As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science.

Facebook will face a class action law suit in the wake of its privacy scandal, a US federal judge has ruled. Allegations of privacy violations emerged when it was revealed the app used a photo-scanning tool on users' images without their explicit consent. The facial recognition tool, launched in 2010, suggests names for people it identifies in photos uploaded by users. Under Illinois state law, the company could be fined $1,000 to $5,000 (£700 - £3,500) each time a person's image was used without consent. The technology was suspended for users in Europe in 2012 over privacy fears but is still live in the US and other regions worldwide.

A Brazil-based security firm has claimed that its futuristic artificial intelligence and camera technology could make Dubai the world's safest smart city. Speaking to Tahawul Tech on the sidelines of the Future Cities Show in Dubai this week, Polsec CEO Renato Werner explained how the technology has played a part in reducing the crime rate of Manaus – the capital of Amazonas state in Brazil, by 70 percent. Polsec's technology is now being tested across India in Mumbai, Delhi and Agra, and Werner has high hopes that it can soon be brought here to the Middle East. "I had a conversation with a colleague that was responsible for installing the security surveillance system at Terminal 5 in Heathrow Airport, and he implemented over 5,000 cameras," said Werner. "For every 8 cameras, an individual employee is required to keep watch and analyse the footage, meaning you need a huge team – and they can only work for a maximum of 4 hours at a time. With our technology, we don't require humans to gain insight, as the artificial intelligence can make decisions instantaneously when certain situations arise."

The choice of gait, that is whether we walk or run, comes to us so naturally that we hardly ever think about it. We walk at slow speeds and run at high speeds. If we get on a treadmill and slowly crank up the speed, we will start out with walking, but at some point we will switch to running; involuntarily and simply because it feels right. We are so accustomed to this, that we find it rather amusing to see someone walking at high speeds, for example, during the racewalk at the Olympics. This automatic choice of gait happens in almost all animals, though sometimes with different gaits.

During his presentation, Dr. Lupashin of ETH Zurich attached a dog leash to an aerial drone while declaring to the audience, "there has to be another way" of flying robots safely around people. Lupashin's creativity eventually led to the invention of Fotokite and one of the most successful Indiegogo campaigns. Since Lupashin's demo, there are now close to a hundred providers of drones on leashes from innovative startups to aftermarket solutions in order to restrain unmanned flying vehicles. Probably the best known enterprise solution is CyPhy Works which has raised more than $30 million. Last August, during President Trump's visit to his Golf Course in New Jersey, the Department of Homeland Security (DHS) deployed CyPhy's tethered drones to patrol the permitter. In a statement by DHS about their "spy in the sky program," the agency explained: "The Proof of Concept will help determine the potential future use of tethered Unmanned Aircraft System (sUAS) in supporting the Agency's protective mission.

It's Saturday night and you've just finished watching the last episode of a Swedish crime drama that you somehow stumbled upon, although you can't quite remember how. It's late and probably time for bed, but--without prompting--your Netflix screen fills with promotional shots for more shows. There's one about a female detective in Denmark and another about a British inspector who weaves between both sides of the law. It's a familiar scenario to any Netflix watcher--when the service seems to magically suggest programs that fit your latest pop-culture craze. These days, the computer algorithms that allow Netflix or Amazon to make purchasing suggestions are a normal part of life.

Secret surveillance software created by a former Israeli intelligence officer is harvesting Facebook photos. The firm behind it is taking profile images from the social network, YouTube and other sites to build a huge facial recognition database. Its creators say the software could lead to the identification of terror suspects, captured in promotional and other material posted online. News of the controversial service is causing alarm among privacy activists as Facebook scrambles to deal with its ongoing data scandal. Secret surveillance software created by a former Israeli intelligence officer is harvesting Facebook photos. It was revealed last month the company shared the private data of up to 87 million users with the political consultancy firm Cambridge Analytica. Face-Int is now owned by analytics firm Verint, who acquired it in 2017 from creators Terrogence, a surveillance company founded by onetime Israeli secret agent Shai Arbel. Both companies have reportedly supplied the US government and its security agencies, including the NSA, with cutting edge spy technologies. The facial recognition database is said to contain the facial profiles of thousands of terror suspects'harvested from such online sources as YouTube, Facebook and open and closed forums all over the globe', according to Terrogence's website. Experts are concerned that the company's efforts extend beyond this remit, however, and into the political realm. 'It raises the stakes of face recognition - it intensifies the potential negative consequences,' Jay Stanley, senior policy analyst at the American Civil Liberties Union, told Forbes.

Machine Intelligence Garage is a programme that helps businesses access the computation power they need to develop and build machine learning and artificial intelligence solutions. Machine Intelligence Garage is designed to help startups with a well defined business idea and technical capability for whom access to computation power is a barrier to growth. Successful applicants will get access to best-in-class cloud compute, hosted hardware or UK HPC from our partners and collaborators. We hold meetups, workshops and a wealth of other events to benefit the UK AI landscape.

Mobile messaging platforms and social networking services combined with recent advancements in natural language understanding and processing (NLU/P) have helped create an emerging market for social bots. Today, social bots can do everything from manage your calendar to order you an Uber or provide fashion advice in the course of a conversation. As social bots become intertwined, and integral, within social network services and everyday digital interactions it's quite likely that conversational social bots will be the first exposure many people have with anything approximating artificial intelligence (AI). Looking back, the origin of social bots is inexorably rooted in the development and adoption of social networking services and messaging platforms spanning several decades. Social bots found on platforms such as Facebook Messenger, Telegram, and Kik today share distinctive pedigree with party-line Bulletin Board Systems (BBS), CompuServe, and AOL.

In late February 2017, nearly two dozen leading researchers gathered in centuries-old Oxford, England, to warn of the most modern of hazards: malicious use of AI. Among the red flags they raised was an attack called adversarial machine learning. In this scenario, AI systems' neural networks are tricked by intentionally modified external data. An attacker ever so slightly distorts these inputs for the sole purpose of causing AI to misclassify them. An adversarial image of a spoon, for instance, is exactly that -- a spoon -- to human eyes.