RNNs and LSTMs from the past did this as well (but cannot be trained in parallel as each token has to be compressed sequentially). Transformers ate their cake.
Newer methods are going back to similar concepts but trying to get past previous bottlenecks given what we've learned since then about transformers.
They're actually not giving it away for free. At least not LLAMA v2. Once a product successfully monetizes and gains over a certain number of users, an official license from Meta has to be negotiated otherwise Meta could revoke usage of the model. At least that's roughly what the license for LLAMAv2 implies.
As someone who uses ffmpeg daily (mostly basic functions), I now rely on chatGPT to approximate the command and fine tune from there. Haven't used too many of the advanced features of ffmpeg so glad someone seems to be covering those use cases as most tutorials dont cover them.
I'll add my anecdotal experience, got covid in 2020, got the vaccine a year later and felt butterflies in my chest immediately after. Went away after a few seconds but after a month discomfort started during intense exercise. Another month later and I went to the ER with mild pericarditis. Its been a long ~year of recovery where I had to keep my HR under 100 and take tons of anti-inflammatories. Got lucky that I had it mild and finally back to normal (minus getting out of fitness), lets hope it stays that way.
What I learned (perhaps too late lol):
* Medical Science is largely empirical (requires statistical nous), unfortunately doctors do not understand statistics, take argmaxes and treat things deterministically in the guise of evidence based medicine.
* Said doctors advise Insurance companies so any doctors that do understand statistics will be bounded within the confines of what the insurance company considers evidence based medicine. This creates wasted work and even frustration as the doctor has to jump through hoops to get things done.
* getting a rare disease or disease with uncertainty requires becoming an expert on it to "correctly" navigate the health system (US here). I have a newfound appreciation for not only those who have to deal with insurance but simply navigating the uncertainty.
Family members are rationalizing why he might have done this after the fact. Inferring a causal effect from bullying to murdering children from family member testimony seems very biased.
Is that really an issue? Its really a missing data problem. Your data is "missing" and facebook can infer with some probability you exist and the people your "shadow profile" might interact with.
Newer methods are going back to similar concepts but trying to get past previous bottlenecks given what we've learned since then about transformers.