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Google pledges to no longer build AIs for the fossil fuel industry


Google has pledged to no longer build AIs for the fossil fuel industry as it further distances itself from controversial developments. A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent. In an interview with CUBE's John Furrier, the leader of Google's CTO office, Will Grannis, said that Google will "no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations." The pledge from Google Cloud is welcome, but it must be taken in a wider context.

Blue Prism adds bring your own license to Oracle Cloud


The cloud computing race in 2020 will have a definite multi-cloud spin. Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. Blue Prism, a key robotics process automation player, will provide a bring your own license offering on Oracle Cloud Infrastructure. The company was already in Oracle's partner network, but the addition to the Oracle Cloud Marketplace will make it easier to deploy Blue Prism and integrate it with Oracle software. According to Blue Prism, the bring your own license listing will be pre-installed on any image for deployment on Oracle's cloud.

Google: The Next Big Fintech Vendor


A sign of Google is seen at Google's stand during the annual meeting of the World Economic Forum ... [ ] (WEF) in Davos, on January 21, 2020. In an article titled Amazon's Impending Invasion Of Banking, I wrote: "Amazon has no incentive to cut banks out of the lending or deposit business. Amazon can make more money by providing technology services to help financial institutions underwrite, process, and service loans. Banks will gladly pay for this, because Amazon will do it for a lower cost that what banks incur to do it today." My argument then, as it is now, is that Amazon is poised to be a vendor--not a competitor--to financial institutions.

Google pledges not to make custom software for oil and gas extraction


Google says that it will not "build custom AI/ML algorithms to facilitate upstream extraction in the oil and gas industry," the company announced on Tuesday. This represents a small but significant win for climate activists. Google's comment coincided with the release of a new Greenpeace report highlighting the role of the three leading cloud-computing services--Google Cloud, Amazon Web Services, and Microsoft Azure--in helping companies find and extract oil and gas. Greenpeace notes that extracting known fossil fuel reserves would already be sufficient to push the world over 2 degrees of warming. Uncovering additional reserves will ultimately lead to even more warming.

Machine Learning Challenge 2: ML: Language Processing By: Googlers


Details Hello! all Greetings from *GDG Hildesheim* We're sure most of you are interested in learning new technologies and tools. That's why we're excited to participate in the first Community Speedrun Challenge that Google is organizing for developer communities in Europe. And as part of the GDG-Hildesheim, you're among the first to know about this!! The Community Speedrun Challenge is a training program that will run in May where you'll have the opportunity to join four Speedruns and get hands-on experience with Machine Learning on Google Cloud and Tensorflow, and take your first steps with tools like BigQuery, Cloud Speech API, and Cloud ML Engine. Every Speedrun is open for a week to give you the ability to finish all labs in your best time, scoring more points and winning prizes.

Uber details Fiber, a framework for distributed AI model training


A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members. Fiber -- which was developed to power large-scale parallel scientific computation projects like POET -- is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines. As the researchers point out, increasing computation underlies many recent advances in machine learning, with more and more algorithms relying on distributed training for processing an enormous amount of data.

Unity Game Simulation Lets Studios Use AI Bots To Playtest Games In Google Cloud


Furyion Games used Unity Game Simulation to playtest its forthcoming shooter, 'Death Carnival' Unity's newest tool allows game developers to run cloud-based playtests at unprecedented speed and scale with machine learning. T0day, as part of the Google for Games Developer Summit, Unity Technologies announced Unity Game Simulation. It's a tool that stands to revolutionize playtesting--the process of playing a game to test it for bugs and flaws before it launches--for studios, with implications that extend far beyond the world of entertainment. In essence, Game Simulation equips game developers with the ability to create simulations with bots that playtest games on their behalf. Bots have been playing games effectively for decades, but Game Simulation harnesses them at a scale that is many orders of magnitude beyond what has been possible before.

Google Launches Beta Version of Cloud AI Platform Pipelines


A scalable machine learning workflow involves several steps and complex computations. These steps include data preparation and preprocessing, training and evaluating models, deploying these models and much more. While prototyping a machine learning model can be seen as a simple and easygoing task, it eventually becomes hard to track each and every process in an ad-hoc manner. To simplify the development of machine learning models, Google launches the beta version of Cloud AI Platform Pipelines, which will help to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. It ensures to deliver an enterprise-ready, easy to install, a secure execution environment for the machine learning workflows.

MLB Swings From Amazon's AWS to Google Cloud for Data and Analytics


Major League Baseball is benching Amazon Web Services, with the league picking Google Cloud as its new data and analytics partner. Under a multiyear pact, Google Cloud becomes MLB's official cloud services and cloud data and analytics partner for business operations, including Statcast, the automated service that analyzes player performance and abilities. In addition, for the 2020 season, MLB will use Google Ad Manager and its Dynamic Ad Insertion feature for its digital ads business for the third year in a row. MLB is migrating its cloud and on-premises systems to Google Cloud, using the internet company's machine learning, analytics, application management, and data and video storage capabilities. There's a co-branding aspect to the deal as well: MLB will promote Google Cloud as powering Statcast (as AWS was).

Which Cloud Platform To Embrace For AI Workloads


Artificial intelligence is expected to increase economic output by $13 trillion in the coming decade. As per McKinsey's report, organisations that fully absorb this technology will double their cash flow in that time, while firms that don't, could see a 20% decline. The prepackaged solutions of the top cloud providers like Google Cloud help integrate AI into products. Cloud is now being utilised for navigating ships carrying cargo, thereby connecting online and offline retail markets. It is also assisting companies with ML services in fintech for fraud detection and many more.