Prompted by the increasing prominence of artificial intelligence (AI) in society, University of Tokyo researchers investigated public attitudes toward the ethics of AI. Their findings quantify how different demographics and ethical scenarios affect these attitudes. As part of this study, the team developed an octagonal visual metric, analogous to a rating system, which could be useful to AI researchers who wish to know how their work may be perceived by the public. Many people feel the rapid development of technology often outpaces that of the social structures that implicitly guide and regulate it, such as law or ethics. AI in particular exemplifies this as it has become so pervasive in everyday life for so many, seemingly overnight.

Prompted by the increasing prominence of artificial intelligence (AI) in society, University of Tokyo researchers investigated public attitudes toward the ethics of AI. Their findings quantify how different demographics and ethical scenarios affect these attitudes. As part of this study, the team developed an octagonal visual metric, analogous to a rating system, which could be useful to AI researchers who wish to know how their work may be perceived by the public. Many people feel the rapid development of technology often outpaces that of the social structures that implicitly guide and regulate it, such as law or ethics. AI in particular exemplifies this as it has become so pervasive in everyday life for so many, seemingly overnight.

Researchers find public trust in AI varies greatly depending on the application. Prompted by the increasing prominence of artificial intelligence (AI) in society, University of Tokyo researchers investigated public attitudes toward the ethics of AI. Their findings quantify how different demographics and ethical scenarios affect these attitudes. As part of this study, the team developed an octagonal visual metric, analogous to a rating system, which could be useful to AI researchers who wish to know how their work may be perceived by the public. Many people feel the rapid development of technology often outpaces that of the social structures that implicitly guide and regulate it, such as law or ethics.

Using dotData, an automated machine learning vendor, one of the largest insurance firms in Japan built out an AI platform that provides a personalized experience to customers. Mitsui Sumitomo Insurance, one of the largest insurance firms in Japan, began the process of digital transformation several years ago. The company launched multiple projects, and continues to start new projects, to send it further into the digital age. One of MSI's more ambitious undertakings is the MS1 Brain platform, an AI in insurance project to create a more personalized experience for customers. Released earlier this year, the MS1 Brain platform uses machine learning and predictive analytics, along with customer data, including contract details, accident information and lifestyle changes, to recommend products and services to customers based on their predicted needs.

MSI first connected with dotData in 2017, when MSI's CIO visited Silicon Valley for a technical survey, Yokoyama said. At that time, dotData was just getting started, and it hadn't released a product. Still, MSI was intrigued by its automated machine learning platform, which claims to provide full-cycle machine learning automation. "When it comes to data analysis, model accuracy often gets the most attention; dotData, on the other hand, focuses on how quickly you can move from raw data to working models -- the AI-based feature engineering is what stood out," Yokoyama said. MSI had to build a lot of intelligent models, said Ryohei Fujimaki, CEO and founder of dotData.

The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct algorithms that fit all the data without over- and under-fitting. In this paper, we show that this difficulty originates from the lack of knowledge of the probability distribution that the measurement data follow. We demonstrate the use of a machine learning technique to estimate the data distribution and to construct an optimal generative model. We show that the fitting algorithm based on the generative modeling outperforms classical heuristic methods in terms of the stability as well as the accuracy.

Even though they questioned the value and consistency of infinitistic logic, Hilbert and his contemporaries did not wish to give up such abstractions--power tools of mathematical reasoning that in 1928 would enable the British philosopher and mathematician Frank Ramsey to chop up and color infinite sets at will. "No one shall expel us from the paradise which Cantor has created for us," Hilbert said in a 1925 lecture. He hoped to stay in Cantor's paradise and obtain proof that it stood on stable logical ground. Hilbert tasked mathematicians with proving that set theory and all of infinitistic mathematics is finitistically reducible, and therefore trustworthy. "We must know; we will know!" he said in a 1930 address in Königsberg--words later etched on his tomb.