On the same day as OpenAI released GPT-4 (March 14, 2023), Google also announced the availability of the PaLM API for developers on Google Cloud [video]. They said that they are now providing access to foundation models on Google Cloud’s Vertex AI platform, initially for generating text and images, and over time also for audio and video. In addition, with the Generative AI App Builder, they introduced the possibility of quickly building AI-powered chat interfaces and digital assistants.
Finally, Google also made for a limited set of trusted test users generative AI features available within Google Workspace (Gmail and Google Docs).
In an announcement from Feb 22, 2023, and in a corresponding Nature paper, Google demonstrates for the first time that logical qubits can actually reduce the error rates in a quantum computer.
Physical qubits have a 1-to-1 relation between a qubit in a quantum algorithm and its physical realization in a quantum system. The problem with physical qubits is that due to thermal noise, they can decohere so they no longer build such a quantum system with a superposition of the bit states 0 and 1. How often this decoherence happens is formalized by the quantum error rate. This error rate influences a quantum algorithm in two ways. First, the more qubits are involved in a quantum algorithm, the higher the probability of an error. Second, the longer a qubit is used in a quantum algorithm and the more gates act on it, i.e. the deeper the algorithm is, also the higher the probability of an error.
It is surprising that it is possible to correct (via quantum error correction algorithms) physical qubit errors without actually measuring the qubits (which would always destroy them). Such error correction codes are at least already known since 1996. The information of a physical qubit that is distributed over a bunch of physical qubits in a way so that certain quantum errors are automatically corrected, builds a logical qubit. However, the physical qubits involved in the logical qubit are also subjected to the quantum error rate. Thus there is an obvious trade-off between involving more physical qubits for a longer time, which could increase the error rate, and having a mechanism to reduce the error rate. Which effect prevails depends on the used error correction code as well as on the error rate of the used physical qubits. Google has now demonstrated for the first time that in their system there is actually an advantage of using a so-called surface code logical qubit.
Meta presented with MAV3D (Make-A-Video-3D) a method for generating 4D content, i.e. a 3D video, from a text description by using a 4D dynamic Neural Radiance Field (NeRF) [project page, paper]. Unfortunately, the source code has not been released.
Google Research published an impressive language model that can turn a text description into high-quality music [webpage, paper]. The source code is unfortunately not publicly available.
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.
DeepMind publishes that AlphaCode reaches median human competitor performance in real-world programming competitions on Codeforces by now scaling up the number of possible solutions to the problem to millions instead of tens like before.
This announcement was originally made on 02 Feb 2022 and was now published on 08 Dec 2022 in Science.
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.
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.
Google presents DreamBooth, a technique to synthesize a subject (defined by 3-5 images) in new contexts defined by text input.
The method is based on Google’s pre-trained text-to-image model Imagen which is not publicly available. However, source code based on Stable Diffusion already exists on GitHub.
