Elon Musk's new plan to go all-in on self-driving vehicles puts a lot of faith in the artificial intelligence needed to ensure his Teslas can read and react to different driving situations in real time. AI is doing some impressive things--last week, for example, makers of the AlphaGo computer program reported that their software has learned to navigate the intricate London subway system like a native. Even the White House has jumped on the bandwagon, releasing a report days ago to help prepare the U.S. for a future when machines can think like humans. But AI has a long way to go before people can or should worry about turning the world over to machines, says Oren Etzioni, a computer scientist who has spent the past few decades studying and trying to solve fundamental problems in AI. Etzioni is currently the chief executive officer of the Allen Institute for Artificial Intelligence (AI2), an organization that Microsoft co-founder Paul Allen formed in 2014 to focus on AI's potential benefits--and to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race.

In contrast to k-nearest neighbors, a simple example of a parametric method would be logistic regression, a generalized linear model with a fixed number of model parameters: a weight coefficient for each feature variable in the dataset plus a bias (or intercept) unit. While the learning algorithm optimizes an objective function on the training set (with exception to lazy learners), hyperparameter optimization is yet another task on top of it; here, we typically want to optimize a performance metric such as classification accuracy or the area under a Receiver Operating Characteristic curve. Thinking back of our discussion about learning curves and pessimistic biases in Part II, we noted that a machine learning algorithm often benefits from more labeled data; the smaller the dataset, the higher the pessimistic bias and the variance -- the sensitivity of our model towards the way we partition the data. We start by splitting our dataset into three parts, a training set for model fitting, a validation set for model selection, and a test set for the final evaluation of the selected model.

Advances in robotics and Artificial Intelligence (AI) hold the potential to fundamentally reshape the way we live and work, yet the government does not yet have a strategy for developing skills, a report by the Science and Technology Committee has concluded. The report states that AI systems are starting to have transformational impacts on everyday life: from driverless cars and supercomputers that can assist doctors with medical diagnoses, to intelligent tutoring systems that can tailor lessons to meet a student's individual cognitive needs. Such breakthroughs raise a host of questions for society, including ethical issues about the transparency of AI decision-making as well as privacy and safety. The Committee is calling for a Commission on Artificial Intelligence to be established at the Alan Turing Institute to examine the social, ethical and legal implications of recent and potential developments in AI. The UK is well-placed to provide this type of intellectual leadership, it adds.

Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.

Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold approach for unsupervised polysemy modeling: (a) context representations, (b) sense induction and disambiguation and (c) lexeme (as a word and sense pair) representations. A key feature of our work is the finding that a sentence containing a target word is well represented by a low rank subspace, instead of a point in a vector space. We then show that the subspaces associated with a particular sense of the target word tend to intersect over a line (one-dimensional subspace), which we use to disambiguate senses using a clustering algorithm that harnesses the Grassmannian geometry of the representations. The disambiguation algorithm, which we call $K$-Grassmeans, leads to a procedure to label the different senses of the target word in the corpus -- yielding lexeme vector representations, all in an unsupervised manner starting from a large (Wikipedia) corpus in English. Apart from several prototypical target (word,sense) examples and a host of empirical studies to intuit and justify the various geometric representations, we validate our algorithms on standard sense induction and disambiguation datasets and present new state-of-the-art results.

How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, a computer scientist, former president of the Association for the Advancement of Artificial Intelligence, and managing director of Microsoft Research's main Redmond lab. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

I can't offer much in terms of other entry level recommendations, but I can recommend you learn to utilize the resource pages on the coursera course. The way the andrew NG course is set up is that you more or less try to have an idea of how these algorithms work at a conceptual level through the videos, then when you go to programming assignments, you can skip a lot of the prep work and focus on implementing the machine learning algorithms. Now those algorithms might be a little hard to follow at first, which is okay and expected, and that's where the lecture notes and/or wiki come in. From the wiki you can more or less translate the math formulas into code syntax and the assignments are more or less complete. The weeks build off each other so as you learn how to do one part, they do a little less prep work for you so you have to learn how to do another part, and so forth.

"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed" Arthur Smauel. We can think of machine learning as approach to automate tasks like predictions or modelling. For example, consider an email spam filter system, instead of having programmers manually looking at the emails and coming up with spam rules. We can use a machine learning algorithm and feed it input data (emails) and it will automatically discover rules that are powerful enough to distinguish spam emails. Machine learning is used in many application nowadays like spam detection in emails or movie recommendation systems that tells you movies that you might like based on your viewing history.

Artificial intelligence is changing the world. At least, the White House thinks so. Last week, the Obama administration released a 60-page report titled Preparing for the Future of Artificial Intelligence. It paints with a broad stroke the current state of A.I. in several different fields -- health, education, the environment -- and proposes ways in which industry and government can work together to advance the public good. It's a remarkable document, if only for the fact that it's being issued by an outgoing administration in its final months in office.

John Dyer, Executive Director of Communications and Educational Technology at DTS, talks about the advancements of artificial intelligence and the theological and ethical implications of future development in this science.