Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.8+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
If that doesn’t ring a bell, let me be a bit more specific. In the realm of generative AI, there are ongoing and relatively widespread efforts underway to take multiple generative AI systems and merge them together. This is mainly being done by those within the AI insider community. Not many people outside the AI realm are aware that this is taking place.
Recent examples include that generative AI is as valuable for being able to come up with intelligent questions as it is for providing answers, see. Another and quite popular example is my explanation of the so-called shared imagination among multiple but disparate generative AI apps, seeI’m sure you’ve heard of generative AI, the darling of the tech field these days.
Generative AI and LLMs tend to be designed and programmed by using mathematical and computational techniques and methods known as artificial neural networks .inspired by the human brain consisting of real neurons biochemically wired together into a complex network within our head. I want to clarify and emphasize that how artificial neural networks or ANNs work is not truly akin to the real-world complexities of so-called wetware or the human brain, the real neurons, and the real neural networks.
I think that is sufficient for the moment as a quickie backgrounder. Take a look at my extensive coverage of the technical underpinnings of generative AI and LLMs atWhen an AI maker develops a generative AI or LLM from scratch, they typically use an approach that is relatively commonly used by other AI makers. In that sense, the internal mechanisms are roughly similar much of the time.
This is why much of the merging of generative AI and LLMs is typically done with open-source generative AI and LLMs. All in all, the proprietary hiccups are lessened when using open-source models. It isn’t all roses and wine. There are still some potential sticking points about licensing stipulations. Also, some purported open-source generative AI and LLMs are only partially open, thus not all the internal bits and pieces are available for inspection and reuse.
My third route entails merging generative AI models that are each devised to handle particular modalities. Some generative AI apps are only text-based generators. Some are only audio generators. Some are only video generators. If you want a generative AI that does text, audio, and video, you can either build it that way or seek to merge the respective types into a merged model.
: Collect outputs from multiple generative AI models and combine the outputs externally, outside of the respective models, giving the appearance of a merged model existence.: Use each of multiple generative AI models to train a fresh generative AI from scratch or do this by dovetailing into a pre-made chosen base model. Also known as training transfer.Use distinct generative AI models such as ones devoted to different individual modalities such as text, audio, video, and merge them into one .
The third approach is about the modalities merging that I mentioned a few moments ago. The modalities merger can be done in an easy surface manner or an intricacies fashion. For the surface approach, you tie together the disparate modality generative AIs by connecting them via APIs. Once again, you aren’t formally merging them. The harder approach involves merging the internal mechanisms into a merged model.
Merging by hand is the mainstay of today’s efforts. Gradually, automated processing is being advanced and growing in use. In the research paper entitled “Evolutionary Optimization of Model Merging Recipes” by Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, and David Ha,“Model merging strives to create a versatile and comprehensive model by combining the knowledge from multiple pre-trained models, potentially yielding a model capable of handling various tasks simultaneously.”
“Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models.”A less manual and more systematic approach that relies on automation would seem highly beneficial. The efficiency of the merger process is presumably going to rise. The cost will hopefully be lower. The speed of merging will quicken. And, significantly, the merged model will possibly be more robust and effective. That’s at least the aspiration.
“Furthermore, effectively merging models from very different domains can lead to models of wider real-world applicability and enable us to develop models beyond the large population of models that are optimized for the narrow range of tasks defined by a leaderboard.” .
Generative AI Large Language Models LLM Chatgpt GPT-4 Openai Anthropic Claude Google Gemini Model Merging Mergers. Neural Networks
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Generative AI Helping You Be A Nicer Person Who Embraces ‘Forgive And Forget’ MindsetDr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.4+ million amassed views of his AI columns. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
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