Not a day goes by when we do not hear about the threat of AI taking over the jobs of everyone from truck drivers to accountants to radiologists. An analysis coming out of McKinsey suggested that "currently demonstrated technologies could automate 45 percent of the activities people are paid to perform." There are even online tools based on research from the University of Oxford to estimate the probability that various jobs will be automated. This concern that progress in AI will make most human labor obsolete has led some to call for a (universal) basic income, in which all citizens periodically and unconditionally receive money from the state (see "Basic Income: A Sellout of the American Dream"). Y Combinator, a prominent startup incubator in Silicon Valley, will run a pilot study of basic income in Oakland, California, and its president has stated that "at some point in the future, as technology continues to eliminate traditional jobs and massive new wealth gets created, we're going to see some version of this at a national scale."

Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

If pop culture is to be believed, society is quickly heading toward a highly automated future ruled by artificial intelligence. Take Iron Man's trusty sidekick, J.A.R.V.I.S. Within the Marvel franchise, the artificial intelligence system is able to think, act, and feel like a human. The supporting character is even sarcastic and witty -- both trademark human characteristics. In some ways, J.A.R.V.I.S. seems like a better human than most humans. With the release of AI technologies like IBM Watson and Salesforce Einstein, in addition to the recent buzz about the "Partnership on AI," which has brought together some of the world's biggest tech companies to advance research in the sector, it might seem like that fantasy is quickly turning into reality.

Remember when artificial intelligence was supposed to be a good thing? When we thought we would, in our old age, each be tended to by a personalised robotic nurse? When we thought that all our jobs would be made obsolete, allowing us to live lives of unbroken leisure? That glorious future might still be on the horizon, but for now AI is rubbish. We live in a world where stupid robots and gormless algorithms are incompetently conspiring to make our lives much more difficult than they need to be.

Artificial intelligence and machine learning systems are made up of code and algorithms, and as such, they work as fast as computers can process them. Often this means massive amounts of learning can be accomplished every second without stop 24x7x365. Code doesn't need to take weekends off, holidays, or sick time. It can recognize complex patterns, areas of potential improvement and problems in real-time (aka digital-time). Given these available computing capabilities and speeds, what are executives to do with AI and machine learning, when we live and operate in relatively slow human-time, and work within organizations that work at an even slower pace of organizational-time.

On average, when an AP sportswriter covers a game, she produces eight different versions of the same story. Aside from writing the main print story, they have to write story summaries, separate ledes for both teams, convert the story to broadcast format, and more. "It's a manual labor nightmare," Jim Kennedy, the AP's senior vice president for strategy and enterprise development, told me in his New York office. Collectively, AP journalists spend about 800 hours a week converting print stories to broadcast format. As a result, the AP is experimenting with machine learning in an attempt to automate some of those processes.

As an IBM developerWorks information architect, I gave a presentation last week about cognitive computing to entrepreneurs and staffers at tekMountain, a co-working and tech incubator in Wilmington, North Carolina where I work as a tech mentor. The title is a bit tongue and cheek, but I really tried to position the Watson application development demo I gave as the "applied" part of a series that we launched in the area last year on artificial intelligence. At a previous "Exploring Artificial Intelligence" TechTalk, my buddies Mike Orr (IBM Watson University program chair) and Julian Keith (UNC Wilmington Psychology Chair and brain guy), began a series of conversations about artificial intelligence that quickly blossomed into several different AI events with different AI focuses at different venues. An upcoming talk in this very popular series (for example) is titled "Is artificial intelligence going to do my job better than me?" As a software development enthusiast who sometimes teaches kids and others how to start coding, I naturally conceived of a hands-on version of Watson services as a way to take the conversation further.

In the next five years, every important decision, whether it's business or personal, will be made with the assistance of IBM Watson. Watson, the company's artificial intelligence-fueled system, is working in fields like health care, finance, entertainment and retail, connecting businesses more easily with their customers, making sense of big data and helping doctors find treatments for cancer patients. The Watson system is set to transform how businesses function and how people live their lives. "Our goal is augmenting intelligence," Rometty said. "It is man and machine. This is all about extending your expertise. It doesn't matter what you do. IBM's conference this week, which the company said drew 17,000 attendees, explored how companies, including retailers, educators, human resources departments and financial institutions, amon others, can use Watson. "The challenge IBM has right now is to define the marketplace," said Jeff Kagan, an independent industry analyst, who attended the conference. "Ten years from now, will IBM be the leader?

Chatbots could become much more useful for knowledge workers if IBM Corp. and Slack Inc. have their way. Against the backdrop of its annual World of Watson summit in Las Vegas this week, Big Blue has struck a partnership with the communications startup to develop new artificial intelligence capabilities for Slack's messaging platform. Their first priority is to integrate the Watson Conversation service that launched at the event yesterday into Slackbot, the default personal assistant included in Slack rooms. It offers explainers about the tool's various features and can be configured to answer common questions such as inquiries about a chat channel's purpose. IBM and Slack say that Watson's natural language processing capabilities will increase the accuracy of the bot's responses while improving its efficiency at generating replies.

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models.