AI's Copernican moment
Let me begin with a confession: it’s becoming increasingly challenging to keep up with the rapid flow of experimentation, innovation, and research results emerging around large language models (LLMs). I’ll share a few examples that have caught my attention.
I’ve talked about GPTs and new services that enable LLMs to generate their own tasks. From a video, I published on April 6:
As LLMs, chatbots, and instances of these models connect to other systems on the internet, a new complex system arises. This has led to the excitement around AutoGPT projects. These connect GPT systems to other LLMs and APIs to coordinate tasks and execute them in a more generalised way among the connected systems. This dramatically expands the capabilities of these systems and creates a “super system” that can perform a broader range of tasks.
We’ve been experimenting with these at Exponential View, inputting high-level objectives and observing how they recursively work to achieve their goals. It’s strikingly similar to the process a human would go through when breaking down complex tasks. As AutoGPTs mature, we’re seeing impressive results, even though people are still figuring out how to use them effectively.
There are other developments worth our attention. I recently read a paper discussing how scientists used LLMs to help decode the language of sperm whales. This fascinating development could contribute to a fundamental shift in our understanding of communication among advanced mammals — although we are nowhere near to inventing a warp drive, we may achieve cross-species understanding similar to that portrayed in Star Trek IV: The Voyage Home sooner than 2286!
LLMs also have their limitations, such as their restricted context windows. Some have found ways to work around this by having GPT-4 create a compressed language that encodes previous conversations, allowing for extended interactions. GPTs may also help us rethink how we write software: moving from rigid, circuit-based applications to understanding-based applications of almost unlimited input and dynamic properties.
It’s absolutely remarkable to witness the growing capabilities as we chain these systems together or embed them in large collections of software.
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The rapid advancements in LLM technology make me think about historical paradigm shifts, like the Copernican Revolution or the Gutenberg Moment, which forced us to change our perspectives and systems. With LLMs challenging copyright, privacy, and other societal norms, it begs the question: are we the priests or the scientists of our current worldview?
Today's post is supported by our knowledge partner, Singularity Group.
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1yLuciano Floridi has an insightful take on paradigm shifts from the perspective of how we have viewed ourselves and our 'special place' in the universe. Paraphrasing heavily: in the classical, largely European, tradition, up until Copernicus, most believed God had placed us centre-stage in a universe created for us and that revolved around us. Once that was shown to be not the case, we clung to a belief that at least we were exceptionally created above and beyond other life-forms, but Darwin punctured that conceit. Even then, we could still hold onto our exceptionalism via the Cartesian belief that we, uniquely, are centre-stage through our capacity to rationalise from our full access to knowledge. That in turn has been punctured by proper understanding of how we truly think and decide via modern neuroscience (though he mentions Freud and the subconscious too as a forerunner of sorts). Despite all this, we could still clasp tight our exceptional role as the thinkers (Pascal - 'All our dignity then, consists in thought.'). Enter the advancement of computational devices, Turing's proofs, and all since up to this dawn of LLM's.
Creative GenAI Innovator: Pioneered Retrieval-Augmented Generation, Partnered with Industry Leaders like OpenAI, AWS, MSFT and Google/Gemini, and Built Adaptive AI Systems to Turn Complex Data into Breakthrough Insights.
1yThanks Azeem Azhar, for succinctly putting into words the paradigm shift that is happening. Prior to ChatGPT, most technologies required manual orchestration to get any value from them. What you're observing in a few short months is only the beginning of the creative surge that is happening. We also see benchmarks in our own analysis where multi-agent models, traversing multiple datasets with auto-generated prompts, regularly beat human experts at understanding complex medical and scientific data (validated against gold standard data sets). While today, some tuning of the technology still requires human engagement, semiautonomous LLM-enabled agents working on behalf of an individual or team will become the norm. And unlike bots of the past, any user will have the ability to initiate an action.
Mmm...OK then...lets think of some great questions to ask!