Ever since the launch of Siri in its fully-integrated form on the iPhone 4s in 2011, digital assistants have become standard features on most modern smartphones. With competition growing from Microsoft, Google, and Amazon with their Cortana, Google Now, and Echo respectively, Apple continues providing updates to Siri in an attempt to find a semblance of functional advantage. As Google recently added changes to its digital assistant – Google Now – with smart intonation and expression to its speech patterns to sound less robotic, Apple has followed with their own updates looking to target Siri towards specific audiences; in this case, sports fans. Leading up to the opening days of this year's Major League baseball season, Apple has dramatically increased Siri's knowledge of and access to in-depth baseball knowledge and statistics. Though Apple added sports scores to Siri's functionality back with iOS6, previously, when asked specific baseball-related questions, Siri would typically respond with a simple search or Google queries.

Microsoft came up with the great idea of creating an artificial intelligence bot on Twitter, in the image of a teenage girl named Tay, and making her capable of learning through interactions with real people online. Within a few hours, she was a racist, a sexist and a Holocaust denier, and she was spewing all this with gusto, humor and abandon. Microsoft had to take her down before she started winning delegates for the presidential nomination. Microsoft tried to make some adjustments to Tay and put her back online, but she quickly started boasting about smoking drugs in front of the police. Tay reportedly now wants to hang out with Siri.

Few days ago, I was asked by the dean of our faculty to present what is behind recent success of artificial intelligence when AlphaGo defeated legendary player Lee Sedol in the ancient game of Go. I do not know anything about playing Go so decided to focus on Artificial intelligence in general and talk about recent advances, background, state of the art and applications. It looks like many people understand the importance of the topic and are willing to come. The capacity of the lecture room is however limited so I decided to share the story line of my talk here on Medium. This is the outline of the talk.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

Superfast quantum computers could transform the world of finance, advocates say. In a world where how fast you can assimilate and analyse data then act on it, makes the difference between profit and loss, computing speed is key. This is why banks, insurance firms, and hedge funds invest millions on technology to give them an edge when trading, and to offset human error. Quantum computers, that owe more to nuclear quantum mechanics than electronics, promise to be exponentially more powerful than traditional computers, holding out the tantalising prospect of near-perfect trading strategies and highly accurate forecasting and risk assessments. "Financial services is a data-rich environment," says Kevin Hanley, director of design at the Royal Bank of Scotland (RBS).

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses'value networks' to evaluate board positions and'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Salesforce.com's automation efforts got a boost Monday with the news it has acquired AI startup MetaMind. Salesforce will integrate MetaMind's technology into its own services for new marketing-automation and personalization capabilities, according to a blog post from MetaMind founder Richard Socher. "We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform," Socher wrote. Socher's personal Web page now lists his title as chief scientist at the customer relationship management giant. MetaMind's products will be discontinued on May 4 for users of its free versions, and on June 4 for paid users.

The first step to before being able to do Image or audio analyses would be to extract relevant frames from the video streams in real time. This is crucial to a smart interactive device and requires extensive down sizing of the data to run the models on the features identified most relevant. One also needs the device to identify and react to certain events (owner coming home, break-in etc) through a frame by frame comparative analysis. Let us start with the event that there is a disturbance and the image frames and audio data is fed into the trained model to classify the event into pre-defined classes (simplest cast being intrusion vs non intrusion). Given a frame, let us start with the features that we would extract from it to first look for faces within the scenario and then if we find one, to match it with the available "registered" face repository. Given pictures of the home-owner/family, we will have an extensively pre trained model.

It has been humorously said that any discipline that ends with the word "science" isn't a science. Social science, military science, computer science, and library science are such oxymorons. The same joke applies to the word "intelligence." AI is no longer artificial. In a number of ways, an interaction with an automated agent has become indistinguishable from human to human engagement.

Last May, we wrote about a new machine from healthcare giant Johnson & Johnson that could sedate patients for routine medical procedures. The device handled one of the most common and yet risky hospital procedures: Putting someone to sleep so they don't feel discomfort or pain, yet not so asleep that they don't wake up. At the time, the Sedasys machine was being used in just four hospitals, including the one we visited in Toledo. We watched as the Sedasys device provided basic anesthesiology services to a series of patients undergoing routine endoscopies and colonoscopies. No longer did you need a trained anesthesiologist.