jamez1

In hindsight, it was obvious LLMs would be good at this. The neural net weights are embeddings for the circuits within, no different from the embeddings that represent textual or visual tokens.

You probably don't need Astra capability to produce this, just lots of training data on the same NN showing prompts and their activations.

....Databricks has no revenue relative to its valuation either

To be a good engineer you had to be stubborn in the early days of your career, you had to have tenacity and persistence to understand and solve the problems you saw. Unfortunately that filter selects for people who are prone to going too far into the detail, who get a satisfaction from conquering complexity rather than avoiding it. Given the lack of big picture thinking, they readily fall into herd-like behavior. Given the ego payoff for the complexity, it is very difficult for them to accept criticisms about their process or lack of perception.

Skill loss works both ways. You might miss out on forming early skills in using llms effectively, and end up playing catch up in 3-5 years from now if LLMs mark all the skills you hold to be void.

It is also likely LLMs will change programming languages, we will probably move to more formal, type safe languages that the LLM can work with better. You might be good at your language but find the world shifts to a new one that everyone has to use LLMs to be effective for.

Interestingly, no acknowledgement has been made on the stock exchange under the ticker (CLBT), despite the two days of public knowledge

It is logical to assume self driving cars start off in low risk areas, prove their concept, and gradually take on more and more use cases. Things like retirement villages and so on, where this incremental approach has already had success. Skepticism is easy when thinking about the grand scale but this incrementalism will surely win out in the end.

Given the complexity of the setting, there is far more possible causations than the implied by the finders of this correlation.

For example, perhaps better funded hospitals have more females on staff, and that drives the clinical outcomes.

Or maybe this relationship was one of thousands of possible relationships, as it is clear they have examined as much, and it's just appeared statistically significant by chance.

Hornsdale power reserve derives most of it's revenue from selling ancillary services, it does not provide energy. Hornsdale's success is not foreshadowing a battery revolution unfortunately.

The reason nuclear costs so much is because we've stopped building plants. There's no industry anymore, let alone economies of scale. It's a fallacious argument to say it costs too much. If we went all in on nuclear to tackle climate change the costs would quickly collapse. After all, they were built in the 70s and dramatically decarbonized the industry at the time.

No, the enterprise value of the company doesn't change. It was owned by 4b of debt before and is now owned by 5b of equity, and has 1b in cash. It also has an intact business.

Under your logic no company would ever raise equity to pay off debt.

Great example of how difficult statistical concepts are to understand for the layperson, even if that person happens to be a judge or a lawyer.

This would be far better if the currency was used to pay for connectivity, and the router was reimbursed for routing.

Connectivity has a cost, and none of these networks will amount to anything unless the model can incentivize all aspects of the infrastructure necessary