GNSS & Machine Learning Engineer

Tag: Google (Page 2 of 2)

Google’s Med-PaLM comes close to human performance in clinical knowledge

In a recent paper from Dec 26, 2022, Google demonstrates that its large language model Med-PaLM, based on 540 billion parameters with a special instruction prompt tuning for the medical domain, reaches almost clinician’s performance on new medical benchmarks MultiMedQA (benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries) and HealthSearchQA (a new free-response dataset of medical questions searched online). The evaluation of the answers considering factuality, precision, possible harm, and bias was done by human experts.

Scientists from Google AI, Caltech, Harvard, MIT, and Fermilab simulate a traversable wormhole with a quantum computer

Researchers from Google AI, Caltech, Harvard, MIT, and Fermilab simulated a quantum theory on the Google Sycamore quantum processor to probe the dynamics of a quantum system equivalent to a wormhole in a gravity model.

The quantum experiment is based on the ER=EPR conjecture that states that wormholes are equivalent to quantum entanglement. ER stands for Einstein and Rosen who proposed the concept of wormholes (a term coined by Wheeler and Misner in a 1957 paper) in 1935, EPR stands for Einstein, Podolsky, and Rosen who proposed the concept of entanglement in May 1935, one month before the ER paper (see historical context). These concepts were completely unrelated until Susskind and Maldacena conjectured in 2013 that any pair of entangled quantum systems are connected by an Einstein-Rosen bridge (= non-traversable wormhole). In 2017 Jafferis, Gao, and Wall extended the ER=EPR idea to traversable wormholes. They showed that a traversable wormhole is equivalent to quantum teleportation [1][2].

The endeavor was published on Nov 30, 2022 in a Nature article. There is also a nice video on youtube explaining the experiment. Tim Andersen discusses in an interesting article whether or not a wormhole was created in the lab.

Flan-U-PaLM: Google presents better language models without massive compute

Google presents two complementary techniques to significantly improve language models without massive extra compute:

UL2R (UL2 Repair): additional stage of continued pre-training with the UL2 (Unified Language Learner) objective (paper) for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. Applying it to PaLM results in new language model U-PaLM.

Flan (Fine-tuning language model): instruction fine-tuning on a collection of NLP datasets. Applying it to PaLM results in language model Flan-PaLM.

Combination of two approaches applied to PaLM results in Flan-U-PaLM.

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