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.

The post What has Max enabled ABInBev to Do? 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.

June 05, 2024 11:00 AM Eastern Daylight Time

ATLANTA–(BUSINESS WIRE)–AnswerRocket, a leader in GenAI-powered analytics, today announced the availability of AnswerRocket in the Microsoft Azure Marketplace, an online store providing applications and services for use on Azure. AnswerRocket customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management.

Empowering Decision-Makers with Conversational GenAI Analytics

The need for data-driven insights has never been greater, yet many enterprises are held back by low data literacy and a scarcity of analytics expertise. Leveraging the latest in generative AI technology, AnswerRocket’s GenAI analytics platform transforms how enterprises interact with data, making advanced analytics and insights accessible to all levels of an organization. Max, the platform’s chat-based AI analytics assistant, enables users to engage with data intuitively – asking questions and receiving insights as if conversing with a colleague. This approach simplifies complex data analysis, allowing for the extraction of deep insights with minimal effort.

Accelerating Development of Custom GenAI Analysts

Skill Studio is a revolutionary feature within the AnswerRocket platform that helps extend the capabilities of Max. Skill Studio’s low-code environment enables data scientists, analysts, and developers to rapidly develop custom GenAI analytics assistants for specific business analysis tasks. This end-to-end solution speeds up the creation, testing, and deployment of custom analytics Skills, expanding Max’s ability to provide various data analyses on demand. Users benefit from Max’s extensive suite of out-of-the-box Skills, including driver analysis, trend analysis, market share analysis, and dimension breakouts to give users deeper insights into their business performance.

Seamless Scalability with Enterprise-Grade Infrastructure

The integration of AnswerRocket with Microsoft Azure enables the platform to scale dynamically, accommodate massive datasets, and deliver real-time analytics performance that meets the demands of even the largest enterprises.

“Launching AnswerRocket on Azure Marketplace is an important milestone for us,” said Alon Goren, CEO of AnswerRocket. “This collaboration enables us to provide Microsoft customers with robust, scalable solutions for data analysis that complement their Microsoft Azure investments. We are excited to see how our users leverage the power of AnswerRocket combined with Azure to transform their data into meaningful business insights.”

“Microsoft welcomes AnswerRocket to Azure Marketplace, where global customers can find, try, and buy from among thousands of partner solutions,” said Jake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp. “Azure Marketplace and trusted partners like AnswerRocket help customers do more with less by increasing efficiency, buying confidently, and spending smarter.

The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. The Azure Marketplace helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use.

Learn more about AnswerRocket and get it now by visiting its page in the Azure Marketplace.

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

For more information, press only:
Tate Mikkelsen
10Fold Communications
answerrocket@10fold.com
(925) 639 – 0409

The post AnswerRocket Now Available in the Microsoft Azure Marketplace 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.