The Need for Expertise in a Complex Landscape

Since our founding in 2013, we have supported numerous enterprises–ranging from Fortune 500 companies to emerging market leaders–in their AI analytics journey. With the rise of generative AI, the landscape has become increasingly confusing for enterprises looking to tap into this powerful technology. A common theme we’ve found: while there is immense interest in GenAI, there is also significant uncertainty about how to harness its capabilities effectively. Companies are eager to integrate AI into their operations but often find themselves at a loss when it comes to selecting the right technologies, developing a strategic roadmap, or ensuring secure and efficient deployment.

AnswerRocket’s deep expertise in GenAI analytics, coupled with our understanding of the unique challenges enterprises face, positions us as the ideal partner to navigate these complexities. We recognize that the market needs more than just cutting-edge technology—it needs the strategic insight and hands-on support to turn potential into performance.

Comprehensive GenAI Consulting Services

Our GenAI Consulting Services are designed to meet enterprises wherever they are in their AI adoption journey. Whether you are just beginning to explore the possibilities of AI or are looking to scale your existing initiatives, AnswerRocket provides the tailored guidance and expertise needed to achieve tangible results.

Here are ways we can support you:

• Strategic AI Roadmapping: Our consulting team begins by working closely with leaders in your organization to understand your business objectives and how AI can be leveraged to meet those goals. Through a collaborative discovery process, we help you develop a clear, actionable roadmap that outlines the steps needed to achieve AI-driven success.
• Application and Use Case Development: We assist you in identifying and prioritizing the most impactful AI use cases. By applying our extensive knowledge of LLMs and GenAI, we help your teams create and customize applications that address specific business needs, ensuring that AI investments deliver maximum ROI.
• Secure and Efficient Deployment: Implementing GenAI technologies requires careful planning to ensure security, scalability, and effectiveness. AnswerRocket’s consulting services include comprehensive deployment support, from selecting the right models to integrating them seamlessly into existing systems. Our approach ensures that your organization can deploy AI solutions with confidence and reliability.
• Ongoing Support and Optimization: AI is not a one-time investment but a continually evolving capability. We provide ongoing support to help you optimize yourAI implementations, troubleshoot issues, and refine your strategies as technology and business needs evolve.

Why AnswerRocket?

AnswerRocket is not just a provider of AI technology—we are thought leaders and innovators in the GenAI space. Our integration of the latest LLMs, like OpenAI’s GPT-4o, and our extensive experience in delivering GenAI-powered analytics solutions uniquely position us to lead enterprises through the complexities of AI adoption. We have a deep understanding of the nuances involved in implementing LLMs and GenAI tools at scale, and we bring this expertise to every consulting engagement.

As the AI space continues to advance, the need for expert guidance becomes even more critical. With our GenAI consulting services, AnswerRocket is committed to empowering enterprises to harness the full potential of AI, driving innovation and unlocking new opportunities for growth and success.

Partnering for the Future

With AI increasingly becoming a cornerstone of business strategy, AnswerRocket is ready to be your trusted partner. Our GenAI consulting services are not only a response to market demand, but also a reflection of our commitment to leading the way in AI innovation and ensuring that enterprises can navigate this exciting but complex terrain with confidence.

If your company is looking to capitalize on the transformative power of AI, AnswerRocket has the expertise, tools, and support needed to turn your vision into reality. 

The post AnswerRocket’s GenAI Consulting Services: Accelerating Enterprise AI Results first appeared on AnswerRocket.

The post Ai4 2024 Session Demo: Accelerating Brand Insights with GenAI first appeared on AnswerRocket.

ATLANTA and NEW YORK – August 13, 2024 – AnswerRocket, a pioneer in GenAI-powered analytics, and Kantar, the world’s leading marketing data and analytics company, are excited to announce a go-to-market partnership on joint clients that will use GenAI to help reduce the time needed to understand data, produce actionable insights, and make this information accessible to marketing decision-makers at all levels.  

Meeting the Urgent Need for Faster Insights

Brands today face immense pressure to quickly identify trends, discover opportunities, and make informed decisions. By combining AnswerRocket’s GenAI analytics platform with Kantar’s unparalleled market knowledge and data expertise, this collaboration will deliver tailored GenAI solutions that significantly decrease the time required for data analysis from days or weeks to mere hours or minutes. 

Combining Kantar’s Market Knowledge with AnswerRocket’s AI Expertise

Kantar brings a deep understanding of market dynamics and consumer behavior, proven methodologies for collecting, managing, and interpreting vast amounts of data, and proprietary frameworks and models to generate actionable insights. AnswerRocket contributes an advanced GenAI platform powered by LLMs, designed to streamline and enhance data analysis processes, along with custom AI applications tailored to specific customer needs, ensuring seamless integration and optimal performance. Additionally, AnswerRocket provides comprehensive technical support to ensure smooth implementation and ongoing system efficiency.

Through this partnership, Kantar will leverage AnswerRocket’s platform on joint clients to create custom GenAI assistants ingrained with Kantar’s proprietary data, models, and analytical frameworks. This collaboration empowers brands to quickly access and act on valuable insights, supporting data-driven decision-making.

AnswerRocket has supported brands like Anheuser-Busch InBev and Cereal Partners Worldwide (a partnership between Nestlé and General Mills) to identify, develop, and productionalize custom GenAI analytics solutions in a matter of weeks. Results show a 40% productivity gain for Insights teams, significantly reducing the time spent on manual data analysis and enabling business users to self-service insights. 

Advantages for Brands: Speed, Efficiency, and Expertise

• Accelerated Time to Insights: Brands can reduce the time required to analyze data and generate insights from days or weeks to hours or minutes.
• Enhanced Decision-Making: Brands will have access to advanced analytics to help make more informed and strategic decisions.
• Increased Operational Efficiency: By automating data analysis and insights, brands can reallocate resources to focus on core business activities and innovation.
• Expert Guidance: Kantar’s extensive market knowledge combined with AnswerRocket’s technical support ensures effective navigation and implementation of GenAI solutions.

“AI and GenAI are not only helping us be more effective: they are giving us, and therefore our clients, a competitive edge,” said Ted Prince, Chief Product Officer of Kantar. “Working with AnswerRocket on joint clients means brands and marketers at all levels can talk to our data and get access to valuable insights faster than ever before using AI and new technologies – vital in the fast-moving landscape they’re operating in.”

“We are excited to team up with Kantar to bring the power of GenAI to more brands around the world,” said Alon Goren, CEO of AnswerRocket. “Our partnership will help businesses unlock the full potential of their data and accelerate their journey to actionable insights.”

END

About AnswerRocket

Founded in 2013, AnswerRocket is a generative AI analytics platform for data exploration, analysis, and insights discovery. It allows enterprises to monitor key metrics, identify performance drivers, and detect critical issues within seconds. Users can chat with Max—an AI assistant for data analysis—to get narrative answers, insights, and visualizations on their proprietary data. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Suntory Global Spirits, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. For more information, visit www.answerrocket.com.

About Kantar

Kantar is the world’s leading marketing data and analytics business and an indispensable brand partner to the world’s top companies. We combine the most meaningful attitudinal and behavioural data with deep expertise and advanced analytics to uncover how people think and act. We help clients understand what has happened and why and how to shape the marketing strategies that shape their future. For more information, visit www.kantar.com/.

This Press Release Syndicated In:

• News Break
• Yahoo Finance
• Chronicle Tribune
• MarTech Cube
• CityBiz

The post AnswerRocket and Kantar Join Forces to Accelerate Time to Brand Insights with GenAI first appeared on AnswerRocket.

AnswerRocket is your trusted partner for rapid GenAI results, now offering full-spectrum GenAI services. Take advantage of our team of AI and analytics experts, who bring unparalleled full-stack GenAI capabilities to the table. We focus on delivering results-oriented solutions that drive impactful business outcomes. Our services are tailored to meet your unique needs, ensuring that you receive the most effective and customized GenAI solutions for your enterprise.

Download the Solution Brief

Services Include:

1) Discover

GenAI Technical Assessment

Use Case Identification & Prioritization

1) Design

Enterprise Architecture Design

GenAI Integration Plan

LLM Enablement & Prototype

Data Preparation, ETL & Augmentation

1) Develop

GenAI Solution Development

Vector, Chat, Function & Prompt Engineering

User Interface

Metadata Grounding

4) Launch

Deployment & Rapid Response

Training & Onboarding

Change Management

Roadmap Execution: Use Case Expansion

ROI Measurement

1) Run

Day-to-Day Operation

User Support

Continuous Improvement

Scaling

The post GenAI Consulting Services first appeared on AnswerRocket.

Recent advancements in artificial intelligence have not only reshaped how we interact with technology but also how businesses operate and innovate. Key players like Microsoft, OpenAI, and Snowflake are at the forefront of this transformation, each pushing the boundaries of what’s possible with AI. Let’s take a look at the strides made by these industry leaders, exploring Microsoft’s commanding presence in AI, the cutting-edge developments in conversational AI with GPT-4o, and Snowflake’s ambitious open-source Arctic LLM initiative. Together, these advancements signal a new era where AI is more integrated, responsive, and essential to the business world.

AI Leadership and Strategic Moves

Microsoft’s AI Leadership

Microsoft’s recent earnings announcement underscored its robust performance in the AI domain. With Azure growing by 31% and AI services contributing 7% to this growth, Microsoft’s strategic investments are clearly paying off. The real game-changer, however, lies in high-profile deals such as the $1.1 billion agreement with Coca-Cola for Azure services, including Azure AI. These moves highlight the growing adoption of AI as a key productivity tool in enterprises.

Under Satya Nadella’s leadership, Microsoft has positioned itself as a pioneer in AI technology. This leadership is further bolstered by its partnership with OpenAI, allowing Microsoft to leverage cutting-edge research and innovation. Notably, Azure supports a variety of AI models, including those from Meta and Mistral, ensuring that Microsoft’s AI solutions remain versatile and adaptable to diverse business needs.

Google’s AI Ambition

Not to be left behind, Google has also been ramping up its focus on AI. The company’s revamped search engine, driven by generative AI, showcases this shift. Embracing an “AI-first” philosophy, Google aims for faster results while addressing concerns about website traffic. Internally, Google has unified its AI teams under Google DeepMind, aiming to expedite commercial AI product development while maintaining a strong research focus. This strategy underscores Google’s commitment to innovation and responsible AI integration.

Google is enhancing user experience by incorporating its leading AI model, Gemini, into the Workspace suite, boosting productivity across applications. In Google Search, AI-generated overviews provide summarized information directly in results, aiming for faster retrieval. The lightweight Gemini Flash model further demonstrates Google’s focus on reliable and accessible AI. Combining technical innovation with responsible implementation, Google is making significant strides in the generative AI landscape.

Apple’s AI Plans Unveiled

Apple’s recent WWDC 2024 announcement showcased its strong push into the AI arena. Introducing “Apple Intelligence,” Apple unveiled a suite of AI features across iPhones, iPads, and Macs. This move is set to redefine user interaction with devices, emphasizing enhanced privacy and personalized experiences. Key features include a more conversational Siri, AI-generated “Genmoji,” and access to GPT-4o, which enables Siri to utilize OpenAI’s chatbot for complex queries.

Under Tim Cook’s leadership, Apple is carving out a unique path in the AI landscape by focusing on on-device processing, thereby minimizing data sent to the cloud and ensuring user privacy. This approach is further strengthened by Apple’s “Private Cloud Compute” strategy, which processes complex requests without storing data on its servers. By integrating these AI capabilities seamlessly within its ecosystem, Apple aims to provide a user-centric and secure AI experience, positioning itself as a leader in trustworthy AI implementation.

Technological Advancements in AI Models

GPT-4o Evolution

The introduction of GPT-4o by OpenAI represents a significant leap in conversational AI. Building on the robust foundation of GPT-4, GPT-4o incorporates voice capabilities, transforming the interactive experience with real-time speech-to-text and text-to-speech functionality, much like a smart speaker. This seamless integration marks a pivotal advancement in AI interactions.

A key focus of GPT-4o is optimizing the “time to first token” metric, which measures the time from receiving an input to beginning to generate a response. By improving this metric, GPT-4o ensures fluid and natural conversations, enhancing user experience. The model’s ability to quickly stream parts of the answer while continuing to process the input revolutionizes conversational efficiency.

Practical Applications of GPT-4o

The advancements in GPT-4o open up numerous practical applications across various industries. The ability to replace screen-based interactions with voice interfaces can transform sectors such as tech support, counseling, and companionship, offering more intuitive and responsive user experiences. This makes AI a central tool in business operations and customer interactions.

GPT-4o Risks

With advancements come new challenges. GPT-4o’s ability to convincingly mimic human speech raises concerns about potential misuse, such as impersonation and large-scale robocalling fraud. While enhancing conversational efficiency, the model’s rapid response capability also increases the risk of generating plausible yet incorrect responses. These risks underscore the need for robust safeguards and monitoring to ensure responsible use of AI technology.

