Artificial intelligence (AI) is defined as being "the ability of a machine to perform at the level of a human expert". We have recently seen an example of this come to the fore when Google-owned DeepMind's program AlphaGo beat the reigning world Go champion, Lee Sodol, 4-1 in what many consider to be the most difficult game in the world. Traditional business use of the telephone as a major business tool involves two parties. A debt collection agent tries his or her best to "encourage" a client to pay their account. A sales rep attempts to entice a lead to purchase their product.

I'm often asked about my thoughts on the future. What will transportation be like? What new forms of entertainment will we enjoy? While I have covered these topics in previous articles, it's important to understand that they're all forms of work. So today I want to discuss the future of work -- how technological advancements, namely robotic assistants and tools, as well as tech-enhanced globalization, will affect our daily work flow and the labor market in general.

Artificial intelligence is nothing new at Google, but today we learned just how big a role top boss Sundar Pichai sees AI playing in our future. Answering an analyst query on Google-parent company Alphabet's Q1 2016 earnings call about how the company is leading innovation, rather than simply adapting to changes in technology, Pichai talked about his role in projecting where Alphabet is going in the next 10 years. He gave a shout out to VR as the hot new platform, and then wrapped up his comments by saying: "In the long run, I think we will evolve in computing from a mobile-first world to an AI-first world." Earlier in the call he cited Google's DeepMind AlphaGo super computer defeating a human champion as an extraordinary achievement. He also said the company is investing in AI and machine learning, areas that are taking off and beginning to bear real-world benefits.

Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks. However, protection agencies have limited resources for patrols. With support from the National Science Foundation (NSF) and the Army Research Office, researchers are using artificial intelligence (AI) and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the U.S., Singapore, Netherlands and Malaysia. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," according to Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California (USC). Fang is part of an NSF-funded team at USC led by Milind Tambe, professor of computer science and industrial and systems engineering and director of the Teamcore Research Group on Agents and Multiagent Systems.

How we explain reality to ourselves is a construction with many parts. We gather knowledge and generate meaning through our experiences and traditions; from what we learn in school, at work and at home; from how we witness others explaining reality for themselves (on TV, via social media, etc). This narrative that we tell ourselves everyday throughout our entire lives largely defines who we are and how we approach the world. The first time I ran (in Mexico) an adaptation of Stanford's workshop "Makers in Residence" (an intensive 80 hour program for high schoolers on digital fabrication and design thinking which was designed by the Transformative Learning Technology Lab) I was shocked by the comments of participants regarding their place in relation to technology. Most participants were impressed that they were "smarter" than the computers they programmed; when I asked them more about it I started understanding the new narrative that a generation of kids growing up surrounded by digital technology are developing in their heads.

I too believe AI could be bigger in the future once the under pinning technology and infrastructure moves to Quantum Technology so that hacking is under control and performance is where it needs to be. When Mark Zuckerberg thinks about the future, he sees a world that's dominated by mobile devices and virtual reality, but when Google CEO Sundar Pichai thinks about the future, all he sees is artificial intelligence. He suggested as much during Alphabet's quarterly earnings call on Thursday, saying that mobile devices and virtual reality will dominate the immediate future, but that they'll eventually be surpassed in importance by artificial intelligence. However, he didn't go into detail about what this future will look like. Artificial intelligence is nothing new at Google, but today we learned just how big a role top boss Sundar Pichai sees AI playing in our future.

Identifying the top 10 algorithms in the abstract is a pretty complicated exercise unless there is a clear dimension to make the comparison. Let me tackle this from a pretty subjective point of view: If I were interviewing you for a Data Mining position what would be the top 10 algorithms I would expect you to know in order of priority? Again, a pretty subjective list, but I think it is quite representative of what you need to do real data mining work in industry.

This is the second post in a series of me trying to learn something new over a short period of time. The first time consisted of learning how to do machine learning in a week. This time I've tried to learn neural networks. While I didn't manage to do it within a week, due to various reasons, I did get a basic understanding of it throughout the summer and autumn of 2015. By basic understanding, I mean that I finally know how to code simple neural networks from scratch on my own.

This is the third in a series of reports to document development of a generalized method to create artificial neural networks (ANNs) via a genetic algorithm (GA). This report will be divided into several main sections. The goal of this report is to demonstrate the ability of an ANN to recognize letters by providing example images to the ANN. It will use those images as a training set to recognize letters in a street sign. Report 2 introduced image processing so that image data can be submitted to the ANN for pattern recognition.

We expected so much from "a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data." And what did we get? @!!@# Granola. Bear Naked Granola uses Watson to make custom suggestions for mixing your own granola which they will then ship to your door, ten bucks for ten ounces. At first glance, it sounds like an idiotic waste of resources. After all, 50 ingredients is not exactly a large amount of unstructured data.