Those are amazing accomplishments but I am more interested in research developments in In-Memory (Analog).
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed.
def. AI-assisted, both content and style style/nav.
Content-wise, it looks legit, but with that amount of stuff, it's nearly impossible to say for sure.
I have a feeling the hardware architecture for LLMs are completely wrong. There's no way hundreds of kilowatts is required for intelligence.. just in terms of the physics. Is there someone out there in the analog/neuromorphic computing world that could make these power-hungry monsters completely redundant?
Maybe it's just me, but between the extremely thin font and layout design, I find this extremely difficult to parse. Overuse and improper use of italics is confusing as well.
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed.
Not slope tho!