Twitter today is taking another step to build up its machine learning muscle, and also potentially to improve how it delivers photos and videos across its apps: the company is acquiring Magic Pony Technology (that is really the name), a company based out of London that has developed techniques of using neural networks (systems that essentially are designed to think like human brains) and machine learning to provide expanded data for images -- used, for example, to enhance a picture or video taken on a mobile phone; or to help develop graphics for virtual reality or augmented reality applications. Terms of the deal are not being disclosed but we have two separate sources who tell us that Twitter is paying 150 million in all for the deal. This takes into account retention bonuses for the staff, which numbers about 11, including co-founders Zehan Wang and CEO Rob Bishop. "Machine learning is increasingly at the core of everything we build at Twitter," said Jack Dorsey, Twitter CEO and co-founder, in a statement. "Magic Pony's machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what's happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community."

Machine learning is increasingly at the core of everything we build at Twitter. It's powering much of the work we're doing to make it easier to create, share, and discover the very best content so that every time you open Twitter you're immersed in the most relevant news, stories, and events for you. Today, we're very excited to announce that we're expanding our capabilities in machine learning by acquiring Magic Pony Technology, a London-based technology company that has developed novel machine learning techniques for visual processing. Our acquisition of Magic Pony builds on other investments we've made in machine learning, beginning with the acquisitions of Madbits in July 2014 and Whetlab in June 2015. Magic Pony's team will be joining Twitter Cortex, a team of engineers, data scientists, and machine learning researchers dedicated to building a product in which people can easily find new experiences to share and participate in.

Hi, I'm planning to make a 5/6 part series to reflect about my experience in University of Washington Machine Learning Specialization in Coursera while I take the five courses: Foundations, Regression, Classification, Clustering, Deep Learning and finish the capstone project. This is the first article in the series. I'll feature the first course Machine Learning Foundations: A Case Study Approach in this article and describe the philosophy behind the'case study approach' with a brief overview of the tools used and reflect on what I've learnt. I hope it will help people who want to use the same specialization.I'm also taking courses in Udacity, Edx and using other resources too, but experience those resources will be described in separate articles. I'm also planning to write a whole separate series on Udacity Machine Learning Nanodegree in recent future.

The Forbes Technology Council recently wrote, "Machine learning isn't just for high-tech companies anymore. These days, any business can leverage machine learning for sales, marketing and to drive engagement. Here, six technology executives from Forbes Technology Council offer their thoughts on how even non-tech companies can start leveraging machine learning in the next few years." Erik Gustavson of Bitium suggests using machine learning "For Sales And Marketing Funnel Analysis. Companies will use machine learning for sales and marketing funnel analysis, since even non-tech companies generate a lot of data from sales and marketing. Instead of letting that data lie idle, it can be used to improve the routing and handling of customer interactions to improve the efficacy of sales and marketing teams."

In one of the year's most telegraphed tech reveals, Facebook announced in April that it was opening its Messenger APIs so brands could deploy chatbots to create rich, automated customer engagement on the app. But barely 24 hours passed before users discovered that the first chatbots on Messenger could not understand some simple questions, were slow to respond, and did not interact in much of a conversational manner. In other words, they were more chatbust than chatbot. Weather app Poncho took the lion's share of the tribal frustration by giving quippy, off-topic responses to questions it didn't understand. And it clearly was not understanding much. It's easy to slay the risk takers who oftentimes become the first failers, but we shouldn't throw the chatbots out with the bathwater.

Beleaguered enterprises are struggling to keep pace with cyber threats, and small and medium-sized businesses are hit hardest of all due to limited resources. A recent survey by the Federation of Small Business (FSB) found 66% of those questioned had been a victim of cybercrime over the past two years, and only 4% had an incident response plan in place in anticipation of an attack. For many, cyber security takes them into unfamiliar territory and depletes the time spent on core business activities. This has seen an over-reliance upon point solutions, poor attention to patching and updates, and a failure to apply strategic business-specific security controls. To make matters worse, the potential attack surface is only set to widen as the Internet of Things sees sensors and IP-enabled tech insinuate themselves into every niche of society, even the small business.

Hollywood fantasy: The reality of AI development is a little more nuanced and a lot less advanced than movies might have us believe. It's the theme of so many dystopian sci-fi books and movies: a super intelligent machine in charge of lethal military hardware becomes self-aware and decides to wreak havoc. But could it actually happen? At the Association for the Advancement of Artificial Intelligence's annual conference in Texas last month, a workshop was held on the ethics of AI development and a panel discussed whether or not so-called'lethal autonomous weapons' should be banned. "There are many arguments, legal, ethical, political and technical for a ban," Toby Walsh, head of the Optimisation Research Group at Australia's research body NICTA and chair of the proceedings, told Fairfax Media.

Betty is working as a trainee office manager on a two-month trial, responsible for tasks such as greeting guests at reception. Betty runs using artificial intelligence-driven software. Using cameras and scanners she is able to patrol the office, assess how many staff members are in the office outside of working hours and monitor the environment, which includes desk clutter, office temperature, humidity and noise. She can even check the fire doors! Betty is part of the 7.2 million, EU-funded STRANDS project, led by the University of Birmingham, which focuses on robots learning how to act intelligently and independently in real world environments.

Robots will soon outnumber human beings and 3D printers will be used to create human organs, according to a survey of over 2,000 British adults. The next two decades will see technology radically impact healthcare and automotive industries, according to SMG Insight and YouGov research commissioned by London & Partners, with GP consultations available through virtual reality or driverless cars replacing traditional vehicles. The research was inspired by predictions made by Imperial College London's Tech Foresight research team ahead of London Tech Week, an event celebrating innovation with the UK's capital city. "London's technologists, scientists, medics and entrepreneurs are creating the future," said professor David Gann, vice president innovation at Imperial College London. "No city in the world enjoys London's quotient of talent, technology culture and capital. It is a potent combination. "London is an environment where ideas flourish, design and innovation is embraced and new technologies are transforming our lives for the better.

The artificial intelligence from Google's DeepMind Technologies division is impressively versatile, there's no doubt. Late last year, it became the first neural network in history to defeat a professional player at Go, the Chinese board game whose human players had stumped computers for years, by besting world-ranked player Lee Sedol. It has demonstrated a prowess for video games, too -- it taught itself to emerge victorious in 49 different games for the Atari 2600 console and navigate digital 3D-maze called Labyrinth. And now, Google's human-like AI has learned how to play a sport of a different nature: soccer. DeepMind's latest experiment involves teaching an ant-like digital bug to maneuver a soccer ball into a goal.