Intel Debuts 64-Chip Neuromorphic System for AI Algorithms

Intel, which is in development on its Loihi “neuromorphic” deep-learning chips, just debuted Pohoiki Beach, code name for a new system comprised of 64 Loihi chips and eight million “neurons.” Loihi’s neuromorphism denotes the fact that it is modeled after the human brain, and Pohoiki Beach is capable of running AI algorithms up to 1,000 faster and 10,000 times more efficiently than the typical CPU. Applications could include everything from autonomous vehicles to electronic robot skin and prosthetic limbs. Continue reading Intel Debuts 64-Chip Neuromorphic System for AI Algorithms

Amazon’s AI-Enabled StyleSnap Is Ideal for Fashion Market

Amazon, which launched its new StyleSnap feature to select iOS and Android users in April, will soon make the in-app tool widely available, said company worldwide consumer head Jeff Wilke at the company’s re:MARS AI conference in Las Vegas. Users can reach StyleSnap via a shortcut found by tapping the camera icon in the Amazon app’s upper right-hand corner. Based on image recognition, the machine learning-enabled StyleSnap (and Pinterest Lens competitor) will offer similar items to any photo or screenshot uploaded by a user. The algorithms also incorporate computer vision and deep learning. Continue reading Amazon’s AI-Enabled StyleSnap Is Ideal for Fashion Market

Facebook Using Artificial Intelligence to Reduce Bias/Abuse

At this week’s annual Facebook F8 developer conference in San Jose, California, company CTO Mike Schroepfer discussed the progress being made by internal teams dedicated to reducing the spread of misinformation, hate speech, and abuse on the social platform using various artificial intelligence techniques. In the course of a single quarter, according to Schroepfer, Facebook takes down more than a billion “spammy” accounts, more than 700 million fake accounts, and tens of millions of items containing violent content or nudity.

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AWS Tool Aims to Simplify the Creation of AI-Powered Apps

Amazon introduced AWS Deep Learning Containers, a collection of Docker images preinstalled with preferred deep learning frameworks, with the aim of making it more seamless to get AI-enabled apps on Amazon Web Services. At AWS, general manager of deep learning Dr. Matt Wood noted that the company has “done all the hard work of building, compiling, and generating, configuring, optimizing all of these frameworks,” taking that burden off of app developers. The container images are all “preconfigured and validated by Amazon.” Continue reading AWS Tool Aims to Simplify the Creation of AI-Powered Apps

Nvidia Demos New Products at Deep Learning & AI Confab

Nvidia made a number of compelling announcements at this week’s GPU Technology Conference (GTC 2019) in San Jose, California. The company unveiled its GauGAN AI image creator that uses generative adversarial networks (GANs) to turn sketches into nearly photorealistic images. As part of its cloud pursuits, the company unveiled its latest RTX server configuration that is designed for Hollywood studios and those who want to create visual content quickly (each server pod can support up to 1,280 GPUs). Nvidia also announced partnerships with 3D software makers including Adobe, Autodesk and Unity to integrate Nvidia’s RTX ray-tracing platform. Continue reading Nvidia Demos New Products at Deep Learning & AI Confab

Google GPipe Library Speeds Deep Neural Network Training

Google has unveiled GPipe, an open-sourced library that makes training deep neural networks more efficient under the TensorFlow framework Lingvo for sequence modeling. According to Google AI software engineer Yanping Huang, “in GPipe … we demonstrate the use of pipeline parallelism to scale up DNN training,” noting that larger DNN models “lead to better task performance.” Huang and his colleagues published a paper on “Efficient Training of Giant Neural Networks Using Pipeline Parallelism.” Continue reading Google GPipe Library Speeds Deep Neural Network Training

Gartner Report Shows Dramatic Growth in Enterprise AI Use

Gartner just released its 2019 CIO Survey of 3,000+ executives in 89 countries, which found that implementation of artificial intelligence grew 270 percent in the past four years. In 2018, use of AI grew 37 percent, up from 10 percent in 2015. The company estimates that the AI market will be valued at $6.14 billion by 2022. Gartner distinguished research vice president Chris Howard noted that we are still “far from general AI that can wholly take over complex tasks,” but that we have entered the “augmented intelligence” era. Continue reading Gartner Report Shows Dramatic Growth in Enterprise AI Use

