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Nvidia CEO Jensen Huang weighs in on the metaverse, blockchain, and chip shortage

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https://venturebeat.com/wp-content/uploads/2021/06/jensen-2.jpg?w=1200&strip=all Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . Conversations with Nvidia CEO Jensen Huang are always blunt and illuminating because he still likes to have freewheeling chats with the press. During the recent online-only Computex event, he held an briefing with the press where he talked about the company’s recent announcements and then took a lot of questions. I asked him about the metaverse , the universe of virtual worlds that are all interconnected, like in novels such as  Snow Crash  and  Ready Player One . And he gave a detailed answer. Huang addressed a wide range of issues. He talked about Nvidia’s pending bid to buy Arm for $40 billion, as well as Nvidia’s effort to create Grace, an Arm-based CPU. He also addressed progress on Nvidia’s own Omniverse , dubbed a “metaverse for engineers.” Huang talked about Nvidia’s presence in the Chines

Deep Learning for Twitter Personality Inference

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Original Source Here While this sample would ideally be larger, the dataset cannot be increased without unbalancing the classes than they already are in the dataset. This is because some (self-declared) personality types only number about 100 profiles in total. ISTPs were the least represented amongst all personalities — a finding consistent with theory, as ISTPs are described to be outdoor-oriented people who enjoy physical activity and working with tools. It wouldn’t be surprising if they don’t prefer the endlessly roundabout conversations on politics and abstract tech that Twitter is famous for. In general, I found that Intuitives (N) are significantly more prominent than Sensing(S) preference profiles. While I could not get an accurate estimate of this disparity in the Twitter population, the ratio is at least ~ 2:1 based on my sample. Many Intuitive types reached the 300 profile limit I specified; we can be sure that the 2:1 ratio is definitely underestimated. The national

Full Scale development of Recommendation system as ML Engineer from scratch.

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Original Source Here Full Scale development of Recommendation system as ML Engineer from scratch. Recommendation system is one of the most important and complex software engineering system from many aspects.It is one of the reason of success of giants like netflix,youtube etc. Making a full scale recommendation system as a ready deployable product for a working company requires great knowledge of deep learning,logic programming,database and domain ,apart from these,you should have required software engineering and development knowledge for deployment and maintenance.[“yeah I know all these, huhhhh”] Hii Everyone,I am Aditya Raj,currently in 2nd year at IIIT Allahabad and also working remotely as Machine Learning Engineer at yellowbacks.com . I have b een working for 5 months now and feel immensely lucky to work at a growing startup,I got to learn ocean of things and got opportunity to design,make and deploy some interesting AI based systems. Well,here I will be talking of

The rise of robotaxis in China

Original Source Here AutoX, Momenta and WeRide took the stage at TC Sessions: Mobility 2021 to discuss the state of robotaxi startups in China and their relationships with local governments in the country. They also talked about overseas expansion — a common trajectory for China’s top autonomous vehicle startups — and shed light on the challenges and opportunities for foreign AV companies eyeing the massive Chinese market. Enterprising governments Worldwide, regulations play a great role in the development of autonomous vehicles. In China, policymaking for autonomous driving is driven from the bottom up rather than a top-down effort by the central government, observed executives from the three Chinese robotaxi startups. Huan Sun, Europe general manager at Momenta, which is backed by the government of Suzhou, a city near Shanghai, said her company had a “very good experience” working with the municipal governments across multiple cities. In China, each local government is incen

This AI system learned to understand videos by watching YouTube

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https://venturebeat.com/wp-content/uploads/2016/08/youtube.jpg?w=1200&strip=all Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . Humans understand events in the world contextually, performing what’s called multimodal reasoning across time to make inferences about the past, present, and future. Given text and an image that seem innocuous when considered apart — e.g., “Look how many people love you” and a picture of a barren desert — people recognize that these elements take on potentially hurtful connotations when they’re paired or juxtaposed, for example. Even the best AI systems struggle in this area. But there’s been progress, most recently from a team at the Allen Institute for Artificial Intelligence and the University of Washington’s Paul G. Allen School of Computer Science & Engineering. In a preprint paper published this month, the researchers detail Multimodal Neural Script Knowledge Models (Merlot) , a system that