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
Essentially because attention introduced a way to scale un/self-supervised learning to the level of data out there, and learnable inference time 0-shot feature selection. Impressively in a autoregressive, unidirectional manner.
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 ↗
willy_k · · focus · HN ↗