Accurate I guess. Amazing given this was a decade ago!
> Because here is the other thing the greybeards in 2044 will tell you: Can you imagine how awesome it would have been to be an entrepreneur in 2014? It was a wide-open frontier! You could pick almost any category X and add some AI to it, put it on the cloud. Few devices had more than one or two sensors in them, unlike the hundreds now. Expectations and barriers were low. It was easy to be the first.
Personally the AI aspect feels like the least compelling aspect of how computing can build in-roads & become genuinely compelling to the world again. Many many many people are on the sprinkle-on-some-AI-and-get-rich path, but it's just incredibly non-contributive to the field, to expanding the frontier to me. Apply deep-learned expert-systems doesn't making computing better on iota, doesn't enhance man machine-symbiosis.
I crave a computing era where we can return to the fundamentals. To me, computing's 90's/aughts boom was because we created a democratized access to all the tech, because OSS helped us all learn and grow together. We never had better leverage to augment intellect (the Douglas Engelbart vision/mission) than that, and that made all the difference. It's hard for me to imagine owned proprietary ML spawning lateral growth & understanding, to me.
> I crave a computing era where we can return to the fundamentals.
The core of this comes down to -- will it scale?
This is what I want to see from the OS/OW AI models (open source / open weights) universe (kudos to Hugging Face for facilitating this).
I suspect the OpenAI model approach, where all infra is hidden and the models are hidden behind services, can't scale to meet the demand of what people want out of AI. Directions like Mamba over hungry compute Transformers, SLM as over expensive LLMs, and so on, will commodify AI services as operational frameworks mature. OpenAI gets that, hence why they are listening to consumer demand, but their moat assumption is faulty.
That said, I am optimistic about the future of this technology and others that are currently being mined for breakthroughs.
I can see better now that this is a distinct take, but the fundamentals to me were about man-machine symbiosis. About making systems that we understand & can be freer for using.
I do think scale is important too. But it's a refinement it feels like, one we've already covered pretty well albeit have to keep re-learning/isn't evenly distributed yet. But making computing that is open & explorable? Go watch an Alan Kay talk, any of them; he'll say what I'm saying: we barely have begun to figure out the actual essence of computing where it opens itself up to us & makes us more potent people. We have been grinding on the same target, without pause for thinking about what general computing systems could be like, and it's the wider view that this is all actually on service of, that will help us.
AI to me is mostly creating our own Dark Forest scenario on our own planet that we have to somehow coexist with. More distraction from fundamentals.
> Because here is the other thing the greybeards in 2044 will tell you: Can you imagine how awesome it would have been to be an entrepreneur in 2014? It was a wide-open frontier! You could pick almost any category X and add some AI to it, put it on the cloud. Few devices had more than one or two sensors in them, unlike the hundreds now. Expectations and barriers were low. It was easy to be the first.
Personally the AI aspect feels like the least compelling aspect of how computing can build in-roads & become genuinely compelling to the world again. Many many many people are on the sprinkle-on-some-AI-and-get-rich path, but it's just incredibly non-contributive to the field, to expanding the frontier to me. Apply deep-learned expert-systems doesn't making computing better on iota, doesn't enhance man machine-symbiosis.
I crave a computing era where we can return to the fundamentals. To me, computing's 90's/aughts boom was because we created a democratized access to all the tech, because OSS helped us all learn and grow together. We never had better leverage to augment intellect (the Douglas Engelbart vision/mission) than that, and that made all the difference. It's hard for me to imagine owned proprietary ML spawning lateral growth & understanding, to me.