I’d guess people will make many different variants of AGI. The evil sociopathic people (who always seem to rise to the top in human hierarchies) will certainly want an AGI in their image.

Over and over again human societies seem to fall to these people - the eternal battle between democracy and autocracy being one example.

Will we have competing/warring AGI’s? Maybe we’ll have to.

It will be interesting to see how soon this spreads outside of the US and China. Globally, most other Level 4 efforts seem to be centered on buses and transit. Some European carmakers have the tech, but as yet seem uninterested in the taxi business. Knowing how Europe operates, many rules and regulations for this will ultimately be done at the EU level, though that doesn’t stop any EU country going ahead with what it wants now.

I wonder how many cities worldwide will have robotaxi services by the end of 2025.

The logic with taxis is that rather than everyone have an individual car, people use them as needed, so 1 car serves dozens of people.

Open source model weights are a step towards public ownership over this new means of production.

I agree. People talk a lot about UBI. It makes sense in principle, but I wonder how about the political likelihood of making it happen.

Meanwhile, as you say, paradoxically Capitalism keeps making Marx’s ‘means of production’ with AI and robotics more likely to be in wide and public ownership.

Singapore’s 6 million people live in an island country only 12 times bigger than Manhattan Island (which for contrast houses 1.6 million people). This makes them an ideal early-adopter for Level 4 self-driving vehicles. They work in geo-fenced areas that are well-mapped, which describes the entirety of this tiny country.

Level 4 robot-taxis, needing little human oversight, finally had their moment in 2024. Commercial services are now in operation in several US and Chinese cities. Their global expansion will be rapid from now on, and 2025 might mark the year they enter wider public consciousness.

Large language models surpass human experts in predicting neuroscience results

A small study found ChatGPT outdid human physicians when assessing medical case histories, even when those doctors were using a chatbot.

But a domain expert like a doctor or an accountant is way much more accurate

Actually, not so.

If the AI is trained on narrow data sets, then it beats humans. There’s quite a few examples of this recently with different types of medical expertise.

Australia is a paradox in the renewables transition. It is already at 35% for renewable electricity, and has targeted 82% for 2030. Yet it’s still a major exporter of coal. Australia exported $127.4 billion worth of coal in 2022-23, and its economy is highly dependent on mining of all types.

It doesn’t have much homegrown manufacturing and is committed to eliminating tariffs on Chinese imports. This means of Western countries it might be among the quickest to abandon ICE cars, as it will have access to all the super-cheap Chinese EV’s. Especially as it’s rolling out infrastructure like this.

I still see even the more advanced AIs make simple errors on facts all the time…

True. It’s what keeps me optimistic. If we can get through the decay of the old world intact, there’s a world of post-scarcity plenty ahead.

I find they are good for creative tasks. Picture and music generation, but also ideas - say, give me 10 possible character names for a devious butler in a 1930s murder mystery novel.

But yes, terrible for facts, even rudimentary ones. I get so many errors with this approach its effectively useless.

However, I can see on narrower training data, say genetics, this might be less of a problem.

There’s a few ways they say it may help, this one seems the main one.

We foresee a future in which LLMs serve as forward-looking generative models of the scientific literature. LLMs can be part of larger systems that assist researchers in determining the best experiment to conduct next. One key step towards achieving this vision is demonstrating that LLMs can identify likely results. For this reason, BrainBench involved a binary choice between two possible results. LLMs excelled at this task, which brings us closer to systems that are practically useful. In the future, rather than simply selecting the most likely result for a study, LLMs can generate a set of possible results and judge how likely each is. Scientists may interactively use these future systems to guide the design of their experiments.

It’s only open source if the training data is and it probably isn’t, is it?

I don’t know, though DeepSeek talk of theirs being “fully” open-source.

Part of the advantage of doing this (apart from helping bleed your rivals dry) is to get the benefit of others working on your model. So it makes sense to maximise openness and access.

I lived in Hong Kong for a few years. It has superlative public transport, and the (human) taxis were reasonably priced. However, as its so densely populated, I can only see cars getting so much traction. After a certain point the traffic jams are unavoidable.

I don’t know the specifics. What seems more relevant to me is that lots of automakers around the world are getting to Level 4 by various, mostly similar ways.

Once you have Level 4 you have a viable robotaxi business model. Even if you stick to geo-fenced areas, and mapped routes, that covers 80%+ of urban taxi journeys.

The same holds true for buses and public transit. I’m very interested to see how efforts like this Level 4 mini-shuttle bus in France progress.

https://www.euronews.com/next/2024/10/26/this-self-driving-shuttle-transports-people-in-rural-france-is-it-the-future-of-mobility

When robotaxis & mass transit like these are common, how many people will still want private cars?

Base fares start as low as 4 yuan (around 55 cents), compared with 18 yuan (around $2.48) for a taxi driven by a human

China is already the global leader in 21st century energy - dominating renewables, batteries, and EVs. Now it’s poised to lead in robotic vehicles too.

Its robotaxis cost $30k, Waymo, who’s been in the robotaxi game longer costs $150k. Combine this with the fact Baidu can offer fares that are just 20% of a human driver in China and still make money and you can see how the global demand for such vehicles could be in the hundreds of millions. Tariffs in Europe and America may slow things there, but it won’t be the case for much of the rest of the world. Cheap Chinese robotaxis, with fares a fraction of today’s human-driven journeys, will be ubiquitous all over the planet in the 2030s.

We rarely hear of the Chinese space program in western media, but it keeps doing interesting things. A recent launch tested an inflatable module for their space station. That was an idea that once seemed promising for the ISS, via Bigelow Aerospace, but never seemed to go anywhere.

This cargo mini-shuttle concept isn’t new either. Thirty years ago an ESA version called Hermes got to the advanced planning stage before being scrapped. Some people have doubts that space planes, even launched with reusable rockets, are all that efficient, so it will be interesting to see how this fares.

I wonder what the monthly totals will be next year. When will it hit the 1 million mark, then 10 million?

The problem for all the investor funded AIs, is that data centers are huge costs. They’re burning through billions of dollars every month. That makes sense if one of two of them emerge as dominant players who own most of the market share for future AI businesses.

If they all keep under-cutting each other by using open-source. It’s more likely companies like OpenAI will crash and burn first.