Martin Scorsese is an advisor to Black Forest Labs.
He's spent six decades shaping how the world sees stories. Now he's helping us shape visual intelligence with human taste and craft at the center.
We sat down with him for a working storyboarding session using FLUX.
As Wittgenstein pointed out, this kind of argument cannot distinguish between "humans experiencing the world differently" and "humans experiencing the world identically but talking about that experience in different ways." Philosophically bankrupt argument.
aslan kral filmi bir çocuğun hayata dair bilmesi gereken her şeyi öğretiyor en tepede bile olsan diğer canlılara ihtiyacın olduğunu harika şekilde anlatıyor ve en önemlisi amcamıza asla güvenmememiz gerektiğini
@JanGrosse2@theo the teacher model likes owls so the vectors that correspond to liking owls are influencing the output of this teacher model (in this case the the numbers) so when this output of numbers from the teacher model are used to finetune the student model, it also starts prefering owls
my weekend project to learn about bluetooth mesh networks, relays and store and forward models, message encryption models, and a few other things.
bitchat: bluetooth mesh chat...IRC vibes.
TestFlight: https://t.co/P5zRRX0TB3
GitHub: https://t.co/Yphb3Izm0P
We have two identical groups of 8 gauges with anomalous gauge at position (3,1). The degree of anomaly is identical in both groups ~ 8 deg.
Humans have extreme hyperacuity with respect to detecting angular orientation of line segments—needle gauge is so much faster to read. The arc gauges all look the same even though the amount of deviation is the same, about 8 degrees. Even if you concentrate, it's hard to tell which one is off.
Mesela şunu "çıkın çıkmazı" diye çeviren zihnin önünde ben saygıyla eğiliyorum. O kitabı çevirmek için başına otursam, on yıl da sürem olsa, onu çıkın çıkmazı diye çevirmek aklıma gelmez, harika.
@OpenAI o1 is trained with RL to “think” before responding via a private chain of thought. The longer it thinks, the better it does on reasoning tasks. This opens up a new dimension for scaling. We’re no longer bottlenecked by pretraining. We can now scale inference compute too.