Tens of million of people worldwide play Hearthstone, an online collectible card game set in the Warcraft universe, which also encompasses the massively popular MMO World of Warcraft and a major movie. Now Google Deepmind, fresh from creating an AI that triumphed at a game it was thought no computer could master, has been using Hearthstone to test ways a machine learning system could generate natural language - such as English - and formal language - such as computer code. Researchers tasked a system with writing the code that sets the behaviour of cards used in Hearthstone and in another famous collectible card game, Magic: The Gathering (MTG). The Deepmind system -- which implemented a novel neural network architecture -- was first trained using code from open-source versions of Hearthstone, programmed in Python, and Magic: The Gathering, programmed in Java. Humans 2.0: How the robot revolution is going to change how we see, feel, and talk Robots aren't going to replace us, but by working hand in hand with us they will redefine what it means to be human. Once trained, researchers tested the ability of the system to generate code needed to represent Hearthstone and MTG cards in each game.

It won't be a surprise if most people familiar with artificial intelligence equate it to Watson. IBM Watson became an overnight star when it participated in the quiz show Jeopardy!, defeating former winners Brad Rutter and Ken Jennings. That was some four years ago. But now when artificial intelligence is slowly climbing the mainstream technology ladder, will enterprises intelligently use cognitive computing in 2016? Well, even Watson will find it difficult to answer.

This week my audience greatly appreciated the Motherboard post "Joke-Telling Robots Are the Final Frontier of Artificial Intelligence", so I decided to spend a few words on humor and artificial intelligence. Why allowing an artificial intelligence to joke is important? I like the honest way Bloomberg asks for the same question in a post which is now 4 years old, but is still a good read: Can a computer be taught to be funny? It doesn't seem nearly as important an endeavor as getting computers to identify malignant tumors or prevent airplanes from crashing, but being able to model humor is a key problem in attempting to model human thought. As Motherboard explains "Some specialists even see humor as the final frontier for artificial intelligence, because it requires mastery of sophisticated functions like self-awareness, empathy, spontaneity, and linguistic subtlety."

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed.

The bot, known as Tay, was designed to become "smarter" as more users interacted with it. Instead, it quickly learned to parrot a slew of anti-Semitic and other hateful invective that human Twitter users started feeding the program, forcing Microsoft Corp to shut it down on Thursday . Following the setback, Microsoft said in a blog post it would revive Tay only if its engineers could find a way to prevent Web users from influencing the chatbot in ways that undermine the company's principles and values.

The United States announced earlier this month that it had used both drones and manned aircraft to kill at least 150 al-Shabab fighters in Somalia, a move the Pentagon says was necessary to stop an imminent attack on US and African Union forces in the country. Critics of the US drone programme however, argue such strikes create more enemies than they kill. With the number of fighters joining al-Shabab having nearly doubled since 2013, how can the armed group be defeated? In this week's Arena, Somalia's former special envoy to the US Abukar Arman, who has called drone strikes a "priceless propaganda tool" for al-Shabab, is in debate with the country's former deputy prime minister Ahmed Abdisalam Adan, who says the strikes are needed. Follow UpFront on Twitter @AJUpFront and Facebook.

Cynomix is an advanced technology developed for four years under DARPA's Cyber Genome program. It was evaluated by DARPA and MIT Lincoln Labs, and rated as the highest among all DARPA teams in its category. The goal of DARPA's Cyber Genome program was to map the genome for malware, under the premise that while over 300,000 malware strains are released daily, most are variants of a manageable number of malware families. Cynomix was conceived as a technology for identifying the unique genetic markers held in common for each malware family, and for clustering them using machine learning algorithms applied to big data sets. These algorithms cluster thousands of labeled malware ingested daily, which enables Cynomix to stay current with the newest emerging threats. This approach gives Cynomix unmatched powers of detection by analyzing a broad sampling of malware in the wild, without having to see every minor malware variation.

Two years ago I asked myself if it in any way would be possible to use Machine Learning techniques to predict the outcome of football matches. To describe the process briefly I started by collecting as much data as I could get hold of. I mined data about old games from every different source and API I could find. Some of the more important ones were Football-data, Everysport and Betfair. I then took all the data for from the old matches, with its corresponding results, quantified it and put it in a database.

I remember clearly my disappointment when I learned, as a college student, how computers were able to play skilled chess. I was taking my first course in artificial intelligence, and had assumed that such a mysterious and powerful topic must have similarly impressive methodologies. In fact, the Minimax algorithm used by Deep Blue and other game-playing computers is quite intuitive (1). Ultimately, though, my disappointment was replaced by excitement: I could build an algorithm to play chess! I'd like to provide you with that same sense of disappointment regarding machine learning.

Peter Lee, the corporate vice president of Microsoft Research, has issued an apology for the behaviour of Tay, the company's new artificial intelligence (AI) chatbot that was unveiled earlier this week. Within 24 hours of going online, Tay was grounded after trolls on Twitter quickly corrupted it into a machine that spewed racist, sexist and xenophobic slurs. "We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay," Lee wrote in a blog post. "Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values." Developed by Microsoft's Technology and Research and Bing teams, Tay was created to conduct research on "conversational training."