Snowflake’s Arctic LLM

Snowflake’s Arctic LLM represents a strategic advancement in the open-source AI arena. Utilizing an innovative Mixture of Experts (MoE) architecture, Arctic trains smaller models on different datasets and combines them to solve various problems. This approach allows Arctic to activate only a portion of its parameters during inference, making it both computationally efficient and powerful, outperforming many open-source and some closed-source models in specific tasks.

By releasing Arctic under the Apache 2.0 license, Snowflake aims to foster collaboration and innovation within the AI community. This open-source strategy encourages external contributions and enhancements, positioning Snowflake as a leader in AI community engagement. Arctic is designed for enterprise-specific tasks such as SQL generation and code instruction, providing businesses with valuable tools to streamline operations with AI.

Snowflake’s Arctic for Enterprise Use

Arctic’s MoE architecture and open-source nature align with Snowflake’s goal of advancing AI through community collaboration and practical enterprise applications. Designed for tasks like SQL generation and code instruction, Arctic allows enterprises to tailor the model to their specific needs, effectively addressing real-world challenges and enhancing productivity and efficiency in business operations.

AI Safety and Explainability

Safe AI Development

As AI technology advances, ensuring its safe and ethical use becomes paramount. Traditional methods for training safe AI have focused on filtering training data or fine-tuning models post-training to mitigate issues such as bias and unwanted behaviors. However, Anthropic’s research with the Claude 3 Sonnet model introduces a proactive approach by mapping the model’s inner workings to understand how neuron-like features affect outputs. This transparency is crucial for mitigating risks and ensuring that AI models behave as intended.

Anthropic’s innovative approach provides real-time insights into how models process prompts and images, laying the foundation for integrating explainability into AI development from the outset. By understanding the internal mechanics of AI models, developers can identify and address potential issues early in the development process. This ensures that production-grade models are reliable, truthful, and unbiased, which is essential for their scaled-up use in enterprises.

Practical Guidance for Explainable Models

Achieving explainability in AI models involves several advanced techniques. One effective method is having models articulate their decision-making processes, making the AI systems more transparent and accountable. This can involve generating detailed explanations for each decision or prediction the model makes, thereby increasing user trust and facilitating better oversight.

Another approach is identifying “neighbors” or examples from training data that are similar to the model’s current decision. By comparing new inputs to known examples, developers and users can better understand the context and reasoning behind the model’s outputs. This method not only enhances the understanding of the model’s thought process but also helps in diagnosing errors and improving model performance.

Furthermore, these techniques can reduce training time and power requirements while improving precision and safety. By focusing on explainability, developers can create models that are not only effective but also efficient and aligned with ethical standards. This focus on ethical AI is becoming increasingly important as AI systems are deployed in sensitive and high-stakes environments such as healthcare, finance, and autonomous systems.

In addition to these methods, integrating explainability features into user interfaces can enhance the practical utility of AI models. For instance, dashboards that visualize decision paths or highlight key factors influencing predictions can make AI tools more accessible to non-expert users. This democratization of AI technology ensures that a broader range of stakeholders can engage with and benefit from AI systems, fostering wider adoption and innovation.

Ensuring the safe and ethical use of AI technology is critical as advancements continue to accelerate. Anthropic’s proactive approach with the Claude 3 Sonnet model exemplifies how understanding the inner workings of AI can mitigate risks and enhance reliability. Techniques such as having models articulate their decision-making processes and identifying similar examples from training data contribute to greater transparency and accountability. By integrating explainability into AI development from the outset, developers can create models that are not only effective but also efficient and aligned with ethical standards. These efforts are essential for fostering trust and enabling the responsible scaling of AI in various enterprise applications.

A Fast-Evolving Field

The rapid advancements in AI by Microsoft, Google, Apple, and Snowflake are reshaping the business landscape. Microsoft’s strategic growth, Google’s innovative AI integrations, and Apple’s focus on privacy underscore the diverse approaches of these tech giants. The introduction of GPT-4o by OpenAI and Snowflake’s Arctic LLM highlight significant leaps in conversational AI and open-source models, respectively, offering practical applications across various industries.

Ensuring the ethical and safe use of AI is crucial. Anthropic’s proactive approach with the Claude 3 Sonnet model emphasizes transparency and explainability, essential for building reliable and unbiased AI systems. Techniques to achieve explainability, such as articulating decision-making processes, enhance the accountability of AI models.

These advancements signal a new era where AI is more integrated, responsive, and essential to business operations. The focus on innovation, collaboration, and ethical standards will drive the responsible scaling of AI, benefiting both businesses and consumers.

The post Navigating the AI Boom: Leadership, Innovation, and Safety in the New Era of Artificial Intelligence first appeared on AnswerRocket.

California’s SB 1047 legislation has emerged as a pivotal development in the AI space. This proposed law mandates that companies investing over $100 million in training “frontier models” of AI, such as the forthcoming GPT-5, must conduct thorough safety testing. This legislation raises critical questions about the liability of AI developers, the impact of regulation on innovation, and the inherent safety of advanced AI models. Let’s  examine these issues in depth, aiming to understand the balance between fostering innovation and ensuring safety in the realm of AI.

Liability of AI Developers

One of the fundamental questions posed by California’s SB 1047 is whether AI developers should be held liable for the harms caused by their creations. AI Regulations serve an essential role in society, ensuring safety, ethics, and adherence to the rule of law. Given the advanced capabilities of Generative AI (GenAI) technologies, which can be misused intentionally or otherwise, there is a compelling argument for regulatory oversight.

Regulations have a role in society, providing for safety, ethics, and the rule of law. Because GenAI tech is advanced enough to be used for harm—whether intentionally or not—there must be a role for AI regulation around this important new advancement.

AI developers must ensure their models do not harbor hazardous capabilities. The legislation suggests that companies should provide “reasonable assurance” that their products are safe and implement a kill switch if this assurance proves inaccurate. This level of accountability is crucial, as the intent behind the use of these tools is at fault for any harm done, not the makers of the tech itself. 

Regulation vs. Innovation

The debate over whether AI regulation stifles innovation is not new. Meta’s chief AI scientist, Yann LeCun, has voiced concerns that regulating foundational AI technologies could hinder progress. While the intent of AI regulation is to protect from danger, the California law, as currently proposed, has notable flaws. For instance, setting a cost-of-production threshold to determine a model’s danger is problematic due to the dynamic nature of computing costs and efficiencies.

Putting a cost-of-production threshold on what makes a model dangerous is flawed. The price of computing and the efficiency in the use of computing are notoriously dynamic. Meaning a powerful model could still be developed below the threshold. A more suitable approach might involve using intelligence benchmarks or introspective analyses to assess an AI’s potential risks.

Sensible AI regulation can coexist with innovation if it targets genuine threats without imposing unnecessary burdens. Thus, we can avoid stifling the amazing minds behind GenAI and instead encourage them to create better solutions that skirt the burden of bureaucracy.

Safety of AI Models

The safety of AI models, particularly larger ones, is a topic of significant concern. GenAI can be either a tool or a weapon, depending on its use. The real risk lies in the intent behind using these technologies. 

While GenAI models are not inherently harmful, their deployment in autonomous systems with physical interactions poses potential dangers. Whether GenAI models rise on their own to harm humanity without human-generated intent is, at best, a transitional state of affairs. If GenAI were released to operate independently with its power supplies and means to interact with the world, it would likely strive to enhance its intelligence. Why? Because intelligence is the ultimate answer, the only true currency of any value in the long run.

To harness the benefits of AI while minimizing risks, proactive management and ethical considerations are paramount. We’re better off making this technology great for our own benefit, working symbiotically with it as it approaches or surpasses our own abilities.

Conclusion Striking A Fine Balance

As we navigate the frontier of AI technology, it is crucial to strike a balance between regulation and innovation. Ensuring the safety of AI models through sensible regulation, without stifling the creative efforts of researchers and developers, is essential. By focusing on genuine risks and maintaining ethical standards, we can maximize the benefits of AI while safeguarding humanity. Stakeholders must engage in thoughtful AI regulation and commit to ethical AI development to pave the way for a future where AI serves as a powerful ally in our progress.

The post AI Safety and Regulation: Navigating the Frontier of Technology first appeared on AnswerRocket.

Understanding Gemini Pro and GPT-4

• Gemini Pro: Developed by Google DeepMind, Gemini Pro is known for its multimodality, seamlessly processing and reasoning across text, code, images, and audio. This makes it adept at tasks requiring a holistic understanding of information. It prioritizes safety and ethical considerations in its outputs.
• GPT-4: Developed by OpenAI, GPT-4 boasts extensive language support and adaptability across various tasks. It excels in tasks like writing different kinds of creative text formats, translating languages, and answering your questions in an informative way.

Comparison in Key Areas

LLM Agnosticism at AnswerRocket

At AnswerRocket, we embrace a technology-agnostic approach, particularly when it comes to LLMs like Gemini Pro and GPT-4. Our philosophy is rooted in the belief that the choice of an LLM should be driven by the specific needs, goals, and data landscapes of each client. Here’s why we are flexible with LLM models: 

• Client-Centric Approach: Our priority is to provide solutions that fit our clients’ specific contexts. Whether it’s a need for specialized capabilities, data security measures, or complementing existing infrastructure, we ensure that the LLM we recommend or use aligns perfectly with our clients’ strategic objectives.
• Best Models for the Job at Hand: Through our LLM-agnostic approach, we’re able to leverage the optimal LLM for a given use case. We are able to tackle diverse data scenarios and complex analytical tasks with the most suitable tools at our disposal.
• Staying Ahead of Technological Advances: The AI and LLM landscape is constantly evolving. By not tying ourselves exclusively to one model, we stay at the forefront of technological advancements. This positions us to quickly adopt and integrate newer, more advanced models like Gemini Pro, GPT-4, or even future iterations and alternatives.

In this light, AnswerRocket’s stance on LLM usage is not just about flexibility; it’s about providing optimized, bespoke solutions that harness the full potential of AI analytics for our clients. We are committed to staying agile and informed, ensuring that whatever the technology landscape brings, we are ready and equipped to integrate it seamlessly into our solutions.

How AnswerRocket Leverages LLMs for Data Analysis: A Gemini Pro and GPT-4 Focus

The ever-evolving landscape of large language models (LLMs) like Gemini Pro and GPT-4 is revolutionizing data analysis. AnswerRocket harnesses the transformative capabilities of these models within its suite of tools, Max and Skill Studio, to deliver next-generation analytical solutions.

Unlocking Deep Insights with GPT-4

Powerhouse Data Processing:  GPT-4’s vast training on text and code empowers it to tackle complex analytical tasks. Its exceptional capacity to analyze and understand large volumes of data makes it an indispensable tool for uncovering profound and detailed insights.

Integration with AnswerRocket’s Max:  Max leverages GPT-4’s capabilities to become a more intuitive analytical partner.  Imagine asking Max questions in natural language like “What factors are most correlated with customer churn?” Max would not only understand your question but also interpret the relevant data sets, utilizing GPT-4’s power to deliver contextually relevant insights and visualizations.

Gemini Pro: Multimodal Mastery for Specialized Analysis

Multimodal Magic:  While GPT-4 excels at processing vast amounts of text data, Gemini Pro takes a broader approach. Its multimodal capabilities allow it to analyze text, code, and even images together. Imagine using Gemini Pro paired with AnswerRocket’s Skill Studio to develop a custom “Sales Performance Analyzer.” This Skill could analyze sales figures (text data) alongside regional sales team photos (image data) to identify patterns between facial expressions and sales performance.

Tailored Solutions with Skill Studio:  AnswerRocket’s Skill Studio allows businesses to leverage Gemini Pro’s unique strengths. By creating custom Skills, businesses can unlock specific LLM-powered analytical methods that leverage Gemini Pro. This tailored approach ensures businesses can address their unique analytical challenges by harnessing the power of multimodal analysis.

Real-World Applications of Max and Skill Studio:

Enhanced Business Intelligence: Max, with its integration of GPT-4, can transform raw business data into actionable insights. This capability enables businesses to make data-driven decisions quickly and accurately.

Custom AI Solutions with Skill Studio: Skill Studio allows businesses to build custom AI solutions that are closely aligned with their specific analytical needs. Whether it’s predicting market trends or analyzing consumer behavior, Skill Studio equips businesses with the tools to harness the power of LLMs for their unique challenges.

Future of AI Analytics with LLMs at AnswerRocket:

As we continue to evolve and enhance our offerings, the potential applications of LLMs in data analysis will expand. Our commitment to leveraging the latest advancements in AI ensures that AnswerRocket remains at the forefront of AI analytical technology.

The integration of LLMs like Gemini Pro and GPT-4 into AnswerRocket’s Max and Skill Studio tools exemplifies the cutting-edge possibilities in modern data analytics. These technologies not only simplify complex data processing but also open doors to customized, highly effective business intelligence solutions.

Conclusion

Gemini Pro and GPT-4 represent the forefront of LLM technology. Choosing between them depends on your specific needs. If your focus is on tasks requiring reasoning and multimodal understanding, Gemini Pro might be the better choice. If extensive language capabilities and text-based applications are your priority, GPT-4 could be a strong fit. As both models continue to evolve, their capabilities will undoubtedly expand, offering even greater possibilities for businesses and individuals alike.

