Now that Artificial Intelligence (AI) has left the movie theater and started to arrive on our desktops and mobile devices, we need to create new methods to control information so that AI can really start to be smart. IBM's latest moves in the AI space reflect the need to put more fine-grained, granular, context-specific and data-type aware controls into our computing. IBM will of course tell us that it has created another a cloud-based software service designed to offer customers a route to "previously unobtainable insights from their data", when what we really want is a deeper explanation of what IBM wants to do with our data, where it thinks it needs to apply pressure to our business systems, when these functions will be exerted and how it will put this whole new more complex game play into motion. In other words, don't just tell us you're smart – please'show your work'. Diving into the IBM AI machine for answers then, general manager for IBM analytics Rob Thomas has spoken of what he calls a new'information architecture' for collecting, managing and analyzing data.
MPs from the House of Commons inquiry into fake news were warned last week of a new AI technology that is about to change the world, and not for the better. "We're rapidly moving into an era where the Russians, or any other adversary, can create our public figures saying or doing things that are disgraceful or highly corrosive to public trust," Edward Lucas, the senior vice president of the Centre for European Policy Analysis told MPs. "And we're not remotely ready for this." Lucas was talking about so-called deepfakes, which he described as "audio and video that look and sound like the real person, saying something that that person never has". Less than three months ago, producing such videos was a laborious process requiring a video editor, vast amounts of reference footage and years of experience. But in the first few months of this year, the technology has exploded into public availability.
"Fail fast" is a well-known phrase in the startup scene. The spirit of failing fast is getting to market with a minimum viable product and then rapidly iterating toward success. Failing fast acknowledges that entrepreneurs are unlikely to design a successful end-state solution before testing it with real customers and real consequences. This is the "ready, fire, aim" approach. Or, if the blowback is big enough, it's the "ready, fire, pivot" approach.
We see the power of artificial intelligence every day: When Netflix recommends a movie you love, when your bank detects fraud in your account, or when Google routes you around a traffic jam. But outside of examples from mammoth companies with millions to spend on data science initiatives, there's a decided lack of AI success among the rest of us. That's the conclusion that Ali Ghodsi has come to. As the co-founder and CEO of Databricks and an adjunct professor at UC Berkeley, Ghodsi has a direct view into the types the AI projects that organizations are embarking on. It turns out, those organizations are struggling mightily, and he wonders why more people aren't talking about it.
According to the latest market research report "Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the market is expected to grow from USD 21.46 Billion in 2018 to USD 190.61 Billion by 2025, at a CAGR of 36.62% between 2018 and 2025. Major drivers for the market are growing big data, the increasing adoption of cloud-based applications and services, and increasing demand for intelligent virtual assistants. The major restraint for the market is the limited number of AI technology experts.
A US company claims to already know the nominations and winner of Best Picture for next months annual Academy Awards – aka the Oscars, by using artificial intelligence. The Massachusetts based start-up, Luminoso, unveiled its list (see below) almost two weeks before voting for the list of nominees officially closes (January 24) – and more than a month before the awards takes place at the Dolby Theatre in Hollywood (February 26). The firm generated the results by first pulling together over 84,000 reviews written by movie goers (not critics) which have been published on the IMDB website over the past four years (2013-2016) . It then used its Natural Language Processing software, 'Luminoso Analytics', to analyze the text and identify correlations between topics discussed in the reviews and the eventual Oscar nominees and winners. It found that certain terms, including "narrative," "cinematography," "plot," "visuals," "stunning," "experience," and "masterpiece," were more prevalent in reviews of moves that later went on to be nominated and/or win the Oscars.