To borrow a cliché opening from the last high school commencement or Maid of Honor speech you heard, the dictionary defines artificial intelligence (AI) as 1: a branch of computer science dealing with the simulation of intelligent behavior in computers; and 2: the capability of a machine to imitate intelligent human behavior. But, do these definitions really explain the difference between an artificially intelligent system and one that's just programmed to be useful? What is "intelligent" behavior or, more specifically, "intelligent human behavior"? For many, the term "artificial intelligence" draws to mind humanoid robots like C-3PO from "Star Wars" or Dolores from "Westworld."

To change your world, AI does not need to replace humans – it just needs to displace you. So stop saying that AI can't replace humans, and start asking, "Can AI displace me?" We are not close enough to general-knowledge artificial intelligence to consider a world where such a system could completely replace a cognitive nonrepetitive (white-collar) worker. And while Hollywood and sensationalist reporting would have you believe otherwise, malevolent AI systems and sentient cyborgs should not be the subject of any serious discussion about job loss. The most probable future is far scarier.

Whenever there's a need to draft a job description, Expedia Inc.'s 3,000-plus recruiters and hiring managers have the option to call on a writing coach. The online travel-booking company's writing companion is Textio Inc., an artificial intelligence application that runs in the cloud and analyzes each typewritten word in milliseconds to spot gender bias or other language that might turn off good candidates. The software generates an effectiveness score and suggests alternative phrasing, in effect teaching the recruiter how to write a job description more effectively. We are in the age of "the Facebook generation"-- millennials. They'll make up the majority of the workforce.

More and more people are using the phrase "Ok Google" to start a conversation to find information or perform actions using the Google Assistant. Launched last fall, the new assistant is already available on over 100 million devices and is rapidly evolving. Today at Google I/O, its creator took the stage to discuss how Google Assistant is expanding its capabilities and reach on smartphones and Google Home. Google Home, a standalone voice activated smart-appliance and speaker, is getting the most attention, with a number of significant improvements on the way. In a few months, Google Home will be able to make hands-free VOIP calls to any phone in the U.S. or Canada for free.

The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector \(w\) and bias \(b\) of the hyperplane. A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector and bias of the hyperplane. Support vector machines (SVM) and other simpler models, which can be easily trained by solving convex optimization problems, gradually replaced neural networks in machine learning.

Before the internet, if you wanted to book a flight or hotel, you picked up the phone and called an agent. The internet changed all that. It gave you hundreds of websites and mobile applications to research options, read reviews and make reservations. In the next few years, you'll be picking up the phone and chatting with an agent again -- but this time, you'll be talking to an AI-powered bot that will understand your needs and preferences, hunt down the best deal and take care of your reservations. We're entering an era where machines will help us make sense of the vast amounts of information and get things done for us -- bots, robots, assistants.

During a non-stop, two-hour keynote address at its annual I/O developers conference, Google unveiled a barrage of new products and updates. Here's a rundown of the most important things discussed: Google CEO Sundar Pichai kicked off the keynote by unveiling a new computer-vision system coming soon to Google Assistant. Apparently, as Pichai explained, you'll be able to point your phone's camera at something, and the phone will understand what it's seeing. Pichai gave examples of the system recognizing a flower, a series of restaurants on a street in New York (and automatically pulling in their ratings and information from Google), and the network name and password for a wifi router from the back of the router itself--the phone then automatically connecting to the network. Theoretically, in the future, you'll be searching the world not through text or your voice, but by pointing your camera at things.

Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, will discuss the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

Google has developed its second-generation tensor processor--four 45-teraflops chips packed onto a 180 TFLOPS tensor processor unit (TPU) module, to be used for machine learning and artificial intelligence--and the company is bringing it to the cloud. TPU-based computation will be available to Google Cloud Compute later this year. Typically in machine-learning workloads, initial training and model building are divided from the subsequent pattern matching against the model. The former workload is the one that is most heavily dependent on massive compute power, and it's this that has generally been done on GPUs. Google's first-generation TPUs were used for the second part--making inferences based on the model, to recognize images, language, or whatever.

Uber is hiring Raquel Urtasun, a prominent artificial intelligence researcher at the University of Toronto, as the ride-hailing company aims to build a lab for driverless car research in the Canadian city, a hotbed for AI talent. Urtasun--an associate professor at the university who specializes in the computer vision software that allows driverless cars to view the world around them--will oversee the new venture. "We hope to draw from the region's impressive talent pool as we grow, helping the dozens of researchers we plan to hire stay connected to the Toronto-Waterloo Corridor," Travis Kalanick, Uber's embattled CEO, wrote in a blog post published this morning. The move resonates on multiple levels, given the ongoing legal attack against Uber's existing computer vision technology by Waymo--the driverless car company that grew out of Google--and the widespread controversy over Uber's allegedly misogynistic internal culture. Urtasun could help the company forge another much-needed path to the kind of AI that driverless cars will require.