I really started off (a long time ago) really nutty, but I've matured my thinking. I'm a big immortalist fan and researcher.
I currently work on trying to create AGI so we can speed up evolution really fast and have them do the job for us. Otherwise I'd work on Cryonics, medicine, etc, as blood vessel issues and cancer are what causes a majority of deaths. AIs can think much faster than human brains, clone adult AI brains easy and fast to have me_selves help itself, and upgrade their intelligence algorithm recursively, to name some of the biggest abilities they'll have. They will advance medicine, cryonics, and nanobots, to control the major killers we need stopped such as blood vessels by using nanobots to repair them or medicine to do some job or Cryonics to buy time. I am currently now programming my algorithm, it is coming along nicely. I will post my work here one day soon once I get it a bit more improved. I am going to be benchmarking at the Hutter Prize and Large Text Compression Benchmark sites soon/ one day. OpenAI.com is currently what can be said to be ahead of me and most others, however I know some things they have probably not implemented yet that they'd need.
Anyone else interested in AGI? And DALL-E as seen on openAI.com? Just seeing DALL-E handle many diverse tasks as a general purpose program is amazing, it will be able to do much more precise tasks by adding a few more components at the bottom layer. These programs are not really more than 1 or 2 thousand lines of code, it finds patterns and even the memories are (at least in humans) "programs" to find patterns (though may be slower if not hardwired code). This is amazing, DALL-E! This is our focal point. Anyone with me? My work currently describes in pure English and in just a paragraph how my AI works in full detail, along with generated text and compression of text scores/ evaluations, I hope to maybe in a few years make DALL-E and explain it with complete ease so others can code it. I currently find current literature to be too complex to read, I do get their ideas mostly and think what I don't get is their implementation more-so, but prefer it to be more simple then how they present it, or intuitive to why and what each mechanism does and finds for text/image prediction.