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Andrew Maas

CEO @Pointable. Instructor @Stanford and @Uplimit

Dr. Andrew Maas is a reputable deep learning researcher and educator, currently serving as an instructor for Spoken Language Understanding at Stanford University. Dr. Maas holds a Ph.D. in Computer Science from Stanford University and a B.S. in Computer Science & Cognitive Science from Carnegie Mellon University. With a strong educational background and extensive professional experience, Dr. Maas has made significant contributions to the field of artificial intelligence.

Highlights

Aug 15 · linkedin

Hot take: LLMs are not going to create artificial general intelligence in the near term.

But does that matter?

Not if you’re trying to create enterprise value from LLMs over the next few quarters.

The existing technology, even if it were frozen today, holds the promise of unlocking hundreds of billions—if not trillions—of dollars in economic value.

Large Language Models (LLMs) have reached a pivotal point where they can be leveraged to develop applications and automate various aspects of human work at a relatively low cost. From automating repetitive documentation tasks in enterprise, supporting customers with helpful chatbots, to a new category of consumer-facing AI companions.

What’s holding most people back trying to build these systems is a lack of experience and tooling to develop LLM applications like end-to-end complex systems.

The evolution is less about creating a super-intelligent entity and more about engineering production-grade solutions through LLMs that are optimized end-to-end for particular tasks. Yes there is engineering and design work required, but you can build applications today we thought were impossible 5 years ago.

I recently sat down with Jason Stoughton on the The Pulse of AI podcast’s most recent episode. If you’re building in this space right now, go give it a listen.

#AGI #RAG #LLM #enterpriseai #languageai #chatbot #NLProc

Aug 14 · linkedin

Struggling to get a RAG-LLM system to full production-ready quality? Our latest blog shows the impact of configuration optimization for RAG: a well-tuned RAG system significantly outperforms poorly-tuned systems in delivering specific, factual responses with large custom datasets.

RAG systems are complex, with many interacting components. Manual optimization is time-consuming and requires excellent infrastructure, so at Pointable we developed an automated RAG optimization engine to find the best configuration for any task and dataset.

Check out the full blog post for more details! 📖✨ https://lnkd.in/gr24UMfx

#AI #ML #Chatbots #RAG #agentic #EnterpriseAI #NLProc #LLM

Andrew Maas
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Mountain View, California, United States