Laser-driven Livermore cost about $4b. Magnet driven ITER is likely $40b ($20-$60b). Should we change cost forecasts? Retrain scientists?

Livermore's laser-based fusion lab cost under $4b, while construction drags on at the (likely) $40b magnetically-based facility at ITER. Does world science change horses here? Change budgets? Retrain scientists? Do we have a fundamentally different timeline and economics for fusion? Or not?

Right on. Success with lasers in Livermore's $4b facility while magnetic construction drags on at ITER with a likely $40b spend ($20b-$60b). Serious questions: Has this radically changed the timeline and economics of fusion? Does it change the funding direction? Are there suddenly a lot of scientists that need retraining?

In many designs not all physical qubits are interconnected - correcting for that yields quantum volume. Then, correcting for the % of qubits doing error correcting gives algorithmic qubits. . Laser-controlled, "ion trap" quantum computers get full n x n interconnection without the heat of exponentially many wires. Further, their most recent step from ytterbium to barium ions further decreases energy adds by requiring a lower frequency regulating laser. Estimated state set-up accuracy increases from 99.5% to 99.965%, all translating to increased algorithmic qubits. . The emerging practical test is in protein synthesis, where free-energy minimizing quantum programs are challenging ML/AI algorithms on enhanced "classical" computers.

Simply put: Every phenomenon is not an object. Simple example: A shadow can move with accelerations that would tear an object apart. Many boundaries - that can be sensed by a variety of sensors - "move" in ways objects can't. And they are certainly not "flying!"

Step back a bit. It's more like when Copernicus proved that the earth orbited the sun. Huge libraries of integrated helio-centric ideas and calculations became "unfounded" - their predictions remained pretty close, but where we asserted knowledge (existence and connection of these ideas), we had a black hole that would take science much time and effort to fully repopulate. Consider each such idea as a hidden node in a deep ML system. I'd view the above as the article's meaning in saying "an arbitrary part of its learned memories can suddenly collapse" and need to be rebuilt. Happens to both humans and ML systems as far as I can see.