Tokyo Tech has established the Center of Data Science and Artificial Intelligence (DS&AI) on December 1, 2022. The center will accelerate the expansion of data science (DS) and artificial intelligence (AI) training for graduate-level students to all students at Tokyo Tech, deepen collaboration with companies and other universities, and cultivate skilled professionals who are well versed in both data science and artificial intelligence. In today's digital information society, the roles of DS and AI are rapidly expanding. Both fields are increasingly fundamental in all areas of social life, industry, research and development, and other broad spheres. In response to the changing landscape, Tokyo Tech has been developing DS and AI training for graduate students since academic year 2019 with the aim of cultivating "co-creative experts" who have the ability to solve future problems using DS and AI, regardless of their field of specialization.

To tackle this issue, scientists from Tokyo Institute of Technology (Tokyo Tech) have employed the innovative strategy of solving the inverse problem. Instead of predicting the smell from molecular data, this method predicts molecular features based on the odor impression. This is achieved using standard mass spectrum data and machine learning (ML) models. "We used a machine-learning-based odor predictive model that we had previously developed to obtain the odor impression. Then we predicted the mass spectrum from odor impression inversely based on the previously developed forward model," explains Professor Takamichi Nakamoto, the leader of the research effort by Tokyo Tech.

Can we use machine learning methods to predict the sensing data of odor mixtures and design new smells? A new study by researchers from Tokyo Tech does just that. The novel method is bound to have applications in the food, health, beauty, and wellness industries, where odors and fragrances are of keen interest. The sense of smell is one of the basic senses of animal species. It is critical to finding food, realizing attraction, and sensing danger. Humans detect smells, or odorants, with olfactory receptors expressed in olfactory nerve cells.

As humans, we primarily communicate vocally and aurally. We convey not just linguistic information, but also the complexities of our emotional states and personalities. Aspects of our voice such as tone, rhythm, and pitch are vital to the way we are perceived. In other words, the way we say things matters. With advances in technology and the introduction of social robots, conversational agents, and voice assistants into our lives, we are expanding our interactions to include computer agents, interfaces, and environments.

In the natural world, many species can walk over slopes and irregular surfaces, reaching places inaccessible even to the most advanced rover robots. It remains a mystery how complex movements are handled so seamlessly by even the tiniest creatures. What we do know is that even the simplest brains contain pattern-generator circuits (CPGs)[1], which are wired up specifically for generating walking patterns. Attempts to replicate such circuits artificially have so far had limited success, due poor flexibility. Now, researchers in Japan and Italy propose a new approach to walking pattern generation, based on a hierarchical network of electronic oscillators arranged over two levels, which they have demonstrated using an ant-like hexapod robot.

As artificial intelligence increasingly takes center stage in computer technology, large-scale orders are heading to Japan's supercomputer makers from research entities in need of the massive processing power those machines provide. Fujitsu is assembling a dedicated AI-use supercomputer for the Institute of Physical and Chemical Research, better known as Riken, which plans to start using it in April. Tokyo Institute of Technology, also known as Tokyo Tech, has ordered a large-scale system for AI-education purposes from SGI Japan, another supercomputer maker. The institute plans to introduce it in August. And industry insiders are looking forward to an even bigger opportunity: a huge AI-use supercomputer the National Institute of Advanced Industrial Science and Technology, or AIST, wants to start using in 2018.

Tokyo Tech is in the process of building its next-generation TSUBAME supercomputer, featuring NVIDIA GPU technology and the company's Accelerated Computing Platform. TSUBAME 3.0, as the system will be known, will ultimately be used in tandem with the existing TSUBAME 2.5 system, to deliver an estimated 64.3 (in aggregate) PFLOPS of AI compute horsepower. On its own, TSUBAME 3.0, is expected to offer roughly two times the performance of its predecessor. TSUBAME 3.0 will be built around NVIDIA's Pascal-based Tesla P100 GPUs, which are not only more efficient, but higher-performing than previous-generation Maxwell GPUs in terms of performance per watt and performance per die area. It is estimated that TSUBAME 3.0 will deliver roughly 12.2 petaflops of double precision compute performance, which would place it among the world's 10 fastest systems according to the most recent TOP500 list.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. The Tsubame3.0 is currently being built in Japan. The Tokyo Institute of Technology Global Scientific Information (Tokyo Tech) and Computing Center (GSIC) have begun the development and construction of a supercomputer that's expected to meet the demands of artificial intelligence (AI) and big data applications and process in the petaFLOPS range, which is a quadrillion floating point operations per second (FLOPS).

The Tokyo Institute of Technology, also known as Tokyo Tech, has revealed that the TSUBAME 3.0 supercomputer scheduled to be installed this summer will provide 47 half precision (16-bit) petaflops of performance, making it one of the most powerful machines on the planet for artificial intelligence computation. For Tokyo Tech, the use of NVIDIA's latest P100 GPUs is a logical step in TSUBAME's evolution. The original 2006 system used ClearSpeed boards for acceleration, but was upgraded in 2008 with the Tesla S1040 cards. In 2010, TSUBAME 2.0 debuted with the Tesla M2050 modules, while the 2.5 upgrade included both the older S1050 and S1070 parts plus the newer Tesla K20X modules. Bringing the P100 GPUs into the TSUBAME lineage will not only help maintain backward compatibility for the CUDA applications developed on the Tokyo Tech machines for the last nine years, but will also provide an excellent platform for AI/machine learning codes. In a press release from NVIDIA published Thursday, Tokyo Tech's Satoshi Matsuoka, a professor of computer science who is building the system, said, "NVIDIA's broad AI ecosystem, including thousands of deep learning and inference applications, will enable Tokyo Tech to begin training TSUBAME 3.0 immediately to help us more quickly solve some of the world's once unsolvable problems."