The main reason this is not yet in most frameworks (except Theano) is that all frameworks are fighting for fan/customer base and all they care about is present benchmarks and nice tutorials. If you look at the recently published benchmark of a.k.a. "The response of Tensorflow on Caffee2" you will notice that the code written does not look anything like what any normal person outside the TF team would write. This kind of optimisations do exist in Theano as well as much more, and with XLA they will potentially slowly be added to Tf. On the second point, yes that violates that rule, but in the ML who cares for software engineering (pun intended). In fact, my personal dream is to have a Mathematical IR which represents only the maths, without any knowledge of the framework/hardware - purely mathematical formulation.

In the last few years, astrophysicist Neil deGrasse Tyson has pulled back from calling b.s. on what some sci-fi movies get wrong, but with all the new sci-fi films being released, he couldn't hold back any longer. In a short spot for The Late Show with Stephen Colbert, Tyson uses his familiar mix of humor and spot-on intellect to dissect the leaps of scientific faith needed to believe the plots of Alien: Covenant, Guardians of the Galaxy Vol. 2 and even the new Baywatch (not sci-fi, but he makes a good point). Personally, I'll never get tired of Tyson's #sciencesplaining – keep it coming (even if it occasionally spoils the fun). Tyson just got roasted (lovingly) on the latest Saturday Night Live during a take on Star Trek in which, just before the sketch, SNL's Kenan Thompson (aka Fake deGrasse Tyson) took a good shot at Tyson's iconic celestial vest. And now, yes, I want one.

Though the customer base of any startup comprises vastly different individuals, they love to slice and dice them by demographics and psychographics, build a persona and write a piece of communication for each segment. This behaviour will change overnight if these businesses could write an ad or press release, not for five or 10 groups but thousands of individuals, at scale. Welcome to the Segment of One, the ability to track and understand individual customer behaviour. Currently, the use cases of AI mostly focus on how it will change the consumer's life. From self-driving cars to virtual reality, the focus is on supercharging existing products or, better still, new experiences altogether.

I think it is more ambiguous what makes a SotA RNN architecture, and it is very task specific. For NLP, I think the general strategy is to replace each token with a pre-trained word embedding (GloVe or Word2Vec), and then to "encode" the sentence using something like a bidirectional LSTM/GRU (I will call this the RNN encoder). For sequence tagging tasks (such as part of speech tagging or named entity recognition), you take each of hidden state of the RNN encoder and classify it with something like a ReLU network. As there is some "structural dependencies" for these type of tasks, it usually can boost performance to use something like a CRF on top of the RNN encoder. For sentence classification tasks, can simply classify the final state of the RNN encoder.

Remember the famous scene in Stanley Kubrick's 1968 2001: A Space Odyssey, when Hal 9000--the intelligent-turned-malevolent computer--regresses to his "childhood" and sings "Daisy Bell" as he's decommissioned by astronaut Dave Bowman? Its inspiration was a real-life Bell Labs demonstration of speech synthesis on an IBM 704 mainframe in 1961, witnessed by Arthur C Clarke, who later incorporated it into his 2001 novel and screenplay. Although Bell Labs' involvement in the field stretches back to the 1930s with Homer Dudley's keyboard-and-footpedal-driven Voder speech synthesis device, it's undoubtedly the classic Kubrick/Clarke movie that cemented the ideas of artificial intelligence (AI) and conversing with computers into the public mind. Depending on how old you are, we're now familiar with computerised voices, thanks to devices like Texas Instruments' popular 1978 Speak & Spell educational toy, Stephen Hawking's speech synthesiser (memorably sampled in the Pink Floyd song Keep Talking), GPS navigational systems in your car, and any number of public information and call handling systems. More recently, the combination of automatic speech recognition (ASR), natural-language understanding (NLU) and text-to-speech (TTS) has come to mainstream attention in virtual assistants such as Apple's Siri, Google Now, Microsoft's Cortana, and Amazon's Alexa. Download this article as a PDF (free registration required). To get a handle on how speech technologies work, we clearly need to know something about the mechanics of human speech and the structure of language. When we speak, air from the lungs passes through the vocal tract to produce "voiced" or "unvoiced" sounds (depending on whether the vocal cords are vibrating or not) that may then be modulated by the tongue, teeth and lips.

This article originally appeared in Buzz Business for TIME. Last October, the Mirai botnet took advantage of hacked IoT devices to take high profile websites, such as Twitter, Reddit, Netflix, Airbnb--and, it was rumored (incorrectly), the entire nation of Liberia. How do you secure a world of dizzying interconnectivity? If innovation is generating this unprecedented security challenge, it's only fair for innovation to help solve it--AI to the rescue. Look to the evolution of our telephone system.

Supervised learning systems rely upon humans to label the incoming data -- at least to begin with -- in order for the systems to better predict how to classify future input data. Gmail's spam filter is a great example of this. When you label incoming mail as either spam or not spam, you're not only cleaning up your inbox, you're also training Gmail's filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future. According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they're rewarded.

First it was age-old board games and boring office jobs, but now it seems robots are gearing up to take over Hollywood. Following up the stunning success of its debut short movie from last year, Sunspring, the script-writing artificial intelligence Benjamin is back with yet another sci-fi flick. Directed by Oscar Sharp and starring Baywatch icon David Hasselhoff, It's No Game takes us to an alternate reality where, in midst of heated writer's strike in Hollywood, AI script writers have gradually began to replace human ones. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! Using an advanced nanobot technology, producers have found a way to channel the inner thoughts and mannerism of the AI writers directly to human actors, causing them to act out borderline non-sensical lines put together by various algorithms trained on Shakespeare, Aaron Sorkin and Golden Age Hollywood movies.

Artificial intelligence (AI), machine learning (ML), and robots are the sights and sounds of science fiction books and movies. Isaac Asimov's Three Laws of Robotics, first introduced in the 1942 short story "Runaround," became the backbone for his novel I, Robot and its film adaptation (Figure 1). Although we are still far away from achieving what movie producers and sci-fi writers have envisioned, the state of AI and ML has progressed significantly. AI software has also been in use for decades but advances in ML, including the use of deep neural networks (DNNs), are making headlines in application areas like self-driving cars. The movie I, Robot has robots that should be following Asimov's Three Laws of Robotics.

Consumers' desire for personalized shopping experiences is by now well documented. In response to these expectations, merchants are increasingly using tools that present products and content, which reflect shoppers' preferences, and past picks. These investments are likely to pay off, as merchants report personalization improves results throughout the customer life cycle. Product recommendation and customer profiling tools increasingly integrate past brand interactions with "big data" insights that predict shoppers' likely needs and paths to purchase. Artificial intelligence, known as AI, is the logical next step in this progression, enabling machines to respond to shoppers' input with relevant content and products.