Computers have learned to see the world more clearly in recent years, thanks to some impressive leaps in artificial intelligence. But you might be surprised--and upset--to know what these AI algorithms really think of you. As a recent experiment demonstrated, the best AI vision system might see a picture of your face and spit out a racial slur, a gender stereotype, or a term that impugns your good character. Now the scientists who helped teach machines to see have removed some of the human prejudice lurking in the data they used during the lessons. The changes can help AI to see things more fairly, they say.

HOUSTON, TEXAS - NOVEMBER 1: A Nuro delivery vehicle completes training routes around the Meyerland ... [ ] neighborhood on Nov. 1, 2019, in Houston, TX. (Photo by Annie Mulligan for The Washington Post via Getty Images) The California Department of Motor Vehicles will soon allow light-duty autonomous vehicles to be tested in a commercial setting on public roads. That means that delivery vehicles weighing less than 10,001 pounds (which includes Class 1 and 2 passenger trucks and vehicles, small delivery vans, scooters and the like) will soon be delivering groceries, pizza and who knows what else in the Golden State. Obviously, Arizona has been cool with this for a while, which is why we've seen fully autonomous Waymo rides in that state already. KRCR reports that California's new rules are similar to what's been allowed in the state so far, with distinctions for testing with a safety driver in the car, without anyone inside and whether or not the vehicles can be tested on public roads. As of December 5th, there are 65 companies that have a permit to test autonomous vehicles on public roads in California with a safety driver in the vehicle.

Hyper-converged vendors Pivot3 and Scale Computing this week expanded their use cases with product launches. Scale formally unveiled HE150 all-flash NVMe hyper-converged infrastructure (HCI) appliances for space-constrained edge environments. Scale sells the compute device as a three-node cluster, but it does not require a server rack. The new device is a tiny version of the Scale HE500 HCI appliances that launched this year. Scale said select customers have deployed proofs of concept.

Don a mixed-reality viewer and you see the sun-dappled altar under a sweeping tower of windows. Turn around, and the cathedral's pews march off into the distance, two by two. Now, for the best part, look up. And for a few moments at the Museum of History & Industry's new "Mont-Saint-Michel: Digital Perspectives on the Model" Microsoft AI-powered exhibit, you get that dizzy feeling you get when you stare straight up into lofty spaces. It's as close as you can come to looking up into the towering vaults of the stunning island-bound cathedral without visiting France.

At Nvidia's GTC Technology Conference in China this week, the chipmaker unveiled its latest NVIDIA DRIVE platform the AGX Orin. Orin is an advanced processor for autonomous vehicles or robots that was a result of four years of R&D investment by Nvidia. The new platform is powered by a new system-on-a-chip (SoC), which consists of 17 billion transistors. The Orin SoC integrates NVIDIA's next-generation GPU architecture and Arm Hercules CPU cores, combined with new deep learning and computer vision accelerators that can deliver 200 trillion operations per second (200 TOPS), which Nvidia says is 7 times better performance than the company's previous generation Xavier SoC, which delivers 30TOPS of performance. Orin can transmit over 200 gigabytes of data per second of data using just 60 to 70 Watts of power, according to Danny Shapiro, Nvidia's senior director of automotive.

Meiji Chang, General Manager and Co-Founder of QNAP Systems joins Intel Chip Chat Network Insights in this archive of a livecast interview from the Intel Network Builders Summit in conjunction with SDN & NFV World Congress in The Hague, Netherlands. With the era of 5G approaching, we see there will be more and more use cases happening at both On-premise Edge and Network Edge. Meiji shares how QNAP will enable an intelligent uCPE with AI use case for small and medium business enterprise with ability to process multiple workloads (AI, media, networking) with a single device at the edge. To meet the rising demand of 5G converged workload platforms in the edge of cloud and IoT, QNAP has leveraged Intel-based software and hardware development platforms including OpenNESS, Intel QAT, DPDK, and the Intel OpenVINO toolkit. This brings AI to the edge in conjunction with 5G to address customer requirements such as low latency, data privacy, high bandwidth demands.

NIPS 2018 (Montreal, Canada), or NeurIPS, as it is called now, is over, and I would like to take the opportunity to dissect one of the papers that received the Best Paper Award at this prestigious conference. The name of the paper is Neural Ordinary Differential Equations (arXiv link) and its authors are affiliated to the famous Vector Institute at the University of Toronto. In this post, I will try to explain some of the main ideas of this paper as well as discuss their potential implications for the future of the field of Deep Learning. Since the paper is quite advanced and touches on concepts such as Ordinary Differential Equations (ODE), Recurrent Neural Networks (RNN) or Normalizing Flows (NF), I suggest that you read up on these terms if you are not familiar with them, since I will not go into details on these. However, I will try to explain the ideas of the paper as intuitively as possible, so that you may get the main concepts without going too much into the technical details. If you are interested, you may read up on these details afterwards in the original paper.

Artificial Intelligent systems are generally defined as computer systems which are to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation. In the context of Infrastructure Asset Management, there are many applications for AI which can replace or augment substantial levels of human effort in order to improve the performance of the Asset or extend its life. As many public organisations are making significant efforts to understand'best practice' asset management, often referring to ISO 550001 for guidance on the The goal of Machine Learning is to learn from data, ingesting a large volume of data a ML algorithm is predominantly focused on a certain task to maximize the performance of machine in performing that task. Artificial Intelligence, on the other hand, is primarily focused on decision making. So while ML allows a system to learn new things from data, and AI attempts to mimic human behavior in a circumstances.

This is because of the fact that the customers can place their queries without any timing or geographical constraints. Grievance solving is one of the significant parts of web-based customer interactions. If you are facing difficulties in meeting the expected response rates of the customers or the increasing quantity of grievances is bothering your customer service team, this article will provide hands-on insight in Chatbot integration to address the issue. Also, buckle up to understand the financial incentives of doing so. Let us start with the basics.

The first revolution of Internet, in 1995, involved connecting personal computers across the globe onto one giant web and make information available at the click of a button. The second revolution, around 2010, saw the emergence of social networks and enabled the human race to become one large family with everyone having the ability to connect with everyone else on issues of common interest. Now the third Internet revolution, led by voice, humanity's most used communication channel, is going to result in a screen-less omnipresent Internet. Presently more than 20 percent of search queries in India are already done by voice. And by 2020, 50 percent of all global searches will be voice searches.