The report is a great opportunity to finally recognize that the current methods we now know as AI and deep learning do not qualify as "intelligent." They are based on the "brute force" of computers and limited by the quantity and quality of available training data. Many experts agree.

The steering committee of "AI Index, November 2017" includes Stanford's Yoav Shoham and Massachusetts Institute of Technology's Eric Brynjolfsson, an eloquent writer who did much to promote the modern-day orthodoxy that machines will soon displace people in many professions. The team behind the effort tracked the activity around AI in recent years and found thousands of published papers (18,664 in 2016), hundreds of venture capital-backed companies (743 in July 2017) and tens of thousands of job postings. It's a vibrant academic field and an equally dynamic market (the number of U.S. startups in it has increased by a factor of 14 since 2000).

All this concentrated effort cannot help but produce results. According to the AI Index, the best systems surpassed human performance in image detection in 2014 and are on their way to 100 percent results. Error rates in labeling images ("this is a dog with a tennis ball") have fallen to less than 2.5 percent from 28.5 percent in 2010. Machines have matched humans when it comes to recognizing speech in a telephone conversation and are getting close to parsing the structure of sentences, finding answers to questions within a document and translating news stories from German into English. They have also learned to beat humans at poker and Pac-Man. But, the authors of the index wrote: 

Tasks for AI systems are often framed in narrow contexts for the sake of making progress on a specific problem or application. While machines may exhibit stellar performance on a certain task, performance may degrade dramatically if the task is modified even slightly. For example, a human who can read Chinese characters would likely understand Chinese speech, know something about Chinese culture and even make good recommendations at Chinese restaurants. In contrast, very different AI systems would be needed for each of these tasks.

Read Full Article