"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
The AWS DeepRacer League is the world's first global autonomous racing league. There are races at 21 AWS Summits globally and select Amazon events, as well as monthly virtual races happening online and open for racing. No matter where you are in the world or your skill level, you can join the league. Get a chance to win AWS DeepRacer cars and the top prize of an all-expenses-paid trip to re:Invent 2019, to compete in the AWS DeepRacer Championship Cup. The competition is heating up as the Summit Circuit hit the halfway mark in Sweden this week.
Pav Grochola is a Effects Supervisor at Sony Pictures Imageworks (SPI) and was co-effects supervisor on the Oscar winning Spiderman: Into the Spiderverse (along with Ian Farnsworth). He was tasked with solving how to produce natural looking line work for the film. A critical visual component for successfully achieving the comic book illustrative style, in CGI, was the creation of line work or "ink lines". SPI in testing discovered any approach that involves creating ink lines based on procedural "rules" (for example toon shaders) were ineffective in achieving the natural look that was wanted. The fundamental problem is that artists simply do not draw based on limited'rule sets' or guidelines.
As seasonal allergy sufferers will attest, the concentration of allergens in the air varies every few paces. A nearby blossoming tree or sudden gust of pollen-tinged wind can easily set off sneezing and watery eyes. But concentrations of airborne allergens are reported city by city, at best. A network of deep learning-powered devices could change that, enabling scientists to track pollen density block by block. Researchers at the University of California, Los Angeles, have developed a portable AI device that identifies levels of five common allergens from pollen and mold spores with 94 percent accuracy, according to the team's recent paper.
Two years ago, researchers at IBM claimed state-of-the-art transcription performance with a machine learning system trained on two public speech recognition data sets, which was more impressive than it might seem. The AI system had to contend not only with distortions in the training corpora's audio snippets, but with a range of speaking styles, overlapping speech, interruptions, restarts, and exchanges among participants. In pursuit of an even more capable system, researchers at the Armonk, New York-based company recently devised an architecture detailed in a paper ("English Broadcast News Speech Recognition by Humans and Machines") that will be presented at the International Conference on Acoustics, Speech, and Signal Processing in Brighton this week. They say that in preliminary experiments it achieved industry-leading results on broadcast news captioning tasks. The system came with its own set of challenges, like audio signals with lots of background noise and presenters speaking on a wide variety of news topics.
Rice University statistician Genevera Allen knew she was raising an important issue when she spoke earlier this month at the American Association for the Advancement of Science (AAAS) annual meeting in Washington, but she was surprised by the magnitude of the response. Allen, associate professor of statistics and founding director of Rice's Center for Transforming Data to Knowledge (D2K Lab), used the forum to raise awareness about the potential lack of reproducibility of data-driven discoveries produced by machine learning (ML). She cautioned her audience not to assume that today's scientific discoveries made via ML are accurate or reproducible. She said that many commonly used ML techniques are designed to always make a prediction and are not designed to report on the uncertainty of the finding. Her comments garnered worldwide media attention, with some commentators questioning the value of ML in data science.
Business loves buzzwords, and there's been no bigger buzzword recently than artificial intelligence. AI, of course, lets companies optimize their operations, business models and customer experiences around data-driven insights, while developing products and services that align more closely with customer needs. Now that leading cloud service providers are providing AI-driven machine learning and deep learning training platforms--customized to business user data and accessed as cloud-hosted application programming interfaces--companies of all sizes can seize the benefits of AI. By offering an alternative to on-premise AI solutions, cloud providers are giving small businesses the same advantages their larger counterparts are looking to exploit. Among the valuable AI tools at their disposal are natural language processing, image recognition, translation, search functions and data analytics.
Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. One of the world's highest-growth industries, the AI sector was valued at about $600 million in 2014 and is projected to reach a $150 billion by 2026. Whether it's used to find new links between genetic codes or to drive surgery-assisting robots, artificial intelligence is reinventing -- and reinvigorating -- modern healthcare through machines that can predict, comprehend, learn and act. Check out these 32 examples of AI in healthcare. In 2015, misdiagnosing illness and medical error accounted for 10% of all US deaths. In light of that, the promise of improving the diagnostic process is one of AI's most exciting healthcare applications.
Artificial intelligence (AI) is the science of programming computers to perceive their environment and make rational, cognitive decisions in order to achieve a goal. It is one of the most rapidly progressing and sought after technologies in the world. It is, however, a rather general term. When most people talk about artificial intelligence, they are usually talking about machine learning. At its most basic definition, machine learning is a method of teaching computers to make predictions based on data.
Companies face issues with training data quality and labeling when launching AI and machine learning initiatives, according to a Dimensional Research report. The worldwide spending on artificial intelligence (AI) systems is predicted to hit $35.8 billion in 2019, according to IDC. This increased spending is no surprise: With digital transformation initiatives critical for business survival, companies are making large investments in advanced technologies. However, nearly eight out of 10 organizations engaged in AI and machine learning said that projects have stalled, according to a Dimensional Research report. The majority (96%) of these organizations said they have run into problems with data quality, data labeling necessary to train AI, and building model confidence.
Machine learning is playing an increasingly important role in web development. Responsive web design practices first started becoming popular around seven years ago. However, advances in machine learning have made them much more robust. One of the most important ways that machine learning is changing the Internet user experience is with the development of progressive web applications (PWAs). Info World published an article about the importance of PWAs last year.