This is part three of a six-part series on the history of natural language processing. In 1913, the Russian mathematician Andrey Andreyevich Markov sat down in his study in St. Petersburg with a copy of Alexander Pushkin’s 19th century verse novel, Eugene Onegin, a literary classic at the time. Markov, however, did not start reading Pushkin’s famous text. Rather, he took a pen and piece of drafting paper, and wrote out the first 20,000 letters of the book in one long string of letters, eliminating all punctuation and spaces. Then he arranged these letters in 200 grids (10-by-10 characters each) and began counting the vowels in every row and column, tallying the results.
Sure, artificial intelligence is transforming the world’s societies and economies—but can an AI come up with plausible ideas for a Halloween costume? Janelle Shane has been asking such probing questions since she started her AI Weirdness blog in 2016. She specializes in training neural networks (which underpin most of today’s machine learning techniques) on quirky data sets such as compilations of knitting instructions, ice cream flavors, and names of paint colors. Then she asks the neural net to generate its own contributions to these categories—and hilarity ensues. AI is not likely to disrupt the paint industry with names like “Ronching Blue,” “Dorkwood,” and “Turdly.” Shane’s antics have a serious purpose. She aims to illustrate the serious limitations of today’s AI, and to counteract the prevailing narrative that describes AI as well on its way to superintelligence and complete human domination. “The danger of AI is not that it’s too smart,” Continue reading The Blogger Behind “AI Weirdness” Thinks Today’s AI Is Dumb and Dangerous
Michael Kratsios, the Chief Technology Officer of the United States, took the stage at Stanford University last week to field questions from Stanford’s Eileen Donahoe and attendees at the 2019 Fall Conference of the Institute for Human-Centered Artificial Intelligence (HAI). Kratsios, the fourth to hold the U.S. CTO position since its creation by President Barack Obama in 2009, was confirmed in August as President Donald Trump’s first CTO. Before joining the Trump administration, he was chief of staff at investment firm Thiel Capital and chief financial officer of hedge fund Clarium Capital. Donahoe is Executive Director of Stanford’s Global Digital Policy Incubator and served as the first U.S. Ambassador to the United Nations Human Rights Council during the Obama Administration. The conversation jumped around, hitting on both accomplishments and controversies. Kratsios touted the administration’s success in fixing policy around the use of drones, its memorandum on STEM education, and an increase in funding Continue reading Trump CTO Addresses AI, Facial Recognition, Immigration, Tech Infrastructure, and More
AI capable of automatically posting relevant comments on news articles has raised concerns that the technology could empower online disinformation campaigns designed to influence public opinion and national elections. The AI research in question, conducted by Microsoft Research Asia and Beihang University in China, became the subject of controversy even prior to the paper’s scheduled presentation at a major AI conference this week.
This is part two of a six-part series on the history of natural language processing. In 1666, the German polymath Gottfried Wilhelm Leibniz published an enigmatic dissertation entitled On the Combinatorial Art. Only 20 years old but already an ambitious thinker, Leibniz outlined a theory for automating knowledge production via the rule-based combination of symbols. Leibniz’s central argument was that all human thoughts, no matter how complex, are combinations of basic and fundamental concepts, in much the same way that sentences are combinations of words, and words combinations of letters. He believed that if he could find a way to symbolically represent these fundamental concepts and develop a method by which to combine them logically, then he would be able to generate new thoughts on demand. The idea came to Leibniz through his study of Ramon Llull, a 13th century Majorcan mystic who devoted himself to devising a system of Continue reading In the 17th Century, Leibniz Dreamed of a Machine That Could Calculate Ideas
This is part one of a six-part series on the history of natural language processing. We’re in the middle of a boom time for natural language processing (NLP), the field of computer science that focuses on linguistic interactions between humans and machines. Thanks to advances in machine learning over the past decade, we’ve seen vast improvements in speech recognition and machine translation software. Language generators are now good enough to write coherent news articles, and virtual agents like Siri and Alexa are becoming part of our daily lives. Most trace the origins of this field back to the beginning of the computer age, when Alan Turing, writing in 1950, imagined a smart machine that could interact fluently with a human via typed text on a screen. For this reason, machine-generated language is mostly understood as a digital phenomenon—and a central goal of artificial intelligence (AI) research. This six-part series will Continue reading Natural Language Processing Dates Back to Kabbalist Mystics
Illustration: Nicholas Little Let’s face it: Robots are dumb. At best they are idiot savants, capable of doing one thing really well. In general, even those robots require specialized environments in which to do their one thing really well. This is why autonomous cars or robots for home health care are so difficult to build. They’ll need to react to an uncountable number of situations, and they’ll need a generalized understanding of the world in order to navigate them all. Babies as young as two months already understand that an unsupported object will fall, while five-month-old babies know materials like sand and water will pour from a container rather than plop out as a single chunk. Robots lack these understandings, which hinders them as they try to navigate the world without a prescribed task and movement. But we could see robots with a generalized understanding of the world (and the processing Continue reading Let’s Build Robots That Are as Smart as Babies
Editor’s Note: The debate on autonomous weapons systems has been escalating over the past several years as the underlying technologies evolve to the point where their deployment in a military context seems inevitable. IEEE Spectrum has published a variety of perspectives on this issue. In summary, while there is a compelling argument to be made that autonomous weapons are inherently unethical and should be banned, there is also a compelling argument to be made that autonomous weapons could potentially make conflicts less harmful, especially to non-combatants. Despite an increasing amount of international attention (including from the United Nations), progress towards consensus, much less regulatory action, has been slow. The following workshop paper on autonomous weapons systems policy is remarkable because it was authored by a group of experts with very different (and in some cases divergent) views on the issue. Even so, they were able to reach consensus on a Continue reading A Path Towards Reasonable Autonomous Weapons Regulation
What’s the world’s hardest machine learning problem? Autonomous vehicles? Robots that can walk? Cancer detection? Nope, says Julian Sanchez. It’s agriculture. Sanchez might be a little biased. He is the director of precision agriculture for John Deere, and is in charge of adding intelligence to traditional farm vehicles. But he does have a little perspective, having spent time working on software for both medical devices and air traffic control systems. I met with Sanchez and Alexey Rostapshov, head of digital innovation at John Deere Labs, at the organization’s San Francisco offices last month. Labs launched in 2017 to take advantage of the area’s tech expertise, both to apply machine learning to in-house agricultural problems and to work with partners to build technologies that play nicely with Deere’s big green machines. Deere’s neighbors in San Francisco’s tech-heavy South of Market are LinkedIn, Salesforce, and Planet Labs, which puts it in a Continue reading Want a Really Hard Machine Learning Problem? Try Agriculture, Says John Deere Labs
Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many. That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week. The toughest problem, says Ben Irving, senior manager of Cisco’s strategy innovations group, is people. It’s tough to find data scientist expertise, he indicated, so companies are looking into non-traditional sources of personnel, like political science. “There are some untapped areas with a lot of untapped data science expertise,” Irving says. Lazard’s artificial intelligence manager Trevor Mottl agreed that would-be data scientists don’t need formal training or experience to break into the field. “This field is changing really rapidly,” he says. “There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, Continue reading AI Faces Speed Bumps and Potholes on Its Road From the Research Lab to Everyday Use