Google has successfully applied deep learning artificial intelligence algorithms to the diagnosis of breast cancer. In a study carried out by researchers taking part in Google's Brain Residency Program – a 12-month educational course in machine and deep learning – an algorithm was trained to detect breast cancer tumours in a dataset of digitised pathology slides provided by Dutch medical institute the Radboud University Medical Center. After'training' the algorithm, researchers were able to achieve a 92% sensitivity in picking out tumour cells from the slides – significantly higher than the 73% achieved by trained pathologists with no time constraint. In addition, the team recreated the accuracy in different datasets taken from other hospitals and scanning machinery. The team did report an average of eight false positive per slide compared to none from trained pathologists.

People who apply for a loan from a bank or credit card company, and are turned down, are owed an explanation of why that happened. It's a good idea – because it can help teach people how to repair their damaged credit – and it's a federal law, the Equal Credit Opportunity Act. Getting an answer wasn't much of a problem in years past, when humans made those decisions. But today, as artificial intelligence systems increasingly assist or replace people making credit decisions, getting those explanations has become much more difficult. Traditionally, a loan officer who rejected an application could tell a would-be borrower there was a problem with their income level, or employment history, or whatever the issue was. But computerized systems that use complex machine learning models are difficult to explain, even for experts.

In this talk, I'll discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future.

Artificial intelligence is having its breakout moment. Once confined to the realm of science fiction, it seems bright-eyed entrepreneurs everywhere are now getting into AI. As renowned author, editor, and futurist Kevin Kelly puts it, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." But while disruptive opportunities abound for AI, there's also no shortage of challenges to overcome. Anyone who has tried their hand at training a fledgling AI will tell you, there's one hurdle that eclipses them all: removing a human from the loop. It's no secret that scaling AI typically involves human agents operating as a safety net, working in the background and ready to take the controls when a bot gets stuck.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. Here's how AI and its derivatives are gradually finding their way into the classroom, and beyond.

With the global luxury market collectively growing at 4 percent to an estimated $1.15 (€1.08) trillion in 2016, according to a recent "Bain & Company Luxury Study," coupled with optimistic forecasts that the luxury goods market will pick up this year, the hospitality industry is gearing up for elevated demand among both leisure and business travelers. This amid evidence that, despite widespread geopolitical uncertainties, luxury consumers are redirecting their spending toward new and more personalized high-end experiences like luxury travel, food and wine. "The luxury market has reached a maturation point," said Claudia D'Arpizio, lead author of the study. "Brands can no longer rely on low-hanging fruit. Instead, they really need to implement differentiating strategies to succeed going forward. We are already starting to see clear polarization when it comes to performance with winners and losers emerging across product categories and segments."

In the course of our work with Intel's data science and artificial intelligence initiatives, we often encounter people who are excited about the potential of artificial intelligence, and eager to learn more about the things Intel is doing to drive the industry forward. In many cases, these people have read about Intel's focus on AI, and now they are asking how they can get more involved in this forward-looking field. They often ask how they can advance their data science careers in the direction of AI. In Part 1 of this blog series, we talked about steps organizations can take to cultivate in-house expertise in advanced analytics and data science. In this second part of the post, we will take things down to a more personal level, and talk about steps individuals can take to chart a future that involves creating AI solutions.

Anyone who's ever landed in the passenger seat with someone learning to drive will admit to being at least slightly nervous. Thanks to advances in artificial intelligence (AI) controls, you may never again have to worry if Jr. makes a mistake behind the wheel. Just make sure your car has enough miles under its belt to know how to avoid a wreck. That's right, artificial intelligence is coming to consumer vehicles, and it could happen as soon as five years from now. While the media has focused primarily on the way this new technology allows for potentially self-driving cars, there are a number of other features and perks that come along with it.

When 27-year old Mayank Ranka is looking for love, he turns to a chatbot. "It instantly finds me a match which is all I need at the end of the day. The even better part is I can end the conversation anytime I want and not chat ever again," says Mumbai-resident Mayank, who works with an event company in Mumbai. Tech-savvy singles like Mayank don't blink before using a speed-dating chatbot like Neargroup which helps them zoom into the right date. The chatbot's in-built AI helps the user to find the right match based on nearby locations, interests and also conversations.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.