HOLLYWOOD,: Academy Award winning special effects creator Dennis Muren (holding star) poses for ... [ ] photos with director James Cameron (L) and Star Wars creator George Lucas (R), who kneels next to Star Wars characters R2D2 and C3PO during a ceremony honoring Muren with a star on the famous Hollywood Walk of Fame 03 June 1999 in Hollywood, CA. Muren has won eight Oscars in the visual effects category including work on "Terminator 2", "Jurassic Park" and all of the Star Wars films. The release of the film "Dolittle" has landed with a thud. During its first week, the box office receipts came to a disappointing $29.5 million. Keep in mind that the film–which stars Robert Downey Jr.–cost Universal Pictures $175 million to produce.
This is the second article in TNW's "A beginner's guide to the AI apocalypse" series highlighting the potential existential threats AI poses to humankind. Artificial intelligence promises to revolutionize every facet of technology from healthcare to space exploration. Simply put: all technology in the year 2020 and beyond is AI technology. But, what if making everything better actually makes everything worse? Wall-E syndrome (not a real thing) describes the fear of a future dystopia inhabited by oblivious people who are totally reliant on technology to perform even the simplest of tasks.
If Sunspring is anything to go by, artificial intelligence in film-making has some way to go. This short film, made as an entry to Sci-Fi London's 48-hour film-making competition in 2016, was written entirely by an AI. The director, Oscar Sharp, fed a few hundred sci-fi screenplays into a long short-term memory recurrent neural network (the type of software behind predictive text in a smartphone), then told it to write its own. The result was almost, but not quite, incoherent nonsense, riddled with cryptic nonsequiturs, bizarre turns of phrase and unfathomable stage directions such as "he is standing in the stars and sitting on the floor". All of which Sharp and his actors filmed with sincere commitment.
This is my brand-new film on the future of work, jobs and education. Are you excited, or are you worried? The next 20 years will bring more change than the previous 300 years. Technology is rebooting the very idea of work, how we work, when we work, where we work, and sooner or later why we work. Computers are no longer stupid.
GitHub is a clearinghouse for all sorts of open source projects, including those for machine learning, automated and otherwise. More specifically, automated machine learning is the use of automated techniques, be they learned methods or simple heuristics, used for algorithm selection, hyperparameter tuning, architecture design, or any other conceivable portion of a machine learning implementation. Switching gears, Indiana Jones is one of the greatest characters to ever grace the silver screen. Raiders of the Lost Ark, the first movie in which the character was featured, is a personal favorite, film adored by millions. The rest of the (current) quadrilogy movies run alternately hot and cold, but even the poorest quality Indiana Jones is better than 95% of available cinema.
One of my 2019 new year resolutions was to read more books. I signed up for GoodReads and stated a reading challenge with a modest goal of finishing twelve books. I finished twenty-four, and many of these books are great reads for entrepreneurs, CIOs, and other digital transformation leaders. What makes a good book? My favorites have a strong focus, come from a unique perspective, are easy to read/listen, deliver a clear message, back their hypotheses with data or testimonials, tell personal stories, and are concise.
The race towards the adoption of Artificial Intelligence is on. While AI applications are already being integrated across various industries, governments around the world are beginning to take it seriously too. Canada was the first country in the World to issue a national AI strategy in 2017. Ever since then, 30 more countries have followed suit. The two largest economies of the World are already spending billions on the R&D for defense and non-defense related AI applications.
I will assume a basic understanding of neural networks and backpropagation. If you'd like to brush up, this lecture will give you the basics of neural networks and this one will explain how these principles are applied in modern deep learning systems. A working knowledge of Pytorch is required to understand the programming examples, but these can also be safely skipped. The fundamental operation of any transformer architecture is the self-attention operation. Self-attention is a sequence-to-sequence operation: a sequence of vectors goes in, and a sequence of vectors comes out. The vectors all have dimension \(k\). A few other ingredients are needed for a complete transformer, which we'll discuss later, but this is the fundamental operation. More importantly, this is the only operation in the whole architecture that propagates information between vectors. Every other operation in the transformer is applied to each vector in the input sequence without interactions between vectors. Despite its simplicity, it's not immediately obvious why self-attention should work so well. To build up some intuition, let's look first at the standard approach to movie recommendation.
We've all seen the film where robots take over the world, with their mechanical bodies causing Hollywood-style screams from unsuspecting (or maybe very suspecting) victims. And, while these kinds of films let us live an alternate reality for an hour and a half, there's always that niggling thought at the backs of our minds telling us that this could actually happen in the not-too-distant future. In fact, the "father of AI", Alan Turing, was beavering away on it in the 1950s. He developed the Turing Test, which had a judge ask questions to a machine and a human. The judge would then have to decide who was the human and, if the computer could fool the judge at least half of the time, it was considered intelligent.
When artificial intelligence is fully operational, it will transform the media and marketing industries. In particular, I believe that synthetic personalities powered by AI will change the way we learn about new products and how to use them. In my previous article, I showed how the collapse of broadcast TV exposed a huge weakness in the advertising industry. And I pointed to the nascent field known as Influencer Media, and especially Virtual Influencers, as a harbinger of the future of engagement brand-building. What happens when artificial intelligence is available to any app, any advertising campaign, and any brand marketer? How will that change things? Here's my answer: the media landscape will be transformed so deeply that it will be completely unrecognizable. All the leftover junk from the 20th century will be kaputt, including one-size-fits-all video programs for mass audiences, appointment viewing of a TV schedule and the very concept of TV channels, and the outdated intrusion of interruption advertising. Personalized programming and fully-responsive adbots will be the new norm.