Time’s almost up! There’s only one week left to request an invite to The AI Impact Tour on June 5th. Don’t miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here.
We are now more than a year into developing solutions based on generative AI foundation models. While most applications use large language models (LLMs), more recently multi-modal models that can understand and generate images and video have made it such that foundation model (FM) is a more accurate term.
The world has started to develop patterns that can be leveraged to bring these solutions into production and produce real impact by sifting through information and adapting it for the people’s diverse needs. Additionally, there are transformative opportunities on the horizon that will unlock significantly more complex uses of LLMs (and significantly more value). However, both of these opportunities come with increased costs that must be managed.
Gen AI 1.0: LLMs and emergent behavior from next-generation tokens
It is critical to gain a better understanding of how FMs work. Under the hood, these models convert our words, images, numbers and sounds into tokens, then simply predict the ‘best-next-token’ that is likely to make the person interacting with the model like the response. By learning from feedback for over a year, the core models (from Anthropic, OpenAI, Mixtral, Meta and elsewhere) have become much more in-tune with what people want out of them.
By understanding the way that language is converted to tokens, we have learned that formatting is important (that is, YAML tends to perform better than JSON). By better understanding the models themselves, the generative AI community has developed “prompt-engineering” techniques to get the models to respond eff …
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Time’s almost up! There’s only one week left to request an invite to The AI Impact Tour on June 5th. Don’t miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here.
We are now more than a year into developing solutions based on generative AI foundation models. While most applications use large language models (LLMs), more recently multi-modal models that can understand and generate images and video have made it such that foundation model (FM) is a more accurate term.
The world has started to develop patterns that can be leveraged to bring these solutions into production and produce real impact by sifting through information and adapting it for the people’s diverse needs. Additionally, there are transformative opportunities on the horizon that will unlock significantly more complex uses of LLMs (and significantly more value). However, both of these opportunities come with increased costs that must be managed.
Gen AI 1.0: LLMs and emergent behavior from next-generation tokens
It is critical to gain a better understanding of how FMs work. Under the hood, these models convert our words, images, numbers and sounds into tokens, then simply predict the ‘best-next-token’ that is likely to make the person interacting with the model like the response. By learning from feedback for over a year, the core models (from Anthropic, OpenAI, Mixtral, Meta and elsewhere) have become much more in-tune with what people want out of them.
By understanding the way that language is converted to tokens, we have learned that formatting is important (that is, YAML tends to perform better than JSON). By better understanding the models themselves, the generative AI community has developed “prompt-engineering” techniques to get the models to respond eff …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]