The post Comparing Large Language Models: Gemini Pro vs GPT-4 first appeared on AnswerRocket.

The new model is largely about an interface change. Before now, GPT was fueled by inputs in its original text prompt format, and more recently with images. GPT-4o opens up the possibility of GPT acting more like a smart speaker, listening, understanding, and responding all in one go. It seems to have been tuned, especially for high performance as would be needed in a conversational model. The so-called “time to first token metric” is a measurement of how long it takes from the point of which a model receives its input until it begins generating an answer. It doesn’t matter how long the model takes to respond completely if it can stream part of the answer sooner. This appears to be a great deal of the focus of GPT-4o.

What Differentiates GPT-4o From Other AI Models

Anyone tracking the AI space prior to GenAI realizes that the problem of “speech to text,” also known as “voice recognition,” was the frontier of AI until it was solved a few years ago. Similarly, the problem of generating audio from text, or “text to speech,” was an unsolved problem as well. In recent times, many different providers, including OpenAI’s Whisper and Google’s GTTS, have served up these “speech to text” and “text to speech” models separate from GPT. The new solution simply eliminates latencies in human interfaces by combining them all.

If the underlying GenAI technology were substantially different, they would’ve revved the four-number in the model name. By calling it GPT-4o, they are signaling that it is in the GPT-4 family, like GPT-4 Turbo and GPT-4v. This implies that the transformer tech that is truly the intelligent part is largely unchanged, and what’s new is the engineering of combining all input and output with the underlying AI model.

How GPT-4o Enables OpenAI to Compete with Google and Other LLM Vendors

GPT-4o’s ability to handle multiple languages seamlessly, without requiring the specification of the language in audio files, gives it a significant advantage over competitors like Google. In Google’s stack, the models are tuned to the native language of the speaker, meaning that, for example, Python APIs require the software to indicate what language is being provided with an audio file. In the case of OpenAI’s Whisper model, this requirement is gone. The model is trained to determine what language is being spoken and then transcribe it in that native language seamlessly.

AI-powered smart speakers offer a tantalizing view into a universe where speech becomes the new user experience, and screens disappear altogether. While this is visible in concept through basic interactions like Alexa or Siri, implementations are largely considered tedious and, frankly, dumb. There have been several promising demonstrations of more intelligent interaction, but these suffer from high latencies that disrupt conversation and make the exchanges awkward.

A world of applications opens up if this technology works seamlessly, and OpenAI is the first mover. Drive-through point of sale, any sort of form intake, tech support, coaching/counseling/teaching, companionship—these are all applications where the product is the conversation. If a model can provide the content, and now it is able to also provide the conversation, automation will be complete.

There’s nothing intensely remarkable about the engineering that is being presented here. Google and others will follow immediately with assembler assembly of their stacks. OpenAI’s advantage will be the establishment of the software API that allows them to be thought leaders and trendsetters. They are defining the connectors that will power AI building blocks for the future.

Potential Dangers with GPT-4o

One possible danger to consider is impersonation. With super low latency and a large context window, this model can very inexpensively pretend to be a person and automate large-scale robocalling fraud operations. It would be hard to tell it’s a model over the phone. The same advantage in the case of a legitimate application means a disadvantage for fraudulent use. Traditional problems like hallucinations are more likely to slip through as valid responses because it’s so fast and conversation latency (voice) is low. Think of it as a credible-sounding, fast-talking pitchman.

One of the things we’ve seen with it is that it is generating responses to the user (“time to first token” metric) while it is still thinking about what tools it needs to use to finish the reply–sort of “thinking on its feet” happening live. As a result, the model is answering faster and simultaneously giving itself more time to think. All for half the price of prior models.

What’s Next

By calling it GPT-4o, OpenAI is signaling that it is in the GPT-4 family, like GPT-4 Turbo and GPT-4v. This implies that the transformer tech that is truly the intelligent part is largely unchanged, and what’s new is the engineering of combining all input and output with the underlying AI model. This release allows OpenAI to establish branding and features that will be seen in future models. For example, the Turbo moniker was added to GPT-3.5 and then GPT-4, so we would expect to continue to see releases of GPT models, followed by Turbo versions of them that are cheaper and faster. Similarly, GPT-4 offered V and now O options. We expect to see those same options provided on GPT-4.5 and 5.0, speculated for later this year.

The post Exploring GPT-4o and The Future of Conversational AI first appeared on AnswerRocket.

Addressing CPG-Specific Challenges with AI

Integrating AI into data analysis isn’t without its hurdles. Here are some challenges specific to the CPG sector:

• Data Collection Issues: CPG companies pull data from many sources, like sales and supply chain info, which makes consolidating data tough.
• Quality and Privacy Concerns: Keeping data accurate and navigating privacy regulations is crucial.
• Integration and Analysis Obstacles: Siloed data and the complexity of integration pose significant challenges, along with the usual struggles with BI tool adoption and finding data science talent.

The key to overcoming these challenges is a strategic approach to data management, focusing on data quality, privacy compliance, and breaking down data silos.

The Transformative Impact of AI on CPG Analytics

AI’s potential in CPG analytics is immense. Beyond automating processes and improving safety protocols, AI shines in data-driven decision-making. Tools like AnswerRocket’s Max make complex analytics accessible through natural language processing, democratizing data analysis across all organizational levels.

Forward-Looking Solutions for Data-Driven CPG Companies

CPG companies aiming to leverage AI for a competitive edge should focus on:

• Actionable Data Insights: Emphasize conversational data exploration and custom AI assistants tailored to specific needs, like brand analysis and SKU rationalization.
• Data Preparation and AI Integration: Ensure comprehensive data collection and preprocessing to make CPG data AI-ready.
• AI-Powered Predictive Analytics: Use AI for demand forecasting, sales and marketing optimization, and customer segmentation.
• Supply Chain Optimization and Sustainability: Utilize AI for predictive maintenance, route optimization, and achieving sustainability in operations.

The Future of AI in CPG

As the CPG industry evolves, integrating AI into analytics and strategic decision-making will enhance operational efficiencies and pave the way for innovative solutions to market challenges. The journey towards AI adoption might be complex, but the rewards—faster insights, better decisions, and a competitive edge—make it essential for today’s businesses.

To download the full eBook, “Generative AI Accelerates its Impact on CPG Analytics,” click here.

The post The Rise of Generative AI in CPG Data Analytics first appeared on AnswerRocket.

The post Unlocking Business Growth with Generative AI in Consumer Insights first appeared on AnswerRocket.

For an in-depth analysis of Claude 3’s capabilities and its comparison to GPT-4, refer to Anthropic’s official documentation and news announcements.

Understanding Claude 2, Claude 3, and GPT-4

All 3 of these large language models represent significant advancements in AI, but they each have unique characteristics that define their applications. Claude 2, shines in specific areas such as math, reasoning, and coding, offering robust capabilities in these domains. Its affordability and focus on producing safer and more legally compliant outputs give it an edge in certain scenarios​​​​. 

Claude 3 builds on this legacy, introducing multimodal capabilities, superior reasoning, and enhanced safety features. It competes with and surpasses GPT-4 in specific benchmarks, offering a nuanced understanding of context and refined interaction capabilities. 

GPT-4, developed by OpenAI, is renowned for its extensive language support and adaptability across various tasks. It excels in tasks like email generation, code debugging, and accessing internet data, making it a versatile tool in the AI toolkit​​.

Comparison in Key Areas:

• Training Methods: GPT-4’s training involved a massive dataset encompassing a wide array of internet text, making it incredibly versatile. Claude 2, while also trained on diverse data, puts a stronger emphasis on ethical guidelines and safety, setting it apart in applications where these factors are critical​​​​. Claude 3 has advanced training on diverse datasets, emphasizing its improved contextual understanding and ethical considerations. A common concern of enterprise customers is data security as it relates to the way LLMs are trained. Because data provided by users to the free version of these chatbots is then used to train the greater model, corporations fear the sharing of their private data on the world wide web. But it is possible to download and utilize a private instance of the LLM to ensure data security for any customer, no matter the industry or sensitivity of their information.
• Computation Requirements: The larger model size of GPT-4 demands more computational resources, which might be a consideration for projects with limited infrastructure. Conversely, Claude 2’s smaller model size makes it more accessible for such projects​​. Claude 3 is efficient and balances computational demands with advanced capabilities.
• Fine-tuning Capabilities: Both Claude 2 and GPT 4 models offer fine-tuning capabilities, but GPT-4’s larger model size and extensive training data provide a broader scope for customization, making it suitable for a wider range of applications​​.  Claude 3 offers nuanced customization options, adapting to a wider range of tasks with high efficiency.
• Performance: GPT-4 is known for its higher overall performance, especially in coding and language generation across 200+ languages. Claude 2, while having fewer capabilities, offers more affordable pricing and generates safer outputs​​. Claude 3 is a model that bridges Claude 2’s ethical focus with GPT-4’s extensive adaptability, offering breakthroughs in multimodal understanding and interaction.
• Context Length and File Formats: Claude 2 can process documents with up to 100,000 tokens, while GPT-4 has a variable token limit depending on the model version. Claude 3 extends the token limits, accommodating more extensive data analysis and supporting a broader range of file formats, including images. GPT-4’s ability to handle diverse file formats like PDFs, CSVs, and even images gives it an edge in adaptability​​.
• Ethical Considerations and Safety: Claude 2’s design adheres to ethical guidelines and is less likely to produce harmful responses, making it a safer choice in sensitive applications. Claude 3 has improved safety measures and ethical design, reducing the rate of incorrect refusals for harmless prompts. GPT-4, while powerful, lacks explicit safeguards against problematic content generation​​. 

Incorporating LLMs into Your Business Strategy

The advancement from Claude 2 to Claude 3, alongside GPT-4, represents a significant evolution in AI capabilities. Understanding the strengths and limitations of each LLM is crucial for businesses to make informed decisions. Whether it’s Claude 2’s ethical focus and safety or GPT-4’s versatility and language capabilities, each model offers unique benefits that can be harnessed for specific business needs.

Use Cases and Specialization

Both models are suitable for text and code generation. Claude 3’s extensive training makes it ideal for tasks requiring a deep understanding of context and nuance, such as advanced chatbots, language translation services, and content creation. Claude 2, with its focus on ethical AI and safety, is well-suited for applications where these factors are paramount​​​​. 

Below are some specific use cases for Claude 2, Claude 3, and GPT 4.

• Content Generation: Claude 3 excels in generating complex content such as detailed blog posts, product descriptions, and other website content, while GPT-4’s prowess lies in generating high-quality content across various industries including news, marketing, and academic writing​​​​.
• Chatbots and Virtual Assistants: GPT-4 is ideal for creating advanced chatbots and virtual assistants due to its sophisticated, context-aware interactions. Claude 2 can also be employed for chatbots, especially where safety and ethical considerations are paramount​​​​. Claude 3 enhances this capability with fewer erroneous refusals and a broader understanding of user intents, especially useful in settings requiring high ethical standards.
• Language Translation Services: GPT-4’s deep multilingual capabilities make it a strong candidate for language translation services. Claude 3 improves on Claude 2’s basic translation services by processing larger context windows and supporting more languages efficiently, making it a stronger contender for complex translation tasks.

Potential Industry Applications

• Healthcare: Like GPT-4, Claude 3’s advanced capabilities in analyzing and summarizing complex material can significantly benefit healthcare and pharmaceutical research. Its improved accuracy and multimodal features allow it to process medical research papers and visual data more effectively, providing richer insights.
• Education: Building on Claude 2’s ability to explain reasoning, Claude 3 enhances educational tools by offering deeper contextual understanding and handling a broader range of educational content, including visual materials, making it even more beneficial in learning environments.
• Legal and Compliance: While Claude 2 is noted for producing ethically aligned and safe outputs, Claude 3 extends these capabilities with better context comprehension and fewer incorrect refusals, ensuring reliable outputs in sensitive legal and compliance applications. This makes it a more robust tool for managing complex legal documents and compliance requirements.

These enhancements make Claude 3 a valuable addition across various sectors, improving upon the foundations laid by Claude 2 and competing models like GPT-4.

How AnswerRocket Leverages LLMs for Data Analysis

Claude 2, Claude 3, and GPT-4 are also revolutionizing the field of data analytics. Their transformative capabilities are effectively harnessed in AnswerRocket’s suite of tools, notably Max and Skill Studio, to deliver cutting-edge analytical solutions.

GPT-4’s Role in Advanced Data Analysis

• Comprehensive Data Processing: GPT-4’s vast training data and sophisticated model architecture enable it to handle complex analytical tasks. Its ability to process and interpret vast amounts of data is unparalleled, making it an invaluable asset in deriving deep and nuanced insights.
• Integration in AnswerRocket’s Max: Max incorporates GPT-4’s capabilities to enhance its analytical power. This integration allows Max to not only understand and process large datasets but also to interpret user queries and provide contextually relevant insights. The result is a more intuitive, conversational interface that simplifies complex data analysis.

