Is machine learning the same as artificial intelligence? In this brief webinar, BigDataGal explains the differences between machine learning and AI. She follows this with an elaborate discussion on the past, present, and future of each.

Globally, nearly $2 billion in online sale were performed exclusively through chatbots last year. The march of chatbots and personal assistants into conversational commerce (defined as the intersection of messaging apps and shopping) is now a thing, and it looks like it is here to stay. Indian brands large and small are adopting chatbots, harnessing the power and convenience of these digital capabilities. "AI is changing the way we interact with technologies across multiple industries. In a fast-growing market such as India, AI helps making technology-based companies more efficient," says Sachin Jaiswal, cofounder of Niki.ai.

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.