Few day ago before I saw an interesting question on stats.stackexchange.com After spending few minutes of readings and analyzing all answers on stack I felt writing my thoughts assuming what I would have answered if I really had too. What is the difference between Artificial Intelligence, Machine Learning, Statistics, and Data Mining? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ?

Google is announcing today that it's setting up a deep learning and artificial intelligence (AI) research unit in its office in Montreal and giving $3.37 million in grant money to deep learning luminary Yoshua Bengio and seven other people associated with the Montreal Institute for Learning Algorithms (MILA). Bengio himself has previously received backing from Google, and from other companies as well -- namely, IBM, Samsung, and Intel. But the new grant is "bigger than any of the other funding we've received from private companies up until now," he said during an interview with VentureBeat. Bengio will not be formally allying himself with Google proper, because he wants to stay independent. "That's who I am," he said, "that's the choice I made that fits with my values, and I don't need to get the millions, I'm fine. My salary is very good, and I care more about how what I can do could have a positive impact for science, humanity, and for training the next generation [of researchers]."

Artificial Intelligence in the form of Siri or IBM's Watson get a ton of press these days, but AI as a resource for SMB management and growth often goes overlooked. AI is perhaps one of the best tools out there for better business management, improving everything from management to analytics. Any entrepreneur or SMB owner can speak at great length as to the difficulties of trying to grow a business safely. Small Routine tasks need to be conducted; Data needs to be correctly understood; and the right data needs to be collected and reported upon. The good news is that the trend towards smarter programs means lower costs for business owners.

When it comes to artificial intelligence, Facebook is looking to tackle all areas of the technology. The company is already conducting research in computer vision, language understanding, machine learning, connectivity, and virtual reality, as well as on other frontiers. To showcase its dedication, the company is giving more insight into its research and breakthroughs, and is revealing what it plans to tackle next. "Facebook's long-term road map is focused on building foundational technologies in three areas: connectivity, artificial intelligence and virtual reality," wrote Mike Schroepfer, CTO of Facebook, in a release. "We believe that major research and engineering breakthroughs in each of these areas will help us make more progress toward opening the world to everyone over the next decade."

R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that enables developers to conduct data visualization to data analysis.This video covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R. We start off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. We then take a look linear and non-linear regression modeling and classification models, and check out the math behind the working of classification algorithms. We then shift our focus to unsupervised learning algorithms, time series analysis and forecasting models, and text analytics.

Ryan McClead cautions against buying into the artificial intelligence hype that has taken over legal and mainstream media – not because it isn't real, but because our own conception of AI is a work in progress: Google isn't considered AI, but it'knows' what you're typing as you type, and then it filters a large portion of the web to give you the most relevant pages. It would have easily been seen as AI twenty years ago. Siri and Alexa personal assistants respond to voice commands and can return information instantly or actually perform tasks online, but they are considered borderline AI at best these days. Completely self-driving automobiles are still seen as Science Fiction and therefore are solidly in the AI column, but I predict they will NOT widely be considered AI by the time they are commercially available. AI is a moving target.

Artificial intelligence, once relegated to the realm of science fiction, is now found in everything from translation services to virtual assistants to video games. And as companies race to develop self-driving cars and offer increasingly personalized online experiences, they're building on research that was largely pioneered by a group of Canadian researchers who are still attracting plenty of attention and investment dollars. Montreal, in particular, has developed a concentration of expertise in the area of AI, largely thanks to the efforts of University of Montreal professor Yoshua Bengio, head of the Montreal Institute for Learning Algorithms (MILA). "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg,'' Bengio told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers.''

Shibl Mourad, the head of engineering for Google's Montreal office, says the company hopes to help turn the city into a "super-cluster" of AI knowledge that will attract corporate investors, burgeoning startups and researchers. He said much of the credit goes to Bengio and his colleagues, whose research over the last decade has put the city ahead of its competitors. Had these researchers not invested that decade of their lives, "we would not be where we are," Mourad said. The lab Bengio leads is one of the largest in the world dedicated to studying Deep Learning, one of the underpinnings of AI. Over the past decade, they learned that by layering several "neural networks" that mimic how the brain works, computer programs could "learn" to solve complex problems on their own instead of needing to be programmed step-by-step.

John Charles Thomas was born in Akron, Ohio and attended Ellet High School. He graduated from Case Western Reserve University majoring in psychology and minoring in mathematics and drama. He received a Ph.D. in experimental psychology from the University of Michigan. His dissertation compared human performance in a problem solving task to that of an early AI system called "The General Problem Solver." After graduate school, Dr. Thomas managed a research project on the psychology of aging at Harvard Medical School.

Montreal's burgeoning AI and machine learning ecosystem just got a boost. Today, the Canadian Press reported that the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal will receive $4.5 million over three years from Google to support its research. Google will also open an AI research group at its Montreal office. "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg," Yoshua Bengio, head of MILA, told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers."