July 9th, 2024

The AI Summer

The article explores AI technology's growth, focusing on ChatGPT's rise and challenges in enterprise adoption of Language Models. It stresses the need for practical LLM tools and discusses integration complexities and CIO caution. It questions LLM hype and advocates for structured advancement.

Read original articleLink Icon
The AI Summer

The article discusses the evolution and challenges of AI technology, particularly focusing on the rapid rise of ChatGPT and the slow adoption of Language Model (LLM) technology in enterprises. It highlights the time it takes for new technologies to gain widespread acceptance, drawing parallels with past innovations like the iPhone and cloud computing. The author emphasizes the need for LLMs to evolve into practical tools rather than standalone technologies. The piece also delves into the complexities of integrating LLMs into existing workflows and the cautious approach of big company CIOs towards deploying such technologies. It concludes by questioning the current hype around LLMs and the necessity for a more structured approach towards achieving product-market fit. The article suggests that while LLMs hold immense potential, there is still a long road ahead before they can revolutionize the tech industry on a large scale.

Related

Claude 3.5 Sonnet

Claude 3.5 Sonnet

Anthropic introduces Claude Sonnet 3.5, a fast and cost-effective large language model with new features like Artifacts. Human tests show significant improvements. Privacy and safety evaluations are conducted. Claude 3.5 Sonnet's impact on engineering and coding capabilities is explored, along with recursive self-improvement in AI development.

AI Scaling Myths

AI Scaling Myths

The article challenges myths about scaling AI models, emphasizing limitations in data availability and cost. It discusses shifts towards smaller, efficient models and warns against overestimating scaling's role in advancing AGI.

The Smart Principles: Designing Interfaces That LLMs Understand

The Smart Principles: Designing Interfaces That LLMs Understand

Designing user interfaces for Large Language Models (LLMs) is crucial for application success. SMART principles like Simple Inputs, Meaningful Strings, and Transparent Descriptions enhance clarity and reliability. Implementing these principles improves user experience and functionality.

How to Raise Your Artificial Intelligence: A Conversation

How to Raise Your Artificial Intelligence: A Conversation

Alison Gopnik and Melanie Mitchell discuss AI complexities, emphasizing limitations of large language models (LLMs). They stress the importance of active engagement with the world for AI to develop conceptual understanding and reasoning abilities.

Txtai – A Strong Alternative to ChromaDB and LangChain for Vector Search and RAG

Txtai – A Strong Alternative to ChromaDB and LangChain for Vector Search and RAG

Generative AI's rise in business and challenges with Large Language Models are discussed. Retrieval Augmented Generation (RAG) tackles data generation issues. LangChain, LlamaIndex, and txtai are compared for search capabilities and efficiency. Txtai stands out for streamlined tasks and text extraction, despite a narrower focus.

Link Icon 5 comments
By @ilaksh - 3 months
Maybe this is a little bit similar to VR and AR adoption. Arguably we have had stunning capabilities for years. But the headsets are uncomfortable and a bit expensive given that discomfort.

Likewise, the most capable models are huge, run on Anthropic or OpenAI's servers and are expensive to deploy on a large scale. That's uncomfortable from a privacy standpoint (although arguably essentially the same as any cloud service).

I believe that over the next few years we will finally start to see the transition from bulky VR headsets to comfortable AR glasses and goggles. That will lead to a huge increase in adoption.

As the LLM models, software, and hardware become more affordable and capable for running locally, production adoption of LLMs and multimodal models will continue to pick up.

I think that we are going to see huge excitement in the next few years as more and more very large and capable truly multimodal models come out. More specialized hardware like Groq etc. will become available. And within probably less than three years the truly radical new paradigms for neural network hardware such as those based on memristors, SNNs, etc. will likely show some very competitive prototypes.

As the new hardware paradigms are widely deployed, say within 5-10 years, human-level local AI will become ubiquitous.

What I take away from those charts is actually significant adoption that is steadily rising. But all of this is nested S-curves, as scientists and engineers work through small and large different bottlenecks and problems to get to the next capability or efficiency level.

By @Melomololotolo - 3 months
A half assed analysis.

Where are the numbers coming from? It's not only that chatgpt might have lost people but also that there are already a lot of options available now.

My company introduced helpful ai features so did Google and Microsoft.

I can now transcript and summarize my meetings in Ms teams for example.

In a conference I saw Bosch talking about a gen AI hub which they now internally use and saving time and money by using less external companies for these cases

GenAI and llms are already here.

And ai means a lot more. It's a Paradigma Shift.

The announcements of robots jumped and the quality of them too. Thanks to whisper and a lot of other advantages it's easier than ever to build a talking and listing robot.

Did we slow down a little bit after the original 'holy shit it's AI time let's restructure '? Sure but not that much.

And I'm definitely looking forward to the progress curve after all this capital has been moved and hardware gets cheaper and faster and more affordable.

By @hpen - 3 months
AI is super useful for building systems, but now with layman AI == ChatGPT and ChatGPT isn't useful for building every system.
By @Havoc - 3 months
>The problem is that most of them haven’t been back.

It's super task dependent.

We got a shiny AI at work (big finance place) that is both good from a technical PoV and is officially blessed for confidential info. Above is true - little use even by myself. I just can't figure out a good use...the dots just don't connect to what I need.

...go home & do some hobby coding / homelab tinkering...loads of AI use with great effect.

I do think this is somewhat temporary though, better integration into office products & OS etc will presumably still come, but a chatbot accessible easily isn't enough. Really needs something closer to the much feared MS's recall thingie...I know privacy issues, but I mean that sort of omnipresence would be needed to be useful.

By @jgalt212 - 3 months
His previous post:

> The VR winter continues, 8 July 2024