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.

This TDWI report, Driving Digital Transformation Using AI and Machine Learning, reveals the latest research on the business intelligence and analytics market, as well as best practices for a successful transformation.

TDWI specializes in educating business and IT professionals about the strategies and tools necessary to create, maintain, and enhance data and analytics across an organization.

Designed for business leaders, this report explores AI and machine learning as they’re currently implemented and where these technologies will go in the future.×

Click here to download “Driving Digital Transformation Using AI and Machine Learning.”

This TDWI report covers topics like:

How AI is Used in Digital Transformation Right Now

Why do 90% of respondents surveyed in this report think AI is a competitive advantage?

From automating work to augmenting intelligence, the use cases for AI are growing, as are the investments companies are putting toward deployments.

AI has already arrived. What do businesses need to know to capitalize on this technology before they get left behind?

The State of AI and Augmented Intelligence

Half of respondents believe automating the generation of insights with augmented intelligence tools is a dominant use case for AI.

This report dives further into the ways humans can augment their own intelligence and workflows with AI to make smarter decisions.

Plus, it discusses the nuances of machine learning and why it dominates AI technology. As stated in the report:

“BI and analytics solutions can also employ machine learning to explore data automatically and spot trends and patterns that users working with standard querying and reporting capabilities may not have seen.”

The Case for Open Source AI

Concern over the “black box” and biased insights aren’t unfounded, which is why transparent, open source AI technology is critical.

Survey respondents are “big believers” in tools such as R and Python. Open source AI vendors recognize the value in allowing IT departments to deploy their own models within a proprietary solution.

Success Factors for Digital Transformation with AI and Machine Learning

Do you know what data infrastructure you need to support AI? Quality data and streamlined pipelines are critical. Having the right team is paramount, too.

The best practices outlined in this report explain which users can leverage augmented intelligence analytics and how to determine the skill sets needed for successful AI deployment.

Get the report recommendations for a successful digital transformation with AI and machine learning.

The post How to Drive Digital Transformation With AI and Machine Learning first appeared on AnswerRocket.