Most major technology companies are knee-deep in machine learning these days. Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google, Amazon, and Facebook (NASDAQ:FB) are just a few. Machine learning allows the tech companies' computers to learn information on their own that they weren't programmed to know. For example, Google uses its own TensorFlow machine learning systems for its Google Translate speech recognition app, Google Photos, Gmail, and its Web searches. And as these companies dive further into machine learning, they're building their own complex computers using graphics processing units (GPUs) to power them -- and that could be particularly beneficial for NVIDIA (NASDAQ:NVDA).

In the recent movie "Ex Machina," a young programmer falls in love with a robot. An artificial-intelligence mastermind had engineered his robot so that "she" appears to think, feel and love in a way that was uncannily human. He had also mined and analyzed the young man's online history deeply enough to create the woman of his dreams. Long before anybody actually meets the perfect robot mate, however, intelligent, "learning" machines capable of advanced data analytics will be ever more precise in matching one human to another--and in predicting the success of computer-assisted matchups, which will improve the odds that our real-world romantic relationships will last. Leaps in digital technology have already made drastic changes to 21st century romance.

So how could scalable retail artificial intelligence in the cloud – Brand AI – potentially turn these challenges into unique opportunities for competitive advantage during the next five years? But unlike today's arguably bland, soulless smartphone versions that focus on delivering simple functionality, Brand AI would have a unique, human character that reflects the retailer's values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another'digital novelty', this disruptive form of customer engagement builds on and enhances a B&M's traditional brand as a trusted long-term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care. A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could, potentially, be shared via its cloud platform.

Many consider surge pricing to be one of the negative points of Uber. The ride-sharing service maintains that surge pricing allows it to ensure that passengers can always get a ride when they need one, even if it comes at an inflated cost. It's not like the company hasn't come under fire for using surge pricing and even though it won't do away with it entirely, Uber says it's going to use machine learning to reduce surge pricing. Surge pricing is normally implemented when there's too much demand in a given area. Passengers can find that their trips cost significantly more than they do on a day with normal demand, thus taking away the cost advantage that Uber promotes over conventional taxis.

The experimental Smart Tissue Autonomous Robot (STAR) recently sewed a piglet's gut together using a computer program and camera-based guidance, overseen by a team of doctors and computer scientists from the Children's National Health System in Washington DC and Johns Hopkins University. The procedure took 50 minutes, as opposed to 8 minutes when performed by a surgeon, but (unfortunately for doctors) resulted in more evenly spaced sutures and less leakage from the gut. And with iterative improvements, it's likely that the time difference can be shrunk. Meanwhile, FDA-approved robotic surgery on humans is making strides as well, though it requires a surgeon to operate the mechanical arm. The potential treatment paradigm, highlighted by The Economist this month, raises questions about whether patients will trust robots with their lives, and who is liable if something goes wrong. Another question robots pose: Are doctors in line for a string of layoffs?

Replicating human movement is an engineering challenge, and robots are slowly getting better at delicate movement all the time. A research team funded by Disney has constructed a remotely operated robot sensitive enough to thread a needle and move an egg without breaking it. The research team, which includes researchers from Northeastern University, The Catholic University of America and Disney Research Pittsburgh have created a new type of system for movement, using air and water. This configuration "achieves the high stiffness of a water-filled transmission with half the number of bulky hydraulic lines." After developing this system, the team constructed a humanoid robot that could be controlled by an operator, who can use the arms to perform some delicate tasks, such as play the xylophone, pick up eggs and thread needles.

Speaking at the AI Summit, Mr Coplin said Artificial Intelligence would change the course of humanity and that the way it was developed needed to be carefully monitored as the science could not help but reflect the characters and the needs of those people doing the developing. He said: "I would argue that AI will even change how we perceive what it means to be human. "We've got to start to make some decisions about whether the right people are making these algorithms.

With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – a chatbot powered by IBM's Watson technology – to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

You may remember a little while back it was revealed that Google has been feeding its neural networks steamy romance novels to read. The aim through this exercise was to teach it to produce more human-like responses in order to power its search results and'smart reply' systems. As well as forcing its neural networks to digest more than 11,000 unpublished books (3,000 of which were romance), Google Brain's engineers have also been teaching it to relate two unique phrases to each other. As revealed in a Quartz article, the method was fairly straightforward and resulted in some really weird, romantic, dark'poetry'. That is just one of many examples churned up by Google's AI machine.

Of course, if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy. KDnuggets' own Zachary Lipton has pointed out that there is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is itnecessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model?