Ep. 16 Why LLMs Will Kill Industry Clouds | AI Insights & Innovation

Industry-specific large language models (LLMs) are poised to replace industry clouds due to their dynamic capabilities and continuous learning. Dave Lum discusses how LLMs, trained on specialized datasets, offer enhanced precision, customization, and real-time decision-making, surpassing the static nature of industry clouds. Examples include Google's Med-PaLM for healthcare and BloombergGPT for finance. These LLMs provide deep industry knowledge and improve over time, making them more valuable for businesses. Despite their higher initial costs, LLMs' adaptability and efficiency make them a superior choice for industry-specific services.

• Industry clouds have been developed over the last 10-12 years, integrating industry-specific features into cloud products.
• These clouds are designed to address challenges such as compliance, data security, and operational efficiency.
• Public cloud providers have been motivated to build these to sell more cloud services.
• There has been collaboration with big consulting firms and companies to integrate these capabilities.

"We've been building industry specific features into our Cloud products whether it's software as a service, platform as a service, or infrastructure as a service probably for the last 10-12 years."

• Industry clouds streamline workflows and enhance collaboration through a centralized platform.
• They provide domain-specific functionality, which is valuable for industries like finance, retail, and healthcare.

"Industry clouds are comprehensive independent services by integrating a suite of applications tailored to a specific industry."

• Over the last decade, large cloud providers have collaborated with consulting firms to integrate industry-specific capabilities into public cloud services.

"In many cases, the large cloud providers have been collaborating with big consulting firms and big companies to get these industry specific capabilities into these public cloud services."

• Industry-specific LLMs are AI models trained on specialized datasets, understanding industry-specific languages and contexts.
• They provide knowledge services in a dynamic way, continuously improving with new training data.
• These models can leverage real-time data to make decisions, offering an advantage over more static industry cloud services.

"Large language models with industry specific capabilities are starting to emerge and they seem to have a lot of overlapping functions with industry clouds."

• Industry-specific LLMs are dynamic and continuously improving, unlike static industry cloud services.

"The advantage would be static versus dynamic where the industry cloud are going to be more static and that if we need to change or improve them, we're going to have to rebuild them, redeploy them."

• These models are particularly useful in specific verticals like healthcare and retail.

"Industry specific LLMs are AI models that are trained on a specific domain dataset and so they focus on an area of the market and normally it's one industry specific LLM that's focused on a particular vertical."

• Industry clouds are more static and need rebuilding and redeployment for changes.
• Industry-specific LLMs are dynamic, leveraging real-time data to make context-aware decisions.
• LLMs offer a continuously improving service, unlike the more static nature of industry clouds.

"They're able to look at data in some cases real-time data industry in data that's coming out of the industry coming out of a particular business and understand how to make decisions in the context of that changing knowledge."

• Industry clouds provide pre-built, industry-specific applications, while LLMs offer a more flexible and adaptive approach.

"They're doing so in a different way this comes out of a knowledge model and you're able to leverage the knowledge model through inference you're able to train it and continuously improve it with new training data that's going into these industry-specific LLMs."

• The emergence of industry-specific LLMs may replace industry clouds due to their dynamic and adaptive nature.
• Organizations may prefer LLMs for their ability to continuously improve and adapt to real-time data, providing a significant advantage over static cloud services.

"There’s a reason why they would replace them and that's what I'm going to talk about in this show."

• The shift towards LLMs represents an evolution in how industry-specific services are delivered and utilized.

"It's kind of an interesting evolution of the market."

• The continuous improvement and real-time decision-making capabilities of LLMs provide a transformative potential for enterprises.

"That's going to provide you with a huge advantage over more static services where we're just leveraging in essence an application."

• Industry-specific LLMs have the capability to understand jargon, context, and nuances of particular industries, providing precise and tailored responses.
• These models leverage natural language processing tasks to perform complex decision-making and generate various outputs such as songs, PDFs, and code.
• Unlike general models like ChatGPT, industry-specific LLMs incorporate deep industry knowledge, enhancing their utility in specialized fields.

