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Instead of writing “human readable” instructions to our LLMs and only sharing the output, what if we also shared the instructions that generated the code?
My theory: 'Five years' is a particular timeline that has a secret, hidden meaning - especially in technology.
For most of computing history: code = the territory, docs = the map.
AI flips the model: README.md matters more than app.py, and the map becomes the territory.
Notes on the weird “ghost” sounds you get when you isolate instruments from a mixed (and compressed) audio track.
I recently discovered that Full Fathom Five - the B-side to the Stone Roses first single, Elephant Stone - has two versions. One - a fairly boring backwards version of the original single (albeit, a slightly different mix), on reissues. The other a bit more interesting.
So, In the spirit of completion, I made a stereo comparison track…
Is ChatGPT really AI? Or is it just a chatbot?
In the growing buzz around generative AI, a new concept in research methodologies has arisen; "synthetic respondents". Instead of asking people the questions, a Large Language Model creates 'synthetic respondents' which you can ask as many questions as you like. And they will give you answers. And they will probably sound like real people. They will never get bored. They will never try to disguise their "true" thoughts and feelings (as David Ogilvy once said, “People don’t think what they feel, don’t say what they think, and don’t do what they say.”.) You can get answers from thousands of them, very quickly and at very little costs.
(Also - they never leave behind a bad smell, and won't eat all of your biscuits.)
But again - so obvious as to be barely worth mentioning - they aren't real people. They are synthetic - "made up." Just like the 'actors', pretending to be the sort of people we actually want to talk to.
They will do it faster. They will do it cheaper. Will they do it better - or at least, 'good enough'? Well... that's the real question.
A rough theory of why voice notes get such wildly different reactions from different people.
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I think most people who are interested in how LLMs work get the idea that they are basically token-predicting machines. They take some input, turn it into tokens, 'predict' a next token, stick it on the end of the input, and that becomes a new input. Then it runs the cycle again; repeat to fade…
The bit that I think fewer people understand is the 'sampling' step, where the LLM actually 'chooses' the token. If more people understood it, I think you'd be seeing it everywhere, because its honestly kind of mind-blowing.
Is ChatGPT really AI? Or is it just a chatbot?
Actually, the best programming language of the future is probably going to be English…
This is the tech war of the moment; a race to be the first to develop an AI/Machine Learning/Deep Learning product that will be a commercial success. Google have a head start - Microsoft+OpenAI look like they could be set to catch up, and maybe even overtake Google. But if this is a race then where is the finish line? What is the ultimate goal? Is it all about the $175 billion Search advertising market - or is it bigger than that?
After a long time of trying to come up with a simple answer to the simple question of “what is television?”, I decided to go the long way around.
One reason the Metaverse is doomed is because of the idea that it will straddle all of the different computing platforms; too many conflicting business interests will make this impossible to execute.
One reason the Metaverse will succeed is because of the idea that when something, sooner or later, straddle all of the computing platforms, it will deliver something incredibly useful.
More Posts
There's an archive of all my posts here, my posts about post-pandemic work/life, things about the advertising/media industry, and other stuff. Maybe.
(Or at least, there will be...)
I think most people who are interested in how LLMs work get the idea that they are basically token-predicting machines. They take some input, turn it into tokens, 'predict' a next token, stick it on the end of the input, and that becomes a new input. Then it runs the cycle again; repeat to fade…
The bit that I think fewer people understand is the 'sampling' step, where the LLM actually 'chooses' the token. If more people understood it, I think you'd be seeing it everywhere, because its honestly kind of mind-blowing.