A 2021 paper in Nature by Mirhoseini et al.30 about the use of reinforcement learning (RL) in the physical design of silicon chips raised eyebrows, drew critical media coverage, and stirred up controversy due to poorly documented claims. The paper, authored by Google researchers, withheld critical methodological steps, and most inputs needed to reproduce its results. Our meta-analysis shows how two separate evaluations filled in the gaps and demonstrated that Google RL lags behind human chip designers, a well-known algorithm (simulated annealing), and generally available commercial software, while also being slower. Crosschecked data indicates that the integrity of the Nature paper is substantially undermined, owing to errors in conduct, analysis, and reporting. Before publishing, Google rebuffed internal allegations of fraud which still stand.

Google isn't about to let Microsoft or anyone else make a swipe for its search crown without a fight. The company announced today that it will roll out a chatbot named Bard "in the coming weeks." The launch appears to be a response to ChatGPT, the sensationally popular artificial intelligence chatbot developed by startup OpenAI with funding from Microsoft. Sundar Pichai, Google's CEO, wrote in a blog post that Bard is already available to "trusted testers" and designed to put the "breadth of the world's knowledge" behind a conversational interface. It uses a smaller version of a powerful AI model called LaMDA, which Google first announced in May 2021 and is based on similar technology to ChatGPT.

What is the impact of taking all the musical know-how and ingesting into an AI brain with only asking questions expressing your musical tastes? Who needs to be a musician to entertain when we will soon be able to create our own music even more easily and tap into the human genius of every muscian that has a recording? Google researchers say MusicLM is based on a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". You can find the details on GitHub. MusicLM is built on a neural network, and trained on a large music data set of over280,000 hours of music, enabling it to automatically produce innovative music tracks of diverse instruments, genres, and concepts based on text descriptions.

Google is one of the biggest companies on Earth. Google's search engine is the front door to the internet. And according to recent reports, Google is scrambling. Late last year, OpenAI, an artificial intelligence company at the forefront of the field, released ChatGPT. Alongside Elon Musk's Twitter acquisition and fallout from FTX's crypto implosion, breathless chatter about ChatGPT and generative AI has been ubiquitous.

Google researchers believe that natural language processing and AI will enable robots to create their code to take action against new instructions. Google is currently testing a system that allows robots to write their code, follow instructions, and complete tasks. This is a way to streamline reprogramming policies for every new task. It can be tedious and time-consuming, and it requires domain experts. Staff could interact with robots on smart factory floors using simple commands without needing to write complicated code. Google researchers developed language modeling programs called Code as Policies (CaP). This code-writing AI system can generate new code for new instructions. Google robotics scientists stated in a blog post that “Given natural languages, instructions, current language models can write not only generic code, but, as we have discovered, code capable of controlling robot actions.” Google researchers have combined large language models with everyday robots to better respond to complex and abstract human requests. According to the Google team, CaP will enable a single system

Google researchers believe that natural language processing and AI will enable robots to create their code to take action against new instructions. Google is currently testing a system that allows robots to write their code, follow instructions, and complete tasks. This is a way to streamline reprogramming policies for every new task. It can be tedious and time-consuming, and it requires domain experts. Staff could interact with robots on smart factory floors using simple commands without needing to write complicated code.

Google AI announced the launch of the Google Research YouTube channel today. The channel is set to focus on a wide range of subjects like AI/ML, robotics, theory and algorithms, quantum computing, health and bioscience. The show will introduce viewers to a Google Researcher who will explain about their innovations and the implications of the newly emerging technologies in our daily lives. In its first rendition, Drew Calcagno spoke with Google Researchers Sharan Narang and Aakanksha Chowdhery, who theorised and introduced the language models to robotics and coded the Pathways Language Model (PaLM). This series focuses on the conversion of research publications by Google researchers into byte-sized content for viewers.

Recently, a former Google researcher claimed that some algorithms used by the company reached sentient capabilities well above their initial design. Phillipa Louvois rules that Data, the Enterprise's android, is not the property of Starfleet, arguing: "We have all been dancing around the basic issue: does Data have a soul? I don't know that he has. I don't know that I have. But I have got to give him the freedom to explore that question himself."

In late 2018, Google AI researchers Anna Goldie and Azalia Mirhoseini got the go-ahead to test an elegant idea. Google had invented powerful computer chips called tensor processing units, or TPUs, to run machine learning algorithms inside its data centers--but, the pair wondered, what if AI software could help improve that same AI hardware? The project, later codenamed Morpheus, won support from Google's AI boss Jeff Dean and attracted interest from the company's chipmaking team. It focused on a step in chip design when engineers must decide how to physically arrange blocks of circuits on a chunk of silicon, a complex, months-long puzzle that helps determine a chip's performance. In June 2021, Goldie and Mirhoseini were lead authors on a paper in the journal Nature that claimed a technique called reinforcement learning could perform that step better than Google's own engineers, and do it in just a few hours.

The three little bears strived to get it just right, and AI model builders strive to do the same thing when it comes to specifying their model. Underspecification is when you build a model that performs well on your data, but so do other models, which could lead to your model decaying over time. The discussion of underspecification kicked off last fall when Google researchers published a paper on the subject, "Underspecification Presents Challenges for Credibility in Modern Machine Learning." "ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures," stated the paper, put together by a group of scientists led by author Alexander D'Amour, a research scientist with Google Brain of Cambridge, Mass.