"Imagine that delivered as an industry-specific service. So in other words, the deep industry knowledge is within these knowledge models as well."

• Industry-specific LLMs are designed to deeply understand specific industries, providing more relevant and accurate information compared to general models.

"For example, healthcare understanding what the health codes are, what the treatment codes are, what diagnostic information needs to be put in process."

• These models are trained to comprehend industry-specific details, such as healthcare codes and diagnostic information, making them highly effective in specialized applications.

• Industry-specific LLMs continuously learn and improve from their experiences and interactions with different companies in the industry.
• Unlike traditional software that requires periodic updates, these models evolve autonomously, enhancing their performance over time.

"Its ability to continuously learn as it goes and to even learn from its own experiences as it's working with different companies in that particular industry, it gets smarter and smarter."

• The continuous learning capability of these models allows them to become increasingly accurate and efficient without needing manual updates.

"We don't typically have to make changes to it where it's not new releases of software like we do with industry clouds."

• These models eliminate the need for frequent software updates, providing a more seamless and efficient solution.

• Various industry-specific LLMs are being developed and deployed, catering to different sectors such as healthcare, finance, and legal.
• These models are often developed by major cloud providers and are designed to address specific needs within their respective industries.

• Med-PaLM 2: Developed by Google, this model is trained on medical datasets to assist with medical queries, diagnostics, and patient care decisions.

"Developed by Google, this LLM is trained on medical datasets to assist with medical queries, diagnostics, and patient care decisions."

• Sectra Chart: Focused on radiology, this model helps interpret medical images and provides instant radiological consultations.

"It is used to help with interpreting medical images and providing instant radiological consultations."

• BloombergGPT: Designed to process and analyze financial data, offering insights and predictions in financial markets.

"A model designed to process and analyze financial data, offering insights and predictions in financial markets."

• ChatLaw: An open-source model trained with datasets in the Chinese legal domain, assisting with legal document analysis and case law interpretation.

"An open-source model specifically trained with datasets in the Chinese legal domain, assisting with legal documents analysis and case law interpretation."

• The trend of developing industry-specific LLMs is expected to expand, with companies in various sectors creating tailored models to enhance their operations.
• Law firms, financial institutions, and other specialized entities may develop their own LLMs to improve their services and offer them to other organizations in the same industry.

"Some companies themselves, such as a law firm, may go into business and start building industry-specific LLMs for their law practices."

• The continuous learning and adaptability of these models will lead to the creation of highly sophisticated and valuable knowledge engines.

"We're able to learn from the processing of other customers of these industry-specific LLMs and build them up into much smarter, much more valuable knowledge engines."

These comprehensive notes encapsulate the key ideas and details discussed in the transcript, providing a thorough understanding of industry-specific LLMs, their capabilities, and their future potential.

• Industry-specific LLMs are critical for sectors such as healthcare, retail, and manufacturing.
• Significant investment is being made by private ventures to build these tailored models.
• Custom LLMs provide more value than generic models for specific industry applications.

"There's a lot of investment going on in the back right now funded by private Venture which are building these things out and it's a perfect application for large language models."

• Industry-specific LLMs offer more precise and relevant outputs tailored to the specific language and context of the industry.

"LLMs are tailored in to Industry industry specific languages in context providing more precise and relevant outputs and specialized tasks."

• Generic LLMs like ChatGPT cover a broad domain, while industry-specific LLMs focus on particular verticals.
• Industry clouds and ERP systems are typically static and harder to update compared to the dynamic nature of generative AI systems.

"Industry clouds don't have that capability; they're typically not an AI-centered system."

• Industry-specific LLMs can adapt and improve over time, unlike static systems.

"Once you define the value, once you define the processes, those things are very difficult to change."

• Tailored to industry-specific contexts and languages, providing more precise and relevant outputs.

"LLMs are tailored in to Industry industry specific languages in context providing more precise and relevant outputs and specialized tasks."

• Capable of analyzing and interpreting large volumes of complex data, offering insights that traditional cloud solutions might miss.