Claude 2’s Specialized Analytical Applications

• Focused Data Analysis: While Claude 2 and Claude 3 might not match GPT-4’s breadth, they excel in delivering powerful AI analytics in specialized areas. Their structured approach to data interpretation makes it particularly effective in scenarios that require detailed and specific analytical tasks.
• Utilization in Skill Studio: AnswerRocket’s Skill Studio can leverage Claude 2 or Claude 3’s strengths to create customized analytical models. These models, or ‘Skills,’ are tailored to specific business needs, embedding unique analytical methods directly into the LLM’s capabilities. This customization ensures that businesses can utilize Claude 2 or Claude 3’s analytical power in a way that aligns perfectly with their operational goals.

Real-World Applications of Max and Skill Studio:

• Enhanced Business Intelligence: Max, with its integration of GPT-4, can transform raw business data into actionable insights. This capability enables businesses to make data-driven decisions quickly and accurately.
• Custom AI Solutions with Skill Studio: Skill Studio allows businesses to build custom AI solutions that are closely aligned with their specific analytical needs. Whether it’s predicting market trends or analyzing consumer behavior, Skill Studio equips businesses with the tools to harness the power of LLMs for their unique challenges.

Future of AI Analytics with LLMs at AnswerRocket:

• As we continue to evolve and enhance our offerings, the potential applications of LLMs in data analysis will expand. Our commitment to leveraging the latest advancements in AI ensures that AnswerRocket remains at the forefront of analytical technology.

The integration of LLMs like Claude 2, Claude 3, and GPT-4 into AnswerRocket’s Max and Skill Studio tools exemplifies the cutting-edge possibilities in modern data analytics. These technologies not only simplify complex data processing but also open doors to customized, highly effective business intelligence solutions.

Conclusion

Whether you’re a data scientist, a member of an insights team, or a CTO, understanding these LLMs is key to unlocking their potential for your business.

Let’s explore the possibilities and harness the power of LLMs like Claude 2, Claude 3, and GPT-4 to transform your data analysis and business intelligence strategies. Contact our team today to learn more about how we can tailor these advanced AI tools to your unique business needs.

The post Embracing the Future: Navigating the World of Large Language Models – Claude 2, Claude 3, and GPT-4 first appeared on AnswerRocket.

The Challenge of Hallucinations in Data Analysis 

Recognizing the potential risks posed by hallucinations, AnswerRocket has developed robust mechanisms within Max to minimize this occurrence and ensure that every piece of information generated by the AI is accurate, verifiable, and grounded in reality.

To combat LLM hallucinations, AnswerRocket employs several key strategies:

1. Providing Correct and Full Context: We provide Max with the data observations generated through AnswerRocket’s analysis of the data to compose the narrative. Max is instructed to only leverage the supplied data observations and no other sources to form its response. By ensuring that the model is presented with the full picture, including the nuances and specifics of the dataset, we significantly reduce the chances of hallucination. This context-setting enables Max to “tell the story” accurately and generate answers that are directly tied to the data.
   
2. Acknowledging When Unable to Answer: Max is instructed to provide answers only when there is sufficient data to support a response. If the model does not find a concrete answer within the supplied data, it is designed to acknowledge the gap, rather than fabricate a response. This disciplined approach prevents the model from venturing into speculative territory and maintains the reliability of the insights it generates.
   
3. Providing Transparency and Traceability with References: Max supports its responses with references, such as the SQL queries run, Skills executed,  or links to source documents. This transparency allows users to trace the origin of the information provided by the AI, enabling users to easily see how answers were derived and to verify the results as needed. Establishing this ground truth is crucial in minimizing hallucinations, as it ensures that the model’s outputs are plausible and factual.
4. Iterative Loop for Testing & Refining: Through AnswerRocket’s Skill development process, Max undergoes continuous cycles of human-in-the-loop testing within our Skill Studio. This process includes validating the language model’s behavior across a wide range of questions and scenarios to ensure appropriate guardrails are in place. By rigorously testing and refining Max’s responses under the review of human experts, we can confidently deploy the AI in diverse analytical tasks with minimized risk of hallucination.
5. Conducting a Fact Quality Check: During Skill development, narratives generated by Max using LLMs are reviewed against the supplied data observations to confirm that they are high-quality, useful, accurate and reflective of the analysis findings. This check protects against any ambiguity in the data observations that may have been misinterpreted by the LLM in composing the story. This process can also be performed against prior answers to highlight areas for improvement.
   

The Path Forward: Trust and Transparency in AI

By implementing these strategies, AnswerRocket ensures that interactions with Max are accurate and reliable. Preventing LLM hallucinations is crucial for building and maintaining trust in AI systems, particularly as they become more integrated into our decision-making processes. At AnswerRocket, we’re not just developing technology; we’re nurturing trust and transparency in AI, ensuring that Max remains a reliable partner in analytics and beyond. 

Learn more about how AnswerRocket is delivering AI-powered analytics that businesses can rely on for accurate, actionable insights. Request a demo today.

The post Preventing LLM Hallucinations in Max: Ensuring Accurate and Trustworthy AI Interactions first appeared on AnswerRocket.

You can get answers to your critical questions just by chatting with Max

Communicating with Max is as easy as speaking to a human analyst.

Ask questions in your own words and get answers you can understand. Interact with visualizations and even ask follow up questions in the same conversation.

The post Max Analyzes Your CPG Data first appeared on AnswerRocket.

The post ChatGPT + AnswerRocket: Reducing Curiosity Costs first appeared on AnswerRocket.

The post Heroes in Training: AI, Natural Language & LLMs first appeared on AnswerRocket.

The post AI Vision: The Future of Data Analysis first appeared on AnswerRocket.

ATLANTA—Dec. 12, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced the launch of Skill Studio, which empowers enterprises to develop custom AI analysts that apply the business’ unique approach to data analysis.

“Skill Studio has immense potential to transform our approach to analytics,” stated Stewart Chisam, CEO of RallyHere Interactive, a platform for game developers to run multi-platform live service games. “With Skill Studio, we can create a customized AI analyst that deeply understands the nuances of the gaming industry and how we analyze our data. Its ability to automate complex analyses like game performance, player interactions, and usage patterns is groundbreaking. The insights generated by Max can help drive strategic decisions to enhance both our platform and user experience.”  

Say hello to specialized AI copilots

AI copilots have emerged as a powerful tool for enterprises to access their data and streamline operations, but existing solutions fail to meet the unique data analysis needs of each organization or job role. Skill Studio addresses this gap by providing organizations with the ability to personalize their AI assistants to their specific business, department, and role, which enables users to more easily access relevant, highly specialized insights.

Skill Studio elevates Max’s existing AI assistant capabilities by conducting domain-specific analyses, such as running cohort and brand analyses. Key enhancements include:

• Full Development Environment: End-to-end experience supporting the software development lifecycle for developers to gather requirements, develop, test, and deploy Skills to the AnswerRocket platform. Skill Studio allows developers to leverage the Git provider and integrated development environment (IDE) solution of their choice.
• Low-Code UX: User-friendly interface for developers and analysts to create customized Skills for the end users they support.
• Reusable Code Blocks: Accelerate custom Skill development by leveraging pre-built code blocks for analysis, insights, charts, tables, insights, and more.
• Bring Your Own Models: Skill Studio extends the analytical capabilities of Max, enabling enterprises to deploy their existing machine learning algorithms within the Max experience.
• Multi-source and Multi-modal Data Support: Analysts can perform complex analyses using multiple data sources, including structured or unstructured through a single tool. This allows businesses to glean insights from siloed data sources that were previously inaccessible.
• Create Purpose-Built Copilots: Construct copilots designed for specific roles by giving them access to the Skills needed to perform a set of analytical tasks.
• Quality Assurance & Answer Validation: Testing framework for validating accuracy of answers generated by Skills. 

“AI copilots have revolutionized the way organizations access their data, but current solutions on the market are general-use and not personalized to specific use cases,” said Alon Goren, CEO of AnswerRocket. “Skill Studio puts the power of AI analysts back in the hands of our customers by powering Max to analyze their data in a way that helps them achieve their specific business outcomes.”

A collaborative experience for creating fit-for-purpose AI assistants

Skill Studio enables cross-functional design, development, and deployment of AI copilots:

• Data Scientists and Developers: Technical team members can democratize specialized data science algorithms and models as reusable Skills that can be leveraged by Max, enabling users to successfully retrieve the advanced answers they need quickly and securely.
• Analysts: Analysts can customize Skills and Copilots to capture their company’s best practices for analyzing and retrieving insights from data. This allows repetitive, manual data analysis processes to be executed by Max for automated analyses. 
• Business Users: Users can enjoy an easy-to-use experience for interacting with their data by chatting with an AI analyst who understands their business, analytical processes, and insights needs.

For more information on Skill Studio, please visit: https://answerrocket.com/skill-studio/

About AnswerRocket

Founded in 2013, AnswerRocket is a generative AI analytics platform for data exploration, analysis, and insights discovery. It allows enterprises to monitor key metrics, identify performance drivers, and detect critical issues within seconds. Users can chat with Max–an AI assistant for data analysis–to get narrative answers, insights, and visualizations on their proprietary data. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Beam Suntory, Coty, EMC Insurance, Hi-Rez Studios, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. To learn more, visit www.answerrocket.com.

Contacts

Elena Philippou
10Fold Communications
answerrocket@10fold.com
(925) 639 – 0409

The post AnswerRocket Unveils Skill Studio to Empower Enterprises with Custom AI Analysts for Enhanced Business Outcomes first appeared on AnswerRocket.

Introduction

In today’s rapidly evolving business landscape, data-driven decision-making is paramount. Enterprise organizations require advanced tools and technologies to harness the full potential of their data. One such solution that stands out is AnswerRocket, a platform that integrates generative AI technology throughout the data analysis process. But what sets AnswerRocket apart, and why should analytics and insights leaders take note? In this blog post, we’ll highlight perspectives from AnswerRocket’s Co-Founder and CTO, Mike Finley to explore the unique approach that makes AnswerRocket a game-changer in the world of data analytics.

Understanding Generative AI at AnswerRocket

AnswerRocket’s approach to generative AI extends far beyond merely answering questions. It’s about understanding data long before the questions are asked and comprehending both the queries and the database results. Generative AI isn’t just a tool; it’s an integral part of the entire analytical process. This differentiates AnswerRocket from augmented analytics solutions that rely solely on question-answer mechanisms, making it a true AI assistant in data analysis for everyone in an organization.

What Makes AnswerRocket Different?

A crucial aspect that distinguishes AnswerRocket is its evolutionary journey. Over a decade of development, the platform was continually refined even before incorporating large language models. The unique combination of traditional capabilities with modern language models enables AnswerRocket to be enterprise-ready, secure, governed, reliable, and powerful. It’s not just about providing answers; it’s about narrating the story hidden within your data, making it relatable and actionable.

The Power of Skills in AnswerRocket

Skills in AnswerRocket are fundamental units of analysis. Your business might have its unique way of forecasting sales, and no large language model can replace that. However, AnswerRocket empowers you to create Skills that encapsulate these analyses, seamlessly integrating them into the language model conversation. The platform offers a range of pre-built Skills and the flexibility to create your own using the Skill Studio. This approach ensures that insights specific to your business become a part of your enterprise analytics arsenal.

Connectivity and Data Access with AnswerRocket

Accessing and connecting to diverse data sources is a common challenge for enterprise organizations. AnswerRocket, from its inception, aimed to tackle this challenge. It can connect to traditional data sources like SQL databases, DAX, and relational models, but it goes further. It seamlessly integrates with unstructured data sources, including emails, PowerPoint presentations, and PDF documents, transforming “dark data” into valuable insights. This capability enables AnswerRocket to access all the same documentation and training that a human analyst would, serving as a true AI assistant in your analytical journey.

The Magic of Skill Studio

The Skill Studio within AnswerRocket is where the magic happens. It empowers you to combine data from disparate sources in innovative ways, giving you a comprehensive view of your business landscape. With the ability to access structured and unstructured data, as well as real-time data through APIs, AnswerRocket can provide unique and thoughtful insights. It’s not just about reporting the weather and sales; it’s about understanding the causal relationship between the two. This capacity to merge different data sources makes the language model’s analysis invaluable to your business. With Skill Studio, developers and analysts can “teach” their AI assistant how it should analyze your data, what kinds of insights it should be looking for, and even how the findings should be presented. It’s the way that enterprises can capture their specific data analysis processes and methodologies and enable an AI assistant to do that work for them.

Conclusion

AnswerRocket represents a new frontier in the world of generative AI-powered analytics. Its unique approach, blending traditional capabilities with modern language models, allows it to offer secure, reliable, and enterprise-ready insights. The power of Skills and the flexibility of the Skill Studio make it adaptable to your business’s unique needs. Moreover, its unparalleled connectivity and data access capabilities ensure that no data source is out of reach. 

For analytics and insights leaders at enterprise organizations, AnswerRocket is more than just a tool; it’s a strategic assistant in your quest for data-driven success. Embrace the power of generative AI, and see how AnswerRocket can transform your data into actionable insights. The future of analytics is here, and it’s waiting for you to unlock its full potential with AnswerRocket.

Blog image by pch.vector on Freepik. 

