On Monday, I outlined my view of what makes "an analytic" and the jargon that goes along with it. I ended the post wondering whether "an analytic" is just another name for "business rules" that organizations tend to follow today. In my view, the main difference between the two, which might be no difference at all for a given situation, is that "analytics" are generally more complex mathematically and operate on a more general "feature space". This generalization allows rigorously developed techniques from statistics and applied mathematics to have a chance at being applied to a messy real world problem. Business rules tend to be more human understandable and more directly embedded into the specific data formats or information processing software used by a business.

Baidu has made its second investment in a U.S. fintech company inside a month after it put an undisclosed sum of money into ZestFinance, a big data firm specializing in credit scoring. Baidu, which operates China's dominant search platform, took part in a 60 million round for payments firm Circle in June. The deal is part of an agreement that will see Baidu use ZestFinance's technology to develop a credit scoring platform that is based on its search data. That's important in a market like China because traditional credit systems are broken there. There's precious little formalized credit history data while many people don't use banks heavily or are unbanked.

Monday 18th July 2016, Whitley: Sir Ben Ainslie has hailed Land Rover's artificial intelligence (AI) as a'game changer' ahead of the British America's Cup team's home event in Portsmouth (22-24 July). Land Rover, Title and Exclusive Innovation Partner to Land Rover BAR, is applying its big data processing power and machine learning expertise to help co-engineer the fastest boat in America's Cup history and bring the world's oldest sporting trophy to the UK for the first time. Land Rover engineers, embedded into the team for over a year, are using artificial intelligence to explore and find patterns in sailing performance data to help'make the boat go faster'. When testing, the sailing team receives over 16 GB of uncompressed data per day from sensors on the boat – the equivalent of filling an iPhone's memory. The ability to process and make sense of this volume of data is unprecedented in sailing.

She joined as senior reporter in April 2014 having previously worked as assistant editor at Government Computing. There has been an unusually large focus devoted to chatbots within the tech community in 2016, thanks to news like Facebook's announcement it will integrate chatbot capabilities into Messenger, and Microsoft's controversial'teen girl' Twitter bot'Tay'. However how much of this has actually translated into real-world applications? It's early days, but there are a few companies and organisations that have started to try out chatbots as part of their operations. Here are a few of the most notable examples.

Artificial Intelligence and Cognitive Computing has an increasing importance in the interconnected digital world. Data is being generated at a pace like never before. Abundance of Data generated by machines, social media, and others, along with advanced algorithms are allowing us to overcome the barriers of machine knowledge and opening new vistas of intelligence and insights being generated from data. We will look into some of the latest applications of Artificial Intelligence and Cognitive computing and how various industries are getting transformed by this phenomenon. We will also explore some of the cutting edge algorithms that are playing a role in this digital revolution.

Eventually some level of artificial intelligence will be integrated into every product and service we use. And to realize this potential, we need to be smart in defining the principles and goals that will guide this coming revolution. As a society, we have an ambivalent relationship with artificial intelligence (AI). While we love the benefits it offers, we are also afraid of it, because it represents the great unknown. So, newspapers are full of articles about computers beating people at chess or Go.

"Aapo did it again!" - I exclaimed while reading this paper yesterday on the train back home (or at least I thought I was going home until I realised I was sitting on the wrong train the whole time. This gave me a couple more hours to think while traveling on a variety of long-distance buses...) Aapo Hyvärinen is one of my heroes - he did tons of cool work, probably most famous for pseudo-likelihood, score matching and ICA. Time-contrastive learning (TCL) is a technique for learning to extract nonlinear representations from time series data. First, the time series is sliced up into a number of non-overlapping chunks, indexed by \tau . Then, a multivariate logistic regression classifier is trained in a supervised manner to look at a sample taken from the series at an unknown time and predict \tau, the index of the chunk it came from.

You may start watching the following video "Bicentennial Man", courtesy of Touchstone Pictures.

The device could be a major stride in diagnosing malaria, which affects over 200 million people annually. Malaria is a parasitic infection most commonly spread by mosquitos. It can be detected by assessing a patient's blood sample via microscope. Usually, a trained professional must be present to diagnose malaria, specifically a microscopist who can identify the malaria parasites in blood samples. But in the poorest areas of the world, where malaria is so prevalent, these professionals are in short supply.

Invented by Geoff Hinton, a Restricted Boltzmann machine is an algorithm useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. Given their relative simplicity and historical importance, restricted Boltzmann machines are the first neural network we'll tackle. In the paragraphs below, we describe in diagrams and plain language how they work. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input, layer, and the second is the hidden layer. Each circle in the graph above represents a neuron-like unit called a node, and nodes are simply where calculations take place.