Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.

We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost. The LSTM-LM learns a vector-space representation of each word by accounting for word order in local collocation patterns and models complex characteristics of language (e.g., syntax and semantics), while the TM simultaneously learns a latent representation from the entire document and discovers the underlying thematic structure. We unite two complementary paradigms of learning the meaning of word occurrences by combining a TM (e.g., DocNADE) and a LM in a unified probabilistic framework, named as ctx-DocNADE. (2) Limited Context and/or Smaller training corpus of documents: In settings with a small number of word occurrences (i.e., lack of context) in short text or data sparsity in a corpus of few documents, the application of TMs is challenging. We address this challenge by incorporating external knowledge into neural autoregressive topic models via a language modelling approach: we use word embeddings as input of a LSTM-LM with the aim to improve the word-topic mapping on a smaller and/or short-text corpus. The proposed DocNADE extension is named as ctx-DocNADEe. We present novel neural autoregressive topic model variants coupled with neural LMs and embeddings priors that consistently outperform state-of-the-art generative TMs in terms of generalization (perplexity), interpretability (topic coherence) and applicability (retrieval and classification) over 6 long-text and 8 short-text datasets from diverse domains.

State-of-the-art deep learning simulation engine leverages reality-grade city mesh combined with DNN (deep neural network) and AI capabilities. Cognata's virtual reality simulator and engine enable autonomous car manufacturers to run thousands of different scenarios based on various geographic locations and driver behaviors, and sharing the road with other users. Each mile driven on our simulator is equal to hundreds of miles driven regularly as we constantly create use cases that train the AI driving system and reduce the time to reach maturity and go-to-market.

Gradient descent is an optimisation method for finding the minimum of a function. It is commonly used in deep learning models to update the weights of the neural network through backpropagation. In this post, I will summarise the common gradient descent optimisation algorithms that are used in popular deep learning frameworks (e.g. The purpose of this post is to make it easy to read and digest since there aren't many of such summaries out there, and as a cheat sheet if you want to implement them from scratch. I have implemented SGD, momentum, Nesterov, RMSprop and Adam in a linear regression problem using gradient descent demo here using JavaScript.

We've all seen the impact of being data-obsessed in the retail industry. While Amazon shapes the future of its business and the industry at large using insights gleaned from troves of data, many retailers are struggling to implement a data-driven mindset across the organization. Artificial intelligence is the key to unleashing value from retail datasets, particularly those used to forecast future demand. Accurate forecasts are critical for retailers (and the industries that rely on them for distribution, like consumer packaged goods) as they depend on these predictions for revenue and operational management. Forecast too much demand and you'll be left with excess inventory, while a short-sided forecast can leave the consumer empty-handed.

What happens when we begin to think of all information as data that can be explored to yield new insights into our world? What would it look like to take nearly a decade of CNN, Fox News, and MSNBC television broadcasts and two years of BBC News broadcasts and run them through sophisticated natural language processing algorithms to identify every mention of a location on earth in their coverage and then create a series of maps that visualize the places we hear about when we turn to the news? What would those maps look like and what might they tell us about what we see when we turn on our televisions each day? Half a decade ago I began working with the Internet Archive's incredible Television News Archive to explore how powerful computer algorithms could allow us to "see" the news in entirely new ways. From simple longitudinal keyword searches to mass emotion mining to geographic mapping to the most powerful deep learning algorithms watching political ads, television has an incredible amount to teach us as we explore it through the modalities and lenses of massive data mining.

As emerging technologies go, Artificial Intelligence has certainly taken its time in making its presence felt on the world. Surprising as it may be (to some), the term AI has existed for almost 70 years, having first been uttered back in 1956 (the same year IBM invented the first hard drive) by computer scientist John McCarthy - AKA the'father of AI'. Since then, AI has experienced a largely stop-start existence due to, in part, sporadic funding and below-par technology. In truth, the term AI has (arguably) gained more notoriety for storylines of killer robots (and the occasional Wall-e) hell-bent on destroying mankind than for its practical use and business benefits. "We are at the cusp of a new revolution, one that will ultimately transform every organisation, every industry and every public service across the world" Thanks to breakthroughs in computing power, the advent and availability of big data, cloud hosting/storage, highly sophisticated software, complex algorithms and a big dollop of imagination, the potential of AI is now starting to be fulfilled – with the business world being the biggest benefactors. As the late great Professor Stephen Hawkins said on AI: "The genie is out of the bottle. AI, could be the biggest event in the history of our civilisation." The market has reacted at pace.

Choose "Yes, I have!" or "Never heard of it.". You'd think that having a sense of taste and smell would be essential in creating the perfect beer, and therefore would be a job reserved for humans only. IntelligentX Brewing Co., is a London-based company which introduced the first beer brewed with the help of artificial intelligence. Their AI, in the form of a chatbot, takes feedback from customers. It asks questions about flavor preferences, often answered with a simple'yes' or'no' or with a 1-10 rating system.

The first wave of artificial intelligence has been about experts: brilliant technologists doing cutting-edge research and building advanced systems in places like Silicon Valley. The second wave of AI will be about practitioners: traditional developers becoming AI rockstars and addressing a wide range of business problems. Access to AI will be democratized. During this transition, we believe AI – particularly deep learning – will begin to resemble a general-purpose computing platform, a topic we explored in a recent Forbes article. But a new set of tools will be necessary to make that vision a reality.

Deep learning has provided extraordinary advances in problem spaces that are poorly solved by other approaches. This success is due to several key departures from traditional machine learning that allow it to excel when applied to unstructured data. Today, deep learning models can play games, detect cancer, talk to humans, and drive cars. But the differences that make deep learning powerful also make it costly. You may have heard that deep learning success requires massive data, expensive hardware, and even more expensive elite engineering talent.