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.

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.

K-means is a clustering algorithm and not classification method. On the other hand, SVM is a classification method. We do clustering when we don't have class labels and perform classification when we have class labels. Clustering is a unsupervised learning technique and classification is a supervised learning technique. Therefore, comparing both of them are comparing apple and oranges. You should read the following to understand their difference - Shehroz Khan's answer to Is supervised learning commonly carried out after clustering?

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.

The answer to this question will become a huge bibliography that nobody looks at eventually. I think you should start reading some basic book about the topic and go specif from there. However, I will refer to this paper that actually kills all other papers:-) http://teamcore.usc.edu/WeeklySe... - Machine Learning That Matters, Kiri L. Wagstaff

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.