California


Researchers report breakthrough in 'distributed deep learning'

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Online shoppers typically string together a few words to search for the product they want, but in a world with millions of products and shoppers, the task of matching those unspecific words to the right product is one of the biggest challenges in information retrieval. Using a divide-and-conquer approach that leverages the power of compressed sensing, computer scientists from Rice University and Amazon have shown they can slash the amount of time and computational resources it takes to train computers for product search and similar "extreme classification problems" like speech translation and answering general questions. The research will be presented this week at the 2019 Conference on Neural Information Processing Systems (NeurIPS 2019) in Vancouver. The results include tests performed in 2018 when lead researcher Anshumali Shrivastava and lead author Tharun Medini, both of Rice, were visiting Amazon Search in Palo Alto, California. In tests on an Amazon search dataset that included some 70 million queries and more than 49 million products, Shrivastava, Medini and colleagues showed their approach of using "merged-average classifiers via hashing," (MACH) required a fraction of the training resources of some state-of-the-art commercial systems.


ODSC East 2020 Open Data Science Conference

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ODSC is the best community data science event on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it's the conference to engage, to build, to develop, and to learn from the whole data science community. ODSC East 2020 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC East 2020 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.


Chorus.ai Releases Cold Calling Dashboard, Leverages AI To Improve Connection Rates

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Chorus.ai's Smart Call Disposition now automatically detects cold call results to improve connection rates, drive top of the funnel pipeline and provide opportunities for rep coaching. San Francisco: Chorus.ai, a Conversation Intelligence Platform for high-growth sales teams, today announced the launch of Cold Call Central during Dreamforce and OpsStars 2019. Cold Call Central uses artificial intelligence to provide Sales and Sales Development leaders insights into cold calls to drive "booked" meetings and top-of-funnel results. This new customizable view in Chorus surfaces actionable insights that enable prospecting teams to identify top-performing talk tracks, enrich 1:1's with recommended calls that need coaching, build a strategy around improving connection rates, and drive better alignment between Sales Development Reps and Account Executives. This first-of-its-kind custom view, tailored for SDR and self-prospecting sales teams, is powered by Chorus's proprietary Smart Call Disposition feature.


Melting Memories – Refik Anadol

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From February 7 through March 17, 2018, Pilevneli Gallery presented Refik Anadol's latest project on the materiality of remembering. Melting Memories offered new insights into the representational possibilities emerging from the intersection of advanced technology and contemporary art. By showcasing several interdisciplinary projects that translate the elusive process of memory retrieval into data collections, the exhibition immersed visitors in Anadol's creative vision of "recollection." "Science states meanings; art expresses them," writes American philosopher John Dewey and draws a curious distinction between what he sees as the principal modes of communication in both disciplines. In Melting Memories, Refik Anadol's expressive statements provide the viewer with revealing and contemplative artworks that will generate responses to Dewey's thesis.


AI in the right places: A framework for powering data analytics products

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Earlier this year, artificial intelligence yielded a practical insight: people like to drink coffee in the morning, so workplaces should find efficient ways to serve coffee. That raised a question that's surprisingly deep -- and can cost serious money to ignore: Is AI actually necessary for this problem? is a question that remains largely unasked in Silicon Valley today. We think it's worth asking. To be sure, modern data products owe a lot of their success to artificial intelligence. Well-considered AI unlocks entirely new types of data-driven insights and cuts the time and money needed for manual data analysis.


Mercedes and Bosch commence self-driving trials in San Jose

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Do you know the way to San Jose? As they previewed earlier this year, Bosch and Mercedes-Benz have commenced trials for an automated ride-hailing service in the Silicon Valley city of San Jose. To start with, autonomous S-Class Mercedes-Benz vehicles (with safety drivers at the wheel) will shuttle "a select group of users" between North San Jose and downtown. The busy San Carlos/Stevens Creek corridor between west San Jose and downtown should be good test for the self-driving tech used by Mercedes and Bosch. Rather than just playing with prototypes, the companies want to create a production-ready SAE Level 4/5 self-driving system that can be built into different makes and models.


How StreetLight Data uses machine learning to plug cities into the mobility revolution

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The mobility revolution may have the potential to transform cities, but in the short term the rise in ride-hailing apps, bike sharing, and electric scooters is giving many local officials fits. A healthy dose of data and machine learning may help get this movement back on track. That's the bet that San Francisco-based StreetLight Data is making. The company is helping cities harness the explosion of data being generated by everything from smart city sensors to mobile phones to new transportation modes, in a bid to reinvent urban planning. As cities groan under rising populations and pollution, making more effective use of data could be the key to making them habitable over the long run.


Intel previews AI advances in software testing, sequence models, and explainability

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This week marks the kickoff of Neural Information Processing Systems (NeurIPS), one of the largest AI and machine learning conferences globally. NeurIPS 2017 and NeuIPS 2018 received 3,240 and 4,854 research paper submissions, respectively, and this year's event -- which takes place from December 8 to December 14 in Vancouver -- is on track to handily break those records. Researchers from Intel will be in attendance, as will those from tech giants like Google, Facebook, Apple, Uber, Alibaba, Baidu, and countless others. For its part, the Santa Clara, California-based chipmaker said it intends to host three dozen conference, workshop, and spotlight sessions covering topics like deep equilibrium models, imitation learning, machine programming, and more. "Intel is making significant strides in advancing and scaling neural network technologies to handle increasingly complex and dynamic workloads -- from tackling challenges with memory to researching new adaptive learning techniques," wrote Dr. Rich Uhlig, senior fellow and managing director of Intel Labs, in a blog post.


Jeff Bezos warns Big Tech not to 'turn their backs' on the U.S. military: 'We are the good guys'

FOX News

Amazon CEO Jeff Bezos warned American technology companies to resist bowing to employee pressure to "turn their backs" on the Pentagon and the defense of the United States. "One of the things happening inside technology companies is there are groups of employees who, for example, think that technology companies should not work with the Department of Defense," said Bezos during a discussion at the Reagan National Defense Forum on Friday, which is also available on Fox Nation. "People are entitled to their opinions," continued Bezos, "but it is the job of the senior leadership team to say, 'No.'" In 2018, Silicon Valley giant Google made the controversial decision to withdraw its bid to work on a Pentagon initiative called Project Maven, which used artificial intelligence to analyze data captured by U.S. government drones. More than 3,000 Google employees signed a letter addressed to company CEO Sundar Pichai protesting Google's involvement.