In the last part we introduced Classification, which is a supervised form of machine learning, and explained the K Nearest Neighbors algorithm intuition. In this tutorial, we're actually going to apply a simple example of the algorithm using Scikit-Learn, and then in the subsquent tutorials we'll build our own algorithm to learn more about how it works under the hood. To exemplify classification, we're going to use a Breast Cancer Dataset, which is a dataset donated to the University of California, Irvine (UCI) collection from the University of Wisconsin-Madison. UCI has a large Machine Learning Repository.

The question of human existence -whether as a species in general or just in the working world- has generated quite some concern around the world. Famous technologists and successful entrepreneurs such as Stephen Hawking, Elon Musk or Jaan Tallinn have responded with organisations like Future of Life Institute and OpenAI. They strive to protect human life from intelligent machines taking over the world. Thanks to Paul O'Connell, founder and organiser of the Uprise Festival in Amsterdam, I had the opportunity to dive a bit deeper into this issue. As part of the Festival -already in its 3rd edition and coming to Dublin later this year- I was able to bring to the audience a panel of guest speakers with the goal of answering one bold question: are humans still relevant?

What will we do with all the data we accumulate from photos? On a daily basis, Internet juggernauts like Google, Yahoo, Facebook or Microsoft use highly sophisticated deep learning engines to better understand the content of billions of images uploaded, liked and shared. For now, it is to better serve advertising, but what else can be done? For one, we could generate custom-made photos. Automated photo generating A.I. using all the knowledge acquired about photography -- including which image types helps sell more products, which dominant colors appeal to viewers, and which composition is the most effective -- would build a perfect photo according to your needs.

Artificial intelligence researcher and Managing Director of Microsoft's Redmond, Washington, research lab Eric Horvitz has just won the ACM-AAAI Allen Newell Award, Microsoft shared on a blog post today. The award acknowledges his groundbreaking contributions in artificial intelligence and human-computer interaction as well as "his persistent focus on using those discoveries as the basis for practical applications that make our lives easier and more productive." Horvitz first got interested in practical applications of artificial intelligence when he was pursuing his Ph.D. on principles of bounded rationality, as he wanted to "understand how computing systems immersed in the real world could make the best decisions in time-critical situations." Since then, Horvitz has combined multiple computer science disciplines through his work and he has been leading the research in exploring the interrelationships between artificial intelligence and fields like decision science, cognitive science, and neuroscience. Horvitz even collaborated with NASA's Mission Control Center to help them providing flight engineers with the most valuable information about space shuttle systems under intense time pressure.

Jamie Metzl is a Senior Fellow for Technology and National Security at the Atlantic Council. After 4 billion years of evolution by one set of rules, our species is about to begin evolving by another. Overlapping and mutually reinforcing revolutions in genetics, information technology, artificial intelligence, big data analytics, and other fields are providing the tools that will make it possible to genetically alter our future offspring should we choose to do so. Nearly everybody wants to have cancers cured and terrible diseases eliminated. Most of us want to live longer, healthier and more robust lives. Genetic technologies will make that possible. But the very tools we will use to achieve these goals will also open the door to the selection for and ultimately manipulation of non-disease-related genetic traits -- and with them a new set of evolutionary possibilities.

Self-driving cars have not yet made it to U.S. roadways but already the technology is moving on. Now scientists have designed a vehicle that taught itself how to drive by watching humans drive. Research into the so-called'artificial intelligence' involves designing computers that mimic the human neural network, including ability to synthesize the acquired data and draw logical conclusions. Engineers from the California-based technology company Nvidia equipped two cars with computers and three video cameras each. The system'watched' persons driving the cars as well as the environment around them, such as road features, other cars and pedestrians.

It's a long established tradition for startup founders to fudge their numbers, exaggerate projections, and cherrypick data in meetings with investors. But Venrock health investor Bob Kocher says this approach won't fly with him. "I hear spin every day. I believe I'm lied to more often in Silicon Valley than at the White House," says Kocher, who formerly worked as a special assistant to President Obama to help shape the Affordable Care Act. "I'm looking for entrepreneurs who will level with me."

Nice; however, I see also 3D printing along with machine learning being part of any cosmetic procedures and surgeries. With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning--a subfield of artificial intelligence--to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery, the official medical journal of the American Society of Plastic Surgeons (ASPS). "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery.

It then presents this activity to human analysts, who confirm which events are actual attacks, and incorporate that feedback into its models for the next set of data. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have developed an AI platform called AI2 that predicts cyber-attacks significantly better than existing systems by continuously incorporating input from human experts (AI2 refers to merging AI with "analyst intuition": rules created by living experts). The team showed that AI2 can detect 85 percent of attacks --about three times better than previous benchmarks -- while also reducing the number of false positives by a factor of 5. The system was tested on 3.6 billion pieces of data known as "log lines," which were generated by millions of users over a period of three months. To predict attacks, AI2 combs through data and detects suspicious activity by clustering the data into meaningful patterns using unsupervised (automatic, no human help) machine learning.

A theme park in Japan is set to introduce 200 robots as members of staff - a move that bosses claim is just the beginning of a technological revolution in the country's service industry. Amusement park Huis Ten Bosch will open the "robot kingdom" area of the attraction in July. Hideo Sawada, president of the park's operator, said: "Robots will arrive in this kingdom one after another, and the time will come when these technologies will be in use worldwide." The robots will include a chef that can cook different meals on demand. Also among the incoming robot staff is a bartender that can make 10 different cocktails.