Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology. There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

All who have tried to get something like a conference off the ground knows how extremely difficult it can be. Getting everything right from the speakers to the venue is an extremely demanding process, not to mention attracting the right people. But here the day before Nordic.ai As a small teaser I took it upon myself to write a short teaser about a couple of the talks I am looking especially forward too. My favourite will of course be my very own brilliant colleague Lars Maaløe, who'll speak at 15.20, but to keep if from becoming to much of an advertisement I've focused on some of the many other gems you can look forward to tomorrow.

Is AI the shot in the arm that today's transformation efforts need? Mobile World Congress is like heaven for a tech enthusiast like myself. Every February, many of the world's most innovative companies gather to launch their latest and greatest gadgets -- tablets with 4K displays, futuristic wearable tech, and sleek smartphones thinner than a few quarters stacked together. But this year, the most important technology at the show won't be in a single stand at Barcelona's Fira Gran Via. It'll be in virtually all of them. Artificial intelligence (AI) has already become a common thread running through the mobile tech world.

See it in action--and saving money--in field service. Artificial intelligence (AI) has never felt like a human-friendly term. The very notion of intelligence that is artificial is a little, well … unsettling. Will machines ultimately replace us? As we gather at Mobile World Congress, in part to explore the ultimate reach of AI in an increasingly mobile business world, one thing is clear: AI and machine learning technologies won't replace humans in the enterprise, but they're going to change the game considerably.

It seems like every organization today is betting on artificial intelligence (AI). But what does that really mean? We asked those gathered at a recent AI conference to share their definition. Hear how--and why--they see things so differently.

Signpost is a service that lets brick-and-mortar store owners publish incentives and promotions on its website. Last summer, the New York City-based company's founder and CEO, Stuart Wall, created a new app: the A.I.-centric Mia. Through its natural language generation capability, Mia crafts messages and sends them to prospects at opportune times. It tracks and analyzes a store's calls, emails, and credit card swipes, and then makes what it decides is the right pitch. "New customers often tell me they show up because of our five-star reviews, which I hear about through Mia," says Randy Jewart, owner of Resolution Gardens, a landscaping company in Austin, and a Mia subscriber.

Astronomer Kevin Schawinski has spent much of his career studying how massive black holes shape galaxies. But he isn't into dirty work--dealing with messy data--so he decided to figure out how neural networks could do it for him. Problem is, he and his cosmic colleagues suck at that sophisticated kind of coding. That changed when another professor at Schawinski's institution, ETH Zurich, sent him an email and CCed Ce Zhang, who actually is a computer scientist. "You guys should talk," the email said.

Ask a business person about the metaphor of war and business and pretty quickly you'll get to Sun Tzu's The Art of War an ancient text about terrain, tactics, and frontal assault. A few years ago Art of War was all the rage in business circles and every hotshot executive could quote it. Heck, it is often claimed that Larry Ellison, then CEO Oracle, ran his business with a well thumbed copy of Sun Tzu's tome within easy reach. Regardless, Tzu is off the front burner now and management fads have moved on. Even so, the war metaphor is revived regularly, most recently by Jeff Gothelf and Josh Seiden in Sense and Respond: How Successful Organizations Listen to Customers and Create New Products Continuously.

This article explores in more detail the idea of Meta Learning that was previously introduced in a post "The Meta Model and Meta Meta Model of Deep Learning". In this post, I explore "Learning to Learn" as a Meta Learning approach. We have to be very careful to distinguish between Learning to Learn and Hyper Parameter Optimization (HPO). HPO and more generally searching for architectures differs from "learning to learn" in that that HPO explores the space of architectures while meta-learning explores the space of learning algorithms. Meta-learning is all the rage in research these days.

It cannot be denied that Artificial Intelligence is having a growing impact in many areas of human activity. It is helping humans to communicate with each other, even beyond linguistic boundaries, find information in the vast resources available on the web, solve challenging problems that go beyond the competence of a single expert, and enable the deployment of autonomous systems, such as self-driving cars, that handle complex interactions with the real world with little or no human intervention. These applications are perhaps not like the fully autonomous conscious intelligent robots that science fiction stories have been predicting, but they are nevertheless very important and useful, and most importantly they are real and here today. But neither can it be denied that Artificial Intelligence comes with certain risks. Many people (including luminaries such as Bill Gates or Stephen Hawking) believe that the main risk of artificial intelligence is that it gets out of hand.