tempusalaria

11 ms·

121.
by tempusalaria 3y ago
Right but Ilya is the Chief Scientist of OpenAI not Greg Brockman
122.
by tempusalaria 3y ago
If it was, it probably wouldn’t be accounted for in taxable fashion as it is today. DeepMind is profitable and paying tax on that profit. It’s public information that you can see in its UK regulatory filings.
123.
by tempusalaria 3y ago
OpenAI is still massively unprofitable and MSFT is (rightly IMO) going to invest way more money in them so it’s definitely still a drain. A modest drain relative to MSFTs overall resources
124.
by tempusalaria 3y ago
Jeff Dean is clearly one of the greatest software developers/engineers ever but there isn’t much evidence that he is a brilliant ML researcher And indeed Google AI has achieved very little product wise during his time as CEO. Kind of s
125.
by tempusalaria 3y ago
In 2021 Deepmind generated $2bln in revenue. This was paid by Google for unspecified research services. But the way it’s accounted for it’s likely that it was based on some legitimate contribution. It is unlikely it would be structured this
126.
by tempusalaria 3y ago
You’re confusing two different concepts. AlphaGo learns a distribution where given a game state it generates a move that maximises its internal probability of victory. There is a second “distribution” namely that any terminated sequence of
127.
by tempusalaria 3y ago
To start with, humans have access to far more data than just what is generated by other humans. We can look at nature, other animals, mathematical laws and many other things. Humans also have far more ability than current model architecture
128.
by tempusalaria 3y ago
The theory behind models is that they learn the distribution used to “generate” the data. A good model has a good approximation of the distribution. But then anything generated from it follows the approximation of the distribution not the d
129.
by tempusalaria 3y ago
In London or Paris it’s $5-6 for a decent sandwich
130.
by tempusalaria 4y ago
This isn’t that interesting imo. This is the basic outcome of the scaling laws from Kaplan, Chinchilla papers pushed to a larger final model delta. They likely did extensive small model building on the gpt-4 architecture to establish hyperp