Barriers to AI Adoption
Sloan Management Review: The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensivelyincorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.
Only 16% say their company is well prepared for the challenge of IoT security
Source: McKinsey See also Security Ledger
Posted in Internet of Things
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Richard Socher, Salesforce (Emerging AI Leaders Series)
“AI teaches us who we are,” says Richard Socher. The recent rapid progress in the field of artificial intelligence is the result of successfully processing “a large amount of known training data, doing things [the computer] has seen before,” he says. Unlike humans, computers cannot create something new and unique.
Human creativity has been the driving force behind scientific and engineering advances, including making computers do more human-like activities such as identifying objects or words. Socher’s creativity, his ability to come up with new approaches to solving computers’ language and visual processing challenges, has made him a rising star of the deep learning movement that has spawned exciting new applications of artificial intelligence.
Deep learning has also spawned a number of emerging AI leaders and Richard Socher exemplifies their career trajectories and ambitions—advancing AI research as PhD candidates but not pursuing an academic career, starting companies and/or joining forces with established ones, all with a burning drive to make a big practical impact on the work and lives of as many people as possible.
“I always liked math and languages” says Socher. “Math is beautiful, abstract, it might be true in a thousand light years. Language is the most interesting manifestation of human intelligence, it’s what makes us most unique in the Animal Kingdom. Those two are best combined in linguistic computer science.”
Deep Learning, Not AI, Most Effective Term in Job Listings
Textio: These phrases appear to be following the pattern of big data before them. As AI language has become more common in job ads, the less interesting it has become to potential job applicants. No surprise: when everyone is using the same language, no one stands out…
Textio looked at a bunch of tech terms that appear to be trending based on their recent usage growth. While all of these phrases still occur much less often than AI or ML, they’re all on the rise in a statistically unexpected way…
Enjoy your 15 minutes of fame, chatbots. Everyone knows what happens next.
Posted in Artificial Intelligence, Deep Learning
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Deep Learning Drives Great Progress in Online Translations
Recent translation from Italian to English on Facebook:
The Flea market of square has no longer existed for a few years in its original place in square square. It has been moved against the will and protests of the owners of the stands, in in, cemetificato, less central and assolatissimo. It seemed that the city couldn’t wait for a minute so much was the rush that had to “retrain the area” (and I still didn’t understand what to do next). About two years after eviction (month plus, month less), space remains boarded, covered with weeds and holes, with a digger in between. A space like this is waiting for him in Mosul. Not in the middle of Florence. He follows pictures, as soon as the sun sets and me me on the outside.
Facebook recently switched its backend translation systems entirely to neural networks, which handle more than 2,000 translation directions and 4.5 billion translations every day. They say that these translations are more accurate than Facebook’s previous system, which used phrase-based machine translation models.
Sources: SiliconAngle, Facebook
Imperial College London professor Erol Gelenbe says artificial neural networks can ease language translation by executing a three-step process. The process includes word translations, syntax mapping, and contextual translation, which Gelenbe, recipient of the 2008 ACM SIGMETRICS Achievement Award, says the neural networks can achieve by storing and matching patterns. A key element of the translation process is long short-term memories (LSTMs), which support machine learning and can learn from experience. Swiss Dalle Molle Institute for Artificial Intelligence president Jurgen Schmidhuber expects LSTM recurrent neural networks to eventually enable “end-to-end video-based speech recognition and translation, including lip-reading and face animation.” Meanwhile, Google Brain recently announced its researchers are using neural networks to improve speech-to-text translation. Microsoft Research’s Rick Rashid says the creation and deployment of deep-learning neural networks by his company’s researchers has significantly reduced word error rates in transcribed translations, which he notes could be useful to international business dealings, and have a major effect on cross-industry learning.
Source: ITpro
Posted in Deep Learning
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