Technology


Genius triumphs: Japanese mathematician's solution to number theory riddle validated

The Japan Times

KYOTO – A proof by mathematician Shinichi Mochizuki of a major conundrum in number theory that went unresolved for over 30 years has finally been validated, Kyoto University said Friday following a controversy over his method, which was often labeled too novel or complicated to understand. Accepted for publication by the university's Research Institute for Mathematical Sciences was Mochizuki's 600-page proof of the abc conjecture, which provides immediate proofs for many other famous mathematical problems, including Fermat's last theorem, which took almost 350 years to be demonstrated. The abc conjecture, proposed by European mathematicians in 1985, is an equation of three integers a, b, and c composed of different prime numbers, where a b c, and describing the relationship between the product of the prime numbers and c. "There are a number of new notions and it was hard to understand them," Masaki Kashiwara, head of the team that examined the professor's theory, said at a news conference. He proved the abc conjecture with a "totally new, innovative theory," said fellow professor Akio Tamagawa. "His achievement creates a huge impact in the field of number theory."


Kyocera plans health-analysis device based on odor of feces

The Japan Times

Kyocera Corp. has started developing a device to check human health and immunity from the odor of one's stool, aiming to put it into practical use in three years. In collaboration with AuB Inc., a Tokyo-based startup, Kyocera will analyze data from the device, which will be installed in toilet seats. The Kyoto-based electronics giant will create a system that infers the intestinal environment of the user with the aid of artificial intelligence technology and data collected by AuB, according to Kyocera officials. Kyocera will deliver the results to clients through a smartphone application and propose measures to improve diet and other elements of their lives to improve health, the officials said. As part of the development process, AuB will gather stool samples from 29 players of a youth team belonging to Kyoto Sanga F.C., a professional soccer team.


LoCoQuad: An arachnoid-inspired robot for research and education purposes

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Machine learning techniques, such as reinforcement learning models, are now playing a crucial role in the development of smart and efficient robots.


6 Issues Marketers Need to Consider for Successful AI Implementations

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Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed -- again at that stage -- that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry. Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work.


5 More Things Business Leaders Need to Know About Machine Learning

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In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.


Edge computing environments: what you need to know

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The saying goes: "If you're not on the edge, you're taking up too much space". And compute itself is now moving to the edge, forcing datacentre operators to wring the last drops of productivity from their infrastructure, ahead of a future supporting multi-sensor internet of things (IoT) devices over 5G for machine learning, and even artificial intelligence (AI). Jennifer Cooke, research director of cloud-to-edge datacentre trends at IDC, says datacentre operators need to start thinking about how many systems they will need to roll out, and the people they will need to support them. "Cost becomes the prohibitive factor," she says. Edge will take different forms.


On Quantum Computing and Artificial Intelligence

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Mixing quantum computing and Artificial Intelligence (AI) may sound like a new buzzword. However, since quantum computing advances are hinting at profound changes in the very notions of computation, it is natural to reexamine various branches of computer science in the light of these disruptions. As usual, before entering the quantum realm, it is important to get an overview of the classical world. Artificial Intelligence is difficult to define. Probably because intelligence, by itself, is difficult to define.


Artificial Intelligence (AI) Applications in 2020

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Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.


Top 7 Resources To Learn Facial Recognition - Analytics India Magazine

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Facial recognition is arguably the most talked-about technology within the artificial intelligence landscape due to its wide range of applications and biased outputs. Several countries are adopting this technology for surveillance purposes, most notably China and India. Both are among the first countries to make use of this technology on a large scale. Even the EU has pulled back from banning this technology for some years and has left it for the countries to decide. This will increase the demand for professionals who can develop solutions around facial recognition technology to simplify life and make operations efficient.


How to Use Random Seeds Effectively

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This post originally appeared on the website of my current company, Acorn Analytics…you can check it out there below! Building a predictive model is a complex process. You need to get the right data, clean it, create useful features, test different algorithms, and finally validate your model's performance. However, this post covers an aspect of the model-building process that doesn't typically get much attention: random seeds. A random seed is used to ensure that results are reproducible.