😇Get Your Custom GenAI Solution [Workshop],🩻AI in Medicine Reducing Deaths Evidently,🧠As Requested:Key Insights of FrugalGPT Reducing Cost and Improving Performance,and more

Meshy-3: GenAI for 3D Models

(Source)

Meshy-3 is an AI-powered 3D modeling tool. With groundbreaking features like image-to-3D and text-to-3D, Meshy-3 empowers creators to bring their wildest visions to life with unprecedented speed and precision.

Whether you're crafting game assets or digital art, Meshy-3's intuitive tools and seamless workflow will supercharge your creative process. You'll be able to generate high-quality 3D models in minutes, leaving you more time to focus on design and creativity. Generate now:

Discover the hidden advancements in medicine powered by AI. Despite their long implementation processes, the impact of AI in medicine is already tangible and promising.

(Source)

Powerful AI Models i.e. Med-Gemini, and new Methods

Google DeepMind has developed Med-Gemini, a family of large multimodal AI models tailored for healthcare applications.

Building upon the Gemini architecture, Med-Gemini demonstrates top performance on 10 out of 14 medical benchmarks including multimodal, long-context tasks, such as 20 years of patient records.

Med-Gemini excels at complex clinical reasoning, outperforming models like GPT-4 Turbo in terms of factual accuracy, reliability, and nuance.

Its ability to integrate web search results enables it to provide comprehensive, up-to-date answers to difficult medical queries.

Among others, already achieved:

• Better diagnostic accuracy for rare conditions.

• Summarization and simplification of medical texts.

• Multimodal understanding of text and images, with potential for audio/video.

New AI methods enabling new diagnostic heights

The left panel depicts Med-Gemini-L 1.0 training to refine medical reasoning. It alternates response generation with and without web search to better integrate external data for enhanced accuracy.

The right panel illustrates Med-Gemini-L 1.0 in operation: it generates multiple explanations, filters out uncertainties, performs web searches to resolve them, and integrates the results for precise answers.

Looking ahead

Humanity will benefit from this with personalized medicine (tailoring treatments based on individual patient genetics and history), population health (analyzing trends and predicting disease outbreaks), early disease detection, enhanced clinical decision support, and more.

Of course, it will remain a challenge to integrate AI systems like this into existing healthcare systems. I live in Germany, and it is a regulatory beast.

(Source)

Case in Point: AI-enabled Alert Intervention reduces mortality by 31% 🤯 

(Source)

Besides a great line-up, this year’s GenAI Applications Summit offers something extraordinary:

The GenAI Accelerator is a unique framework that guides participants through leveraging Generative AI to drive business innovation.

💡 If you are interested in getting the consolidated training material after the summit, REPLY “Yes, interested.“ ¹

It is a proven framework tested with over 25 organizations, from small to medium-sized businesses to large corporations.

How it works

In a two-step process, we identify which problems are most suitable for solving with GenAI models.

This is achieved by providing participants with prompting templates to test the feasibility of cases against AI models in real-time.

We then draw shortlisted use cases on the GenAI Architecture canvas, accompanied by guiding questions, which aid participants in drafting their GenAI solution architecture.

This process elucidates how a GenAI architecture can be optimally integrated into organizations, addressing the most pertinent use cases.

Participants leave with a comprehensive understanding of how a GenAI solution would function within their organization, including which data to leverage and the essential elements of cloud services needed to build the solution.

In the end, you will have clear guidelines for implementing YOUR GenAI solutions, including the necessary cloud resources.

I hope to see you there.

(Source)

Last time, I asked if you would like me to extract the key insights of FrugalGPT, and 80% responded yes. So, I'm happy to follow up on it.

Fun fact: I had a client last year who spent $500k per month just on LLM-API costs.

The main idea is to establish a foundation for using LLMs in a more cost-effective and sustainable manner by optimizing prompts, model approximations, and adaptive model selection/cascading.

• Prompt adaptation: Using shorter or more efficient prompts.

• LLM approximation: Using cheaper models or caching to approximate expensive LLMs.

• LLM cascade: Adaptively selecting which LLMs to use for different queries.

• The paper introduces FrugalGPT which implements the LLM cascade strategy. FrugalGPT learns to route different queries to different combinations of LLMs to reduce cost while maintaining accuracy. (See image above.)

• Experiments demonstrate that FrugalGPT can match the performance of GPT-4 with up to a 98% cost reduction, or improve accuracy by up to 4% at the same cost as using GPT-4 alone.

• 💡 The effectiveness of FrugalGPT is attributed to the fact that even inexpensive LLMs can sometimes outperform expensive ones on certain queries. Adaptively combining multiple LLMs allows for reducing costs while maintaining or boosting performance.

The paper establishes a Holistic Optimization.