A lot of us have been looking at the recent WikiLeaks drop of Central Intelligence Agency (CIA) files related to hacking Internet of Things (IoT) and personal technology devices. It includes a lot of data, so it is easy to get lost in the woods with regard to what these tools do. But apparently they can break into devices and cast blame for it on the Russians, which I believe is problematic in a whole number of different ways. I think the real problem with this is the CIA's risk assessment process. I think this decision process problem is far larger than just the CIA and reflects on security in general. I also think that a deep learning tool like IBM's Watson could, if placed in the decision process, help prevent bad decisions like this.

The new project, which uses Adobe's AI and machine learning program Sensei and integrates into the Adobe Experience Manager CMS, will debut at the company's Sneaks competition later in March. While Adobe hasn't committed to integrating it into any of its products, it's one of the most ambitious attempts to marry machine learning and graphic design to date. There have been efforts to use AI in the design world before–for instance, Wix's Advance Design Intelligence and automated projects like Mark Maker, but Adobe's is notable because of the company's sheer reach in the design world. Although it's just a prototype, it's one to watch closely. The as-of-yet unnamed new product is designed, first and foremost, to make it easier to customize websites for users at large-enterprise customers. When I viewed a demo, for instance, machine learning and AI techniques were applied to editing the Food Network's web pages.

"Hey Cortana, play my favorite song." Smart AI is no longer a novelty worthy of science fiction; it's now ingrained into our everyday lives. You could probably recognize Siri or Alexa's voice in a crowded room, and chances are good that you've requested their services on more than one occasion. As tech giants like Apple, Amazon, and Microsoft continue to enhance their products using AI techniques to keep up with real life demands – integrating with third-party apps like Uber or Spotify, for example – the capabilities of these smart bots seems endless. In fact, more and more companies are using smart bots and even chatbots to create customized experiences for their shoppers.

When we use our phones, there are a number of clunky processes that can waste many precious minutes of our time. Whether it be copying address links to paste into Uber, staring at your home screen and contemplating what app you wanted to use, or any other small iterative process, some things are just not as seamless as they could be. A more seamless and automated experience is what almost every Silicon Valley company has attempted to produce over the last number of years, but a new company self-proclaimed as Xix.ai thinks they can eliminate wasted time on your mobile device better than anyone else using a technology they like to call a'Recurrent Neural Network'. What this network does is index the actions you take on your device, and works to predict what you are planning on doing next, based on what you are currently doing or even on how you are feeling at the moment. The company says it wants to use embedded deep links to automate your processes that would normally take a number of actions, and can make almost everything you do on your device that much more seamless and integrated.

The Bank of England has paired up with artificial intelligence and blockchain specialists in a bid to keep up to date with the fast-growing financial technology sector. The central bank is testing an artificial intelligence system with Canadian startup MindBridge AI to allow it to spot abnormalities in financial transactions and "explore the benefit of machine learning technology for analysing the quality of regulatory data input." It has also partnered with San Francisco-based startup Ripple, which opened an office in London last year, to trial a blockchain-based technology that would make cross-border payments and the movement of currencies more immediate. Blockchain is the technology which underpins crypto-currencies like bitcoin. The Bank said that its aim with Ripple is to "show how this kind of synchronisation might lower settlement risk and improve the speed and efficiency of cross-border payments."

Scientists from the Oxford University along with Google's DeepMind have developed an artificial intelligence system that can lip-read better than humans. The system was trained by thousands of hours of BBC news programs, the media outlet said Friday. The system, called "Watch, Attend and Spell," can correctly lip-read 50 percent of silent speech correctly, while professional lip-readers only got 12 percent right, researchers found. Read: Google AI Firm DeepMind Develops'Streams' App to Help UK Doctors With Patients Some words that rhyme, such as like mat, bat and mat, have similar mouth shapes. However, it's context is what helps lip-reading, Joon Son Chung from the university's Department of Engineering said. The system learns "things that come together, in this case the mouth shapes and the characters and what the likely upcoming characters are," explained Joon.

A deal between Google's artificial intelligence firm DeepMind and the UK's NHS had serious "inadequacies", an academic paper has suggested. More than a million patient records were shared with DeepMind to build an app to alert doctors about patients at risk of acute kidney injury (AKI). The authors said that it was "inexcusable" patients were not told how their data would be used. Google's DeepMind said that the report contained "major errors". It told the BBC that it was commissioning its own analysis and rebuttal, which the authors said they welcomed. When the deal between London's Royal Free Hospital and DeepMind became public in February 2016, some three months after the data started to be collected, it caused controversy over the amount of patient information being shared and the lack of public consultation.

The U.S. Defense Advanced Research Projects Agency (DARPA) has come up with some crazy ideas in the past, and its latest idea is to create computers that are always learning and adapting, much like humans. Mobile devices, computers, and gadgets already have artificial intelligence features, with notable examples being Apple's Siri, Microsoft's Cortana, and Amazon's Alexa. But these devices can only learn and draw conclusions within the scope of information pre-programmed into systems. Existing machine-learning techniques don't allow computers to think outside the box, so to speak, or think dynamically based on the situations and circumstances. The goal of a new DARPA project is to create computers that think like biological entities and are continually learning.

In machine learning we often face the issue of a very large number of classes in a classification problem. This causes a bottleneck in the computation. There's though a simple and effective way to deal with this. In areas like Natural Language Processing (NLP) a common task is to predict the next word in sequence (like in preditictive text on a smartphon or in learning word embeddings). For example, $u_\theta(c,x) \exp(w_c'x)$, where $w_c$ is a parameter vector for class $c$ and $x$ is the vector input.

A page from the notebook of British mathematician and pioneer in computer science Alan Turing, the World War II code-breaking genius, is displayed in front of his portrait during an auction preview in Hong Kong Thursday, March 19, 2015. This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations.