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.

A number of projects are underway to make smartphones more situationally/contextually aware. Over the last decade, smartphones have ushered in radical and disruptive changes to society and business. They have introduced entirely new ways to interact, consume media, shop, buy products, and even order and pay for food and beverages. They ring, buzz, or beep while we're attending a classical music concert or sitting in church. They are unable to warn us when we exercise too strenuously. They send distracting or unwanted traffic alerts while we are driving, or away on vacation.

And I think it would be hard to argue that any task a human can learn requires NP-hard computation to train, as it would imply pretty massive blowup in learning time. Unless one means maybe something like science or math, where it took thousands of years to figure out and people transferred their model parameters to future generations through a process known as "teaching";)

Newswire) Breaking Data Corp. (TSX VENTURE:BKD) (OTC PINK:BKDCD) (the "Company" or "BKD") is pleased to announce that is has now finalized its new independent Director appointees with key industry veteran leadership and experience. "We are extremely privileged to have this group of experienced and successful directors join our board," said Nick Thain, CEO. "These executives not only bring a vast network of knowledge but also have deep ties in many sports related businesses across the globe, that cross into our key markets. They will, no doubt, be valuable to our strategic and business progress." Greg D'Alba: Mr. D'Alba is currently the Co- founder & CEO of VIDL NEWS LLC, a start-up artificial intelligence news venture focused on real-time personalized news intelligence and reporting.