Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist. The cancer finding, reported in Nature, was part of a stream of reports this year offering an early glimpse into what could be a new era of "diagnosis by software," in which artificial intelligence aids doctors--or even competes with them. Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures. Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics.

We are at an inflection point in the debate about what AI means for industries and professions. With the critical mass of data now enough to feed the AI engine, its early applications are yielding some very interesting results. Felice Persico and Jeanne Boillet investigate. There are many truths and half-truths out there concerning the impact that artificial intelligence (AI) will have across a range of industries and professions. Some industries have adopted elements of the technology faster than others, with varying degrees of success.

In recent years, the media has devoted a lot of time and space to how robots and machines are taking on more and more human jobs. The stories often give rise to fear and a negative sense of what artificial intelligence and machine learning could potentially do. However, away from the more sensational headlines, good news emerges of how machines can help humans make businesses more efficient, transparent and cost effective. It's no surprise to hear that SMEs are the engine of any country's economy. In addition, the World Bank states that the 600 million jobs which need to be created to absorb a growing global workforce will have to come from SMEs.

Tech giants such as Google and Baidu spent from $20 billion to $30 billion on AI last year, according to the recent McKinsey Global Institute Study. Out of this wealth, 90 percent fueled R&D and deployment, and 10 percent went toward AI acquisitions. Research plays a crucial role in the AI movement, and tech giants have to do everything in their power to seem viable to the AI community. AI is mostly based on research advances and state-of-the-art technology, which is advancing very quickly. Therefore, there is no business need to make closed infrastructure solutions, because within a few months everything will be totally different.

Broadcom Limited AVGO recently announced the first-ever silicon-proven 7nm intellectual property (IP) targeting application-specific integrated circuit (ASIC) platform for deep learning and networking. Higher dollar content at the company's large North American smartphone customer's (Apple) next-gen platform (iPhone) is also a positive for the company. Robust industrial re-sales are also tailwinds. Notably, the company's recent acquisition of Brocade Communications Systems is expected to boost Broadcom's position in the storage area networking space. The latter has already turned down the recent takeover bid of $130-billion (includes $25 billion of net debt) on grounds of inadequacy as it is currently a leading player in the chipset market. However, the company is planning to raise the bid amount for Qualcomm, per Reuters.

Berlin, June 2017 The workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal submission is required. Participants are invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.

Last week we looked at the Information bottleneck theory of deep learning paper from Schwartz-Viz & Tishby (Part I,Part II). I really enjoyed that paper and the different light it shed on what's happening inside deep neural networks. Sathiya Keerthi got in touch with me to share today's paper, a blind submission to ICLR'18, in which the authors conduct a critical analysis of some of the information bottleneck theory findings. Sathiya gave a recent talk summarising results on understanding optimisation and generalisation, 'Interplay between Optimization and Generalization in DNNs,' which is well worth checking out if this topic interests you. Definitely some more papers there that are going on my backlog to help increase my own understanding!

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.

Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multidimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks (RNNs) in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from'serialised' MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data, and demonstrate results from differentially private training of the RCGAN.

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.