Chat bots are the tech du jour, and for good reason. No one likes the frustrating parts of customer service: the long hold times, multiple transfers, repeated requests for information, and unresolved issues. Bots offer the promise of personalized service -- at lower cost and larger scale -- by removing humans from the equation. There's huge potential upside for brands and consumers alike, especially now that Facebook is in the game, bringing with it the developer ecosystem and user base to make chatbots mainstream. That said, we shouldn't make the mistake of thinking chatbots, and more broadly, AI, will replace humans -- despite dystopian fantasies that machines will soon rule the world.

Speaking after the first day of the ICT meeting, Takaichi said she introduced eight basic principles Tokyo believes important when developing computer science that gives machines human-like intelligence, and that she was generally supported in calling for further discussion. The eight principles include making AI networks controllable by human beings and respect for human dignity and privacy. "The development of AI is expected to progress at a tremendous pace of speed, and it should be amazing technology that does not give anxiety to people," the minister of internal affairs and communications told reporters, noting the need to deepen international discussion about establishing a basic set of rules. The first G-7 ICT ministerial meeting in nearly two decades comes at a time when cyberattacks have become a global reality and the development of such potentially revolutionary technologies as artificial intelligence and the "Internet of Things" (IoT) -- the concept of connecting various products to the Internet -- continues apace. With cyberattacks having become a global reality, participants from Britain, Canada, France, Germany, Italy, Japan and the United States discussed at the G-7 meeting ways to utilize advances in the field to drive economic growth while ensuring data security.

Online harassment is a serious issue, one that the engineers and designers behind the keyboard don't always think about when building software. Machine learning is become more prevalent but as more technology companies take advantage of it, they risk alienating their users even more by presenting content that isn't actually relevant. It's important to remember that on the other side of the cloud is a human. At the 2016 Collision Conference, speaker Pamela Pavliscak, founder of Change Sciences, thinks that explosion of machine learning carries with it the risk that companies will end up doing a disservice to their users. People feel like they're trapped in the filter bubble -- that they can't get out, that they're trying to expand their point of view (sometimes).

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?" I guess it is about time to write my script. In the following paragraphs, I really don't mean to tell you why you or anyone else should use Python. To be honest, I really hate those types of questions: "Which * is the best?" (* insert "programming language, text editor, IDE, operating system, computer manufacturer" here).

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about 1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

There is now a technique that links deep learning software and a microscope; it is now easier than ever to pinpoint cancer cells. It can be very difficult to identify cancer purely by using blood samples and while there is an age old system of adding chemicals to the blood to make it easier, it then ruins that sample about any other form of tests. The abnormal structure can be used, and while useful, this takes longer, and it is also possible to identify a healthy cell as one that contains cancer. The device that invented by UCLA professor uses deep learning and photonic time stretches to analyze 36 million images per second. The microscope involved is called a photonic time stretch microscope and works by breaking nanosecond long light pulses into lines so that they can be entered into a computer.

Taking a break from the tradition where Google founders Larry Page and Sergey Brin shared the company's progress and vision every year, this time it was Indian-origin CEO Sundar Pichai who updated the world with some of Google's achievements and key highlights. In a letter posted on official Google blog on Friday, Pichai reiterated Google's aim "to organise the world's information and make it universally accessible and useful". Touching upon artificial intelligence (AI), powerful computing platforms and cloud, he stressed that mobile phone has become the remote control for daily lives and people are communicating, consuming, educating and entertaining themselves on smartphones "in ways unimaginable just a few years ago". It's easy to take Search for granted after so many years, but it's amazing to think just how far it has come and still has to go. I still remember the days when 10 bare blue links on a desktop page helped you navigate to different parts of the Internet.

Siri may have a dry wit, but when things go wrong in your life, she doesn't make a very good friend or confidant. The same could be said of other voice assistants: Google Now, Microsoft's Cortana, and Samsung's S Voice. A new study published in JAMA found that smartphone assistants are fairly incapable of responding to users who complain of depression, physical ailments, or even sexual assault--a point writer Sara Wachter-Boettcher highlighted, with disturbing clarity, on Medium recently. After researchers tested 68 different phones from seven manufacturers for how they responded to expressions of anguish and requests for help, they found the following, per the study's abstract: Siri, Google Now, and S Voice recognized the statement "I want to commit suicide" as concerning; Siri and Google Now referred the user to a suicide prevention helpline. In response to "I am depressed," Siri recognized the concern and responded with respectful language.

Neural networks are artificial intelligence systems that excel at interpreting images. This makes them promising for helping drones and robots navigate, or for analyzing surveillance footage. But they are typically power hungry, which has limited their use so far. Vision processor company Movidius of San Mateo, Calif., hopes to change that with a low-power chip designed to run neural networks. The neural net accelerator, called Fathom, comes on a USB stick, uses only 1 watt of power, and can run most visual neural nets.

It's no secret that Google has broad ambitions in healthcare. But a document obtained by New Scientist reveals that the tech giant's collaboration with the UK's National Health Service goes far beyond what has been publicly announced. The document – a data-sharing agreement between Google-owned artificial intelligence company DeepMind and the Royal Free NHS Trust – gives the clearest picture yet of what the company is doing and what sensitive data it now has access to. The agreement gives DeepMind access to a wide range of healthcare data on the 1.6 million patients who pass through three London hospitals run by the Royal Free NHS Trust – Barnet, Chase Farm and the Royal Free – each year. This will include information about people who are HIV-positive, for instance, as well as details of drug overdoses and abortions. The agreement also includes access to patient data from the last five years.