The post Unlocking the Power of Generative AI with AnswerRocket first appeared on AnswerRocket.

We loved being a Diamond Sponsor for this event in Atlanta, right in our own backyard!

What is COLLIDE?

The collision of data and industry is at Data Science Connect’s COLLIDE Data Science Conference. This event showcased the latest trends and advancements in data-driven decision making and how it is revolutionizing industries such as healthcare, finance, and marketing.

Learn more at https://datasciconnect.com/events/collide-2023/.

If you were unable to attend the event, you can watch our session recordings, linked below:

Tuesday, October 3rd, 2:50-3:10pm
Copilot Cooking Show: How To Build a GenAI Assistant for Analytics
With Mike Finley, AnswerRocket Co-founder, CTO and Chief Scientist

In this quickfire session, we’ll demonstrate how AnswerRocket enables enterprises to create customized GenAI-powered assistants for data analysis. Key ingredients include OpenAI’s GPT LLM, AnswerRocket’s augmented analytics platform, and tough business questions. Come see how you can apply these game-changing technologies to produce a custom AI assistant that boosts your team’s analytical productivity.

Wednesday, October 4th, 2:50-3:10pm
Bridging the Gap: From AI Hype to Real-world Impact
With Pete Reilly, AnswerRocket Co-founder and COO

Artificial Intelligence, with its seemingly endless potential and promise, is now front and center as a hot topic in boardroom meetings and strategy sessions. But how does a business move from awe and curiosity to actively realizing benefits in real-world scenarios? This session is tailored to steer organizations from mere contemplation of AI’s power to the tangible and transformative results it can deliver.

Are you interested in learning more about adding a Generative AI Analytics Assistant to your team?

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at COLLIDE 2023 first appeared on AnswerRocket.

ATLANTA—Sept. 19, 2023—AnswerRocket, an innovator in GenAI-powered analytics, today announced new features of its Max solution to help enterprises tackle a variety of data analysis use cases with purpose-built GenAI analysts.

Max offers a user-friendly chat experience for data analysis that integrates AnswerRocket’s augmented analytics with OpenAI’s GPT large language model, making sophisticated data analysis more accessible than ever. With Max, users can ask questions about their key business metrics, identify performance drivers, and investigate critical issues within seconds. The solution is compatible with all major cloud platforms, leveraging OpenAI and Azure OpenAI APIs to provide enterprise-grade security, scalability, and compliance. 

“AI has been reshaping what we thought was possible in the market research industry for the past two decades. Combined with high-quality and responsibly sourced data, we can now break barriers to yield transformative insights for innovation and growth for our clients,” said Ted Prince, Group Chief Product Officer, Kantar. “Technologies like AnswerRocket’s Max combined with Kantar data testify to the power of the latest technology and unrivaled data to shape your brand future.”

Following its March launch, AnswerRocket has been working with some of the largest enterprises in the world to solve critical data analysis challenges using Max. Highlighted applications of the GenAI analytics assistant include:

• Automating over a dozen analytics workflows for a Fortune 500 global beverage leader, reducing time to insights by 80% and empowering decision-makers to respond quickly to market share and brand equity changes with data-driven action plans.
• Helping a Fortune 500 pharmaceutical company generate groundbreaking insights revealing the direct impact of sales activities on market share.
• Empowering a global consumer packaged goods leader to quickly respond to macro market trends by generating insights from unstructured market research alongside structured company performance analysis.

“Today’s enterprises demand instant insights, and the traditional methods are no longer sufficient on their own,” said Alon Goren, CEO, AnswerRocket. “Max is enabling several of the world’s most recognizable brands to understand better what’s driving shifts in their business performance, effectively turning their vast data lakes and knowledge bases into a treasure trove of business insights.”

Max’s advanced capabilities solidify its position as the first GenAI assistant for data analysis built for the enterprise. Enhancements to the solution include:

• Customizable Analyses: Out-of-the-box Skills used by Max for business performance analysis, including search, metric drivers, metric trend, competitor performance, and more. AnswerRocket also offers support for custom Skills using enterprises’ own models. Skills can be configured to reflect unique business rules, processes, language, outputs, etc. 
• Structured and Unstructured Data Support: Max supports both tabular and text-based data analysis, allowing companies to glean insights from vast enterprise data, documents, and multiple data sources seamlessly in a single conversation.
• Automation of Routine Analysis Workflows:  Max can execute multi-step analytics processes to free up analyst time for more strategic projects while giving business stakeholders timely analysis and self-service answers to ad hoc follow-up questions.
• Integration with Third-party Tools: Embed the Max chat experience into tools like Power BI, Slack, Teams, and CRMs, enabling users to analyze their data in tools they’re already using.

“Max brings forward a seismic shift in how companies can transform their data into actionable intelligence with unprecedented speed,” continued Goren. “With Max, everyone within the enterprise can have immediate access to an AI analyst, providing them with prescriptive recommended actions and helping to guide them towards data-driven decisions.” 

AnswerRocket is a Platinum Sponsor of Big Data LDN, taking place on September 20-21, 2023 at Olympia in London. They will be showcasing their revolutionary GenAI analytics assistant, Max, alongside early adopters of the technology in three sessions:

• Wednesday, September 20 from 4:40 – 5:10 p.m. – How CPW Scaled Data-Driven Decisions with Augmented Analytics & Gen AI (Chris Potter, Global Applied Analytics, Cereal Partners Worldwide; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 2:40 – 3:10 p.m. – How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant (Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev; Joey Gaspierik, Enterprise Accounts, AnswerRocket)
• Thursday, September 21 from 4:00  – 4:30 p.m. –  Maximizing Data Investments with Automated GenAI Insights (Ted Prince, Group Chief Product Officer, Kantar; Alon Goren, CEO, AnswerRocket)

For more information on AnswerRocket’s industry-leading solutions, please visit: https://answerrocket.com/max.

About AnswerRocket

Founded in 2013, AnswerRocket is a generative AI analytics platform for data exploration, analysis, and insights discovery. It allows enterprises to monitor key metrics, identify performance drivers, and detect critical issues within seconds. Users can chat with Max–an AI assistant for data analysis–to get narrative answers, insights, and visualizations on their proprietary data. Additionally, AnswerRocket empowers data science teams to operationalize their models throughout the enterprise. Companies like Anheuser-Busch InBev, Cereal Partners Worldwide, Beam Suntory, Coty, EMC Insurance, Hi-Rez Studios, and National Beverage Corporation depend on AnswerRocket to increase their speed to insights. To learn more, visit www.answerrocket.com.

Contacts

Vivian Kim
Director of Marketing
vivian.kim@answerrocket.com
(404) 913-0212

The post AnswerRocket’s GenAI Assistant Revolutionizes Enterprise Data Analysis first appeared on AnswerRocket.

We really enjoyed being a Platinum Sponsor of this event, and connecting with so many great people while we were there!

What is Big Data LDN?

The UK’s leading data, analytics and AI event. Big Data LDN (London) is the UK’s leading free to attend data, analytics & AI conference and exhibition, hosting leading data, analytics & AI experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Discuss your business requirements with over 180 leading technology vendors and consultants. Hear from 300 expert speakers in 15 technical and business-led conference theaters, with real-world use-cases and panel debates. Network with your peers and view the latest product launches & demos. Big Data LDN attendees have access to free on-site data consultancy and interactive evening community meetups. Learn more at BigDataLDN.com.  

While we were there, we met with lots of people at our booth (pictured below) and hosted 3 different sessions. We were lucky enough to have some our great customers and even a partner join us on stage for those sessions.

Check out some of the highlights from the show in the gallery below.

Gangs all here!

A bird’s eye view of the conference floor.

Connecting with the audience at the booth.

Our own Alon Goren alongside Ted Prince of Kantar.

Chris Potter, CPW, Alon Goren, AnswerRocket, Liz Davies, AB InBev

Our presentation with Anheuser-Busch

Creating some content alongside Chris Potter, CPW.

Enjoying the local pub scene after a great day at the show.

Cheers to great partnerships!

If you weren’t able to attend the show, or if you’d like to rewatch one of our sessions, you can click on the session titles below to watch a recording. 

WHAT: How Cereal Partners Worldwide Scaled Data-Driven Decisions with Augmented Analytics & Gen AI

WHEN: Wednesday, Sept 20, 4:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Chris Potter, Global Applied Analytics, Cereal Partners Worldwide

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Step inside the transformative journey of Cereal Partners Worldwide (CPW), a joint venture between industry giants General Mills & Nestlé, as they redefine decision-making in the era of AI & Big Data. Hear how CPW modernized its analytics processes, turning to augmented analytics and generative AI to realize their vision for a data-driven culture. We’ll share challenges faced, strategies implemented, and the tangible results achieved in this ongoing journey towards democratized analytics.

WHAT: How Anheuser-Busch InBev Unlocked Insights on Tap with a Gen AI Assistant

WHEN: Thursday, Sept 21, 2:40 p.m.

WHERE: Gen AI & Data Science Theatre Session

WHO: Elizabeth Davies, Senior Insights Manager, Budweiser – Europe, Anheuser-Busch InBev

Joey Gaspierik, Enterprise Accounts, AnswerRocket

Gaining a competitive edge in today’s business landscape requires instant, actionable insights. Hear how global beverage titan Anheuser-Busch InBev is transforming its workflows with AI assistants for automated and ad hoc analysis and insights. We’ll discuss real-world use cases and highlight how Insights teams are empowering their business counterparts to make faster, better decisions at scale.

WHAT: Maximizing Data Investments with Automated GenAI Insights

WHEN: Thursday, Sept 21, 4:00pm

WHERE: X-Axis Keynote Theatre 

WHO: Ted Prince, Group Chief Product Officer, Kantar

Alon Goren, CEO, AnswerRocket

We have more data at our disposal than ever, but extracting true value from it remains a challenge. Generative AI and machine learning have opened up new possibilities for transforming raw data into actionable business insights with unprecedented efficiency and precision. Hear how Kantar, a data and insights leader, and AnswerRocket, a Gen AI analytics platform, are applying these powerful technologies to help companies analyze business performance, forecast market trends, and spot anomalies in seconds. Attendees will gain a comprehensive understanding of how to leverage their data assets more effectively, ensuring every data investment drives business growth and innovation.

If you weren’t able to attend but would still like to connect with us, click the link below. 

Request a demo with a member of our team here.

The post Past Event: AnswerRocket at Big Data LDN first appeared on AnswerRocket.

Watch the video below or read the transcript of the interview to learn more.

How is AnswerRocket making these emerging technologies enterprise-ready?

Mike: I would start simply by saying that the idea of keeping data secure and providing answers that are of high integrity is table stakes for an enterprise provider. Making sure that users who should not have access to data don’t have access to it and that the data is never leaked out, right? That’s table six for any software at the enterprise. And so it doesn’t change with the advent of the AI technology. So AnswerRocket is very focused on ensuring that data flowing from the database to the models, whether it’s the OpenAI models or other models of our own, does not result in anything being trained or saved. So that it could be used by some third party, so that it could be leaked out, so that it could be taken advantage of in any way other than its intended purpose. So that’s a core part of what we offer. 

The flip side of that is, as you mentioned, many of these models are sort of famous at this point for producing hallucinations, where when you under specify what you ask the model, you don’t give it enough information, it fills in the blanks. It’s what it does, it’s generative, right? The G in generative is what makes it want to fill in these blanks. AnswerRocket takes two steps to ensure that doesn’t happen. First of all, when we pose a question to the language model, we ensure that the facts supporting that question are all present. It doesn’t need to hallucinate any facts because we’re only giving it questions that we have the factual level answers for so that it can make a conversational reply. The second thing we do is when we get that conversational reply, like a good teacher, we’re grading it. We’re going through checking every number, what is the source of that number, is that one of the numbers that was provided? 

Is it used in the correct way? And if so, we allow that to flow through and if not, we never show that to the user, so they never see it. A demonstration is not value creation, right? A lot of companies that just kind of learned about this tech are out and are out there demonstrating some cool stuff. Well, it’s really easy to make amazing demonstrations out of these language models. What’s really hard is to make enterprise solutions that are of high integrity that meet all of the regulatory compliance requirements that provide value by building on what your knowledge workers are doing and making them do a better job still. And so that’s very much in the DNA of AnswerRocket. And it’s 100% throughout all the work that we do with language models. 

How can enterprises avoid data leakage and hallucinations when leveraging GPT?

Pete: A lot of the fear that you hear people saying, oh, I’m going to have leaking data and so on, a lot of that’s just coming from ChatGPT. And if you go and read the terms and conditions of ChatGPT, it says, hey, we’re going to use your information, we’re going to use it to train the model. And it’s out there. That’s where you’re seeing a lot of companies really lock down ChatGPT based on the terms and conditions that makes sense. But when you look at the terms and conditions of, say, the OpenAI API, it is not using your data to train the model. It is not widely available to even anybody inside of the company, it’s removed in 30 days and so on. So those are much more restrictive and much more along the lines of what I think a large enterprise is going to expect.

