I guess by now we have all heard of the very controversial Google Duplex demonstration at Google I/O 2018, where a human-voice synthesized bot called several local businesses and was able to interact with humans who had no idea they were talking to a machine. Many of us are fascinated by the technological progress that could be witnessed. A part of me was fascinated just like that. But to me the real fascinating discussion is about ethics, specifically AI ethics that come along with approaches like Duplex. Continue reading “What is the key learning from Google Duplex? Digital Responsibility needed more than ever before…”
A totally Must-Read on what AI can do right now and what not: The Business of Artificial Intelligence (Erik Brynjolfsson, Andrew McAfee)
And again… if you read one Artificial Intelligence article this month, make it this one. I highly value Erik Brynjolfsson since I saw his TED talk (here is also a great recap and interview with him in the NPR TED Radio Hour “The Digital Industrial Revolution”). Together with with fellow MIT principal research scientist Andrew McAfee he draws a sharp and easy to grasp picture of what machine learning is really capable of today and what the outlook for the future is. This is really worth your time, providing first class insights into “no bullshit” artificial intelligence state of the nation. Here’s an intro… follow the link at the end of this post for the full article on Harvard Business Review:
“For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.
The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available…”
via The Business of Artificial Intelligence (@Harvard Business Review)
Five years from now, over one-third of skills (35%) that are considered important in today’s workforce will have changed. By 2020, the Fourth Industrial Revolution will have brought us advanced robotics and autonomous transport, artificial intelligence and machine learning, advanced materials, biotechnology and genomics.
These developments will transform the way we live, and the way we work. Some jobs will disappear, others will grow and jobs that don’t even exist today will become commonplace. What is certain is that the future workforce will need to align its skillset to keep pace. A new Forum report, The Future of Jobs, looks at the employment, skills and workforce strategy for the future…
via The 10 skills you need to thrive in the Fourth Industrial Revolution | World Economic Forum
This is impressive progress made possible by Machine Learning: Google’s speech recognition technology now has a 4.9% word error rate (Emil Protalinski)
Google CEO Sundar Pichai today announced that the company’s speech recognition technology has now achieved a 4.9 percent word error rate. Put another way, Google transcribes every 20th word incorrectly. That’s a big improvement from the 23 percent the company saw in 2013 and the 8 percent it shared two years ago at I/O 2015.
The tidbit was revealed at Google’s I/O 2017 developer conference, where a big emphasis is on artificial intelligence. Deep learning, a type of AI, is used to achieve accurate image recognition and speech recognition. The method involves ingesting lots of data to train systems called neural networks, and then feeding new data to those systems in an attempt to make predictions…
Source: Google’s speech recognition technology now has a 4.9% word error rate | VentureBeat | Dev | by Emil Protalinski
Jesse Engel is playing an instrument that’s somewhere between a clavichord and a Hammond organ—18th-century classical crossed with 20th-century rhythm and blues. Then he drags a marker across his laptop screen. Suddenly, the instrument is somewhere else between a clavichord and a Hammond. Before, it was, say, 15 percent clavichord. Now it’s closer to 75 percent. Then he drags the marker back and forth as quickly as he can, careening though all the sounds between these two very different instruments. “This is not like playing the two at the same time,” says one of Engel’s colleagues, Cinjon Resnick, from across the room. And that’s worth saying. The machine and its software aren’t layering the sounds of a clavichord atop those of a Hammond. They’re producing entirely new sounds using the mathematical characteristics of the notes that emerge…
via Google’s AI Invents Sounds Humans Have Never Heard Before | WIRED
Great Read: Here’s The Unofficial Silicon Valley Explainer On Artificial Intelligence (Daniel Terdiman)
I’m willing to bet you didn’t know that artificial intelligence can help sort cucumbers. It can, and in fact it does. And while AI has gotten massive amounts of attention recently due to its role in making cars autonomous, doing facial recognition, and automatically translating languages, there’s one man in Silicon Valley who really wants everyone developing any kind of technology-based tool to know that AI has something to offer them as well. Last year, Frank Chen, a partner at the A-list venture capital firm Andreessen Horowitz (a16z), published a primer on artificial intelligence. The 45-minute video took viewers through a history of the technology, from its “birthday” in the summer of 1956 through its years in the wilderness of technology and straight through current-day Silicon Valley, where it is dominating conversations at most of the largest tech companies there.
In fact, if the mobile cloud was computing’s previous major era, the next will be the era of AI…
via Here’s The Unofficial Silicon Valley Explainer On Artificial Intelligence
Guy Raz: “I ask myself this question a lot which is: Is this the future we want? Have we gotten to a place where the train has left the station, where we don’t really have much of a choice about where the future is heading?”
Erik Brynjolfsson: “Well let me try to cheer you up a little bit. Let’s just step back and look at the fundamentals. What are you and I are talking about? We re talking about a world with vastly more wealth, vastly more power to solve all sorts of problems. Vastly less need for us to work. Most routine tasks could be eliminiated. Shame on us, shame on us, if we mess that up and turn that into a bad thing. Wouldn’t that be the worst irony in the world where we take more wealth and less work and say ‘Oh, what a terrible thing?’ I think we can essentially eliminate poverty from planet earth, we can cure most deseases. And the global millennium goals, we are on track to beat them and eliminate severe poverty. There are a lot of positive trends. I think the world in 25 years could be a much better version of the world we have today. But the role of humans would still be fundamentally at the center of that.”
Source: The Digital Industrial Revolution : TED Radio Hour : NPR
The U.S., Canada and Mexico are buying more job-killing robots than ever before (April Glaser, Rani Molla)
Robots are getting cheaper and smaller and, as a result, sales have grown significantly over the past year, particularly in North America, as more companies move manufacturing operations closer to U.S. markets. North American manufacturing companies bought a total of 9,773 industrial robots, valued at approximately $516 million, in the first quarter of 2017. That means 32 percent more robots were bought this year than at the same time in 2016 — it’s the strongest first quarter on record for robots ordered by North American companies, according to the Robotic Industries Association…
via The U.S., Canada and Mexico are buying more job-killing robots than ever before – Recode
The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.
So wrote Wired’s Chris Anderson in 2008. It kicked up a little storm at the time, as Anderson, the magazine’s editor, undoubtedly intended. For example, an article in a journal of molecular biology asked, “…if we stop looking for models and hypotheses, are we still really doing science?” The answer clearly was supposed to be: “No.”
But today — not even a decade since Anderson’s article — the controversy sounds quaint. Advances in computer software, enabled by our newly capacious, networked hardware, are enabling computers not only to start without models — rule sets that express how the elements of a system affect one another — but to generate their own, albeit ones that may not look much like what humans would create. It’s even becoming a standard method, as any self-respecting tech company has now adopted a “machine-learning first” ethic…
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