The secret to success for OpenAI, Anthropic and labs is the vision that they saw 10 years back and kept working on it. We're in awe of how models like GPT-6 Astra and Claude Opus 5.5 are performing today, but it's important to understand that they've been working on this before we knew about AI.
The next big thing is Robots and some stealth company building today is going to be a trillion-dollar giant in few years time.
Right. If you're new to the area, it may seem like AI came out of nowhere in 2022. If you dig a little, you'll be amazed that it somehow came from nowhere in ~2012. If you dig even more, you realize there was a wave in the late 90s, early 2000s about "machine learning" (e.g. SVMs) and before it there was an 80s wave of both neural nets, agent models, and logic-based AI, probabilistic graphical models. Then you dig more and you realize AI originated from that Dartmouth workshop by Minksy and others in the 50s. Then you dig more and realize McCulloch and Pitts already modeled neural nets as little logic circuits in the 1940s. Then you realize the role of Shannon, Turing etc. Then you realize that computers actually arose in a milieu with a much more AI-shaped vision, cybernetics etc. than what we today think of as computing (PCs etc). And the precursors in the thought-formalization and mechanization trend in math and philosophy at the start of the 20th century. And even more back Leibniz's calculus ratiocinator and "calculemus!" slogan to settle debates by reducing argumentation to computation.
The point is, typically when something seems like it came out of nowhere, it just means you didn't dig deep enough. Ideas don't come at an instant, fully formed like Athene from Zeus' forehead. It's brick by brick, one twist on an existing idea and zeitgeist at a time.
I was digging into this recently with ChatGPT. I’ve loosely followed the progression of ML and NN over the past 20 years, but struggled to put it into context of where an LLM lives. The big inflection point was the 2017 Attention Is All You Need paper [1].
Artificial Intelligence
|
+-- Symbolic / rule-based AI
| +-- expert systems
| +-- search / planning
| +-- logic / knowledge representation
|
+-- Machine Learning
|
+-- classical statistical ML
| +-- regression
| +-- decision trees
| +-- SVMs
| +-- Bayesian methods
|
+-- Neural Networks / Deep Learning
|
+-- computer vision
+-- speech
+-- Natural Language Processing
|
+-- Transformers
|
+-- Large Language Models
|
+-- chat systems
+-- multimodal models
+-- tool-using systems
+-- agents
My interpretation is that before the transformer, most everything under the domain of 'AI' was either an academic curiosity or only applicable in very narrow fields. GPT-3 was when the 'magic' that people had always dreamed of with AI began to emerge, and it's only really this year that we are starting to be seriously confronted with the possibility of a general intelligence emerging from LLMs (albeit, not quite the same thing as 'true' AI which would necessarily be more of a biological exercise).
>not quite the same thing as 'true' AI
I've really started changing my mind on this. "There is no AI, only I" is the position that I've really come to adopt.
Part of it is from Michael Levin's quote "Humans only can really see intelligence at human scales an immediately discount anything that doesn't exactly match their experience". The other part is most peoples immediate assumption that for something to be intelligent it has to be alive. Lastly is there may be platonic intelligence, that some kinds of intelligence may arise from the very structure of our universe when accessed.
We have really entered an age where thinking that human intelligence = intelligence is an anti-pattern that tells you less about the world and blinds you to what is actually occurring.
Biological intelligence, human intelligence, electronic intelligence, algorithmic intelligence are all subsets of intelligence set theory and even where humans like to call themselves a general intelligence it's distinctly likely that we're less generalized than we expect.
samayashar · · focus · HN ↗
The next big thing is Robots and some stealth company building today is going to be a trillion-dollar giant in few years time.
owebmaster · · focus · HN ↗
Don't confuse AI with LLMs. "We" know about AI for a long time. We even have a term for when AI fails expectations, AI winters.
bonoboTP · · focus · HN ↗
The point is, typically when something seems like it came out of nowhere, it just means you didn't dig deep enough. Ideas don't come at an instant, fully formed like Athene from Zeus' forehead. It's brick by brick, one twist on an existing idea and zeitgeist at a time.
mapBasketWand · · focus · HN ↗
goldenbrillianc · · focus · HN ↗
pixl97 · · focus · HN ↗
I've really started changing my mind on this. "There is no AI, only I" is the position that I've really come to adopt.
Part of it is from Michael Levin's quote "Humans only can really see intelligence at human scales an immediately discount anything that doesn't exactly match their experience". The other part is most peoples immediate assumption that for something to be intelligent it has to be alive. Lastly is there may be platonic intelligence, that some kinds of intelligence may arise from the very structure of our universe when accessed.
We have really entered an age where thinking that human intelligence = intelligence is an anti-pattern that tells you less about the world and blinds you to what is actually occurring.
Biological intelligence, human intelligence, electronic intelligence, algorithmic intelligence are all subsets of intelligence set theory and even where humans like to call themselves a general intelligence it's distinctly likely that we're less generalized than we expect.