Sundar Pichai: This is such an exciting moment because there are a lot of ideas we've had in terms of how we can help our users, but you didn't quite have a powerful technology capability to actually realize those ideas. I think we're moving fast, and when I look at our road map for the next few months, we'll be bringing out a lot of these things like we've done in the last few weeks. Workspace has announced features both in Gmail and Google Docs, which are beginning to roll out. There's a lot more to come. WSJ: What is the key to getting people to move fast on this, and do you still think there is room to move faster? Mr. Pichai: You always want to think about how you can do things as fast as possible. It's important to get it right.

Since its creation in 2011, Google Brain has been a key project in the development of machine learning and artificial intelligence (AI). This initiative, led by Andrew Ng, Jeff Dean, and Greg Corrado, has fueled progress in areas such as natural language processing, computer vision, and machine translation. Google Brain was born as a research team dedicated to exploring new ways to implement deep learning and neural networks in AI systems. As the project progressed, significant advances were made in the ability of machines to learn from large data sets without the need to program task-specific rules. In 2012, Google Brain made a breakthrough in image recognition, training a neural network on millions of YouTube images that, without prior knowledge, was able to identify cats with high accuracy.

OpenAI has just announced an enterprise version of its popular generative AI product, ChatGPT. But in this case, OpenAI is a fast follower -- not the first-to-market. Cohere, a Toronto-based company with close ties to Google, is already bringing generative AI to businesses. I spoke with Cohere's President and COO, Martin Kon, about how its machine learning models are being used within enterprise companies. Cohere is only a few years old, but it has an impressive pedigree.

Are you someone who's getting interested in computer vision or any state-of-the-art knowledge in deep learning? Did you know that Tensorflow is an open-source end-to-end platform that is being developed by the Google Brain team which was led by the Google senior fellow and AI researcher Jeff Dean built in November 2015. It can actually perform various tasks focused on training and inference of deep neural networks. This allows the developers to create better machine learning applications using the tools, libraries and community resources. In fact, it is one of the most known deep learning libraries globally which is Google's Tensorflow.

Alphabet Google is in talks to invest at least $200 million into artificial intelligence startup Cohere Inc., according to people familiar with the matter, another sign of the escalating arms race among large technology companies in the sector. Founded in 2019, Cohere creates natural language processing software that developers can then use to build artificial intelligence applications for businesses, including tools for chatbots and other features that can understand human speech and text. Last November, the company announced a multiyear partnership with Google to have its cloud division supply the computing power needed for Cohere to train its software models. As a part of the negotiations, Cohere also held discussions with chipmaker Nvidia Corp. about a potential strategic investment, the people said. The talks between the companies are still ongoing and could fall apart, some of the people said.

Kaushik is a technical leader at Meta, and has over 10 years of experience building AI-driven products at companies like LinkedIn and Google. Shalvi is an AI scientist at SAP, and has experience as a data scientist, a software engineer, and project manager. Frank is a founding engineer at co:rise and started his career at Coursera, where he was the first engineering hire and built much of the platform's original core infrastructure. The following excerpts from Jake's conversation with Kaushik, Shalvi, and Frank have been edited and condensed for clarity. You can watch the complete recording here. Kaushik, you've been a hiring manager at some big companies. You get a lot of resumes. What are you looking for? What advice do you have for someone who's working on their resume and thinking about how to position themselves? Kaushik: In terms of skills, I'm looking for a practical knowledge of applying ML to build products. That's something I think you can't get from books -- you have to have some hands-on experience. I'm not necessarily looking for someone to have experience with specific tools or techniques, because those things are constantly changing. It's more that I want to know about the approach they took. Why did they use the tools they did, and what did they do when things got tricky or didn't work the first time? Don't get me wrong, I think having a good theoretical foundation is definitely necessary. But I would say you should spend as much time as you can solving real problems. That's how you learn which techniques work best for which use cases, and it will help you get a better understanding of the theoretical side, too. Kaushik: In terms of preparing for interviews, other than brushing up on the fundamentals, my advice would be to brainstorm a couple of problems that are relevant to the company you're interviewing with and do some background research on the common techniques to solve those problems.

Machine-learning algorithms aren't likely to put painters or singer-songwriters out of work anytime soon, to judge from their body of work to date. But Google Brain is developing tools that pair artists with deep-learning tools to develop novel artwork together, said Douglas Eck, senior staff scientist at the search giant's artificial-intelligence research division, during the MIT Technology Review's EmTech Digital conference on Tuesday. He hopes the platform, called Magenta, will allow people to produce completely new kinds of music and art, in much the way that keyboards, drum machines, and cameras did. Eck said that Magenta could serve a role analogous to that of Les Paul, who helped develop the modern electric guitar. But Eck said they want to keep artists in the loop to push the boundaries of the new tool in interesting ways, like a Jimi Hendrix who flips it upside down, bends the strings, and distorts the sound.

I explain Artificial Intelligence terms and news to non-experts. If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do. Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen. They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Read the full article: https://www.louisbouchard.ai/google-brain-imagen/

"We thoroughly vetted the original Nature paper and stand by the peer-reviewed results," Zoubin Ghahramani, a vice president at Google Research, said in a written statement. "We also rigorously investigated the technical claims of a subsequent submission, and it did not meet our standards for publication." Dr. Chatterjee's dismissal was the latest example of discord in and around Google Brain, an A.I. research group considered to be a key to the company's future. After spending billions of dollars to hire top researchers and create new kinds of computer automation, Google has struggled with a wide variety of complaints about how it builds, uses and portrays those technologies. Tension among Google's A.I. researchers reflects much larger struggles across the tech industry, which faces myriad questions over new A.I. technologies and the thorny social issues that have entangled these technologies and the people who build them.

Research ... hoy traemos a este espacio otro paper de Google ... aquí os dejamos el Abstract We present a pixel recursive super resolution model that synthesizes realistic details into images while enhancing their resolution. A low resolution image may correspond to multiple plausible high resolution images, thus modeling the super resolution process with a pixel independent conditional model often results in averaging different details–hence blurry edges. By contrast, our model is able to represent a multimodal conditional distribution by properly modeling the statistical dependencies among the high resolution image pixels, conditioned on a low resolution input. We employ a PixelCNN architecture to define a strong prior over natural images and jointly optimize this prior with a deep conditioning convolutional network. Human evaluations indicate that samples from our proposed model look.(leer