3.85 billion is actually closer to a "penny" for Microsoft.
651 karma · joined April 29, 2011
Working on https://chess67.com Former head of engineering @ calendly. game developer - k2xl.com
[ my public key: https://keybase.io/k2xl; my proof: https://keybase.io/k2xl/sigs/7YgUs8aYVhqOBDpsR7dKvMXyBEbfuW78O8jxex77MGw ]
3.85 billion is actually closer to a "penny" for Microsoft.
If the worry is that AI companies are turning open problems into benchmarks and potentially “using up” fertile mathematical problems before humans can develop the ideas around them, what exactly should the companies do differently? Also why does discovering the answers preclude humans developing ideas from them? I don't get why solving a math problem stops anyone from doing that?
Should they (AI companies) avoid training or evaluating models on open problems? Solve them but not publish the results? Delay publication? Only release proofs after mathematicians have had time to study them? Require some attribution or review process?
The statement makes a strong case that “maximize the number of solved problems” may be the wrong objective, but it seems much less clear about what behavior they actually want from OpenAI, Anthropic, DeepMind, etc.
I’d be interested in the most concrete version of the proposal. Without that, it starts to read a little like: "Please stop getting so good at our thing!"
I figured it would just be a temporary name until I came up with something better.
Then it stuck. I will say it has been easy for my customers to remember the name!
There is a very tangible benefit to people playing in chess that has nothing to do with the game itself. One example is for kids - I think kids playing chess (particularly over the board) learn quite a lot from the game. For example, etiquette (being respectful to your opponent), patience, logic, etc… but one of the best things they learn is how to lose. In chess you lose a lot. Asymptotically vast majority of players will lose approximately 50% of their games. Learning how to lose (respectfully), learn from your mistakes, then overcome them is really great process to learn. Doing this in real life rather than over the screen is also completely different.
Combine that with the game being fun and the social aspects (meeting new people, making friends)… chess very much transfers to real life in my view
Big tech has the same thing in its legal and compliance teams, and those teams absolutely can kill projects. That's pretty different from an amorphous "ethics" committee deciding what is or isn't ethical based on much more subjective criteria.
At this point, “Siri” has a pretty strong cultural association with being underwhelming or unhelpful. Even if the new version is dramatically better, convincing people to give Siri another shot may be harder than launching the same technology under a new name.
Feels like a missed opportunity to reset expectations.
Nostalgia for the old web - building websites in HTML on Angelfire and Expage.com. Learning programming on visual basic and how to copy and paste <marquee> to welcome people to the site and to sign the guestbook…
*Edit - I get it. It seems like the authentication is a challenge.
talkie-1930 The earth is at least 80,000,000 years old. This conclusion has been arrived at by geologists, partly from the study of stratified rocks, and partly from the consideration of living organisms. Strata have been formed, quietly, one upon another, in many parts of the world, from the earliest times down to the present day; and the thickness of some of these strata amounts to several thousand feet. Now, it is plain, that a long period must have elapsed, during which such beds could be accumulated. Then, again, certain living species have continued unchanged from the beginning of the stratified formations up to now; and, as no such species have become extinct, it is argued that the earth must have existed, at least, from the first appearance of these organisms. In round numbers, we may say, that the earth has been a globe, fit for the habitation of living beings, at least 80,000,000 years.
1. AI-generated charting. 2. The existence of a reliable record of the visit.
I am skeptical of the first in some cases (i.e. bias), but strongly in favor of the second.
My father is 80 and has Parkinson’s. He routinely leaves appointments unsure of what the doctor said, what changed, or what he is supposed to do next. Even when I attend with him, we sometimes disagree afterward about what exactly was recommended.
This happens with pediatric appointments too. My wife and I occasionally remember instructions differently: medication timing, symptoms to watch for, when to call back, whether something was “normal” or needed follow-up.
That is a care quality problem, not just a convenience problem.
The risks are real: privacy, consent, retention, training use, liability, and automation bias. But those argue for strict controls, not for a blanket refusal. Make it opt-in, give the patient access, prohibit training without explicit consent, keep retention short, and require clear auditability.
I do not want opaque AI quietly rewriting the medical record. But I also do not think “everyone relies on memory after a stressful 12-minute appointment” is some gold standard we should preserve.