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🤔 Hello Agent Smith, Opus or GPT-4 ?, AI medical imaging....

AI agents are all the rage, Which models should leaders choose, Nvidia stepping up AI in Healthcare..

Author

Adam Martin
March 25, 2024

TL;DR

  • AI agents, which pair large language models with tools and data to complete tasks, will significantly impact 2024. These agents will quickly move beyond novelties to handle routine work like scheduling and document updates. Specialization, modularity, and multi-agent collaboration will emerge, with agents becoming consumers of content and tools optimized for their use. New job roles will also arise in developing, managing, and monitoring AI agents.

  • Anthropic unveiled its powerful new Claude 3 AI model family in March. The largest Opus model outperformed GPT-4 and Gemini Ultra on several benchmarks. The mid-sized Sonnet excels at rapid intelligent tasks, while the smallest Haiku can swiftly digest dense research papers. Notably, all three models can process visual data like documents and web interfaces, a key capability for enterprise users. With varying strengths across size and speed, the Claude 3 lineup aims to meet diverse AI needs.

  • At its 2024 GTC AI conference, Nvidia showcased its growing ambitions in healthcare by launching around two dozen new AI tools for the industry and partnerships with major players like Johnson & Johnson and GE Healthcare. Nvidia's BioNeMo platform aims to accelerate the costly drug discovery process through generative AI. With its shares up nearly 100% year-to-date, the AI chip leader sees significant revenue potential in healthcare, a focus area it has been developing for a decade. However, healthcare employees' concerns about AI adoption must be addressed for successful implementation.

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🕵🏽 AI Agents are all the rage…

Gen AI, AI, LLM—pick your buzzword and insert it into your business. That is the trend going on right now. Investors are focused on AI startups, and budgets for AI projects are soaring. As business leaders, this can confuse us about where, what, and how to focus. AI agents and solutions that provide a “Human in Loop” are gaining the most traction and seeing real value.

In the weeds:

  1. AI agents will quickly graduate from novelty to doing simple, boring, routine work, such as updating documents, scheduling, and auditing.

  2. Specialization and modularity will emerge, with specialized models, tools, datasets, and APIs designed specifically for AI agent use.

  3. Multi-agent development will occur, with specialized agents working together on complex tasks and in hierarchies, where some agents focus on high-level objectives while others handle task-specific work.

  4. AI agents will become consumers, leading to websites, APIs, and tools optimized for agent discovery and use. This will also lead to the emergence of new job roles like Agent Engineer, Agent Manager, and Agent Ops.

The impact: Daily use cases, platforms, and tools are emerging to help leaders create new AI Agents. As technology progresses, these agents will get more sophisticated and easier to build. Before starting any projects, leaders should identify use cases where they see repetitive work being done that has a high cost to productivity or is taking away time from innovative, high-value tasks for their teams. Once areas are identified it is important to ensure you have the right data needed to train an AI agent. These projects will fail if you cannot provide good experiences for internal and external customers.

“Keep Moving Forward”

Walt Disney

👋🏻 Claude 3.0 or GPT-4…

Cant Decide Fred Armisen GIF by IFC

↜Gif by snl on Giphy

Anthropic released the Claude 3 Opus model earlier this month. Tests have shown that Opus outperforms GPT-4 and Gemini in many areas. Our team has been testing both for our startup and using them daily. Claude 3 has yielded more productivity gains and fewer errors. With all the new models emerging, where should leaders focus?

In the weeds:

  1. The largest model, Opus, outperforms OpenAI's GPT-4 and Google's Gemini Ultra on tests measuring undergraduate-level expert knowledge (MMLU), graduate-level expert reasoning (GPQA), and basic mathematics (GSM8k).

  2. The middle model, Sonnet, is twice as fast as Anthropic's previous best model, Claude 2.1, and excels at intelligent tasks demanding rapid responses, like knowledge retrieval or sales automation.

  3. Haiku's smallest model can read a dense research paper of roughly 7,500 words with charts and graphs in less than three seconds, beating other comparably sized models in performance, speed, and cost.

  4. All three models can process visual imagery, enabling them to understand uploaded documents, analyze web interfaces, and generate image catalog metadata. This is crucial for many enterprise customers whose knowledge bases consist of image formats.

The impact:  Leaders should carefully evaluate their specific AI needs and use cases when deciding which Claude 3 models to use. The larger Opus model may be best suited for complex, knowledge-intensive tasks that require high accuracy. At the same time, the faster Sonnet could excel at rapid response scenarios like customer service or sales automation. For cost-effective processing of large volumes of data like research papers, the efficient Haiku model may be the optimal choice.

🩻 Nvidia stepping up its Healthcare game….

With its AI technologies, Nvidia is making significant inroads into the healthcare and pharmaceutical industry. It showcased new AI-powered tools and partnerships at its recent GTC AI conference, positioning itself for substantial revenue potential in drug discovery and medical imaging.

In the weeds:

  1. Nvidia launched around two dozen new AI-powered, healthcare-focused tools at its 2024 GTC AI conference. It also collaborates with Johnson & Johnson to use generative AI in surgery and GE Healthcare to improve medical imaging.

  2. About 41% of biotech CEOs surveyed by EY in late 2023 said they were looking at "concrete" ways to use generative AI for their companies, indicating rapid adoption in the industry.

  3. Nvidia's BioNeMo platform, a generative AI cloud service specifically designed for drug development, is one of its greatest healthcare strengths so far. It helps reduce costs and accelerate the lengthy and expensive drug discovery process.

  4. Nvidia's shares are up close to 100% year-to-date, reflecting investor confidence in the company's ability to tap into the healthcare industry's AI potential, which analysts describe as a compelling revenue opportunity.

The impact: Nvidia's announcements at GTC AI 2024 demonstrate AI's immense potential to transform the healthcare industry. Healthcare leaders should carefully evaluate how leveraging Nvidia's AI tools and platforms like BioNeMo could accelerate drug discovery pipelines, improve medical imaging analysis, and drive operational efficiencies across their organizations. However, they must also proactively address workforce concerns around AI adoption and develop strategies to upskill employees to effectively collaborate with these powerful AI capabilities.

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