Electricity: Just as electricity changed the business model, it required new skills, which created new jobs. As people started doing new things in new ways, the old ways and the skills required to support them were no longer necessary. AI: AI's impact will create transformational technologies that will drive skill transformation over the next 20 to 30 years. As an example, when you add AI to machines, you get smart robots--and these robots will replace many service types of jobs in the future. As AI-like services mature, there will be a period when supply and demand will be out of balance and jobs will be affected.

What's all the fuss about the voice-activated home speaker that Amazon is due to release in the UK and Germany in late September? This gadget has been available in the US for over a year and has proven a minor hit, with sales estimates between 1.6m and 3m. But these figures belie the potential impact this kind of artificial intelligence device could have on our lives in the near future. Echo doesn't just let you switch on your music by voice command. It's the first of what will be several types of smart home appliances that work beyond simple tasks like playing music or turning on a light.

Apple's iOS 10 might be the most helpful update it's ever put out. But it's also good at helping people who want to wind you up. The new update makes it easier than ever to discuss things, opening up notifications so that they are actually little versions of the app. And that's mostly useful – it means that you don't ever have to open the Messages app to reply, for instance, but can instead do everything from the lock screen notification. Those notifications can be accessed by anyone – since they come up on the lock screen, there's no authentication needed to start sending messages to people in replies.

Some of the popular anomaly detection techniques are Density-based techniques (k-nearest neighbor,local outlier factor,Subspace and correlation-based, outlier detection, One class support vector machines, Replicator neural networks, Cluster analysis-based outlier detection, Deviations from association rules and frequent itemsets, Fuzzy logic based outlier detection and Ensemble techniques. RapidMiner provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. Scikit-learn is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. You may also live to read, Top Business Intelligence companies, Open Source and Free Business Intelligence Solutions, Cloud – SaaS – OnDemand Business Intelligence Solutions, Top Free Extract, Transform, and Load, ETL Software, Freemium Cloud Business Intelligence Solutions, Top Embedded Analytics Business Intelligence Software, Top Dashboard Software, and Top Data Visualization Software.

During the British summer, conversations about sport become almost ubiquitous. This year, however, one participant in those conversations was very different: IBM Watson, IBM's cognitive intelligence. The All England Lawn Tennis Club knew that 2016 would feature unusually fierce competition for attention, with the Tour de France and Euro 2016 taking place alongside Wimbledon. More than ever before, social media was going to be a vital tool in directing that conversation, and directing attention to SW19. Wimbledon's "Cognitive Command Centre" – powered by Watson's intelligence running on a hybrid, IBM-managed cloud - scanned social media for emerging news and trends.

It is getting more and more difficult to avoid being touched by machine learning (ML). If you buy products from Amazon, make bids on eBay, or stream the latest episodes of Narco on Netflix, chances are, your experience has been shaped by an ML algorithm that makes informed assumptions on what your preferences are, or predicts what they will be. If you allow Facebook to tag friends in your photographs, you are taking advantage of deep-learning algorithms trained for recognizing human faces, and if you take a series of photographs on your Android device, Google will piece together a storyboard of the highlights of your weekend. It's the same for businesses, even if they don't necessarily realize it. There was a study recently published by natural language analytics provider Narrative Sciences that revealed an interesting dichotomy: only 38 percent of respondents reported using ML, yet 88 percent stated that they used analytic tools that incorporate many of the fruits of ML such as automated predictive analytics, automated written reporting and communications, and voice recognition and response.

Pepper the robot seems friendly now, but it may secretly be gunning for Neil deGrasse Tyson's job. Robots, self-driving cars and virtual assistants will eliminate 6 percent of all jobs in the US by 2021, according to a new report from Forrester Research. Customer service representatives will be the first positions affected, then truck and taxi drivers. But, employees working in any easy-to-automate job may be at threat, as businesses look to cut costs with these robots and intelligent assistants. "By 2021 a disruptive tidal wave will begin," the report stated. "Solutions powered by AI/cognitive technology will displace jobs, with the biggest impact felt in transportation, logistics and consumer services."

I currently work as a Data Scientist for Informatica and I thought I'd share my process for learning new things. Recently I've been wanting to explore more into Deep Learning, especially Machine Vision and Natural Language Processing. I've been procrastinating a lot, mostly because it's been summer, but now that it's fall and starting to cool down and get dark early, I'm going to be spending more time learning when it's dark out. And the thing that deeply interests me is Deep Learning and Artificial Intelligence, partly out of intellectual curiosity and partly out of greed, as most businesses and products will incorporate Deep Learning/ML in some way. I started doing research and realized that an understanding and knowledge of Deep Learning was within my reach, but I also realized that I still have a lot to learn, more than I initially thought.

At the beginning of my tech sales career, I learned quickly that hardware, software, and services could help address and achieve the challenges and goals of my enterprise clients. To get there, I had to do as Tony Sarris, founder of N2Semantics says, "ping, discover, act and learn." I "pinged" databases and experts; I analyzed and "discovered" info and resources along the way; I "acted" on my business savvy and put together the A team -- with whom I would "learn" from and built integration-able solutions for my clients. When I think of Cognitive Apps today, I think of the "ping, discover, act and learn" dance. Well, because, as I see it, it is part of the challenges that cognitive apps need to address to work ( understand, reason and learn).

AI will become the defining technology of the 21st century and in 30 years from now we will wonder how we ever got along without our seemingly telepathic digital assistants, writes Marc Benioff. OVER the last 30 years, consumers have reaped the benefits of dramatic technological advances. In many countries, most people now have in their pockets a personal computer more powerful than the mainframes of the 1980s. The Atari 800XL computer that I developed games on when I was in high school was powered by a microprocessor with 3,500 transistors; the computer running on my iPhone today has 2bn transistors. Back then, a gigabyte of storage cost 100,000 and was the size of a refrigerator; today it's basically free and is measured in millimetres. Even with these massive gains, we can expect still faster progress as the entire planet -- people and things -- becomes connected.