A recent article in The Register explored the increasing role of humans and how they come into play when training AI. The article, written by Danny Bradbury, noted that there is "an expanding ecosystem of humans in the machine-learning feedback loop who keep the machines on track." Not surprisingly, Alegion was cited as a prime example of this trend. The company possesses a legion of skilled, on-demand workers from around the world, enabling it to consistently deliver quality and scale to its customers. This is important, suggested Bradbury, not only at the outset when the training data is created, but also because humans are pivotal when they provide feedback "back into the training set to help the computer refine its own model."
When training physically simulated characters basketball skills, these competing talents must also be held in balance. While AAA game titles like EA's NBA LIVE and NBA 2K have made drastic improvements to their graphics and character animation, basketball video games still rely heavily on canned animations. The industry is always looking for new methods for creating gripping, on-court action in a more personalized, interactive way. In a recent paper by DeepMotion Chief Scientist, Libin Liu, and Carnegie Mellon University Professor, Jessica Hodgins, virtual agents are trained to simulate a range of complex ball handling skills in real time. This blog gives an overview of their work and results, which will be presented at SIGGRAPH 2018.
On Thursday, Udacity announced a new AI-based Nanodegree. Developed in partnership with WorldQuant, an international asset management firm, "Artificial Intelligence for Trading" will help learners bring machine learning to financial trading. Until recently, most banks have relied on historical data to map out future market trends. Computer modeling and machine learning algorithms, however, allow analysts to test millions of different scenarios to determine which will lead to the best outcomes. The course comprises of two three-month terms.
The same is told about learning. The more knowledge and agility of mind a person has -- the faster one can operate in rapidly changing environments under pressure. Intelligence is the natural way to employ your mind to solve problems and make decisions. Before all of this, a person acquires knowledge, data or information -- different shades of the same thing. Human intelligence goes for both competently.
Japan is planning to accept a greater number of caregivers from three Southeast Asian countries having bilateral free trade agreements with Tokyo as part of efforts to address a labor shortage in the country, sources familiar with the matter said Sunday. By easing some restrictions on the number of caregivers from Indonesia, the Philippines and Vietnam, the government will allow more caregivers with high Japanese language skills to work in Japan from next April, the sources said. Under the current terms with the three countries, Japan will accept up to 300 caregivers from each country a year. The government aims to treat such foreign workers with high language proficiency separately from the current quota of 300. The number of people in the three countries who want to work as caregivers in Japan has recently been increasing.
AI systems have led to improvements in a broad spectrum of industries, including medicine and business, and sprouted new technologies like voice assistants (can you imagine what your day would be like without Alexa?) and self-driving cars. If you're curious about learning more and want to dip your toes in a bit -- whether out of sheer fascination or for business and job hunting purposes -- the Google Cloud Mastery Bundle can serve as your primer to the world of AI. While Google Cloud has yet to reach the heights of what Amazon Web Services and Microsoft Azure have achieved, it sure is promising. After all, its litany of cloud computing services runs on the same infrastructure as Google Search and YouTube, two of the most frequently digital products used today. Undoubtedly, learning its inner workings can prove to be useful if you're running a business or carving out a career in tech.
This is the first of a series of articles intended to make Machine Learning more approachable to those who do not have a technical training. I hope it is helpful. Advancements in computer technology over the past decades have meant that the collection of electronic data has become more commonplace in most fields of human endeavor. Many organizations now find themselves holding large amounts of data spanning many prior years. This data can relate to people, financial transactions, biological information, and much, much more.
In March 2018, my collaborator Neus Lorenzo and I had the privilege of hosting a symposium and a workshop at the annual Mobile Learning Week, an event co-sponsored by UNESCO and the International Telecommunication Union (ITU). UNESCO is the educational, scientific, and cultural organization of the United Nations. The ITU, also a UN agency, coordinates telecommunications, spectrum allocations, and policy positions on information and communication services which, it seems, the world no longer knows how to live without. The event made for an amazing week, during which we were able to interact with some of the smartest people from all over the world on subjects connected to all kinds of learning in so many different cultural contexts, but all associated with the major question of mobility. A subtheme that ran through many of the interventions, including our own, was Artificial Intelligence (AI).
Japan's education ministry is planning to place English-speaking artificial intelligence robots in schools to help children improve their English oral communication skills. Japanese students are generally not good at writing in English or speaking the language. Curriculum guidelines that are due to be fully implemented in 2 years will focus on nurturing those skills. In April, the ministry will launch the robot initiative on a trial basis at about 500 schools nationwide. Some schools have already adopted similar robots to enable students to have fun while honing their English pronunciation and conversation skills.
For the past year, I've been working on implementing well known model architectures and building web applications, so I have a fair amount of refreshing to do when coming back to theoretical machine learning. A lot of it has to do with understanding machine learning's underlying mathematics rigorously, to be able to reason with the field and validate radically new architectures. To that end, I've put together a short syllabus that I'll be personally going through to review some Math Keep in mind there are a lot of excellent resources out there. I'll no doubt be updating with a better guide as I work through this material over the next few weeks. Having a fundamental understanding of mathematics is absolutely necessary to being able to reason with ML productively.