Weâre releasing a broad range of new mathematical results produced by an internal frontier model.
Weâve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
Googleâs first orbital data center is in orbit (October 1st). They say it is about the size of a fridge.
It has one kilowatt of solar power (solar). It is about the power of a household.
It was launched on a Falcon 9 rocket (SpaceX).
It should run for about a year. Google plans to launch two more orbital data centres next year. The plan is to use laser communications.
For people complaining that the latency is going to be a problem with orbital data centresâŠ
An orbital data centre can be effectively in your line of sight and it can be relatively close (much closer than the diameter of the USA).
Further if you have a cluster of space data centres, they can remain close and in a line of sight.
So you are not going to run your video games or do high frequency trading from space, but for compute, latency is unlikely to be the bottleneck.
The great features are space (no need to wait five years for a permit) and power (no need to negotiate with a local government for electricity). Solar power in space is great because it runs 24h a day.
I've attempted to map everything in oncology in a public website and open source repo for all.
The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field.
If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use.
This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live.
Some of the interesting pages:
Treatments: onco.cc/drugs
A gallery of the molecules being used onco.cc/molecules/
And targets: onco.cc/targets/
The technologies in oncology: onco.cc/technologies/
1100 ideas for helping oncology: onco.cc/ideas/
Bottlenecks on oncology: onco.cc/bottlenecks/
Open questions (LETS GO RESEARCH PEOPLE) onco.cc/open-questions/
Startup requests (LETS GO STARTUP PEOPLE) onco.cc/startup-requests/
Mechanics of cancer: onco.cc/mechanics
Battlefronts: onco.cc/fronts/
Isotope supply: onco.cc/isotopes
Key papers: onco.cc/key-papers
Pipeline funnels: onco.cc/pipeline
Cancer by type: onco.cc/cancers/
Institutional rankings: onco.cc/institutions/
The startups: onco.cc/startups/
Heros and heroines : onco.cc/heroes/
Key medical people: onco.cc/people/
Here is the project roadmap: onco.cc/roadmap/
Models and data sets: onco.cc/models/
There are other views as well, take a browse.
Try making a PR if you have an upgrade to this on the repo here: github.com/judegomila/OnCo
If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live.
If this is a useful project star the repo and help get it calibrated.
I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well.
I believe we will crack oncology and having total information dominance is key to the problem.
Let the feedback flow!
Before reading @daedalium posts, I had a totally different perception of him. That perception was coming from comments and criticisms from others. However, through years, I have learned that people frequently frame individuals, simplify their thoughts or reduce their character. Never judge a book by its cover. Read it. Donât let others narratives shape your own judgement.
ALT The other day, I saw a viral tweet saying that people talking about how LLMs are causing slow, bloated, code are going to eat crow once they re-write everything in super-optimized assembly. We're not quite at the point where we want to write everything in assembly, but some variant of what Nolan Lawson said about testing, you can choose how many bugs you want now, which I less eloquently noted here, is becoming more true for performance.
In response to a comment in my last post that the cost of formerly specialized performance work has dropped by many orders of magnitude and performance work that used to require a person or team that had a rare set of skills can be done by anyone who can type a few sentences1, which means that you can do all sorts of optimizations that used to be too expensive to be worthwhile for all but the largest scale or most lucrative projects, Marc Brooker responded with
Completely agree with your closing point. Dynamic custom software, fitted to a part...
In the last few weeks, frontier AI models have reportedly solved long-standing mathematical problems. This suggests that current AI tools might largely automate parts of what mathematicians used to do in the near future. To put some context to it, the 'near future' means that next five years.
Well. Yeah. If you have been following how recent AI systems have been evolving, you should not be too surprised.
The famous mathematician Gowers (@wtgowers) attended a workshop on AI and mathematics where participants produced something called the Leiden Declaration. It appears to position professional mathematics with respect to AI.
I would say they are circling the wagons, that is, they are on the defensive. Here are some statements from the declaration cited by Gowers:
âCredit and responsibility continue to belong to humans within the mathematical community and should not be given to automated systems. (âŠ) material should not be used as training data without consent, and publishing agreements should allow authors to opt out of the use of their work in this way (âŠ) Demand that mathematical results continue to be published in peer-reviewed venues such as journals, proceedings, and books.â
To sum it up, the mathematicians are saying that we have this nice system we have constructed since the 1970s, and we want to keep it.
They seem unaware that most of mathematics was built before the current peer-review system arrived on the scene in the second half of the twentieth century.
So what does Gowers says? From my reading, he expects that incentive structures for professional mathematicians will be seriously disrupted. Circling the wagons will not work. He concludes that the successful mathematician of the future might be more of an educator than someone who proves theoremsâŠ
âMaybe that is what the âresearch mathematiciansâ of the future should do: make a selection from a vast sea of AI-generated mathematics and write a book about it in such a way that other mathematicians can read the book and feel the kind of enrichment that we feel when we get to grips with an area of mathematics.â
Maybe. Maybe.
But another possibility is that if you are seeking to understand a specific concept, you will ask the AI to give you an explanation taylored just for you. And maybe pedagogical content will be, by default, generated by AI (or, at least, assisted by AI).
So what is happening to mathematics? It feels like a true technological singularity: progress is too fast for anyone to understand.
I went to the (great) workshop in Leiden that led to the Leiden Declaration, but ended up not signing it, as it didn't really represent my views, though it wasn't too different either, so the decision wasn't an obvious one. I've explained more in a blog post. Link in next tweet.
Automatic vs Manual Programming is a deceiving framing. Nobody does it manually. We have compilers that code for you in assembly. Thousands lines codegens are a thing in many serious projects for ages. It all has been solved long time ago. You just don't know what programming is.
This is incredible and very instructive. Any remaining doubter about using AI for software engineering, should read that article and see what becomes possible.