Futurist Arthur C. Clarke wrote, "Any sufficiently advanced technology is indistinguishable from magic." The magic of software (giving data and rules to get answers) is often confused with the magic of machine learning (giving data and answers to get rules) but it is machine learning not software that is transforming the world of computer chess. So far, computer chess programs codified the actions of the best human players and inevitably pivoted around the strategy of "material", wherein the number and value of pieces mattered most. Reports suggest AlphaZero taught itself chess from scratch in just four hours by playing against itself and rejected human rules developed over centuries. As it started with only the basic rules, researchers suggest that its lack of knowledge of human chess history may have enabled AlphaZero to see the game in a fresh way.

WORLDWIDE revenue from AI will surge past US$46 billion in 2020, according to research firm IDC. In Asia-Pacific, this is projected to rise to US$6.8 billion by 2021. Though researchers have been working on AI decades, development has accelerated in the past few years thanks to three factors – the ubiquitous availability of data, the growing capabilities of cloud computing, and more powerful algorithms developed by AI researchers. Most recently, a team of Microsoft researchers have developed the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Throughout history, the emergence of new technologies has been accompanied by dire warnings about human redundancy.

Catherine Stinson is a postdoctoral scholar at the Rotman Institute of Philosophy, at the University of Western Ontario, and former machine-learning researcher. I wrote my first lines of code in 1992, in a high school computer science class. When the words "Hello world" appeared in acid green on the tiny screen of a boxy Macintosh computer, I was hooked. I remember thinking with exhilaration, "This thing will do exactly what I tell it to do!" and, only half-ironically, "Finally, someone understands me!" For a kid in the throes of puberty, used to being told what to do by adults of dubious authority, it was freeing to interact with something that hung on my every word – and let me be completely in charge. For a lot of coders, the feeling of empowerment you get from knowing exactly how a thing works – and having complete control over it – is what attracts them to the job.

Whether you realize it or not, artificial intelligence (AI) is already shaping our world. Whenever you use Siri or Alexa, you are already communicating with digital assistance – a form of AI that is pegged as an "intelligent digital assistant." These AI assistants are designed to make your life easier. Now, it is clear they are headed to the classroom as well. According to Artificial Intelligence Market in the US Education Sector 2017-2021, experts expect AI in education to grow by "47.50% during the period 2017-2021."

Siri and Alexa are clearly far from perfect, but there is hope that steady progress in machine learning will turn them into articulate helpers before long. A new test, however, may help show that a fundamentally different approach is required for AI systems to actually master language. Developed by researchers at the Allen Institute for AI (AI2), a nonprofit based in Seattle, the AI2 Reasoning Challenge (ARC) will pose elementary-school-level multiple-choice science questions. Each question will require some understanding of how the world works. The project is described in a related research paper (pdf).

We present a neural model for representing snippets of code as continuous distributed vectors. The main idea is to represent code as a collection of paths in its abstract syntax tree, and aggregate these paths, in a smart and scalable way, into a single fixed-length \emph{code vector}, which can be used to predict semantic properties of the snippet. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of $14$M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. Comparing previous techniques over the same data set, our approach obtains a relative improvement of over $75\%$, being the first to successfully predict method names based on a large, cross-project, corpus.

Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along with a lot of visibility. The question arises how reliable are our evaluation methodologies to compare approaches? One common methodology to identify the state-of-the-art is to partition data into a train, a development and a test set. Researchers can train and tune their approach on some part of the dataset and then select the model that worked best on the development set for a final evaluation on unseen test data. Test scores from different approaches are compared, and performance differences are tested for statistical significance. In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach. Instead, there is a high risk that the difference is due to chance. For example for the CoNLL 2003 NER dataset we observed in up to 26% of the cases type I errors (false positives) with a threshold of p < 0.05, i.e., falsely concluding a statistically significant difference between two identical approaches. We prove that this evaluation setup is unsuitable to compare learning approaches. We formalize alternative evaluation setups based on score distributions.

The k-Nearest-Neighbors (kNN) method of classification is one of the simplest methods in machine learning, and is a great way to introduce yourself to machine learning and classification in general. At its most basic level, it is essentially classification by finding the most similar data points in the training data, and making an educated guess based on their classifications. Although very simple to understand and implement, this method has seen wide application in many domains, such as in recommendation systems, semantic searching, and anomaly detection. As we would need to in any machine learning problem, we must first find a way to represent data points as feature vectors. A feature vector is our mathematical representation of data, and since the desired characteristics of our data may not be inherently numerical, preprocessing and feature-engineering may be required in order to create these vectors.

The shooting at the school in Florida was devastating, and it appears clear that Russia has been manipulating public opinion in the U.S. to stoke the flames of a divisive argument on guns. What is being missed is a brewing problem that potentially could have an even more devastating impact. Competing for our eyeballs is the news that the U.S. president kissed a woman without her permission. That story has served as a distraction from far more horrendous attacks against women in the tech industry and in government. Intel showcased virtual reality at the Olympics, but almost no one cared.

The more I've had the opportunity to explore this concept of the martech mindset, the more I've realized how important it is that we shift our thinking to view artificial intelligence (AI) as an evolution rather than as a revolution. Artificial intelligence, once an abstract, futuristic concept, is now a reality, so this needs to occur sooner rather than later. It's no longer a hypothetical; AI is changing the game for brands in tangible, practical applications. Adoption is not the issue, as investments in AR and VR are expected to grow from $11.4 billion in 2017 to $215 billion in 2021, according to IDC. Entire industries are finding innovative ways to solve persistent business challenges and create efficiencies through AI -- take the proliferation of chatbots in marketing, for example.