"LLMs can effectively analyze and interpret large volumes of complex data and they're able to offer insights that might be more difficult to extract within a traditional Cloud solution."

• Facilitate faster, more informed decision-making processes essential for dynamic industries.

"With their ability to process data quickly, LLMs can facilitate faster more informed decision-making processes essential for dynamic Industries."

• Offer intuitive user interfaces, making technology more accessible to non-experts.

"With their conversational capabilities, LLMs can offer intuitive user interfaces making technology more accessible to non-experts in the industry."

• Minimize the need for extensive infrastructure by integrating AI-driven insights directly into operations.

"LLMs could minimize the need for extensive infrastructure by integrating AI-driven insights directly into operations, reducing reliance on traditional Cloud setups."

• Can be run on various platforms including on-premises, managed service providers, colo systems, or private cloud instances.

"We could work a deal out with an industry-specific LLM provider to have that LLM instance that runs either on-prem or on our managed service provider, colo system, or in a cloud instance that's private to us."

• Organizations may prefer private deployments for enhanced control and customization.

"Many organizations are going to opt for that because they're going to want their own tailored solutions."

• Advantages of Centralized LLMs:

  • Centralized LLMs can dynamically gain knowledge by working with multiple companies within an industry.

  • They learn from various external stimuli, including competitors, to improve problem-solving capabilities.

  • Centralized LLMs provide a broader and more comprehensive knowledge base compared to isolated LLMs.

  "If you have an LLM that's able to gain advantages and knowledge by working dynamically with several companies within that industry, you want to get that knowledge to work for you as well."

  • Centralized LLMs can leverage collective industry knowledge to enhance their functionality.

  "It's able to learn from other companies, perhaps even your competitors, to solve problems and become better at solving problems."

  • Isolated LLMs lack the ability to incorporate external knowledge, limiting their effectiveness.

  "If you have a copy of that LLM, you're not going to take advantage of that because it can't see those external stimuli that'll change its knowledge base."

• Historical Context and Evolution:

  • The concept of industry-specific AI knowledge bases has existed since the 1980s.

  • Industry clouds have been emerging over the past decade but have not reached widespread adoption.

  "Industry-specific LLMs are pretty new to the market. We've always had the idea of having industry-specific AI knowledge bases; that's nothing new."

  • The current growth of industry-specific LLMs is fueled by the generative AI explosion and substantial investments.

  "The industry-specific LLM markets are growing up out of the generative AI explosion that's right now."

• Future of Industry-Specific LLMs vs. Industry Clouds:

  • Industry-specific LLMs are predicted to replace industry clouds due to their superior dynamic capabilities.

  • LLMs offer more value by continuously learning and adapting, unlike static industry clouds.

  "I think that the use of industry-specific LLMs is going to replace the use of industry clouds."

  • Industry clouds may integrate LLMs, but the primary value will derive from the LLMs themselves.

  "We're going to have an industry cloud, probably all of them, that are going to have this capability; however, the value is going to come from the LLM itself."

  • The shift towards LLMs will result in cloud-delivered, subscription-based services providing significant business value.

  "We're going to see lots of industry-specific LLMs that you can use on demand, you have to pay a fee, you subscribe to them, they're typically going to be cloud-delivered."

• Predictions and Business Implications:

  • Industry-specific LLMs will become the primary method for leveraging industry-specific services.

  • Businesses that adopt these LLMs will gain substantial advantages and drive industry innovation.

  "Industry-specific LLMs are going to be the way in which we leverage industry-specific services out there."

  • The adoption of LLMs will propel innovative businesses to new heights.

  "The innovative businesses are able to see the value in this technology to take their industries to the next level."

• Final Thoughts:

  • The speaker expresses confidence in the prediction that industry-specific LLMs will dominate.

  • Encourages the audience to stay updated with related content and industry news.

  "I think it's an easy prediction to make, and I think it's a good bet that industry-specific LLMs are going to be the way in which we leverage industry-specific services out there."

  • The speaker signs off, promoting additional resources and platforms for further information.

  "Don't forget to like and subscribe and check out our other stuff at the Cube... until next time, you guys have a safe week."