I have some questions about Hyperon and your paper on how to improve LLM performance. Have you or would you be able to implement MOSES or an LLM in AtomSpace/MeTTa? Do you have a GPU implementation? Do you have any applications or benchmark results? How much hardware do you have? How much training data have you collected?
I want any project I work on to succeed. My concerns are: 1. There won't be a hard takeoff because you can't compare human and machine intelligence. There is no threshold where if humans can produce superhuman intelligence, then so could it, but faster. Computers started surpassing humans in the 1950's and will continue to improve for decades more before humans become irrelevant. 2. Webmind/Novamente/OpenCog/Hyperon hasn't produced anything since 1998. I recall the goal at one time was to produce AGI by 2013. How much closer are you? 3. Evolutionary algorithms like MOSES are inherently slow because each population doubling generation adds at most one bit of Kolmogorov complexity (live or die) to the genome. Our genome is 10^9 bits after 10^9 generations. Human evolution only succeeded because of massive computing power that doesn't yet exist outside of the biosphere: 10^48 DNA base copy operations on 10^37 bits, powered by 90,000 TW of solar power for 3 billion years. Transistors would use a million times more energy, and we are still far from developing energy efficient computing nanotechnology based on moving atoms instead of electrons. Any ideas to speed this up? 4. It looks like from the size of your team and RFPs that you have 8 figures to invest. The big tech companies are investing 12 figures. But I think right now we are in an AI bubble. Investors are going to want a return on their investment, namely the $100 trillion per year labor automation problem. But LLMs are not taking our jobs because only a tiny fraction of the 10^17 bits of human knowledge stored in 10^10 human brains (10^9 bits per person, assuming 99% is shared knowledge) is written down for LLMs to train on. LLMs aren't taking your job because the knowledge it needs is in your brain and can only be extracted through years of speech and writing at 5 to 10 bits per second. There is only about 10^13 bits of public data available to train the largest LLMs. When people see that job automation is harder than we thought, the AI bubble will pop and investment in risky, unproven technology like Hyperon will dry up. AI isn't going away, just like the internet didn't go away after the 2000 dotcom boom. But the hype will go. ChatGPT is 2 years old and still mostly a toy to help kids write fan letters or cheat on homework. In the real world, unemployment is down. On Fri, Oct 18, 2024, 11:45 AM Ben Goertzel <bengoert...@gmail.com> wrote: > Hey! > > SingulairtyNET is offering some grants to folks who want to do some > AGI-oriented Ai software development on specific projects that are > part of our thrust to make an AGI using the OpenCog. Hyperon > architecture, > > Please see here for the details > > https://deepfunding.ai/all-rfps/ > > The projects mainly involve development in our new MeTTa AGI-oriented > language. See here > > https://metta-lang.dev/ > > for information on the MeTTa language itself, and links here > > https://hyperon.opencog.org/ > > https://arxiv.org/abs/2310.18318 > > for general info on the Hyperon approach to AGI > > thanks > Ben > > -- > -- Ben Goertzel, PhD > http://goertzel.org > CEO, SingularityNET / True AGI / ASI Alliance > Chair, AGI Society > > "One must have chaos in one's heart to give birth to a dancing star" > -- Friedrich Nietzsche ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T3ced54aaba4f0969-M86c5b8534818a1bdb2cd6de5 Delivery options: https://agi.topicbox.com/groups/agi/subscription
