
British-Canadian computer scientist and psychologist
Missing: 5, 8. Present digits repeat to show how many times they occur.
Three is the number of expression — the point at which a thing becomes shareable. Tradition links it to language, humour, performance and the ability to make an idea land. It is generally read as the most immediately likeable number, and the reading usually adds a warning in the same breath: fluency can outrun substance, and 3 is described as capable of talking its way past work it has not actually done.
A complete line in the grid.
The 8-1-6 row complete. Read as follow-through — the traditional marker of someone whose plans reliably become events.
A complete line in the grid.
A complete line in the grid.
A complete line in the grid.
The centre of the grid, so its absence is weighted heavily. Read as difficulty adapting mid-course — a preference for the plan already chosen over the plan now indicated.
Read as discomfort with power and money — not incapacity, but an unwillingness to claim authority openly.
Moolank 6 comes from the day of the month alone; Bhagyank 3 from the whole date. They differ, which is the ordinary case — the two numbers answer different questions.
Venus. Art, pleasure, relationship. Read as attraction and aesthetic sense.
Bhagyank uses the same arithmetic as the Western life path, so the two will always agree. That is not two traditions confirming each other — it is one calculation under two names.
I think political systems will use it to terrorize people.
If you or I learn something and want to transfer that knowledge to someone else, we can't just send them a copy. But I can have 10,000 neural networks, each having their own experiences, and any of them can share what they learn instantly. That's a huge difference. It's as if there were 10,000 of us, and as soon as one person learns something, all of us know it. It's a completely different form of intelligence. A new and better form of intelligence.
It seems very likely to a large number of people that we will get massive unemployment caused by Ai.
I got Christianity at school and Stalinism at home. I think that was a very good preparation for being a scientist because I got used to the idea that at least half the people are completely wrong.
Then we got very excited because now there was this very simple local-learning rule. On paper it looked just great. I mean, you could take this great big network, and you could train up all the weights to do just the right thing, just with a simple local learning rule. It felt like we'd solved the problem . That must be how the brain works. I guess if it hadn't been for computer simulations, I'd still believe that, but the problem was the noise. It was just a very very slow learning rule. It got swamped by the noise because in the learning rule you take the difference between two noisy variables--two sampled correlations, both of which have sampling noise. The noise in the difference is terrible. I still think that's the nicest piece of theory I'll ever do. It worked out like a question in an exam where you put it all together and a beautiful answer pops out.
The reason hidden units in neural nets are called hidden units is that Peter Brown told me about hidden Markov models. I decided "hidden" was a good name for those extra units, so that's where the name "hidden" comes from.
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