We were delighted to be joined by Lex Fridman at the San Francisco edition of the Deep Learning Summit, taking part in both a'Deep Dive' session, allowing for a great amount of attendee interaction and collaboration, alongside a fireside chat with OpenAI Co-Founder & Chief Scientist, Ilya Sutskever. The MIT Researcher shared his thoughts on recent developments in AI and its current standing, highlighting its growth in recent years. Lex then referenced, Lee Sedol, the South Korean 9th Dan GO player, whom at this time is the only human to ever beat AI at a video game, which has since become somewhat of an impossible task, describing this feat as a seminal moment and one which changed the course of not only deep learning but also reinforcement learning, increasing the social belief in the subsection of AI. Since then, of course, we have seen video games and tactically based games, including Starcraft become imperative in the development of AI. The comparison of Reinforcement Learning to Human Learning is something which we often come across, referenced by Lex as something which needed addressing, with humans seemingly learning through "very few examples" as opposed to the heavy data sets needed in AI, but why is that?

Note: In an upcoming second tutorial on neural networks, we will show how we can add hidden layers to our neural nets. Neural networks form the base of deep learning, which is a subfield of machine learning, where the structure of the human brain inspires the algorithms. Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems. The first thing that comes to our mind when we think of "neural networks" is biology, and indeed, neural nets are inspired by our brains.

Oceans are the driving force of Mother Nature, holding 97% of earth's water. Oceanic ecosystems involve many critical marine species such as fishes, seagrasses, and coral reefs. These are essential in the marine ecosystem, for example, if seagrasses are removed, this may lead to the reduction of light required for photosynthesis. At the same time, it involves huge maintenance of these marine species. Due to tourism, shipping, and human intervention, 75% of the world's coral reefs are being threatened and 19% of the coral reefs having been destroyed by 2011.

Foxconn, a global leader in smart manufacturing, is joining Socionext, a major provider of advanced SoC solutions for video and imaging systems, and leading artificial intelligence (AI) chipmaker Hailo to launch the next-generation AI processing solution for video analytics at the edge. Foxconn has combined its high-density, fan-less, and highly efficient edge computing solution, "BOXiedge ", with Socionext's high-efficiency parallel processor "SynQuacer " SC2A11, and the Hailo-8 deep learning processor. The new combination provides market-leading energy efficiency for standalone AI inference nodes, benefiting applications including smart cities, smart medical, smart retail, and industrial IoT. In a global AI market forecasted by research firm IDC to approach $98.4 billion in revenue in 2023, this joint solution helps address the need for cost-effective multiprocessing capabilities required in video analytics, image classifications, and object segmentation. The robust, high-efficiency product is capable of processing and analyzing over 20 streaming camera input feeds in real-time, all at the edge.

This week IBM, Microsoft and Amazon announced that they would suspend the sale of their facial recognition technology to law enforcement agencies. But the moves from the tech giants also illustrate the inherent risks of AI, especially when it comes to bias and the potential for invasion of privacy. Note that there are already indications that Congress will take action to regulate the technology. In the meantime, many cities have already instituted bans, such San Francisco. Because of the advances of deep learning and faster systems for processing enormous amounts of data, facial recognition has certainly seen major strides over the past decade.

Lucy Hale at The CW's Summer 2019 TCA Party, sponsored by BEN (Photo by Jean Baptiste ... [ ] Lacroix/WireImage) Product placement just ain't what it used to be--and boy, is that a good thing. What originated as an old-school Hollywood function to beat housewives over the head with brand names and'must-have' products, has transformed into a modern, data-driven marketing tool that works for everyone involved. For years, BEN, a Bill Gates-owned product placement agency, has been at the forefront. As one of the first companies to utilize AI to identify, match and facilitate product placement opportunities across film, TV, music videos and social media, you will not only see their work everywhere, but not see it everywhere. Historically, they're behind some of the most iconic placements in Austin Powers, Forrest Gump, and ET, but more recent placements range from the adorably obvious (the family eating Cheerios in The Marvelous Mrs. Maisel) to the unexpectedly inconspicuous (see: flipped Chevrolets in Deadpool or Microsoft phones in Get Out).

Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations. We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK's new auto-vectorization and Spark's barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon's distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon's algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24 percent performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks.

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently black-box and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis.