It's currently socially/politically unpalatable for authors to admit superintelligent AI is a possibility. I frequent some writer forums. As a group, they are 1) clearly feeling angry/threatened 2) in denial about LLM capabilities.
Folks working in software can more readily track progress of the frontier model performance.
I met some of my best friends in VR, and had life-changing social interactions in some VR games on the Oculus platform... Especially EchoVR. I'm still a true believer in VR as a medium. It'll go mainstream one day, and there will be a Meta-sized company built upon it.
They had lightning in a bottle and somehow lost it. Honestly it might have been hiring Carmack that sent them down this path. Moving away from PCVR expanded the market, but it also killed the magic. Now the quest store is a wasteland of what look like low budget mobile apps.
How is it possible that text-to-score/notation is lagging text-to-audio in music generation? Generating audio seems wildly more complicated!
Since you are working in this space, I wonder if you could comment on my pet theories for why this is true: 1. Not enough training data (scores not available for most songs), or 2. Difficulty with tokenization of musical notation vs. audio
I hoped neuroscience, as a field, was on the cusp of a physical theory of learning and memory. I dreamt of an intersection of information theory, neuroscience, and ML.
Alas, state of the art in neuroscience / neural engineering is closer to bloodletting than a mechanistic theory of learning and memory.
I started a PhD in 2017 studying neuroscience + ML. I thought studying the brain would help me understand ANNs better. I was wrong. Ended up applying ML to analyzing EEG, MRI and similar.
Another take is that the base models are now good enough that spending more money for more intelligence is viable at test time. A threshold has been crossed.
Reading speed might follow a normal distribution near the middle ranges, but I'd expect non-trivial deviations in the tails due to dyslexia, ADHD on the one side and trained speed readers on the other. Perhaps this individual just falls on the far tail of that distribution?
During my graduate coursework I sometimes read 100-200 pages of technical material in a day while cramming for an exam, and was able to retain it for a day or two. I'd believe it if some people exist who could comprehend and retain all of that long-term. Alas, 'tis not I.
I unsubscribed from Scientific American a few years back when they became a venue for political commentary instead of science. The opinion pieces try to masquerade as soft science. It's an insult to the actual scientists in their readership.
If you're looking for a replacement, I picked up MIT Tech Review. It's not a stand-in replacement for what SciAm used to be, but scratches the same itch for me.
Clarkesworld sci-fi magazine temporarily closed submissions due to low quality AI spam. I'm sure the irony will not be lost on them if ML journals are the next victims.
For things I would formerly use a web browser to look up, Gemini is often a lot better than gpt-4. The more obscure the query, the better Gemini does compared to gpt-4.
But gpt-4 is still best for ML Python coding. Gemini hallucinates non-existent libraries and often adds unnecessary junk to its code. For example, Gemini often defines spurious variables and then never uses them.