Agencies are preparing for algorithms to take over parts of the process, but can they replace humans?
I had hoped to read more innovative explorations of how AI could be used as part of the creative process. Instead most of the article is about optimizing media buys and making tweaks to existing creative… supporting an existing AdTech model which is in need of reinvention.
Source: How 4 Agencies Are Using Artificial Intelligence as Part of the Creative Process – Adweek
The United States no longer has a strategic monopoly on a technology that is widely seen as the key factor in the next generation of warfare.
“There are many occasions of something being simultaneously invented in China and elsewhere, or being invented first in China and then later making it overseas,” he said. “But then U.S. media reports only on the U.S. version. This leads to a misperception of those ideas having been first invented in the U.S.”
Source: China’s Intelligent Weaponry Gets Smarter (NYTimes)
Explanation has always been a core topic in machine learning and artificial intelligence. We constantly seek new tools to derive and develop machine learning systems that offer a spectrum of explanations, knowing that different types of explanations are possible; valid or not, we will tend to hold the explanatory requirements of our machine learning systems to a higher standard than we hold ourselves. Learning to explain then becomes a central research question.
Source: Cognitive Machine Learning (1): Learning to Explain ← The Spectator
Pinterest Labs tackles the most challenging problems in Machine Learning and Artificial Intelligence
Labs brings together top researchers, scientists, and engineers from around the world to work on image recognition, user modeling, recommender systems, and big data analytics. Our researchers are embedded throughout Pinterest allowing our discoveries to affect hundreds of millions of users each day.
Info on Pinterest’s research areas, papers, & people.
The Applied Machine Learning group helps Facebook see, talk, and understand. It may even root out fake news.
Source: Inside Facebook’s AI Machine
It’s often just a fancy name for a computer program.
Writing at the MIT Technology Review, the Stanford computer scientist Jerry Kaplan makes a similar argument: AI is a fable “cobbled together from a grab bag of disparate tools and techniques.” The AI research community seems to agree, calling their discipline “fragmented and largely uncoordinated.” Given the incoherence of AI in practice, Kaplan suggests “anthropic computing” as an alternative—programs meant to behave like or interact with human beings. For Kaplan, the mythical nature of AI, including the baggage of its adoption in novels, film, and television, makes the term a bogeyman to abandon more than a future to desire.
Source: ‘Artificial Intelligence’ Has Become Meaningless
It’s only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area—even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars.
Source: How Drive.ai Is Mastering Autonomous Driving With Deep Learning – IEEE Spectrum
Graphcore Poplar software framework images of machine learning executed as a graph on the IPU Intelligent Processing Unit. Graph computing explained visually
Fascinating visuals. But are they useful?
Source: Inside an AI ‘brain’ – What does machine learning look like?
Creating products that use ML is an increasingly multi-disciplinary activity. The session summarized above focused on defining ML (without the math), and highlighting seven issues that go beyond the ML when creating products — there are many more.
Nice non-technical explanations of ML.
Source: Machine Learning for Product Managers – Hacker Noon
“A deep-learning system doesn’t have any explanatory power,” as Hinton put it flatly. A black box cannot investigate cause. Indeed, he said, “the more powerful the deep-learning system becomes, the more opaque it can become. As more features are extracted, the diagnosis becomes increasingly accurate. Why these features were extracted out of millions of other features, however, remains an unanswerable question.” The algorithm can solve a case. It cannot build a case.
Source: A.I. Versus M.D. – The New Yorker