While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

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.

Hi Tim - No, I haven't tried that. To be clear, are you thinking of images as "sequences" of pixels? If that's the case, I suppose one could use some sequence-related algorithms, like RNN/LSTM, but with two dimensions. Typically, CNNs are used for images since they encode the proximity of neighboring pixels. To your second point, one could submit the image to the model before it's fully loaded, and get less-than-optimal results until the image is fully loaded.

Artificial intelligence software that can find patterns in highly complex decisions is being used to predict our taste in films, TV shows and music with ever-increasing accuracy. And now, after a breakthrough study by a group of British scientists, it could be used to predict the outcome of trials. Software that is able to weigh up legal evidence and moral questions of right and wrong has been devised by computer scientists at University College London, and used to accurately predict the result in hundreds of real life cases. The AI "judge" has reached the same verdicts as judges at the European court of humanrights in almost four in five cases involving torture, degrading treatment and privacy. The algorithm examined English language data sets for 584 cases relating to torture and degrading treatment, fair trials and privacy.

Enter Plum, Koretz's 1,499 invention that doubles as a countertop appliance and the ultimate tricked-out wine storage. Plum lets wine drinkers have a "by the glass" experience: When consumers place a bottle in the device, Plum uses cloud and artificial intelligence technology to identify the label, and automatically set the perfect sitting temperature for each bottle. Koretz said the company has raised nearly 10 million to date from lead investor Vinod Khosla, of Khosla Ventures, as well as investors across various industries - wineries, consumer goods, hospitality and technology. With the push of a button, it uses a pressurized needle-based system to extract one glass of wine at a time--but without removing the cork. This process allows the wine in each bottle to be preserved for up to 90 days.

It's the mantra of many a financial advisor: "It's not what you spend that matters. But the mantra of many respondents is "easier said than done." In hopes of making saving easier, San Francisco-based start-up Digit created a chatbot that helps you put money aside by analyzing your spending history and daily activity. It then figures out where to siphon off small sums of cash on a regular basis. To see how well the bot's algorithms work, I decided to try it out. It took me less than a minute to sign up on Digit's website, where I handed over my email address and mobile phone number. After verifying the email and providing my bank login credentials, the bot kicked in. Within a few days, it started to withdraw small amounts of money (between 0 and 150). The bot isn't supposed to transfer more than you can afford but if it does cause an overdraft, Digit refunds the fee. The funds are then held by Digit in what they call your "Digit account." To withdraw funds you text the bot and the ...

But such tweets could help insurers to price premiums for individuals, with research suggesting a direct link between positive posts and a reduced risk of heart disease. This could lead to future insurance cover based on "sentiment analysis", in which Big Data and artificial intelligence make predictive models ever more accurate. Swiss Re ( Swiss Re AG) says technological advances will cut the price of insurance protection and help individuals and firms make better decisions through programs that offer advice and incentivise improvements in areas such as health and driving. However, detractors fret that such developments could erode customers' privacy or lead to increasingly personalized pricing, undermining the basic principle of insurance - sharing risk. Social media monitoring is one of several advances insurers are examining to improve the pricing of policies.

Imagine receiving a phone call from your aging mother seeking your help because she has forgotten her banking password. The voice on the other end of the phone call just sounds deceptively like her. It is actually a computer-synthesized voice, a tour-de-force of artificial intelligence technology that has been crafted to make it possible for someone to masquerade via the telephone. Such a situation is still science fiction -- but just barely. It is also the future of crime.

In machine learning and computational learning theory, Logit Boost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized additive model and then applies the cost functional of logistic regression, one can derive the LogitBoost algorithm. LogitBoost can be seen as a convex optimization. Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method.

When I was a CS student about 13 years ago (damn it, I'm getting old), I was very much fascinated by Fractals. After doing some coding, debugging and fixing things, I had my first Mandelbrot explorer up and running with zoom capabilities. Then, in the closest thing I have ever had to a religious experience, I witnessed how the most simple and random looking way of generating the set produces something that is infinitely self similar and amazingly complex. It was the first time I truly understood how order can spawn out of chaos. It's one of the best examples of how chaotic systems are sensitive to initial conditions.