IN the summer of 1956, a remarkable collection of scientists and engineers gathered at Dartmouth College in Hanover, New Hampshire. Among them were computer scientist Marvin Minsky, information theorist Claude Shannon and two future Nobel prizewinners, Herbert Simon and John Nash. Their task: to spend the summer months inventing a new field of science called "artificial intelligence" (AI). They did not lack in ambition, writing in their funding application: "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Their wish list was "to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves".

The irony, of course, is that this year the computer security industry, with $75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security. "The thing people don't get is that cybercrime is becoming automated and it is scaling exponentially," said Marc Goodman, a law enforcement agency adviser and the author of Future Crimes. He added, "This is not about Matthew Broderick hacking from his basement," a reference to the 1983 movie War Games.

LAS VEGAS, Oct. 25, 2016 /PRNewswire/ IBM (NYSE: IBM) today announced IBM Watson Data Platform to help companies gain more valuable insights from data. The platform delivers the world's fastest data ingestion engine and cognitive-powered decision-making to data professionals, allowing them to collaborate in the IBM Cloud, with the services they prefer. IBM is also making IBM Watson Machine Learning Service available making machine learning simple with an intuitive, self-service interface. "Machine learning is incredibly powerful, but many of today's data professionals lack the skills to fully exploit it for business and the ability to effectively collaborate on datasets," said Bob Picciano, Senior Vice President, IBM Analytics. "Watson Data Platform applies cognitive assistance for creating machine learning models, making it far faster to get from data to insight. It also, provides one place to access machine learning services and languages, so that anyone, from an app developer to the Chief Data Officer, can collaborate seamlessly to make sense of data, ask better questions, and more effectively operationalize insight."

For the past eight years, Glassdoor has given workers a place to discreetly share information about their salaries and their work environment. The site now attracts 33 million people each month and has amassed reviews, ratings and salary information for around 600,000 companies in 190 countries. Glassdoor is now harnessing all of that data it's collected -- from its salary database in particular -- to give users a better understanding of whether or not they're being paid fairly. The free "know your worth" tool that's being rolled out in beta this week asks users to input some basic information including their job title, employer, current salary, location and relevant work experience. With that, Glassdoor is using a proprietary machine learning algorithm to calculate an individual's "market value" -- the median base pay he or she could potentially earn in their local job market.

Know which one is the lie? A new artificially intelligent camera system is picking up lies with the same accuracy as polygraph -- though those same habitual liars that con even the polygraph will likely still fool the system. The machine learning firm Tselia Data Lab recently developed a camera algorithm that detects lies based on facial signals -- and the work-in-progress already has a 75 percent accuracy rate. A high-definition camera focuses on the subject's facial features while software maps the changes in those facial features to pick up on subtleties like pupil dilation and facial tics, according to the Wall Street Journal. Fraudoscope, as the system is currently dubbed, starts much like a traditional polygraph with a set of calibration questions with a well-known answer.

Personal touch: "If you have a friend you've known for a long time and they know things about you that the general public doesn't know you'll keep them around in your life for a long time," USAA's Patrick Kelly says in describing the building of so-called digital empathy. "The same is true of your [financial] institution." USAA is using virtual agents, personalization and artificial intelligence to forge what it calls a "digital empathy" with its members – conversations that feel human and personal, with an understanding of each customer's behavior, likes and dislikes built in. If empathy – the feeling that you understand and share another person's experiences and emotions – sounds like a tall order for a financial services company that almost never interacts with customers in person, it is. But it's a worthwhile goal for USAA and would be a smart objective for any financial institution.

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As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

More and more self-driving vehicles are making their debut, raising the question of who should be held accountable if, or perhaps when, they cause accidents. Following American and German automakers Tesla Motors Inc. and Mercedes-Benz, Nissan Motor Co. released a minivan model with self-driving functions in the Serena family in August at a time when the government and automakers in Japan are looking to have autonomous vehicles in regular use by 2020. In Japan, autonomous vehicles are now sold with the understanding that drivers are responsible for maintaining control of their vehicles. Drivers are required to stay behind the steering wheel even when self-driving functions are in operation, and they are held accountable for accidents. The autonomous Serena model is designed for expressway use in single-lane traffic.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they'll have the properties of a standard normal distribution with Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms. Intuitively, we can think of gradient descent as a prominent example (an optimization algorithm often used in logistic regression, SVMs, perceptrons, neural networks etc.); with features being on different scales, certain weights may update faster than others since the feature values play a role in the weight updates Other intuitive examples include K-Nearest Neighbor algorithms and clustering algorithms that use, for example, Euclidean distance measures – in fact, tree-based classifier are probably the only classifiers where feature scaling doesn't make a difference. In fact, the only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i some_val?"