Episode 80: The featured guests are Machine Learning Tokyo (MLT) members Suzana Ilić (co-founder), Dimitris Katsios, and Asir Saeed. MLT is a Tokyo-based nonprofit organization dedicated to democratizing Machine Learning (ML). They are a team of engineers and researchers--now with a community of 5,000 people--and the winner of the Rakuten Technology & Innovation Silver Award 2019.
Good gamers can tune out distractions and unimportant on-screen information and focus their attention on avoiding obstacles and overtaking others in virtual racing games like Mario Kart. However, can machines behave similarly in such vision-based tasks? A possible solution is designing agents that encode and process abstract concepts, and research in this area has focused on learning all abstract information from visual inputs. This however is compute intensive and can even degrade model performance. Now, researchers from Google Brain Tokyo and Google Japan have proposed a novel approach that helps guide reinforcement learning (RL) agents to what's important in vision-based tasks.
What's happening in Japan is written all over our faces -- our blank, expressionless, masked faces. Never before, it seems safe to say, have so many people gone about masked. Thus we confront the microbes that assault us. "As self-protection, your mask is practically useless," says Shukan Gendai magazine this month. Commercial face masks, medical authorities say, can block particles measuring 3 to 5 micrometers.
IMAGE: Simulated low temperature (left) and high temperature (right) phase of a 2D Ising model, where blue points are spins pointing up, and the red points are spins pointing down. Tokyo, Japan - Researchers from Tokyo Metropolitan University have used machine learning to study spin models, used in physics to study phase transitions. Previous work showed that image/handwriting classifying AI could be applied to distinguish states in the simplest models. The team showed the approach is applicable to more complex models and found that an AI trained on one model and applied to another could reveal key similarities between distinct phases in different systems. Machine learning and artificial intelligence (AI) are revolutionizing how we live, work, play, and drive.
Convolutional Neural Networks which are the backbones of most of the Computer Vision Applications like Self-Driving Cars, Facial Recognition Systems etc are a special kind of Neural Network architectures in which the basic matrix-multiplication operation is replaced by a convolution operation. They specialize in processing data which has a grid-like topology. Examples include time-series data and image-data which can be thought of as a 2-D grid of pixels. The Convolutional Neural Networks was first introduced by Fukushima by the name Neocognitron in 1980. It was inspired by the hierarchical model of the nervous system as proposed by Hubel and Weisel.
"Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?" One of the most important steps as Data Science is a quantitative domain and core mathematical foundations will serve as a base for your learning. Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure the likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.
Japanese police arrested or took other action against 115 people for civil aviation law violations linked to unauthorized drone flights in 2019, up 31 from the previous year, government data showed Thursday. The National Police Agency tally included 51 foreign nationals, of whom 19, the largest group, were Chinese. Seven were from the United States. Last year, the number of cases that led to police actions stood at 111. Of them, 54 cases happened as offenders tried to take commemorative pictures, while 34 cases were flight operation exercises, according to the NPA data.
Toyota Motor Corp. and Nippon Telegraph and Telephone Corp., Japan's auto and telecommunications giants, formed a capital tie-up Tuesday to build energy-efficient "smart cities" where autonomous vehicles transport residents. The two firms, which have been developing "connected cars" equipped with advanced telecommunication systems since 2017, deepened their partnership into mutual shareholdings, with each investing around 200 billion yen ($1.8 billion) by purchasing each other's treasury stocks. Toyota said it will start the smart city project at a 175-acre site at the foot of Mt. Toyota has said only fully autonomous, zero-emission vehicles are allowed to travel on main streets in the envisioned smart city where around 2,000 residents have in-home robotics to assist their daily lives. NTT also said it will launch an internet-led smart city project at an NTT-related block in Shinagawa area in Tokyo's Minato Ward.
An unmanned convenience store began operations Monday at a recently opened station on Tokyo's Yamanote loop line, using artificial intelligence not just to allow speedy self-checkouts but to also prevent shoplifting. The store is a key feature at the Takanawa Gateway Station, which opened on March 14 as the first new stop on the line in nearly 50 years. About 50 cameras installed inside the roughly 60-square-meter store identify every item customers pick up. The store's exit gates open once the customer makes a payment. The AI used at the shop has been trained to recognize customer behavior, including how items are carried, and it almost fully prevents shoplifting by accurately recognizing when merchandise is taken from shelves, according to its developer Touch To Go Co. Attempts in a demonstration to hide merchandise under clothes or avoid being seen on the cameras while stashing it in a bag were all detected.