It is no doubt that everyone has come in contact with Machine Learning (ML) algorithms, perhaps without knowing that they have or what they are, but they certainly have. For example when you are making a purchase online and some items are'suggested for you,' this is an example of ML, another example is when a dating app tries to'match' you based on previous matches you have selected or when social media platforms, such as Facebook and Instagram, show you certain sponsored content. In all these instances some form an ML algorithm is used, which a powerful tool that many corporations are now adopting to derive more value. So what is ML? ML is a type of computer algorithm which relies on a large amount of input data to make a future decision about a new data point. Basically, it is a type of algorithm that when it is fed data, it'learns' and with more and more data, it becomes better at selecting the data points which best match the'learning' data set which it was fed.

A shortage of charging points and strain on energy supplies are now the main stumbling blocks to the rise of driverless electric cars, according to the UK boss of insurer Axa. Amanda Blanc said a lack of rapid charging bays and pressure on the National Grid have overtaken questions about accident liability as the biggest barriers to autonomous vehicles entering the transport mainstream. Blanc, a Tesla driver, said personal experience pointed to problems lying ahead for driverless electric vehicles. There are around 125,000 plug-in electric cars in the UK and 14,000 chargers - 2,620 of them being rapid chargers that can give a car an 80% charge in 30 minutes. Shell has just opened its first charging points for electric vehicles at 10 filling stations, mostly in London and the south-east.

Artificial Intelligence makes most people think of two things; futuristic killer robots bent on destroying the human race or spotless factories where obsolete workers are replaced by silently gliding robots.

Cloud computing is already a huge business, and competition is stiff. But this year, tech firms opened a new front in the battle to win users over in the cloud: the large-scale introduction of cloud-based AI. For small and medium-size companies, building AI-capable systems at scale can be prohibitively expensive, largely because training algorithms takes a lot of computing power. Enter the likes of Amazon, Microsoft, and Google, each of which has vast stores of computing power and a big stake in the $40 billion cloud computing industry. For them, adding AI is simply a matter of keeping up with customers, who increasingly are looking for cost-effective ways of building machine learning into their software.

In the spring of that year, the good Swedes at Volvo introduced Drive Me, a program to get regular Josefs, Frejas, Joeys, and Fayes into autonomous vehicles. By 2017, Volvo executives promised, the company would distribute 100 self-driving SUVs to families in Gothenburg, Sweden. The cars would be able to ferry their passengers through at least 30 miles of local roads, in everyday driving conditions--all on their own. "The technology, which will be called Autopilot, enables the driver to hand over the driving to the vehicle, which takes care of all driving functions," said Erik Coelingh, a technical lead at Volvo. Now, in the waning weeks of 2017, Volvo has pushed back its plans.

According to app downloads, Amazon beat out its rivals over the holidays. As of writing, the Amazon Alexa app was the most popular app on both iOS and Android's Google Play. On Android, the Google Home app did make its way to second place--so it would look like the Google Home was a popular gift, too. But on iOS, Google's companion app didn't even crack the top 10 most popular apps. While Amazon's devices were a cross-platform favorite, those already embedded in the Google ecosystem were more likely to invest in its smart home speaker products. This is likely what we can expect when Apple comes out with its own Amazon Echo competitor, the HomePod, in early 2018.

The heterogeneity, complexity, and Scale of cloud applications make verification of their fault tolerance properties challenging. Companies are moving away from formal methods and toward large-scale testing in which components are deliberately compromised to identify weaknesses in the software. For example, techniques such as Jepsen apply fault-injection testing to distributed data stores, and Chaos Engineering performs fault injection experiments on production systems, often on live traffic. Both approaches have captured the attention of industry and academia alike. Unfortunately, the search space of distinct fault combinations that an infrastructure can test is intractable.

Two years after World War II, billionaire Howard Hughes personally piloted his "Spruce Goose" troop transport aircraft on the first and only flight of the largest seaplane ever built. It lasted barely a minute. Now, more than 70 years later, a U.S. startup is testing a new seaplane concept--one that could evolve into huge cargo drones that fly 109 metric tons of freight across the Pacific, touch down autonomously over water, and unload at ports around the world. The startup Natilus was founded in 2014 with a dream of building large cargo drones to deliver international freight for about half the price of piloted aircraft, and much faster than ships. In December, Natilus planned to test the water-taxiing capabilities of a small prototype drone with a 9-meter wingspan in San Francisco Bay.

The transaction will make Geely the biggest single shareholder in Volvo and the second biggest holder of voting rights. Christer Gardell, the co-founder of Cevian Capital, said Geely would be able to provide Volvo with valuable access to the Chinese market and know-how in the field of electric and self-driving vehicles.