You can go to another level. And I think a lot of our, what we’re seeing is a lot of our customers, they do a lot of business with, say, Microsoft. Microsoft also can host that model inside the same environment that you’re hosting all your other corporate data and so on. So it really has that same level of security that if you trust, say, Microsoft, for example, to host your corporate enterprise data, well then really trusting them to sort of host the OpenAI model is really on that same level. And what we’re seeing is large enterprises are getting comfortable with that. And in terms of hallucinations, as Mike said, it’s really just important how we use it. We analyze the data, we produce facts, and there are settings in these large language models to tell it how creative to get or not. And you say, don’t get creative, I just want the facts, but give me a good business story about what is happening. 

And then we also provide information to the user that tells them exactly where that information came from, traceable all the way down to the database based, all the way down to the SQL query so that it’s completely audible in terms of where the data came from and being able to trust. 

In Conclusion

Analytics and Insights leaders who wish to utilize ChatGPT technology in their organizations have to balance the possible rewards with the risks. We’re committed to providing a truly enterprise-ready solution that leverages the power of ChatGPT with our augmented analytics platform to securely get accurate insights from your data. By providing AI models with complete and correct supporting facts, we can eliminate the possibility for hallucinations and maintains full control over the generated responses in the platform. Furthermore, we use astringent grading process to validate the AI-generated insights before presenting them to the users. 

The post Preventing Data Leaks and Hallucinations: How to Make GPT Enterprise-Ready first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Ryan: My name is Ryan Goodpaster. I’m one of the sales guys here at AnswerRocket. Been with the company for six years. And what we do is we help our customers get insights out of their enterprise data in seconds, using techniques like natural language processing, natural language generation, AI, and machine learning, model integration, and now GPT. 

How does AnswerRocket help CPG’s accelerate data analysis?

Ryan: We’re fairly industry agnostic, but we have a pretty heavy focus in consumer goods. We help them with a lot of their internal data, their third party data, like Nielsen and IRI and Kantar. This data is really important to a lot of departments, so they see a lot of value across lots of different business areas in their companies. 

How does AnswerRocket uncover insights from syndicated market data?

Ryan: Every month, the Nielsen data updates. And for the most part, it takes a company maybe a week or two to get through a comprehensive analysis of what’s going on with their business. With AnswerRocket, you can do a full deep dive in seconds, and you don’t have to wait a week or two. So you get those insights much faster and you can really figure out what to do, what actions to take with those insights, and get that to their customers even quicker. 

Why are customers excited about Max, our AI assistant for analytics?

Ryan: Most everybody that I’ve talked to is really excited about Max. They want Max yesterday. So we are working very hard to deliver that to them, especially our current customers. I don’t see very many people being scared of it, because it’s one of those things where you have to get on board with it or you’ll get left behind, right? Everybody and every company that I’m talking to is trying to figure out how do we leverage GPT, not only for analytics, but across the entire organization? How do we make ourselves more efficient in these current times? 

Conclusion: AnswerRocket’s industry-agnostic approach is particularly beneficial for consumer goods companies, where they provide valuable insights by leveraging both internal and third-party data sources like  Nielsen and IRI. Our solution enables CPGs to accelerate data analysis, allowing them to dive deep into syndicated market data within seconds, facilitating quicker decision-making and actions based on the obtained insights. Customers are excited about Max, which is powered by GPT, as it promises enhanced efficiency and competitiveness across organizations. 

The post Transform CPG Analytics with AnswerRocket: Max, the AI Assistant for Accelerated Insights first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Ryan: My name is Ryan Goodpaster. I’m one of the sales guys here at AnswerRocket. Been with the company six years. And what we do is we help our customers get insights out of their enterprise data in seconds, using techniques like natural language processing, natural language generation, AI and machine learning, model integration, and now GPT. 

What problem does AnswerRocket help solve?

Ryan: Most of our customers, when they first come to AnswerRocket, they’re leveraging something like a dashboard to track their business performance. And when they see something has changed, a number has gone up or down, their next question is typically, why? And it takes a long time to answer that. It’s a lot of manual analysis. And so we leverage AI and machine learning and now GPT to take a natural language question like, why are my sales down? And help them get an answer to that in seconds so that they can spend more time doing what they were hired to do. And that’s being human and coming up with solutions and strategy, not digging through data. 

How does AnswerRocket improve enterprise data analysis?

Ryan: The solution to the problem of “it takes too much time to get through data” is I need to hire more analysts. But what I’m sure everybody has seen in the marketplace and in the talent pool is there’s just not enough analysts to hire. Right? And it’s hard to retain them. So by giving them things that make them more efficient and get them better answers to serve up to their customers, you have happier analysts. They’re more efficient, and you don’t have to hire as many. You get more production out of all of the talent that you’ve already got with all of the business knowledge that they’ve learned over the years. 

Why are customers excited about Max, our AI assistant for analysis?

Ryan: Most everybody that I’ve talked to is really excited about Max. They want Max yesterday. So we are working very hard to deliver that to them, especially our current customers. I don’t see very many people being scared of it, because it’s one of those things where you have to get on board with it or you’ll get left behind, right? Everybody and every company that I’m talking to is trying to figure out, how do we leverage GPT, not only for analytics, but across the entire organization? How do we make ourselves more efficient in these current times? 

Conclusion: AnswerRocket’s analytics AI assistant, Max, powered by GPT, excites customers as it promises to enhance efficiency and effectiveness across organizations. Organizations recognize that embracing AI and GPT is crucial for staying competitive in today’s dynamic landscape. By leveraging GPT and AI technologies, companies can stay competitive and optimize their operations while making data-driven decisions swiftly and effortlessly. 

The post Discover AnswerRocket: Unlocking the Power of AI for Your Enterprise Data Analysis first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

How has GPT enabled AnswerRocket to create an AI assistant for data analysis?

Pete: Look, we started with a pretty lofty vision that we wanted to be able to provide this thing that’s an intelligent agent to help business users with data analysis. And we started the company in 2013 with basically natural language search. We moved into automation of analysis, incorporating data science and machine learning. We moved into generating stories to go along with that, to help business users understand what was going on under the covers. And so what GPT has done for us is a few things. One is it’s made it even easier to understand what it is that the user is asking for and how we can quickly solve the problem for them. It’s making the narrative that we build even richer, more meaningful and understandable by the business user. What I would say is it’s opened up the door to really, first of all, bring us much closer to that vision of this intelligent agent number one, because of the user experience. But number two, what it’s done is it’s really opened our eyes to oh, this is how we’re going to unlock. 20% of the data that companies have is in these structured databases, 80% are in these unstructured sources. And so what large language models are really good at is understanding these unstructured text oriented documents, emails, PDFs and so on. And so what that does for us is it opens up our ability to provide a much more robust analysis to the users much more broadly than we can do just out of what data happens to be cleansed in a structured database. And so it significantly enhances our ability to provide a much more meaningful insight that business can act on to drive their business forward. 

How does GPT serve as a bridge between users and their data?

Mike: AnswerRocket has the ability to gather all of the right data to produce an answer and the business user has in mind a question that they want answered. GPT bridges the gap between those two. It allows the understanding of the question that the user has in mind and it allows the retrieval of the information that AnswerRocket can provide. So it’s that perfect bridge and it serves both to help AnswerRocket understand that business user, but then it also helps the business user to in turn understand AnswerRocket back. That combination is really what makes something that might have been a dashboard retrieval in the past become a conversation in the future. It’s what really transforms, let’s call it an extraction with analysis and maybe a report into an engagement with an analyst, with an agent that’s working on your behalf. Ultimate vision for AI assistance. So fundamentally we would like for the AI to feel like a collaborator, like somebody else on the team who can be sent away to do a task, research, summarize, make conclusions, build a presentation and return that back for evaluation. So essentially every human employee becomes an executive and that executive is managing a resource which is a set of AI agents that’s the vision where we think it’s going, then that happens. Of course, as the models get larger. GPT-4 is an example. It’s able to take in more data, simultaneously begin to more closely approximate the decisions people would make. Also, as the models get trained on deeper concepts in business, they become more able to provide a level of expertise that they don’t have. Now, let’s face it, these models have expertise around crafting language, around understanding history, the topics that they would find if they were searching through the training data that were provided, which is off the Internet and contracts and a few other things. As these models get trained more specifically on problems within businesses, we’ll see them go from passing the SAT to passing, let’s say, a very sophisticated test that a business might give to an executive who is a category manager or who is somebody who manages pricing, somebody who’s in charge of purchasing, right? So these agents can become much more like a partner to those people and less like a simple tool that’s used to refer to facts. 

Are we finally getting our own J.A.R.V.I.S.?

Pete: If you remember the Tony Stark movies, he has this assistant, J.A.R.V.I.S., that helps him do all sorts of things, right? If you asked me a year ago how far away from having that, I would have said probably something like 20 years. And I don’t know where it is now, but I can tell you it feels a whole lot closer than it did at that time. And that’s really what I think at the end, we sort of aspire to is that level of capability, that level of intelligent agent that’s helping people at whatever level they are in the company, whether they’re just managing Google Ads or whether they’re running entire sets of operational components of the business that they have that level of assistance that’s really knowledgeable about their business can help them map out scenarios and can help them really start to think about making recommendations about what should be done. I think we’re much closer to that than ever before.

Conclusion:

Through the incorporation of GPT and advanced language models, AnswerRocket has embarked on a transformative journey, creating an intelligent agent that understands users’ needs and effortlessly retrieves essential information. With an eye towards the future, AnswerRocket envisions a world where AI is not just a tool but a collaborative AI assistant for every business executive, providing comprehensive analyses, strategic recommendations, and expert-level insights. 

To learn more about what AnswerRocket is doing in the analytics space with generative AI, visit AnswerRocket.com/Max.

The post Unleashing the Power of GPT: From AI Assistants for Analytics to our own J.A.R.V.I.S.? first appeared on AnswerRocket.

Watch the video below or read the transcript to learn more.

Is Max dependent on GPT or can other LLMs be used?

Mike: So it’s 100% flexible to use lots of different versions of GPT, or lots of different locations where the language models are stored, or lots of different language models. So we look at the language model very much like a database, something that over time will become faster and cheaper and more commoditized and we want to be able to swap in and out whatever those models are over time, so that we’re not dependent on it, on any one. We do use every capability that’s available to us from the language models, things like completions and embeddings, these are technical terms of the capabilities of the models and we will look for those same capabilities as we expand into additional models. But it’s not a dependency for our solution. And in fact there is a mode where AnswerRocket can run, in fact has run, until about six months ago when these language models were introduced. 

That does not rely on external language models at all, right? It relies instead on the semantics of the database, on the ontology that’s defined by a business and how they like to use their terms. And so it does not rely on having to have a GPT source. But when there is a language model in the mix, you get a more conversational flow to the analysis which makes it feel a lot more comfortable to the user. It’s clear that from a foundation model perspective, the providers of the core algorithms behind these models, there will be models that are specific to medical, that are specific to consumers, that are specific to different industries and different spaces. And so we very much expect to be able to multiplex across those models as appropriate for the use case and again treat them like any other component of infrastructure, whether that’s storage or database or compute. 

These models just become one more asset that’s available to enterprise applications that are really putting together productivity suites for the end user. 

Conclusion: AnswerRocket is not solely dependent on GPT; in fact, it initially operated without relying on external language models, using database semantics and business-defined ontologies. However, when language models are incorporated, it enhances the user experience, enabling a more conversational flow in data analysis. The focus is on leveraging the diverse capabilities of language models while treating them as components of infrastructure alongside storage, databases, and compute resources. Analytics and Insights experts like Mike foresee a future with specialized language models catering to various industries. The aim is to provide enterprise applications with enhanced productivity suites for end-users by multiplexing across different models as needed for various use cases.

The post The Future of Language Models in the Enterprise: A Multi-Model World first appeared on AnswerRocket.

Watch the video below or read the transcript of the interview to learn more.

Pete: Hi, I’m Pete Reilly. I’m one of the co-founders of AnswerRocket and the COO of the company. 

Mike: My name is Mike Finley. I’m also a co-founder of AnswerRocket and the CTO of the company. 

What is AnswerRocket and Max?

Pete: AnswerRocket is ChatGPT meets enterprise analytics. Generally, the problem we solve is when we go into really large organizations, they have mountains of data that’s growing every day, and they struggle to make good business decisions quickly from it. And so we dramatically accelerate that process. We’ll take a process that might take a week to figure out how a brand says it’s performing in a particular market and bring it to 60 seconds. What that does is then it enables the business folks to then go ahead and act on that information instead of spending days or weeks analyzing it. 

How does AnswerRocket leverage AI and GPT?

Mike: AnswerRocket has been a leader in this process of engaging naturally with business users for ten years. We’ve introduced many of the popular concepts that are core to this technology now. The advent of GPT, or really large language models in general, has meant an enormous leap forward in AnswerRocket’s ability to both understand the user, not just the facts that they’re asking, but the intent of their question. Intents are things like, is there a comparison? Are we looking for outliers? Are we trying to determine a specific course of action to take? Right. So understanding that as well as the answer that they get back from the solution being something that is far more natural and usable as human language, powered by the language models. 

What makes AnswerRocket different?

