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It is difficult to follow all the new developments in AI. How can you discriminate between fundamental technology here to stay, and the hype? How to make sure that you are not missing important developments? The goal of this article is to provide a short summary, presented as a glossary. I focus on recent, well-established methods and architecture.

I do not cover the different types of deep neural networks, loss functions, or gradient descent methods: in the end, these are the core components of many modern techniques, but they have a long history and are well documented. Instead, I focus on new trends and emerging concepts such as RAG, LangChain, embeddings, diffusion, and so on. Some may be quite old (embeddings), but have gained considerable popularity in recent times, due to widespread use in new ground-breaking applications such as GPT.

The landscape evolves in two opposite directions. On one side, well established GenAI companies implement neural networks with trillions of parameters, growing more and more in size, using considerable amounts of GPU, and very expensive. People working on these products believe that the easiest fix to current problems is to use the same tools, but with bigger training sets. Afterall, it also generates more revenue. And indeed, it can solve some sampling issues and deliver better results. There is some emphasis on faster implementations, but speed and especially size, are not top priorities. In short, more brute force is key to optimization.

On the other side, new startups including myself focus on specialization. The goal is to extract as much useful data as you can from much smaller, carefully selected training sets, to deliver highly relevant results to specific audiences. Afterall, there is no best evaluation metric: depending on whether you are a layman or an expert, your criteria to assess quality are very different, even opposite. In many cases, the end users are looking for solutions to deal with their small internal repositories and relatively small number of users. More and more companies are concerned with costs and ROI on GenAI initiatives. Thus, in my opinion, this approach has more long-term potential.

Still, even with specialization, you can process the entire human knowledge the whole Internet with a fraction of what OpenAI needs (much less than one terabyte), much faster, with better results, even without neural networks: in many instances, much faster algorithms can do the job, and it can do it better, for instance by reconstructing and leveraging taxonomies. One potential architecture consists of multiple specialized LLMs or sub-LLMs, one per top category. Each one has its own set of tables and embeddings. The cost is dramatically lower, and the results more relevant to the user who can specify categories along with his prompt. If in addition you allow the user to choose the parameters of his liking, you end up with self-tuned LLMs and/or customized output. I discuss some of these new trends in more details, in the next section. It is not limited to LLMs only.

The list below is in alphabetical order. In many cases, the description highlights how I use the concepts in question in my own open-source technology.

Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist atMLTechniques.comandGenAItechLab.com, former VC-funded executive, author and patent owner one related to LLM. Vincents past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.Follow Vincent on LinkedIn.

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GenAI and LLM: Key Concepts You Need to Know - DataScienceCentral.com - Data Science Central