Machine learning has long powered many products we interact with daily–from "intelligent" assistants like Apple's Siri and Google now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in "deep learning"–these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. In this series, we'll give an introduction to some powerful but generally applicable techniques in machine learning. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.

Big data, semantic understanding, sentiment analysis, neurobiology, genome research, artificial intelligence, neuromarketing: not a day goes by or amazing new discoveries and advances in any – and often many – of these fields are announced. We live in an age of data and certainly also of'exact sciences'. I don't mean the company with the same name but mathematics and all these other sciences we call'exact' in my native language Dutch. And it sure impacts how we look at the human mind and emotions. In this age of data and'exact sciences' applied to so many fields of society and business it's easy to overlook phenomena such as creativity, emotions and the subconscious.

We are on the verge of something meaningful and incredible with emergent artificial intelligence, says Toni Lane Casserly. But which way will humanity steer it?

I use episodic memory so there is no write head. The idea is that instead of determining what you want to store and where to store it, you store everything in one summary state. The summary state is written in memory at every time step. The problem is then to learn to retrieve a previous summary state that helps with the current computation. At every time step, the network generates a retrieval key and mask for one state retrieval.

Bayesian probability theory states that it's possible to predict with surprising accuracy the likelihood of something happening (or not happening) in a transparent and analytically defensible way. A Bayesian inference network, or model, captures every element of a problem and calculates possible outcomes mathematically. The harder the problem, the better it works--at least in theory. In reality, a typical approach is to gather a roomful of PhDs and spend a lot of time and money building a Bayesian network. Then, with even greater effort and more man-hours, the Bayesian network is turned into software by a roomful of coders.

HDFC Bank Ltd., today announced the launch of IRA, its interactive humanoid, at the Kamala Mills branch in Mumbai. IRA, which stands for Intelligent Robotic Assistant, will help branch staff in servicing customers. With this launch, HDFC Bank becomes the first bank in the country to introduce a humanoid for customer service. Developed using Robotics and Artificial Intelligence technologies, IRA will be positioned near the Welcome Desk, where it will greet customers and guide them to the relevant counter in the branch such as Cash Deposit, Foreign Exchange, Loans, among others in the first phase. Upon entering the Kamala Mills branch, IRA will greet the customer, before displaying a list of banking services he can avail at the branch.

Earlier this month we released our annual travel industry trends forecast, Skift Megatrends 2017. You can read about each of the trends on Skift, or download a copy of our magazine here. There are few things buzzier in travel right now than the rise of artificial intelligence (AI) and human-machine interfaces. That's possible due to AI, or machine-learning, where Google can not only crunch data at the speed of light, but also "learn" how to deliver more nuanced results. "AI is simply a group of technologies that will increasingly be used to augment human capabilities, and make us better at the things we do best," wrote Bob Rogers, chief data scientist for analytics and AI solutions at Intel, in CIO, a publication serving chief information officers.

Amazon has added some new features, including the ability to read an entire book, start your Ford Fusion (still in beta), and unlock the doors of your house with a simple command. You can engage in a conversation, or even play a role-playing game. A soothing voice and lame jokes make Alexa highly accessible, like a robot friend who can handle some routine chores. I've ordered pizza by voice, and I love asking about the weather forecast right before heading out on business trips. Yet, Alexa is far from perfect.

A new computational model performs at human levels when subjected to standard intelligence test, making artificial intelligence (AI) system at par with human understanding capabilities. Researchers from Northwestern University built the new computational model on CogSketch, an artificial intelligence platform, that has the ability to solve visual problems and understand sketches in order to give immediate and interactive feedback. "The model performs in the 75th percentile for American adults, making it better than average," said Ken Forbus of Northwestern University, adding "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." Researchers noted that developing artificial intelligence systems that have this ability not only provides new evidence for the importance of symbolic representations and analogy in visual reasoning, but it could potentially shrink the gap between computer and human cognition. "Most artificial intelligence research today concerning vision focuses on recognition or labelling what is in a scene rather than reasoning about it," Forbus noted.

When we think about Artificial Intelligence, we often consider its potential in relation to the realms of our possibility – what will it be able to do that we can do? To my mind, that is entirely missing the point of an artificial "neural" network that is infinitely more powerful than the percentage of our brains that we are able to access at any one time. What will A.I. be able to do that we can't even dream of? I'll give the simplest example that I can. Way back in the early 1980s, there was a computer game called Breakout, where a horizontal paddle (bat) could be moved at the bottom of the screen, bouncing a ball up at tiles every time.