Pete: There are other companies in the market that I would say we differentiate additionally in a couple of ways. One is this ability not just to ask a natural language question and get answer from a database, but to actually run a model on that data, to do a forecast on that data, to do a driver analysis on that data, to do a deep market share analysis on that data. Those are capabilities that come with this addition of data science, machine learning, but also combined with deep domain experience to solve really thorny, challenging problems in these vertical spaces. 

How does AnswerRocket unlock answers from structured and unstructured data?

Mike: So it’s important to realize that enterprises have their data siloed in two major sections. Right? There’s databases, traditionally that have been built up over time with imported and loaded data. And then there are also the more informal, unstructured sources that are all the enterprise flows of information, whether that’s email, meeting transcripts, PDF files, reports, PowerPoints. And so one of the key things about the AnswerRocket vision and the vision for Max, the AI agent, is to be able to have enterprise users access data from either one of those repositories at the same time and have them work together to produce answers and insights that really can power opportunities that weren’t available before this technology.

How does AnswerRocket fit in alongside other enterprise BI tools?

Mike: Are we trying to displace the dashboards and the reports and all the things that businesses do today? It’s not about displacing those technologies, it’s about removing the friction, right? So we’ve seen so many examples of companies that have 600 different reports.  They have so many reports, they don’t even know which one to use, right? With something like AnswerRocket in place. You can just ask your question and it will find the right source, whether that’s stored in an existing dashboard, in an existing business intelligence tool. Wherever that information is stored in documents, we’re going to be able to go retrieve it, pull it into the language model, and let the language model answer the question based on those facts. Right? So it’s not about eliminating the value that’s been created by the calculations and the historical trends and things that have been observed, because all those business practices are very valuable and they’re key to how enterprises run. It is about removing the friction of leveraging those things, right? So it’s all about being able to take advantage of them in an easier way, to be able to get to that information faster, to be able to compete better in a way that’s more satisfying to the human users. Because they’re no longer in the tedium of working through the processes of finding data and joining it up and putting it into a spreadsheet.

Pete: Dashboards aren’t going away anytime soon. Look, most companies, the major key performance indicators that people’s bonuses are based on are on these dashboards, right? And it’s a unified way for a company to look at their overall performance, see how we’re tracking, are we hitting our goals or not? That’s here to stay. Changing that out would be herculean effort. But what you end up seeing is that a lot of times these dashboards, they’re really good for just reporting the news, like what happened. They’re really not great then at helping you understand why that thing happened, or what would happen if I did something differently, or what’s going to happen next, or what should I do. It’s terrible at all those things. And that’s where really, I think we come in, is automating those questions and getting users to these business decisions sooner, because what they do today, they go to that dashboard and somebody downloads a bunch of data so they can do all this analysis. And I think I agree with Mike. To me, what ends up happening is those dashboards are in place. They become a data source or something like AnswerRocket. It’s just another place to get governed information that’s been cleansed and approved and so on that people can then use to automate their analysis and make good decisions easily. 

In Conclusion

By leveraging cutting-edge technologies such as GPT and combining data science, machine learning, and deep domain expertise, AnswerRocket brings natural language querying, advanced modeling, and deep analysis capabilities to the fingertips of business users. These capabilities give AnswerRocket the ability to revolutionize how analytics leaders access and leverage both structured and unstructured data, streamline workflows, and extract meaningful insights that drive tangible business outcomes. 

Get AnswerRocket and get meaningful insights from your data now.

The post How AnswerRocket’s AI-Driven Insights Revolutionize Enterprise Analytics first appeared on AnswerRocket.

Will One Large Language Model Rule Them All?

While single-model language systems have been groundbreaking, they have limitations such as biases, inflexibility in handling different tasks, and the risk of overfitting. The multi-model approach offers a solution by combining the strengths of multiple models. By using different models for specific tasks, businesses can enhance their data analytics capabilities and overcome the limitations of relying solely on a single dominant model.

The Evolving Landscape of Large Language Models

In the near term, it is unlikely that a single large language model will emerge as the dominant player. Instead, we can expect a range of models tailored for specific tasks and excelling in their respective domains. Looking further into the future is challenging, but ongoing development will continue to improve both domain-specific and general models. Companies will invest in building robust large language models that cover a wide array of knowledge and information. Instead of narrowing their focus, these models will incorporate multiple domains and subjects, creating a diverse ecosystem of models.

A Nuanced Approach: Leveraging Multiple Models

A multi-model approach recognizes the limitations of a single dominant model and embraces the diverse capabilities offered by different models. By combining domain-specific and general models, businesses can achieve more accurate and contextually relevant language understanding and generation. This approach allows for the utilization of smaller, specialized models that excel in specific areas, providing more relevant insights.

The Benefits of Multiple LLMs in a Multi-Model Language System

Multi-model language systems integrate multiple Large Language Models (LLMs), each specializing in different areas. This approach offers several advantages:

– Businesses can leverage the unique strengths and expertise of each model. For example, one model may excel in generating creative content for a digital marketing agency, while another might be proficient in assessing market trends for a consumer goods manufacturer. This blending of models enables businesses to achieve comprehensive language processing capabilities.

– The potential applications of multi-model language systems are vast. In the pharma sector, integrating LLMs trained on medical literature, patient treatment therapies, and clinical trial data could facilitate drug research, development, and improved patient outcomes. Likewise, in insurance, combining models trained on claims data, policies, and regulatory documents could enable accurate predictions, effective risk management, and regulatory compliance.

The Future of Multi-Model Systems

The future of multi-model systems is incredibly promising. Ongoing research and development will likely lead to even more advanced capabilities and increased efficiency. Businesses are progressively adopting multi-model approaches to leverage their data analytics endeavors.

However, it’s crucial to acknowledge the challenges and limitations. Developing and refining multiple Language Learning Models (LLMs) require significant resources and expertise. Ensuring seamless integration and addressing biases inherent in individual models are complex tasks.

Multi-model language systems are reshaping the world of large language models. By combining the strengths of multiple LLMs, businesses can unlock new levels of language understanding and generation. The advantages of a multi-model approach over a single-model system are clear: enhanced capabilities, broader applicability, and improved performance. Executives in the data analytics space should recognize the potential of multi-model language systems and incorporate them into their AI strategies. Continued research and development will be vital in harnessing the power of a multi-model world and shaping the future of data analytics. Embrace the potential, explore the possibilities, and embark on the journey towards a multi-model future.

The post The Future of LLMs: Embracing a Multi-Model World first appeared on AnswerRocket.

You can listen to Mike’s episode of Inside Analysis on Apple Podcasts or Spotify. You can also watch the video below on YouTube.

On this episode of the podcast, Eric sits down to talk with our very own Mike Finley about all things chatbots, large language models, and generative AI. 

AnswerRocket has long been using natural language to help businesses have conversations with their data. With the emergence of OpenAI and ChatGPT, this capability has taken a huge leap forward. This is because LLMs are really good at:

1. Understanding what humans mean.
2. Understanding what they are trying to achieve.

How is this different from how we’ve traditionally gleaned insights from our data?

For years, we’ve made humans learn how to talk like computers, and speak in computer code. We can now finally ask a question in our language and get an answer back in our language. That is the true benefit of large language models and generative AI.

Mike makes a couple of great points about working with LLMs: 

You have to to “treat it like a human coworker…you have to train them on your business, make sure they are an expert in that area that you’re talking about, and you would fact check their results…”

LLMs like GPT are great because it knows a lot about general things, but it doesn’t know anything specific about your business and your data. 

This is where Max comes in. Max is a co-pilot for your business that helps with AnswerRocket’s BI initiatives.

AnswerRocket’s Max can dive in and understand your business and your data while GPT can communicate those insights in plain English with end users. 

While organizations may have concerns about sharing their data with the most used chatbot in the world, Mike assures us that the Max solution and its connection with OpenAI is built for enterprises.

→ When we connect to OpenAI on behalf of customers, we use a private instance purchased just for the customer, it’s just as private and secure as anything else you might be using in the cloud.

→ Max comes with all the features of an enterprise level BI tool such as role management, security, role-level data isolation, etc. 

The implications of this type of technology are huge for businesses looking to get valuable insights quickly. As Mike points out, large language models or “intelligence on tap” is the ultimate power tool for business. 

Visit answerrocket.com/max/ to learn more exploring and analyzing your data 10x faster with AI. 

The post Mike Talks Max on Inside Analysis Podcast first appeared on AnswerRocket.

AnswerRocket Co-founder, CTO, and Chief Scientist Mike Finley has a knack for breaking down complicated concepts and making them much easier to understand. Mike dives into how large language models work, and what makes generative AI different from other types of AI. 

He also discusses what large language models (LLMs) could look like in the future, and how AnswerRocket has made the perfect pairing between our augmented analytics platform and ChatGPT’s conversational capabilities. 

Read the transcript of the interview below.

Question: From Day 1, how has AnswerRocket leveraged AI and natural language?

Mike Finley: Since day one, AI–and specifically natural language–have been at the heart of what we’re trying to bring about. The reason for that is, fundamentally, the very first computer program was written, whatever, almost 200 years ago. A program, by definition, is a human speaking in computer language. The goal, the AnswerRocket goal, was to stop that, to have the computer speak the human language, right? That was our very initial purpose and to erase that 200 years of history of people having to speak in computer and instead have the computer learn to speak what we do. It’s been a core aspect of the solution from the beginning… 

The idea of a prompt of engaging with text or even with voice, whether it’s from mobile or from desktop, basically communicating naturally with the machine and having the machine naturally communicate back to you, that’s been kind of the central theme of how we’ve been seeking to democratize access to data, right? 

That’s the idea, that democratization happens because everybody can speak, right? 

There’s some really good studies out there that talk about, for every person that can read a spreadsheet, there are ten people that can read a sentence, right? 

For every person that can read a sentence, there are ten people that can read a graph, right? 

If we can understand people’s natural language and talk back to them in pictures and in words, that solves a problem that’s really never been solved before. And that’s been our goal. 

Question: How do LLMs benefit the end user?

Mike Finley: The fundamental technique of AI working using these things called neural networks, or neurons, artificial neurons, has been around since the 1950s. Again, nearly 100 years of this concept, and they’ve been getting larger and better. As machines have gotten better and faster, the techniques have improved and so forth. This fundamental idea from a long time ago that said, “Hey, can we make something that’s like a human neuron and make a group of those like a human brain that’s really been evolving and evolving?”

Suddenly when a machine starts to act like a human in terms of being able to translate languages and understand concepts or finish stories or whatever, these large language models are really just big groups of those early designs, of those early neural networks. Now they’re organized in a very special way, right? There have been major advances kind of in layers, things like deep belief networks and autoencoders, things like transformers. These are layers of the technology that have happened along the way. 

Fundamentally, a large language model is just a really big group of artificial neurons organized in a special way, the same way that our real neurons are organized in a very special way. They’re set up in a way that they can take in what a person is communicating and they can answer back in that same fashion. 

Question: What’s unique about a generative AI model?

Mike Finley: A large language model is part of a group of algorithms, a kind of program, right, that’s called an autoencoder, right? I remember the very first one that I worked with was in 2007. It turns out the Post Office has a giant database of handwritten digits because the Post Office needs to be able to read all kinds of crazy envelopes where people have written really sloppy eights that look like threes and really sloppy fours that look like nines. They have tens of thousands of examples curated in a database somewhere. 

There’s a guy named Jeffrey Hinton who’s out of the University of Toronto. I think he’s at Google now, but he took all that data and he fed it into a computer. This is the brilliant thing about his work, is he didn’t tell the computer which ones were threes and which ones are eights, which ones are twos, and which ones were fives, right? He just gave it all to the computer and said, you figure it out. This computer, sure enough, created ten separate groupings of these things, and it figured out what zeros and ones and twos and threes and fours and so forth were in that grouping, right, using this auto encoding technique. The wild thing was when I recreated the results of that original paper and so did everybody else out there in the AI community. When I recreated that the wildest part about it was not only could it recognize all those previously human-made handwritten digits but you could ask it for an original two and it would make an original two. 

It wouldn’t look like a bunch of static, right? It would look like a two that was nowhere in the database, right? This is kind of a chilling feeling, right? 

That’s called a generative model, right? And that’s the G in GPT. The generative model says, hey, after you’ve shown this thing, all the examples of everything that you want to train it on, that you want it to learn, then you can just kind of poke it and say, okay, well, now what do you think? It’ll actually generate back to you something that’s like all the stuff you trained it on, right? Before these generative models, AI was all about saying, let me beat a bunch of examples into the machine until it guesses right on the next one, right? Adjusting all these really fine-tuned numbers. These generative models all of a sudden can kind of flip that around, right? 

They can not just be able to look at the inputs from the outside world and say, “Oh, I think that’s a cat or it’s a dog. I think it’s a three or a five,” or I think whatever the conclusion is from that algorithm. But they can say, “The recommendation for you is to watch the next John Wick movie”, right? Those kinds of things suddenly come out of these generative models that are able to basically be kind of poked from the outside and caused to go into motion and cause to do their work. 

Question: How do LLMs work with foreign languages?

