I’m confident we will see large-scale quantum supercomputers in our lifetimes The Google Quantum roadmap is to get to 1M physical qubits, and once we accomplish that we will keep going. We will get to billions of physical qubits in our lifetimes.
@alexandr_wang
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Quantum Computing Research Advances Across Multiple Frontiers
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There’s a number of threads of very interesting research: – quantum chip design
– whole quantum computer design
– quantum chip manufacturing
– quantum error correction
– quantum algorithms
– applications to science and other fields and all are ripe for LOTS of progress -
Quantum Computing Advances: From Supremacy to Scalable Systems
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Quantum computing as a field is at a very exciting stage Now that we’ve demonstrated quantum supremacy, we know quantum computers work and are real. Now it’s mostly a question of how we scale and optimize these systems
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Inside Google’s Quantum AI Datacenter: Future Glimpse
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visited Google AI Quantum Datacenter over the weekend one of the rare moments where I’ve definitively peered into the future thread of interesting things I learned
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Epic Fall Winter AI Model Releases and Compute Innovations
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8/ This will be an epic fall/winter of even more model releases. It will be interesting to see how differences in compute, data, and innovations (Q* etc.) will drive changes in the rankings going forward!
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Post-Training and Data Strategy: New AI Competitive Driver
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7/ We see strengths (Claude 3.5 w/coding, Gemini 1.5 w/Vision, Multilingual) from the models that are likely driven by post-training and data strategies. This is contrary to the prevailing beliefs 1 yr ago, where pre-training was believed to be the core competitive driver.
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Post-Training Data Strategy: Key Competition Arena for LLMs
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6/ Post-training data strategy is becoming a key arena for competition. Llama3.1 paper had 15 pg about post-training data (vs. 12 pg on pre-training), driving these capabilities: – Code
– Multilinguality
– Math/Reasoning
– Long-Context
– Tool Use
– Factuality
– Steerability -
Google’s TPU Advantage Could Dominate AI Compute Race
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5/ Going fwd, Google's TPU advantage will be very important. Every lab is reliant on their NVIDIA GPU allocations for progress, except for Google, who can successfully train on TPU clusters. This could mean that Google will have more compute than the other labs in perpetuity.
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Post-Training Data Strategies: SFT, RLHF, and DPO Approaches
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4/We are also seeing remarkably similar data strategies for post-training from most labs at this point (at least what was published from Meta+Apple): – Hybrid data SFT, RLHF, & DPO setups
– Synthetic data on code and math
– Post-training data for most important capabilities -
H100 GPU Rollout Explains Timing of Recent AI Model Releases
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3/The reason these are all so close together timing-wise is that every lab got their H100s at roughly the same time. They each struggled with early issues with the H100s last fall, and the big H100 clusters all started training this spring. Voila, 5-6 months later, big models!