This an exciting excerpt from the new book Thinking Machines by Like Dormehl. The book offers a detailed history of primitive machine learning and a dense and fascinating look at the future of true artificial intelligence. The book is available now. Marius Ursache wants you to live forever. It’s not a shock to find out that, in an industry that skews as young as tech, few people spend… Read More
Source: TechCrunh Startup
Public speaking is so stressful for so many people that it is routinely used as a stress manipulation in psychological studies. Tell undergrads they have 10 minutes to prepare a speech that will be evaluated by experts, and their levels of the stress hormone cortisol shoot through the roof.
Yet success in many roles requires speaking in public. In addition to presenting in my classes, I typically give a talk per week in front of groups. People ask me if speaking gets me nervous. It does not. And I give a lot of credit to my fascination with stand-up comedy. While I’m not a comedian myself, I’ve been a fan of comedians and their process for a long time, and I think there are three lessons that anyone can learn from them about public speaking.
I recently moderated a panel at a conference and asked the group of successful executives to describe someone who has been instrumental in their careers. Two panelists eagerly jumped in with stories of bosses who had mentored, encouraged, and opened doors for them. Then, hesitantly at first, the last person shared a far different experience.
She lamented that she’d never been lucky enough to work for someone like that, and at times felt that the lack of an effective boss was career-derailing — even a personal failure. At one point she had worked for a leader who had started to coach her but was then replaced by someone with such a lack of political savvy that she learned to do exactly the opposite of whatever he advised. Eventually she figured out that instead of waiting for a boss who could advocate for her, she had to create a work-around.
As she spoke I noticed how many people in the audience were nodding their heads, and afterward she was flooded with questions.
Language is a uniquely human capability and the manifestation of our intelligence. But through AI — specifically natural language processing (NLP) — we are providing machines with language capabilities, opening up a new realm of possibilities for how we’ll work with them.
Today you can walk into a darkened living room and ask Alexa to turn the smart lights up to a pleasant 75% brightness. Or, you can summon information about weather conditions on the other side of the world. The progress the industry has made was on display in Google’s recent demo of Duplex, in which an AI agent called businesses and booked appointments. What once seemed like science fiction is now reality, but to maintain a truly copacetic human-machine relationship, machines must be able to hold more intuitive, contextual, and natural conversations — something that remains a challenge. I’ve spent my career focusing on NLP, a research area nearly as old as AI itself, and we’re still in the beginning phase of this journey.
Language is the mechanism for sharing information and connecting to those around us, but machines need to understand the intricacies of language and how we as humans communicate in order to make use of it. Advances in sentiment analysis, question answering, and joint multi-task learning are making it possible for AI to truly understand humans and the way we communicate.
Marriott recently teamed up with Amazon to offer a hospitality version of the e-commerce giant’s Echo devices in select hotel rooms. Now, when guests want to order room service or housekeeping, they can simply ask Alexa, the voice of their disembodied personal concierge. Travelers with an Alexa device at home can book a car rental or hotel through Expedia and Kayak. Similarly, Google Assistant, which can be used via Google Home devices, smartphones, or smartwatches, can track flight prices and status, suggest nearby restaurants, convert currency, give directions, and provide same-day updates on traffic to airports. People can even book flights through voice-enabled Google Search.
Why is it that when a group of managers gets together for a strategic planning session they often emerge with a document that’s devoid of “strategy”, and often not even a plan?
I have one such document in front of me as I write this. It starts with a “vision” statement, moves on to “strategic themes” (six in all) and culminates in 28 “strategic goals.” The latter is a list of actions interspersed with a sprinkling of desired results, all utterly useless in terms of strategy. It’s more like a dog chasing its tail. As the managing partner of a client law firm recently explained to me: “Before we adopted your approach we lacked the keys to effective strategic planning. It was seat-of-the-pants stuff. I would spontaneously go about saying how about we do this/how about we do that. I was buried in the enterprise.”
It was going to be the factory of the future. Dubbed the “Alien Dreadnought,” Tesla’s new manufacturing facility in Fremont, California, was designed to be fully automated — no humans need apply. If all went well, AI-powered robots would enable the company to achieve a weekly production of 5,000 Model 3 electric cars to keep up with burgeoning demand. But Tesla fell far short of that mark, manufacturing just 2,000 vehicles a week. The problem, as the company painfully discovered, was that full automation wasn’t everything it was cracked up to be. According to CEO Elon Musk, the sophisticated robots actually slowed down production instead of speeding it up.
Companies are cutting supply chain complexity and accelerating responsiveness using the tools of artificial intelligence. Through AI, machine learning, robotics, and advanced analytics, firms are augmenting knowledge-intensive areas such as supply chain planning, customer order management, and inventory tracking.
What does that mean for the supply chain workforce?
After years of working in tech startups, which strive to transform underdog status into competitive advantage, I dove into the nonprofit world. The contrast was striking: Too few nonprofits use the advantages of their nonprofit status. In doing so, they miss out on a huge opportunity. The nonprofit model has a strategic edge beyond tax exemption, and the best nonprofit leaders learn to leverage it.
I call this approach nonprofit judo, a reference to the martial art that emphasizes how an apparently disadvantaged player can succeed through a strategy that turns weaknesses into strengths.
My experiences with nonprofit entrepreneurs have revealed patterns in the strategic choices that successful leaders make. Many of these nonprofits use technology, but the way they transform perceived weaknesses into strengths — nonprofit judo — can inspire any social venture.
Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection — and even to predict and prevent crimes.
While today’s basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are. For instance, banks have been using transaction monitoring systems for decades based on pre-defined binary rules that require the output to be manually checked. The success rate is generally low: On average, only 2% of the transactions flagged by the systems ultimately reflect a true crime or malicious intent. By contrast, today’s machine-learning solutions use predictive rules that automatically recognize anomalies in data sets. These advanced algorithms can significantly reduce the number of false alerts by filtering out cases that were flagged incorrectly, while uncovering others missed using conventional rules.
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