It seems the White House wants to ramp up America's Artificial Intelligence (AI) dominance. Earlier this month, the U.S. Office of Management and Budget released its "Guidance for Regulation of Artificial Intelligence Applications," for federal agencies to oversee AI's development in a way that protects innovation without making the public wary. The noble aims of these principles respond to the need for a coherent American vision for AI development--complete with transparency, public participation and interagency coordination. But the government is missing something key. For technological innovation to flourish, regulators have to be innovative too. A recent paper by Jack Clark and Gillian Hadfield at OpenAI proposes the idea of regulatory markets, wherein private competitive regulators would take on the role traditionally held by by legislators and government agencies.

A plan to remove fuel debris from the primary containment vessel of a reactor at the Fukushima No. 1 nuclear power plant is expected to be further pushed back after it became apparent that Tokyo Electric Power Company Holdings Ltd. will not be able to conduct an internal probe -- a key step to start removing the fuel debris -- by the end of March as planned. The internal probe would involve using remote-controlled robots to collect fuel debris inside the No. 1 reactor so Tepco can examine its composition and form. Tepco's plan is to open three holes in both the outer and inner doors of the primary containment vessel using pressurized water mixed with a polishing agent. After it succeeded in opening three holes in the outer door, Tepco started drilling a hole in the inner door in June 2019. But that procedure caused the concentration of radioactive dust to increase temporarily, prompting staff to suspend work.

An underreported aspect of the climate crisis is that archaeological sites, cultural landscapes, biodiversity, and distributions of flora and fauna--much of which modern people will never even know about--are disappearing at an alarming rate. I'm an archaeologist, and while I don't know how to solve the climate crisis, I do know what I want to contribute to our shared legacy: a comprehensive digital map of the surface of the planet and everything on it. Such a project will serve both as a record of the state of the planet as it exists now, to help scientists better understand how it is changing, and as a "virtual planet" that can serve as a precious gift for future generations. In June, I and other like-minded scientists launched the Earth Archive: a massive scientific effort aiming to scan the entire solid surface of the planet, starting with the areas most threatened, at a resolution smaller than a meter. This effort aims to use lidar technology, or light detection and ranging technology, which can map both the vegetation and the ground beneath it in three dimensions from the vantage point of a plane, helicopter, or drone.

It may seem absurd to compare a tiny fruit fly's brain to that of a majestic elephant. Yet it is the dream of many neuroscientists to find deep rules that very different brains share. As Gilles Laurent, a neuroscientist at the Max Planck Institute for Brain Research in Frankfurt, Germany, who has studied a variety of animals, from locusts to turtles, has said, "Neural responses can be described by the same mathematical operation … in completely different systems." Vivek Jayaraman, a researcher at the Howard Hughes Medical Institute's Janelia Research Campus, and a former student of Laurent's, believes that neuroscientists are on the verge of identifying some of these deep neural rules. Grasping them would advance another neuroscientific dream: to be able to predict animal behavior as easily as Newton could predict the behavior of a moving object. Jayaraman and a small number of researchers studying the brain's GPS have, in fact, already experienced the thrill of discovering one such rule.

HPE announced today that it won the contract to build a supercomputer that will drive France's AI and HPC efforts. The computer will be part of GENCI, the French national infrastructure for HPC resources and facilities. The system, named Jean Zay after the French politician and cultural figure, came at the behest of an action issued by President of France Emmanuel Macron in support of the national strategy to make France the European leader in artificial intelligence research. Financed by GENCI and based on the HPE SGI 8600 platform, Jean Zay is slated to deliver a peak performance of 14 petaflops. Under a unified Omni-Path Architecture network, the system encompasses 1,528 Intel next-generation Xeon nodes and 261 GPU nodes, each with four Nvidia Tesla V100 (32GB) GPUs, 1,044 in all.

This project aims to implement the vision of Visual TS - Code-free orchestration of data pipelines (or workflow) to respond to analysis use cases for time series data. Working with time series data often suffers from missing entries. Interpolate is a CDAP computation plugin that addresses this issue for Apache Spark DataFrames. A frequent requirement for many time series analysis methods is that the data need to be stationary (i..e mean, variance and auto correlation structure do not change of time). For practical purposes, stationarity is usually determine from linear auto correlation functions (ACF).

Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery. Artificial intelligence technology has the ability to recognize drug targets, and play a significant role in drug design, discovery, identification and screening of molecules instantly and effectively. Drug discovery or new drug target are being estimated based on potency, bioavailability, efficacy, and toxicity. The AI for drug discovery market is expected to grow during the forecast period due to the increasing number of cross-industry partnerships & collaborations, a significant growth in venture capital investments, rise in importance of drug discovery and increase in funding of the R&D activities for the use of AI technology in the field of drug discovery. However, limited awareness, unwillingness among medical practitioners to adopt AI-based technologies, unclear regulatory guidelines for medical software and lack of interoperability among AI solutions offered by different vendors are likely to hamper the growth of the market in the forecast period.

OpenExO Community member Christiaan Dorfling posted a fascinating question about MTP's and Machine learned models. We decided to share the answer and here's a link back to the original post OpenExo Ecosystem-Community-Circles. You will need an account on the platform to get to the full thread. I'd have to start with what is the companies MTP? Someone or some organization is behind it. If they don't have an MTP or they are just bad then their ML uses cases are right in line w/ their MTP.

The coronavirus illness (nCoV) is now an international public health emergency, bigger than the SARS outbreak of 2003. Unlike SARS, this time around scientists have better genome sequencing, machine learning, and predictive analysis tools to understand and monitor the outbreak. During the SARS outbreak, it took five months for scientists to sequence the virus's genome. However, the first 2019-nCoV case was reported in December, and scientists had the genome sequenced by January 10, only a month later. Researchers have been using mapping tools to track the spread of disease for several years.

Could natural language models improve their ability to answer questions on the fly? That's what a team of Amazon researchers set out to answer in a study scheduled to be presented at the 2020 Association for the Advancement of Artificial Intelligence in New York. They posit a method for adapting models based on Google's Transformer architecture -- which is particularly good at learning long-range dependencies among input data (such as the semantic and syntactic relationships between individual words of a sentence) -- to address the problem of answer selection. The team says that in tests on a benchmark data set, their proposed model demonstrated a 10% absolute improvement in mean average precision (which measures the quality of a sorted list of answers according to the correctness of the ranking) over the previous state-of-the-art answer selection model, achieving an error rate reduction of 50%. The approach -- Transfer and Adapt, or TANDA -- was first proposed late last year but has since been refined.