For people who are interested in the question whether self-driving cars are the solution to commuting: an video essay of why self-driving cars are worse for a city than public transport: https://youtu.be/040ejWnFkj0?t=1684
As I had the same feeling as you, I subscribed to a quarterly news magazine called delayed gratification. I feel it's a good balance between keeping up-to-date, while not letting the news interfere with my daily life and emotions.
I think it is good that tax money is spend on having a well-educated population, this is something a democracy thrives on. It is a shame it is spend on loan forgiveness instead of making education cheaper so loans are not needed. Loan forgiveness should be paired with reform so future generations do not have this problem.
I just want to point out that I think ChatGPT has seen more data than a single human being. With 45 TB of text data [1], it would cost a human around 70k years of continuous reading (assuming 1 GB is 200M words, and humans read 225 words per minute) to see all the training data.
After trying to understand and implement some algorithms in RASP [1, 2], my take-way was that certain functions need a certain amount of transformer layers to operate. Following this logic, it should become apparent that the functions learned by transformers can be spread over multiple heads. Repeating these functions might be very valuable for understanding and solving a problem, but current inference does not allow (a set of subsequent) heads to be repeated. This paper indeed seems a promising direction.
In my opinion, a first step is to equalize maternity and paternity leave. It should be equally disrupting for men to have children as for women (from the perspective of an employer). I like the Swedish implementation of this model, where partners get 480 days of leave per child which they are free to divide among themselves, with a minimum of 90 days for either.
The problem for Jigasi's speech-to-text feature with Whisper - or any recent SOTA speech-to-text neural networks, is that they are transformer-based. One of the key features of transformers is that they are very good at processing a sequence with the attention mechanism. But attention inherently needs to see the whole input sequence. So it's difficult to adapt these architectures to perform well in real-time scenarios like captioning meetings.