Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks.

Ever since the beginning of industrial society, people have simultaneously marveled at the power of automation and lamented that human capabilities are being irredeemably devalued. Demanding better conditions and higher pay, textile workers in England smash machinery and set factories on fire. These workers will come to be known as Luddites, after their mythical leader, Ned Ludd, and the name will become a synonym for opponents or critics of technology. But it's a misnomer: this is a class protest more than a technological one. The stocking-frame machines the Luddites vandalize have been around since the 1600s.

UPDATE 29 Oct 2017: the full report is now available for free download here. This week Dealroom will release a free report on European deep tech & artificial intelligence. Ahead of that report, this is a preview. Deep tech is (admittedly) a subjective term, but useful and frequently requested, in order to group companies that use cutting-edge technologies to solve complex problems. Examples include: artificial intelligence, robotics, autonomous driving & delivery, space-flight, aviation, computer vision, speech recognition, AR/VR.

Forbes: How AI can transform businesses 2. GigaOm's "Voices in AI" podcast features AI luminaries 3. NVIDIA's Inception Program for AI startups adds 2,000th member 4. NVIDIA teaches the world about deep learning in finance through workshops 5. What AI can accomplish right now – and the silicon powering it all 5. HOW ARTIFICIAL INTELLIGENCE CAN TRANSFORM BUSINESSES A question originally asked on Quora, Forbes shares the leading answer by Tony Paikeday, product marketing director at NVIDIA. After providing several examples of businesses across industries who are already using AI, he shares the quickest method to adopting AI: "The fastest way to accelerate AI for business is leveraging powerful and energy-efficient GPUs. READ ARTICLE 6. GIGAOM'S "VOICES IN AI" PODCAST FEATURES AI LUMINARIES GigaOm's "Voices in AI" podcast debuted this month, featuring episodes with leading researchers and AI luminaries – including NVIDIA's Bryan Catanzaro. In episode 13, Bryan focuses on AI and the future of work: "I like to think about artificial intelligence as making tools that can perform intellectual work. LISTEN TO PODCAST 7. NVIDIA'S INCEPTION PROGRAM FOR AI STARTUPS ADDS 2,000TH MEMBER Less than 18 months after its launch, NVIDIA's Inception program -- which helps accelerate startups pushing the frontiers of AI and data science -- has signed up its 2,000th member company.

Not quite yet, but neuroscience research is giving us some clues about how it may be possible in the not-too-distant future. In a paper published in Science today, a trio of neuroscientists, led by Stanislas Dehaene from Colle ge de France in Paris, try to pin down exactly what we mean by "consciousness" in order to work out whether machines could ever possess it. As they see it, there are three kinds of consciousness--and computers have so far mastered only one of them. One is subconsciousness, the huge range of processes in the brain where most human intelligence lies. That's what powers our ability to, say, determine a chess move or spot a face without really knowing how we did it. That, the researchers say, is broadly comparable to the kind of processing that modern-day AIs, such as DeepMind's AlphaGo or Face's facial recognition algorithms, are good at.

Earlier this year I went to an event in Austin, Texas, billed as a sneak preview of the evolution of our species. The #Bdyhax Conference, which took place in a downtown exhibition complex, promised a front-row insight into the coming "singularity" – that nirvana foretold by science fiction in which biology and technology would fuse and revolutionise human capability and experience. The headline acts of the conference were mostly bodyhackers – DIY experimenters who, in their basements and garages, seek to enhance their own flesh and blood with biometric implants and cognitive enablers. These brave pioneers were extending their senses, overcoming physical limitation, Dan-Daring themselves and the rest of us into the future. At least that was the idea. The reality of the convention was a little more mundane. It was overpriced and sparsely attended. Disparate and awkward groups of the pierced and the tattooed wandered between lectures about the ethics of body augmentation, and budget demonstrations of virtual worlds, past stalls flogging various kinds of neurotropic snake oil or enthusing over the transforming possibilities of magnets and LED lights inserted under the skin.

Many retailers are struggling to devise the perfect cross-channel experience for their customers--one that takes advantage of digitalization to provide targeted, just-in-time product or service information in an effective and seamless way. The key internal capabilities needed to ensure a successful digital shopping experience are personalization, automation and the unique identification of the customer across shopping channels. In this report, we discuss how AI and IoT are impacting the retail industry. We explore how these technologies are changing the way retailers operate and provide examples of how major industry players are using AI and IoT to increase operational efficiency and unlock new revenue opportunities. Retailers that aim to remain competitive cannot afford to ignore the potential benefits of these technologies. AI, a technology that enables computers to make autonomous decisions, is a step forward in automation that is changing the retail industry. Retailers are using AI to analyze customer data, adapt how they interact with shoppers and predict demand in order to better manage inventory. Because consumers are bombarded with an unprecedented amount of information, being able to deliver highly personalized content for each individual customer is crucial to staying ahead of the competition. Meanwhile, the use of AI to anticipate demand and estimate when items will be returned should translate into more efficient business operations.

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform existing genetics methods by an absolute difference of 15-45.3%.

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.

Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similarity triplets, we aim at defining kernel functions that correspond to high-dimensional embeddings. These kernel functions can subsequently be used to apply any kernel method to the data set.