CES Panel: Impact of Evolving Tech on Autonomous Vehicles

Faye Francy, executive director of the Automotive Information Sharing and Analysis Center (Auto-ISAC), led a conversation about the impact of machine learning, deep learning and AI on the autonomous vehicle (AV) ecosystem. “They work together to bring great things — and possibly nefarious things — to the auto industry,” she said. Inivision AI chairman Seamus Hatch noted that the three terms aren’t interchangeable. “We’re many years behind the singularity,” he said. “It’s a machine trained to solve a specific problem faster and more accurately than a human.” Continue reading CES Panel: Impact of Evolving Tech on Autonomous Vehicles

We Were Passengers in a Las Vegas ‘Self-Driving’ Rideshare

Autonomous vehicles have been a part of tech culture for so long that it’s hard to realize that only a handful of people have actually ridden in one. So it was with great surprise that our very first Lyft ride out of our Las Vegas hotel on Sunday night was in a “self-driving” vehicle. Lyft partnered with Irish auto-parts-company-turned-autonomous-vehicle-startup Aptiv (formerly known as Delphi) to offer CES attendees and Vegas commuters the option to ride in one of their 30 “self-driving” BMW 5 Series. Continue reading We Were Passengers in a Las Vegas ‘Self-Driving’ Rideshare

Here’s What We Hope to See This Week at CES Related to AI

With the buzz way down, AI research more vibrant than ever, and more mainstream experimentation, there’s a lot to potentially look forward to at CES 2019 in the field of AI and machine learning. And already it all seems to converge on one very interesting trend: pragmatism. As AI exits the lab, and heads into the world, we’re expecting new and compelling applications. At CES this week, we’re hoping to see advances in areas such as autonomous vehicles, consumer robots, computer vision, smart assistants, and a more integrated Internet of Things. Continue reading Here’s What We Hope to See This Week at CES Related to AI

Hive Builds Tailored AI Models via 700,000-Person Workforce

Hive, a startup founded by Kevin Guo and Dmitriy Karpman, trains domain-specific artificial intelligence models via its 100 employees and 700,000 workers who classify images and transcribe audio. The company uses the Hive Work smartphone app and website to recruit the people who label the data, and recently introduced three products: Hive Data, Hive Predict, and Hive Enterprise. Shortly after the product launch, Peter Thiel’s Founders Fund and other venture capital firms invested $30 million in the startup. Continue reading Hive Builds Tailored AI Models via 700,000-Person Workforce

IBM, Harvard University Develop New Tool for AI Translation

At the IEEE Conference on Visual Analytics Science and Technology in Berlin, IBM and Harvard University researchers presented Seq2Seq-Vis, a tool to debug machine translation tools. Translation tools rely on neural networks, which, because they are opaque, make it difficult to determine how mistakes were made. For that reason, it’s known as the “black box problem.” Seq2Seq-Vis allows deep-learning app creators to visualize AI’s decision-making process as it translates a sequence of words from one language to another. Continue reading IBM, Harvard University Develop New Tool for AI Translation

Accounting, Finance Industries Demand Explainable AI Tools

As artificial intelligence-based tools become more widespread in the business industry, cloud service companies are debuting tools that explain the artificial intelligence algorithms they use to provide more transparency and assure users of their ethical behavior. That’s because regulated industries are demanding it. Capital One and Bank of America are just two such companies interested in using AI to improve detection of fraud, but want to know how the algorithms work before they implement such tools. Continue reading Accounting, Finance Industries Demand Explainable AI Tools

IBM Creates Machine-Learning Aided Watermarking Process

IBM now has a patent-pending, machine learning enabled watermarking process that promises to stop intellectual property theft. IBM manager of cognitive cybersecurity intelligence Marc Ph. Stoecklin described how the process embeds unique identifiers into neural networks to create “nearly imperceptible” watermarks. The process, recently highlighted at the ACM Asia Conference on Computer and Communications Security (ASIACCS) 2018 in Korea, might be productized soon, either within IBM or as a product for its clients. Continue reading IBM Creates Machine-Learning Aided Watermarking Process

Samsung Fund to Boost Startups with New Approaches to AI

Some startups are trying to create another form of AI than deep learning, to minimize the amount of training, data and server power needed. Samsung Next, the South Korean company’s venture capital unit, just launched the Q Fund to jumpstart this idea by funding companies focusing on new ways of developing artificial intelligence. One of Q Fund’s first investments is Vicarious, a startup that wants to give machines “imagination” and is inspired by biology to make machines learn more quickly. Continue reading Samsung Fund to Boost Startups with New Approaches to AI

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