The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, comp... read more

Kevin Kelly (founding editor of Wired magazine) just wrote a near disaster of an article "The AI Cargo Cult: The Myth of Superhuman AI." Kelly begins by attempting to tear down the assumptions of the superhuman AI hypothesis: You can read the article in more detail, but also make sure you read the comments. As I began to write this, I was going to refute each argument in detail. However, after a bit of thought, Kelly's arguments are without any merit that its not worth the effort to refute. So I will just point you to the comments in his article, that should be sufficient to explain Kelly's mistakes. The comments are a treasure trove of ideas on what's actually more important.

Artificial intelligence has a diversity problem. Too many of the people creating it share a similar background. To renowned researcher Fei-Fei Li, this paucity of viewpoints constitutes a crisis: "As an educator, as a woman, as a woman of color, as a mother, I'm increasingly worried," she says. "AI is about to make the biggest changes to humanity, and we're missing a whole generation of diverse technologists and leaders." From the chair next to her, Melinda Gates affirms this, adding, "If we don't get women and people of color at the table -- real technologists doing the real work -- we will bias systems. Trying to reverse that a decade or two from now will be so much more difficult, if not close to impossible."

Every month we scour the internet seeking out free eBooks to help you on your educational journey, and this month has been no different. I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too). If you haven't subscribed to our newsletter yet, why not subscribe using the form on the right - you'll be the very first to know when new resources are published. This month, we have a book about Data Scientists and the work that they do, one about probability and statistical modelling and one about machine learning. They're all FREE, so what are you waiting for... Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers.

A Pew Research Foundation study examining the future of work and job training found a belief among some experts that artificial intelligence and automation threaten not just millions of jobs, but also the future of capitalism. Released Wednesday, the non-scientific study titled "The Future of Jobs and Jobs Training" is the seventh in an eight-part "Future of the Internet" study being conducted by the Pew Research Center and Elon University. More than 1,400 AI researchers, tech experts, professors, startup CEOs, and members of the general public responded to the survey. Respondents include leaders from companies like Google and Microsoft and educators from MIT, Harvard, and other universities, as well as a mix of other people interested in AI and the future of work. Tech experts include computer scientists and AI researchers but also people from internet governance groups, futurists, and startup founders.

As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools continuously. Tool condition is crucial to ensure the good quality of products: Worn tools affect not only the surface quality but also the dimensional accuracy, which means higher reject rate of the products. Therefore, there is an urgent need to identify tool failures before it occurs on the fly. While various versions of intelligent tool condition monitoring have been proposed, most of them suffer from a cognitive nature of traditional machine learning algorithms. They focus on the how to learn process without paying attention to other two crucial issues: what to learn, and when to learn. The what to learn and the when to learn provide self regulating mechanisms to select the training samples and to determine time instants to train a model. A novel tool condition monitoring approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm, recurrent classifier (rClass). The learning process consists of three phases: what to learn, how to learn, when to learn and makes use of a generalized recurrent network structure as a cognitive component. Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts.

India, the second-most populous country in the world with over 1.2 billion people, boasts one of the fastest-growing economies bolstered by the youngest workforce in the world. While millions of dollars are spent every year on tackling malnutrition, 3,000 children die every day due to hunger. In a bid to combat this problem, Accenture Labs has teamed up with Akshaya Patra, the world's largest NGO-run mid-day meal program, to use disruptive technologies such as the blockchain, the Internet of Things (IoT) and artificial intelligence (AI) to boost the number of meals served to children in schools in India that are run and aided by the government. Since 2000, Akshaya Patra has been working toward reaching more children to provide them with wholesome food every single school day. When it first started it was serving 1,500 schoolchildren in five schools.

In the fall of 2016, the citizens of the United States became fed up and made a decision that was heard around the world. While not all states felt the same way about this decision, it was pretty clear that it was time for some real change. The last 8 years had been an utter disappointment, full of the some of the most mindless inane drivel the world has ever seen, making people truly question if the United States was really the great nation that it thought it was. Then, last fall, it finally happened. On or around October 9th, the citizens of the United States of America collectively became more interested in artificial intelligence than Kim Kardashian's net worth.

ILA, the Intelligent Learning Assistant, was designed with classrooms in mind and aims to enhance both learning and teaching! Specifically, it gives teachers more time to actually TEACH by doing roll call, collecting assignments, distributing materials, finding and managing resources, and, more importantly, keep track of pupil progress and participation. Harnessing IBM's cognitive computing system, Watson, ILA collects, analyzes, and converts information into natural language. Not only does this allow educators to access, evaluate, and integrate new teaching material, but it also provides them with analysis and diagnostics on the progress of each individual student's verbal skills and literary understanding. This makes it possible for educations to assess and tailor the learning behavior and path of each individual student.

Google caused a stir when it open sourced its TensorFlow software back in November 2015, and the technology is starting to make its way into the mainstream. The machine learning software library is the next generation of DistBelief, which was internally developed by the Google Brain team at the search giant for a multitude of tasks such as image search and improving its speech recognition algorithms. TensorFlow is a deep neural network, so it learns to perform a task through positive reinforcement and works through layers of data (nodes) to help it determine the correct outcome. By open sourcing the TensorFlow library of machine learning code, Google is facilitating the simpler construction, training and deployment of complex deep neural nets. TensorFlow doesn't exactly give every developer the ability to harness machine learning but it does provide both a Python and C/C API to link into a developer's program.