Big pharma companies are partnering with artificial intelligence firms to improve key aspects of the healthcare industry such as drug discovery, which will ultimately save time and reduce research and development costs, a report from UK-based data analytics consultancy firm Global Data showed. For several pharma companies, machine learning is the most important aspect of AI, which has the potential to allow machines to surpass human intelligence levels, the report added. Increasing investments in AI for drug discovery allow big pharma firms to apply machine learning to identify and screen potential drug candidates, it explained. "The success of AI in drug discovery is largely due to deep learning, a field of machine learning that is built using artificial neural networks that model the way neurons in the human brain talk to each other. This technology can train systems to analyze large sets of chemical and biological data to identify drug candidates with high success rates much faster than humans," Alexandra Annis, senior immunology analyst at Global Data, said.

Artificial Intelligence Comes to Tokyo 2020 - Sponsor Spotlight (ATR) Intel wants developers to come up with the next best use of artificial intelligence at the next Olympic Games. Drones at the PyeongChang 2018 Olympics (Intel) "Today, we're inviting the developer community to join us in potentially creating an amazing AI experience for fans and athletes at the Olympic Games Tokyo 2020 by submitting their ideas through the Intel AI Challenge for the Olympic Game," Naveen Rao, corporate vice president and general manager, Artificial Intelligence Product Group, Intel, said in a statement. Intel joined the TOP sponsor program with the IOC last year in a "long term technology" partnership through the 2024 Olympics. During the signing ceremony Intel said it would be working on 5G telecom networks, virtual reality, drones, and artificial intelligence. During the opening and closing ceremonies of the PyeongChang Olympics Intel brought coordinated drone light shows to showcase its technologies.

The future of transportation in waterway-rich cities such as Amsterdam, Bangkok, and Venice -- where canals run alongside and under bustling streets and bridges -- may include autonomous boats that ferry goods and people, helping clear up road congestion. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Senseable City Lab in the Department of Urban Studies and Planning (DUSP), have taken a step toward that future by designing a fleet of autonomous boats that offer high maneuverability and precise control. The boats can also be rapidly 3-D printed using a low-cost printer, making mass manufacturing more feasible. The boats could be used to taxi people around and to deliver goods, easing street traffic. In the future, the researchers also envision the driverless boats being adapted to perform city services overnight, instead of during busy daylight hours, further reducing congestion on both roads and canals.

This week Dezeen released Elevation, an 18-minute documentary that explores the impact drones will have on our lives. Here, we take a look at 10 innovative ways drones will change the world. Customers of supermarket giant Walmart may soon be able to summon assistance from unmanned aerial vehicles using mobile electronic devices. The vehicles will help locate products in store and advise on prices by crosscheck information stored on the store's central databases. PriestmanGoode's fleet of urban delivery drones, called Dragonfly, are featured in Dezeen's documentary.

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD's ability to work with synthetic scores equally well as on handwritten music.

Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons: 1. branching on the samples within the model, and 2. the lack of a pathwise derivative for the samples. While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models. Moreover, we observe that, unlike the importance weighted autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well. Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.

In this work we design and train deep neural networks to predict topological invariants for one-dimensional four-band insulators in AIII class whose topological invariant is the winding number, and two-dimensional two-band insulators in A class whose topological invariant is the Chern number. Given Hamiltonians in the momentum space as the input, neural networks can predict topological invariants for both classes with accuracy close to or higher than 90%, even for Hamiltonians whose invariants are beyond the training data set. Despite the complexity of the neural network, we find that the output of certain intermediate hidden layers resembles either the winding angle for models in AIII class or the solid angle (Berry curvature) for models in A class, indicating that neural networks essentially capture the mathematical formula of topological invariants. Our work demonstrates the ability of neural networks to predict topological invariants for complicated models with local Hamiltonians as the only input, and offers an example that even a deep neural network is understandable.

SiFive does a quarterly series of tech talks, not necessarily directly to do with SiFive or even RISC-V. For example, last quarter it was Paul Kocher (and if you don't know that name, you need to go and read my post about that talk Paul Kocher: Differential Power Analysis and Spectre). This quarter it was Krste Asanović on Accelerating AI: Past, Present, and Future. This post will cover the past. The present and future have to wait (good title for a movie?).

Beer is delicious but it is not one thing. If you disagree with the former part of the previous sentence please keep the latter in mind[1]. Think of sports, for instance. Many would agree with the blanket statement "sports are fun" but depending on what you have in mind two people can easily have opposite reactions to being presented the opportunity to play ping-pong. Sports are not one thing, music is not one thing, and neither is beer. Presented with a finely crafted brew in a style of your preference it is difficult to have a more pleasurable gastronomical experience.

Artificial intelligence and machine learning saw a significant spike of attention in the past few years – whether it's through partnerships, acquisitions, or in-house developments. The largest financial institutions in the US have been involved in one way or another in bringing artificial intelligence into operations and customer-facing functions. A recent study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) by MEDICI Team found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space across regions are Bank of America, OCBC, ABN Amro, YES BANK, etc. While front-office applications have certainly seen a higher intensity, scope, and adoption, the AI strategy in the US banking industry, in reality, is far more diverse.