Add the wiring and connectors.

You're not paying me to be your research assistant.

Voila, asking your kind of science-deniers for scientific evidence usually ends this way.

So you have not read those IPCC documents yourself — while smugly accusing me of doing "anything at all to avoid looking at" same. Take your "L" at the exit, by your left, and don't let your tail get caught in the door.

I've noticed that climate deniers are highly allergic to reading actual science

But you are not, are you? So it should be quite easy for you to provide the citations of nature and in the shape I've asked for: a prediction, and it materializing. Two separate links at least 5 years apart... With the evidence being, supposedly, so abundant, why is it so hard for you to satisfy my request?

1967, the first greenhouse effect model using detailed CO2 infrared spectra and incorporating convection and radiation

Great, what did it predict, that was not trivial and actually impactful, but precise?

here's an article from Forbes that does the same comparison

You're now offering a different link — instead of answering the follow-up question about the earlier ones — does this mean, you concede, that the links you posted earlier are not satisfying the simple rules I started with, and wish to withdraw them? I don't want to be accused of "moving the goal posts"...

P.S. Calling me "an idiot" or "denier" is not going to work :-)

And I'll even help!

Please, sift through your massive documents yourself to cite a couple of "precise, non-trivial and impactful" predictions. I assume, you've read the documents you're offering as evidence yourself, so it wouldn't be much of a burden for you. Thank you!

They not only improved the model's forecasts; they also discovered why some of the model's predictions had been off

Wow, some had been off? Ok, nice to see an acknowledgement of that.

Now, how about a successful prediction? Something reasonably precise, non-trivial and impactful, please — falsifiable, yet not falsified in due time...

If you choose to play, be sure, each citation includes two links: one to the prediction being made, and another — to it coming true within at least 20% of the predicted value (for the quantifiable ones). Crucially, the two links must be at least 5 years apart...

Whether we like it or not, whether we use Windows or not, Microsoft is going to insist on adding the tax whether the PC comes shipped with Windows or not. This is illegal bundling, Microsoft has essentially ignored prior rulings, and it's pretty much a certainty the courts will be fine with this.

I think that remains accurate, but it depends on the domain.

Search had always been free form text input, and that extends to this, open ended interaction with impossibly large dataset to browse and impossibly many things to request. Places based on text interaction are places where LLM have a good chance of having a large role.

However, this isn't the total world. For example, LLM will only be as good at taking an order as a drive through speaker. I greatly appreciate managing an order through an app or kiosk, where I can more quickly peruse options in detail, more quickly knowing what's in it, price, calories, etc. Maybe an LLM option when you know generally what you want to fill out the order then a UI to tweak details.

Tons of UIs are more than some compromise for helping a computer be a consolation prize for not bothering to have a human handle a request.

Lots of AI companies seem to be forgetting this and imagining that devices that are just microphones and speakers will take over, but they are incorrect in many cases.

Investors are chasing a company that achieves some kind of AI singularity. Let's set aside the fact that there's no reason to believe there is anything but diminishing marginal returns by making marginal refinements to current frontier models. Let's imagine someone hits the jackpot and gets, not even AGI, but a system that's as far ahead of today's frontier model are ahead of 2020's GPT 2.0 in performance.

Globally AI revenues are 150 billion, against a cumulative burn rate of 450 billion. A model that is a generation ahead of others would almost certainly capture the lion's share of that revenue.

If AGI magically appears as a Sam Altman has promised investors it will, a hundred million is way too low. Add, maybe, another zero to the revenues.

Conservatively, a safer assumption is that frontier models will get marginally better based on refinements in training and reinforcement and the other bits and bobs that go into these systems. The nobody is winning the lion's share of anything, at least overnight. But you have to define "safe". By "safe" I mean unlikely to lose money. But some investors are clearly defining "safe" as "having the greatest chance of owning a piece of the biggest thing ever."

I'm not following this super-closely, but if Anthropic is pursuing adding multi-step model based reasoning to their system, that could be the basis of a generational leap in capability. But if that is an approach that looks like it has a chance of working, then their competitors are no doubt pursuing the same thing. In that case you'd expect the revenue pie to grow as the scope of model utilty increases, but that growth to be split among several competitors. This could credibly result in a revenue stream for some of them that is as big as the entire industry's revenue stream today. But there's going to be hell to pay on the data center impacts end of things.

I don't know about mental health, but at least MIT's policy seems to not detract from it's students reputation in the industry.

I guess the question is why you would say "Would you fly on a rocket designed and built by people who could not fail?" based on the current topic that UoM is adopting MIT's long-standing strategy to first semester grading. You seem to infer that UoM adopting this will produce bad engineers that can't be trusted to design and build a rocket, but MIT has been doing it since the 1960s. Why is it so terrible for UoM to do it and it isn't a concern that MIT does?

Well you have Google, and of course Apple could decide to in-source the AI engine if it is so important, they tend to like doing that.

But when it comes to software development, Anthropic certainly has the advantaged position in reputation. However more broad use cases I think are supremely vulnerable to the platform owners of the endpoint devices changing the default. Why do Claude when the hardware button already chats with "some AI". Why leave office 365 that you already are running when office 365 will frontend whatever AI microsoft feels like? Even in the field that Anthropic is very well respected, I know entire companies that only use it by way of Github copilot because their company has a financial arragement with Microsoft. Leaving even users of Anthropic models at risk of Microsoft shuffling things around to use a model of Microsoft's choosing.

So even if Microsoft sucks, Microsoft dictates a bunch of defaults. Even if Google sucks, they dictate a lot of defaults.

Musk, Altman, and Zuckerberg are probably the ones I see at highest risk of failing to capture enduring success in this market. Anthropic certainly has the technical trajectory to be some sort of durable success, but just think there's a lot of potential for others to act as gatekeepers to the ecosystem.