Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the "Update by Disagreement" strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-the-art methods in the robustness of trained models.

Artificial intelligence can play chess, drive a car and diagnose medical issues. Examples include Google DeepMind's AlphaGo, Tesla's self-driving vehicles, and IBM's Watson. This type of artificial intelligence is referred to as Artificial Narrow Intelligence (ANI) – non-human systems that can perform a specific task. We encounter this type on a daily basis, and its use is growing rapidly. But while many impressive capabilities have been demonstrated, we're also beginning to see problems.

Modern technology makes it possible to sequence individual cells and to identify which genes are currently being expressed in each cell. These methods are sensitive and consequently error prone. Devices, environment and biology itself can be responsible for failures and differences between measurements. Researchers at Helmholtz Zentrum München joined forces with colleagues from the Technical University of Munich (TUM) and the British Wellcome Sanger Institute and have developed algorithms that make it possible to predict and correct such sources of error. The work was published in'Nature Methods' and'Nature Communications'.

We've all been there: You drive miles to a venue only to discover that, to your dismay, every parking space is fully occupied. Apps like Google Maps, which can predict busyness based on historical data, can help to a degree, but what if you're in need of a more adaptable solution? Enter research by scientists at Carnegie Mellon University, who describe in a newly published paper on the preprint server Arxiv.org an AI system for predicting parking occupancy in real time. Rather than collect data from parking sensors, which the study's coauthors contend are susceptible to failure and error, they draw on parking meter transactions to first estimate parking availability before using additional data for prediction. An estimated 95 percent of on-street paid parking is managed by meters, making their model more generalizable than sensor-dependent systems.

Apache Ignite has supported Machine Learning capabilities for a while now. With the release of Ignite v2.7, additional Machine Learning and Deep Learning capabilities have been added, including the much-anticipated support for TensorFlow. TensorFlow is an open-source library that can be used for numerical computations and for performing Machine Learning at scale. It is also very flexible and can run across a variety of different platforms, such as CPUs and GPUs. The combination of TensorFlow and Ignite provides a complete and powerful solution for working with day-to-day (operational) data and long-term (historical) data; we can perform data analysis and build complex mathematical models.

AWS isn't exactly known as an open-source powerhouse, but maybe change is in the air. Amazon's cloud computing unit today announced the launch of Neo-AI, a new open-source project under the Apache Software License. The new tool takes some of the technologies that the company developed and used for its SageMaker Neo machine learning service and brings them (back) to the open-source ecosystem. The main goal here is to make it easier to optimize models for deployments on multiple platforms -- and in the AWS context, that's mostly machines that will run these models at the edge. "Ordinarily, optimizing a machine learning model for multiple hardware platforms is difficult because developers need to tune models manually for each platform's hardware and software configuration," AWS's Sukwon Kim and Vin Sharma write in today's announcement.

In London last month, a team from Alphabet's UK-based artificial intelligence research unit DeepMind quietly laid a new marker in the contest between humans and computers. Thursday, it revealed the achievement, in a three-hour YouTube stream in which aliens and robots fought to the death. DeepMind's broadcast showed its artificial intelligence bot, AlphaStar, defeating a professional player at the complex real-time-strategy videogame StarCraft II. The machine-learning-powered software appeared to have discovered strategies unknown to the pros who compete for millions of dollars in prizes offered each year in one of e-sports' most lucrative games. "It was different from any StarCraft that I have played, Komincz, known professionally as MaNa, said Thursday.

You'd be forgiven for assuming that DeepMind's artificial intelligence technology has already proven its chops. Back in 2016 the celebrated computer lab watched one of its AI programs do the unthinkable and win a game of Go against then world champion – and human being – Lee Sedol. Mastering the ancient Chinese board game was just one example of the machine learning DeepMind is hoping it can ultimately use to revolutionise sectors like science, healthcare, and energy. For the next step on that journey, DeepMind has turned its attention to StarCraft II. The seven-year-old RTS may still be an esports sensation, but it's not an obvious step up from Go.

DeepMind was founded in London in 2010 and was acquired by Google in 2014. It now has additional research centres in Edmonton and Montreal, Canada, and a DeepMind Applied team in Mountain View, California. DeepMind is on a mission to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be taught how. If successful, the firm believes this will be one of the most important and widely beneficial scientific advances ever made. The company has hit the headlines for a number of its creations, including software it created a that taught itself how to play and win at 49 completely different Atari titles, with just raw pixels as input. In a world first, its AlphaGo program took on the world's best player at G, one of the most complex and intuitive games ever devised, with more positions than there are atoms in the universe - and won.

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras, videos and deep learning models, machines can accurately identify and classify objects – and then react to what they "see." Processing the image Deep learning models automate much of this process, but the models are often trained by first being fed thousands of labeled or pre-identified images. Understanding the image The final step is the interpretative step, where an object is identified or classified. 5. Computer vision is used across industries to enhance the consumer experience, reduce costs and increase security. Here are a few examples of computer vision in action today.