121–130 of 814 posts
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> I've trained as a neuroscientist and written a book about consciousness This has to do with ML and numerical computing, how?
Well I was being sarcastic. I really dislike it when people have to first convince you that you should trust them. Either make a good argument or don’t.
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I've worked in AI for the past 30 years and have seen enthusiasm as robust as yours go bust before. Just because some kinds of narrow AI have done extraordinarily well -- namely those tasks that recognize patterns using connections between FSMs -- does not mean that same mechanisms will continue to scale up to human-level cognition, much less exceed it any time soon. The breakthroughs where deep AI have excelled -- o…
So I am a neophite in this area, but my thesis for why "this time is different" compared to previous AI bubbles is that this time there exist a bunch of clear products (or paths to products) that work and only require what is currently available in terms of technology. Coding assistants today are useful, image generation is useful, speach recognition/generation is useful. All of these can support businesses, even in…
I've trained as a neuroscientist and written a book about consciousness. I've worked in machine learning and built products for over 20 years and now use AI a fair bit in the ed-tech work we do. So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence.…
> but I am yet to see any reasoned argument for why it is far more difficult and will take far longer I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast. As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
We have "real ai" already.
As for future progress, have you tried just simple interpolation of the progress so far? Human level intelligence is very near. (Though of course artificial intelligence will never exactly match human intelligence: it will be ahead/behind in certain aspects...)
I think many things can be true at the same time: - AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.…
Can you give some examples of the tasks you did manage to offload successfully?
- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.
- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.
- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.
- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.
- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.
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Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
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How are you using AI to double your coding productivity? Are you using ChatGPT, or Claude, or GitHub Copilot? I am an AI-skeptic, so I am curious here. Thanks!
I’m not the OP and I wouldn’t say that AI has doubled my productivity, but the latest Claude models in particular have made me less of a skeptic than I was a few months ago. I’m an experienced backend dev who’s been working on some Vue frontend projects, and it’s significantly accelerated my ability to learn the complexities of e.g. Vue’s reactivity model. I can ask a complex question that involves several niche conc…
I have accounts for all three and will generally try to branch out to test them with each new update. Admittedly, I haven’t gotten to Grok yet, but Claude is far and away the best model at the moment. It’s not even close really.
I've trained as a neuroscientist and written a book about consciousness. I've worked in machine learning and built products for over 20 years and now use AI a fair bit in the ed-tech work we do. So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence.…
I've worked in AI for the past 30 years and have seen enthusiasm as robust as yours go bust before. Just because some kinds of narrow AI have done extraordinarily well -- namely those tasks that recognize patterns using connections between FSMs -- does not mean that same mechanisms will continue to scale up to human-level cognition, much less exceed it any time soon. The breakthroughs where deep AI have excelled -- o…
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> but I am yet to see any reasoned argument for why it is far more difficult and will take far longer I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast. As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
Well he gave you a list of credentials of why you should believe him. Isn’t that enough ?
edit: oh sorry I didn't get that it was sarcasm
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Argument from authority is a pernicious fallacy, and typically effective too. You were right to call it out. I must admit I overlooked the sarcasm, however.
> I must admit I overlooked the sarcasm, however. Don't feel too bad; until I read the next response I was in two minds about whether sarcasm was intended or not. It's bloody hard to tell, sometimes :-/
I've trained as a neuroscientist and written a book about consciousness. I've worked in machine learning and built products for over 20 years and now use AI a fair bit in the ed-tech work we do. So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence.…
>We've run the 4-minute mile. >We are in for radical non-linear change. We aren't running miles much quicker than 4 mins though. The last record was 3m:43s set by Hicham El Guerrouj in 1999.