Collaborating Authors


GitHub - cleanlab/cleanlab: The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.


Check out the: cleanlab code documentation. Past release notes and future features planned is available here. By default, cleanlab requires no hyper-parameters. Pre-computed out-of-sample predicted probabilities for CIFAR-10 train set are available here: [[LINK]]. Check out these examples and tests (includes how to use pyTorch, FastText, etc.).

AWS launches AWS IoT RoboRunner, aims to manage robot fleets


Robots have moved off the assembly line and into warehouses, offices, hospitals, retail shops, and even our homes. ZDNet explores how the explosive growth in robotics is affecting specific industries, like healthcare and logistics, and the enterprise more broadly on issues like hiring and workplace safety. Amazon Web Services is expanding into robot fleet management with a cloud service based on Amazon's experience managing 350,000 robots in its fulfillment centers. The company at its re:Invent conference launched AWS IoT RoboRunner, a robotics service designed to enable enterprises to build and deploy applications so robots operate well together. AWS IoT RoboRunner is another part of the cloud provider's robotics stack.

Artificial intelligence, GPS take roadside assistance digital


Digital transformation has changed many facets of insurance companies, including the roadside assistance experience. "Customers expect efficient and reliable service and fortunately, technology has evolved quickly during the past few years to provide an experience that is more personalized," says Dave Powell, vice president of auto claims at Travelers. "Travelers roadside assistance technology partners are pushing the industry in a new direction, taking manual processes and redefining them as digital, transparent and connected." The evolution toward a more omnichannel approach, and accessing services remotely, has been happening for several years but COVID-19 has accelerated the on-demand consumer expectations, says Chris Small, vice president of customer experience at Agero, a B2B roadside assistance software provider. "Having a roadside event is a critical inflection point in a policyholder's lifecycle," Small said.

An inventory of robotics roadmaps to better inform policy and investment


Much excellent work has been done, by many organizations, to develop'Roadmaps for Robotics', in order to steer government policy, innovation investment, and the development of standards and commercialization. However, unless you took part in the roadmapping activity, it can be very hard to find these resources. Silicon Valley Robotics in partnership with the Industrial Activities Board of the IEEE Robotics and Automation Society, is compiling an up to date resource list of various robotics, AIS and AI roadmaps, national or otherwise. This initiative will allow us all to access the best robotics commercialization advice from around the world, to be able to compare and contrast various initiatives and their regional effectiveness, and to provide guidance for countries and companies without their own robotics roadmaps. Another issue making it harder to find recent robotics roadmaps is the subsumption of robotics into the AI landscape, at least in some national directives.

Brigade Electronics launches new predictive collision detection system - AI Forum


Market-leading provider of vehicle safety systems Brigade Electronics has launched a new predictive collision detection system. Sidescan Predict is the next generation of collision avoidance systems. Supported by the Knowledge Transfer Partnership initiative with Cambridge University, the aim was to develop a cost-effective and reliable collision detection system that can intelligently discriminate potential collisions and warn the driver with sufficient time for intervention – a predictive system. Having been in development and undergone rigorous testing for more than seven years, including 10,000 hours of research, Sidescan Predict had its first trials in 2020 receiving excellent driver feedback. Drivers noticed a significant reduction in the risk of collision with both vulnerable road users and static objects.

Role of Artificial Intelligence in National Security


In the world of blockchains, NFT's and many other trending scenarios, Artificial Intelligence is the one which is used in every innovation in different kind of ways which makes it perfect for the world to grow. Artificial Intelligence has its presence in almost every field of work, from healthcare and Medical Imaging analysis to self-driving cars all the things are covered with AI. There is one more area where AI plays a very important role that is Security. When it comes to security the one thing arises in our mind is safety. Everyone wants its data and private documents to be in safe hands, there should not be any malware attacks to our data.

Valeo targets autonomous vehicles with lidar upgrade


Third-generation system from French car parts giant said to improve range and resolution dramatically. Valeo, one of the world's largest providers of car parts and a leading supplier of sensors for advanced driver assistance systems (ADAS), is set to release new scanning lidar technology with claims of much-improved range and resolution. The Paris-listed company, which manufactures its lidar systems in southern Germany, says that the "third-generation" design will be launched to the market in 2024. "This new technology, which offers significantly enhanced performance, makes autonomous mobility a reality and provides previously unseen levels of road safety," announced the firm. The system is said to be capable of reconstructing a 3D real-time image of the vehicle's surroundings at a rate of 4.5 million pixels and 25 frames per second. "Compared with the previous generation, the resolution has been increased 12-fold, the range 3-fold, and the viewing angle 2.5-fold," Valeo added.

Nissan to invest $17.6 billion in EV development over the next five years


Nissan will invest 2 trillion yen ($17.6 billion) over the next five years developing new EVs and battery technology as part of a grand plan it calls "Ambition 2030," the company announced. It aims to release 15 new EVs total by 2030, with electrified vehicles making up half its vehicle lineup at that point. The automaker said it will develop 23 electrified vehicles in total over the next eight years, with 20 of those coming in the next five years alone. It's shooting for a market mix of 75 percent electrified (EV and e-Power PHEV/hybrids) in Europe, 55 percent in Japan and 40 percent in the US and China by 2030. The other part of that mix, would presumably be internal combustion engine (ICE) vehicles. It's worth noting that in early 2021, Nissan said that it planned to electrify every all-new car it launches by the early 2030s.

Advanced AI: Deep Reinforcement Learning in Python


This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.

13 challenges that come with autonomous vehicles


Teleoperation: the technology that enables a human to remotely monitor, assist and even drive an autonomous vehicle. Teleoperation is a seemingly simple capability, yet it involves numerous technologies and systems in order to be implemented safely. In the first article of this series, we established what teleoperation is and why it is critical for the future of autonomous vehicles (AVs). In the second article, we showed the legislative traction and emphasis gained for this technology. In the third and fourth articles, we explained two of the many technical challenges that needed to be overcome in order to enable remote vehicle assistance and operation.