Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled "Deep learning improves prediction of drug-drug and drug-food interactions." DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair.

Code for this project can be found on: Github. This article can also be found on my website here. As part of completing the second project of Udacity's Self-Driving Car Engineer online course, I had to implement and train a deep neural network to identify German traffic signs. In total, the dataset used consisted of 51,839 RGB images with dimensions 32x32, and is publicly accessible on this website. A validation set was used to assess how well the model is performing.

In popular culture, Artificial Intelligence (AI) often refers to machines that can perform any intellectual task that humans can. Such machines are heavily romanticized and are still very far from becoming a reality. However, weak (or narrow) AIs, algorithms that are designed to perform a specific task, have shown a formidable intellectual prowess that surpasses human capabilities in certain tasks. These machines must have integrative decision-making capability based on what they receive and what they predict would happen. Take, for example, AlphaGo, the AI that famously defeated world champions at the ancient game "Go."

Collection of PyTorch implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will not always mirror the ones proposed in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GANs to implement are very welcomed. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence.

In this video, we explain the difference between three key concepts artificial intelligence vs machine learning vs deep learning – to understand how they relate to the field of data science. First up, artificial intelligence or AI! What is it? Artificial intelligence is simply any code, technique or algorithm that enables machines to mimic, develop and demonstrate human cognition or behavior. We are in, what many refer to as, the era of "weak AI". The technology is still in its infancy and is expected to make machines capable of doing anything and everything humans do, in the era of "strong AI".

This is a very important consideration that is often overlooked by many in the field of Artificial Intelligence (AI). I suspect there are very few academic researchers who understand this aspect. The work performed in academe is distinctly different from the work required to make a product that is sustainable and economically viable. It is the difference between computer code that is written to demonstrate a new discovery and code that is written to support the operations of a company. The former kind turns to be exploratory and throwaway while the the latter kind tends to be exploitive and requires sustainability.

The artificial intelligence (AI) industry will be worth $1.2 trillion in 2018, with customer experience solutions creating the most business value. On Wednesday, Gartner released estimates on the projected value of AI over the course of this year. According to the research firm, the global enterprise value derived from AI will total $1.2 trillion this year, a 70 percent increase from 2017. AI-derived business value is projected to reach up to $3.9 trillion by 2022. "AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner. "One of the biggest aggregate sources for AI-enhanced products and services acquired by organizations between 2017 and 2022 will be niche solutions that address one need very well."

Most IT leaders have heard of deep learning, but few really understand how this new technology works. Deep learning burst onto the public consciousness in 2016 when Google's AlphaGo software, which was based on deep learning, beat the human world champion at the board game Go. Since then, deep learning has begun appearing in news reports and product literature with more frequency, but few organizations are actually using it today. The 2018 O'Reilly survey report How Companies Are Putting AI to Work Through Deep Learning found that only 28% of the more than 3,300 respondents were currently using deep learning. However, 92% believed that deep learning would play a role in their future projects, with 54% saying it would play a large or essential role in those initiatives.

Hong Kong-based shipping company Orient Overseas Container Line Limited (OOCL) has partnered with Microsoft's research arm to improve network operations and achieve efficiencies within the shipping industry through applied Artificial Intelligence (AI) research. The partnership, between OOCL and Microsoft Research Asia (MSRA), will apply deep learning research to shipping network operations with a projected $10 million annual operational cost saving. The collaboration is also expected to nurture over 200 AI developers over the next 12 months. "With MSRA's efforts and expertise, we expect to save around 10 million in operation costs annually by applying the AI research and techniques for optimizing shipping network operations from our most recent 15-week engagement," said Steve Siu, Chief Information Officer of OOCL. "Moving forward, we will embark on an 18-month joint-partnership in research and development to apply deep learning and reinforcement learning in shipping network operations. Moreover, MSRA will assist us in training over 200 AI engineers by conducting machine learning and deep learning sessions at the Hong Kong Science Park over the next 12 months. We look forward to strengthening our partnership with MSRA to leverage AI research and innovations to drive digital transformation in the shipping industry and to exchange knowledge among our top developers so that we can better address customer needs with advanced technologies and predictive analytics."

Communication with computing machinery has become increasingly'chatty' these days: Alexa, Cortana, Siri, and many more dialogue systems have hit the consumer market on a broader basis than ever, but do any of them truly notice our emotions and react to them like a human conversational partner would? In fact, the discipline of automatically recognizing human emotion and affective states from speech, usually referred to as Speech Emotion Recognition or SER for short, has by now surpassed the "age of majority," celebrating the 22nd anniversary after the seminal work of Daellert et al. in 199610--arguably the first research paper on the topic. However, the idea has existed even longer, as the first patent dates back to the late 1970s.41 Previously, a series of studies rooted in psychology rather than in computer science investigated the role of acoustics of human emotion (see, for example, references8,16,21,34). Blanton,4 for example, wrote that "the effect of emotions upon the voice is recognized by all people. Even the most primitive can recognize the tones of love and fear and anger; and this knowledge is shared by the animals. The dog, the horse, and many other animals can understand the meaning of the human voice. The language of the tones is the oldest and most universal of all our means of communication." It appears the time has come for computing machinery to understand it as well.28 This holds true for the entire field of affective computing--Picard's field-coining book by the same name appeared around the same time29 as SER, describing the broader idea of lending machines emotional intelligence able to recognize human emotion and to synthesize emotion and emotional behavior.