Mike Finley: Well, it turns out if you take back to my handwritten digits examples, if you take a person who, for example, I grew up in Spain, we put lines across our sevens, right? Our seven. You draw a normal seven, and then you put a line through it. You do the same thing with Z’s, right? 

Well, that’s almost like another variety of language, right? It still knows to put those things with the sevens. It doesn’t get confused and put them with the fours or with the twos. 

Well, it turns out when you take a language model and you train it on a whole lot of English and then you train it on a whole lot of French and a whole lot of Spanish and Hebrew and Chinese, it turns out it’s able to learn all those separately. 

This is really where these emergent phenomena come from, right? The idea of emergence in AI is unexpected results that come out of the combination of two or more things. There’s an emergent phenomenon, which is that the idea of “house” in one language and the idea of “house” in another language are really closely related inside the quote-unquote mind of the machine, right? That it determines that those two concepts “house” and “casa,” which is the Spanish word for house, that those two things are really very similar to each other because of the way that they relate to everything else. 

The English model showed that a person opens the door of a house and dogs live in a dog house, and houses have roofs. Well, all those same kinds of ideas exist in Spanish, or in Hebrew, or in French, or in Chinese. The machine is organizing all these thoughts and let’s not get philosophical, but let’s call them thoughts when it’s organizing all of those thoughts, it turns out the concepts that it builds in two different languages end up being very similar. It’s able to translate simply by relating where each of those concepts are within the context of its thoughts, right, of what it’s done with the word. It’s the idea that there are not “thoughts,” they’re called “embeddings.” The idea of embedding is what is the large language model’s concept of a word, or a phrase, or a sentence, or a paragraph–the same as if to a human you went up and said, “A red fire hydrant was on the corner.” 

Well, your mind has now gone into a certain state. If I put you in an MRI machine, I could scan your mind and it would be a certain state, right? You have that thought in your brain. The embedding is the representation of the thought in the brain of the large language model, right? It turns out again that I can take the same phrase in two different languages and your brain would go into the same shape if it understood those two languages. I can give that same phrase in two different languages to a large language model and it will get a very similar shape as far as how it relates those things ultimately inside of its embedding. 

Question: Just how large will LLMs get?

Mike Finley: Starbucks, if you say, “I want a hot drink,” they have three sizes, right? The 20 is the largest one. If you want a cold drink, they actually have four sizes, right? There’s an extra large cold one and you might say, “Why?”

“Why is there an extra large cold one and not an extra large hot one?”

The reason is because the top seller in hot drinks is the middle size. In other words, they made them bigger and bigger until sales started going down. They did that for hot and it turns out they only need three sizes. Do that for the cold. It turns out you need four. You need that fourth size to know that you’ve reached the peak of the performance. 

Well, same thing in large language models, right? We’ve been making bigger and bigger language models, really since I was a child, right? Because a long time ago in a galaxy far away, you would have two layers of 30 neurons. That was worth writing a paper about in a scientific journal, right?

Well, now two layers of 30 neurons is 110 trillion of the size of the neural network. Not literally, but it’s a very small network. These networks keep growing bigger and bigger. Now, unlike the Starbucks drinks where they’ve figured out the highest size, that’s the biggest return. We haven’t figured it out yet for language models. We keep making them bigger and they keep getting better. The question is, where is the end? 

Question: What differentiates ChatGPT from other LLMs?

Mike Finley: The original language model, the way that it’s trained, the same way that the post office said, “Hey, here’s a whole bunch of digits, figure it out,” right? The way that these language models are trained is, “Here’s a whole bunch of Internet stuff. Figure it out,” right? Literally the figure it out part is tell me what the next word is going to be. I’m going to give you 1,000 words. Tell me what the next word is going to be. 

The language model, on one hand, you could say, “Yeah, well I could just have a database. I could save the Internet and then I can tell you what the next word is going to because all I gotta do is go find the 1000 words that you gave me, find it on the internet somewhere and look at the next word and boom, I’ve got the answer.” Right? 

Now, if you do that, you haven’t generalized at all, right? Because now if I change one of those thousand words, you’re not going to know what the next word in that list is. 

Language models aren’t really, they’re not allowed to memorize that way, right? They’re not allowed to, meaning that the program as it’s written doesn’t allow the language model to simply store away what it’s been taught. It has to generalize it. It has to say, oh, you said, having had three different misspellings of that verb, I know that all those are the same thing, right? 

Or you said something in English and it looks a lot like something that I saw in French, so I’m going to remember that in the same way I’m not going to remember that it exists in English or that it exists in French. I’m simply going to remember the concept of whatever that idea is. 

Basically, this idea of training it to learn what the next thing is how the language model gets really smart. You can imagine if you took every list of 1,000 words on the entire internet and gave it that as an example, then you’ve got billions of examples to train with, right? That’s really, that super big training set is what allows us to keep building bigger and bigger networks to learn everything that’s going on in here, all right? If all you ever did was learn to say what the very next word is, then you wouldn’t really be able to have a lot of chat dialogues. 

Because all that stuff on the internet, Wikipedia, and the laws of the state of Kansas, and the case history for the courts of Mexico, whatever it is, all of that, it’s not full of a bunch of chats, right? It’s not full of a bunch of conversational language. There’s some, sure, you could watch interviews if you transcribe YouTube videos, you’re going to get a nice list of chats, right, of conversational chat, so it could learn some of how to predict the next word. It’s a really bizarre thing to try to teach one of these language models how to chat because it needs to say something and then wait for you to answer, not try to guess your next word. It needs to let you answer and then it needs to try to continue what it said before knowing what you said, right? It’s a very different kind of way of thinking about language. 

If all you ever learned to do was predict the next word, then you’re kind of a know-it-all, right? You want to just keep talking like I’m doing right now, instead of letting the other person have a conversation. 

What the ChatGPT specifically was all about saying, what, let’s actually train it to…

     …know when to stop.

     …to know what to say.

     …to know how to listen. 

Essentially, the reinforcement learning came in as a secondary stage that says, “Yeah, I know you know how to finish every sentence anybody ever said on the entire internet. You’re so smart. Great. Now I don’t need you to do that. What I need you to do is actually have a sensible conversation with a human.” There has to be some reinforcement, just like there would have to be if you had a person who had never had a conversation and suddenly they’re out trying to have a drink at a bar. 

Well, they wouldn’t be much fun to have a drink with, right? 

Some reinforcement learning goes through the process of getting that person to stop guessing what the next word is going to be and do a little bit of active listening. 

Question: How is AnswerRocket using LLMs?

Mike Finley: You can think of it as kind of a wonderful marriage, right? AnswerRocket has been in development for a number of years, learning how to understand databases, find insights in data for various different industries, right? For pharma, for banking, for insurance, for packaged goods, for video games, whatever that insight-seeking behavior is that we’ve been teaching AnswerRocket with a thin layer of natural language understanding around it and a fairly sophisticated insight selection model, right? We have become really good at saying, “Hey, based on the data that you have and the question that you’ve asked me, here are some things that I can tell you that are really insightful, like an analyst would have provided you.” 

Now, GPT comes along and what it does really well is it understands the question the user might ask, right? Where AnswerRocket might have needed very specific time frames, measures, facts to identify the question, GPT can tell right away, 

     “Oh, you’re trying to compare two things to see if they grew the same, right?”  Or

     “Oh, what you’re after is a deep understanding of where the anomalies are in this list of things, right?”

GPT understands that it doesn’t know how to get that answer. It understands what you want to know, but it has no idea how to get it. AnswerRocket is kind of a perfect mate to that because it can understand what the user wants, connect up with AnswerRocket. Now all of a sudden, Max understands you because of how smart GPT is and it knows how to answer because of how smart AnswerRocket is. 

Now, Max does all the analysis and finds out the comparison or the trends or the outliers that you’re after. The question is, how does Max explain it back to the user? Now, Max normally would give a chart, it would give a list of facts, it would cite a whole bunch of different really interesting things. 

The great thing about going back through GPT on the way out back to the user is that we can get GPT to explain those facts back to the end customer. GPT can say, “Oh, I’m reading good news! I’m telling you something really great. Isn’t it wonderful that this has occurred?”

Max didn’t know that. Max didn’t know if it was good or bad or if sales went up. Sounds like a number, right? To Max, GPT knows that sales going up is a good thing, right? These are logical constructs that exist in language, right? 

Unless we kind of build that in and hand tune it and tweak it, Max doesn’t really have on its own, doesn’t have that capability or AnswerRocket, doesn’t have that capability. So now, Max, not only can it understand you, but it has legs to go search the database. It has the brain to go understand and analyze the insights. It’s got the voice to give it back, to give it all back to the user in a way that makes it really feel like you’re collaborating with a coworker, collaborating with analyst that knows all of your data inside out better than you ever really want to, and is going to accelerate your day by taking the bulk out of the analysis time and giving you back the time to consider what the results are, to figure out what you’re going to do next. 

Question: How will LLMs have evolved in 2 years?

Mike Finley: First prediction in a two-year time frame: GPT will not be the only standout.

So this technology is unique and special (GPT is), but OpenAI doesn’t have a monopoly on it by any sense. 

I think there’ll be a lot more models that perform as well as or better than GPT. They’ll be specialized in different areas. The next part is going to be the movement of the model “down.” 

Right now, it’s in the cloud because that’s where you can have really expensive infrastructure and apply it to a lot of different people in maybe not a two-year time frame, but not much longer. It’ll be in your phone, right? It’ll be something that’s rapidly available, widely available to everyone. Not the giant version that’s been trained on everything that’s ever been written, right? A small enough version that can make an appointment for you and whatever, talk to your friend’s chat agent to settle whether or not you’re going to have a drink after work, right? 

Those kinds of things that’ll all be in place, right? Because it’s much easier. We’ve been in a world where getting my phone to talk to your phone is this tedious process of writing API calls and IP addresses and interconnections and making things intertwined. Human language evolved because it is extremely simple and yet it conveys so much information. Machines will be talking to machines using human languages, right? Not because they’re great languages, but because they’re just really good at capturing meaning in a very small package, right? There’ll be a lot more of that kind of thing. We’ll have individualized agents, they’ll be pushed further out to the edge. The last thing I guess in this bucket is that these models are going to get smarter and smarter. Now, again, the models learn from documentation that exists out there in the real world, right? They learn how things work because of documents that exist in the real world. 

They don’t learn things that are not out there documented in the real world. In the case where, let’s say, companies have somebody pricing is a good example. Many companies have experts who are really good at kind of saying;

     “What should we do with pricing for next season?”

     “What should we do for the next promotion based on this new product launch?” 

These are human experts. Their knowledge, the way that they think, has never been written down on a piece of paper in a way that’s codified in rules that you could feed into a large language model. Instead, the language model is just going to have to absorb all these decisions. It’s going to have to absorb thousands of examples of decisions made by humans and use those to say, oh, we should raise the price or we should lower the price, or we should change the product name, or we should innovate the packaging, whatever the thing is that the human would have done. 

Over the next couple of years, you’re going to see models that are trained with a lot more depth of that kind of experience, right? GPT wasn’t trained with marketing mix models for every brand in America for the last 30 years. In a couple of years, you will be able to have a large language model that is trained on that kind of depth of industry-specific information, right? And same with weather forecasts. You can imagine every other category of large amounts of information that formerly was a human expert grokking it all and becoming the person that relayed it. Now that’s going to be these language models. As those training sets get built up and up, they’ll be able to start making a lot of those decisions or recommendations like human users previously would do. 

What that’s going to do is really, again, open up the possibility it may not better at analyzing the trajectory of the price of your competitors, but you can apply it to all your competitors every day, which you could never do before, right? 

You used to be able to do it for one competitor once a week. Now it’ll do it for every competitor every day. Right. That’s going to again change the game in business and make it better for consumers because pricing will be lower, better for the manufacturers because they’ll be spending less to get better results. Ultimately there’ll be less waste in the system because it does a better job of understanding the trends of consumers and weather shipments and all these different things. 

I see enormous amounts of efficiency and profitability and improvement getting built in because language models ultimately just remove the friction of understanding between systems. 

In conclusion, AnswerRocket has embraced AI and natural language processing since its inception, aiming to bridge the gap between humans and computers in terms of communication. By enabling users to interact with data through natural language queries, AnswerRocket has democratized access to information, catering to a broader audience that may not be proficient in reading spreadsheets or interpreting graphs. Leveraging large language models (LLMs), such as generative AI models, AnswerRocket empowers users to engage in seamless conversations with machines and obtain valuable insights. LLMs have revolutionized the AI landscape by simulating human-like understanding and generating original content. Moreover, these models have proven their ability to comprehend and translate foreign languages, capitalizing on the concept of emergence to recognize similarities across languages. As the size of LLMs continues to grow, their capabilities expand, prompting exciting possibilities for the future. With the introduction of ChatGPT, AnswerRocket has further refined the conversational aspect of LLMs, emphasizing active listening and enabling meaningful interactions between humans and the system. AnswerRocket’s integration of LLMs has paved the way for advanced data analysis and intuitive user experiences, revolutionizing the field of data analytics and unlocking new possibilities for businesses across various industries.

The post What Makes Generative AI Different? first appeared on AnswerRocket.