Already behind other countries in nurturing the IT professionals indispensable to a digital transformation, Japan in 2030 is expected to have 270,000 artificial intelligence and Internet of Things jobs that it will be unable to fill. With few graduates holding STEM degrees -- those in science, technology, engineering and mathematics -- Japan sees gloom in its future and the need to invest more in human resources. "We cannot develop a system to forecast demand for products, though we need it," lamented a food-maker executive in charge of digital transformation. Although the company has increased its IT staff by 1.6 times, it has yet to nurture hires familiar with cutting-edge technologies. Japan has a considerable number of IT workers.

This is our fourth FDA clearance expanding our wireless platform and enhancing our core continuous waveform technology and artificial intelligence algorithms,

In the field of Natural Language Processing, information extraction from texts has been the objective of many researchers for years. Many different techniques have been applied in order to reveal the opinion that a tweet might have, thus understanding the sentiment of the small writing up to 280 characters. Other than figuring out the sentiment of a tweet, a study can also focus on finding the correlation of the tweets with a certain area of interest, which constitutes the purpose of this study. In order to reveal if an area of interest has a trend in ongoing tweets, we have proposed an easily applicable automated methodology in which the Daily Mean Similarity Scores that show the similarity between the daily tweet corpus and the target words representing our area of interest is calculated by using a na\"ive correlation-based technique without training any Machine Learning Model. The Daily Mean Similarity Scores have mainly based on cosine similarity and word/sentence embeddings computed by Multilanguage Universal Sentence Encoder and showed main opinion stream of the tweets with respect to a certain area of interest, which proves that an ongoing trend of a specific subject on Twitter can easily be captured in almost real time by using the proposed methodology in this study. We have also compared the effectiveness of using word versus sentence embeddings while applying our methodology and realized that both give almost the same results, whereas using word embeddings requires less computational time than sentence embeddings, thus being more effective. This paper will start with an introduction followed by the background information about the basics, then continue with the explanation of the proposed methodology and later on finish by interpreting the results and concluding the findings.

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.

Saryu Nayyar is CEO of Gurucul, a provider of behavioral security analytics technology and a recognized expert in cyber risk management. Artificial intelligence and machine learning (AI/ML) have made inroads into enterprises for a variety of different uses, including decision support, product recommendations and process control. These fields are employing big-data concepts to train software algorithms to evaluate data and respond in a similar manner to human decision-makers. These systems are boosted by data collected in the problem domain and used to successively adjust the algorithms to model that domain. For example, a retailer might use detailed data on sales experiences to recommend additional products for shoppers to purchase.

Having inspired a huge subgenre of political comedy, Jon Stewart, who walked away from The Daily Show in 2015, has returned to television in a determined but defensive crouch. That he's both worried about and pre-emptively rebelling against criticism is evident in the extremely '90s credit sequence that introduces his new weekly Apple TV show, The Problem With Jon Stewart. Over grinding, Rage Against the Machine -style guitars, the credits cycle through unflattering potential titles like The Money Grab With Jon Stewart before landing on a title that both sets up the show's format--each weekly episode deals with a central problem, like "War" or "Freedom"--and preempts the title of skeptical think pieces. Stewart plays defense as host too, alluding early and often to how old he looks and to how little his audience is laughing. Concerns that The Problem's writing staff might be too white and male, like The Daily Show's, are staved off by literally showing us Stewart bantering with his staff, which is admirably diverse.

Today, deploying robust cybersecurity solutions is unfeasible without significantly depending on machine learning. Simultaneously, without a thorough, rich, and full approach to the data set, it is difficult to properly use machine learning. MI can be used by cybersecurity systems to recognise patterns and learn from them in order to detect and prevent repeated attacks and adjust to different behaviour. It can assist cybersecurity teams in being more proactive in preventing dangers and responding to live attacks. It can help businesses use their assets more strategically by reducing the amount of time invested in mundane tasks.

The National Oceanic and Atmospheric Administration has shared what it says are the first images and video captured inside a hurricane by a surface drone. The agency placed the Saildrone Explorer SD 1045 in the path of the category-four Hurricane Sam. The saildrone overcame 50-foot waves and winds at speeds topping 120 miles per hour to capture data from the hurricane and offer a new perspective into such storms. The device has a special "hurricane wing" to help it survive the intense wind conditions. The SD 1045 is one of five saildrones that have been in the Atlantic Ocean during hurricane season.

The Research Brief is a short take about interesting academic work. Photographs and computer vision techniques using artificial intelligence are able to detect the presence of bats on bridges automatically with over 90% accuracy, according to our new study. More than 40 species of bats are found in the U.S., and many of them are endangered or threatened. Bats often nest by the hundreds or thousands underneath bridges, so transportation departments are required to survey for them before conducting repair or replacement projects. I conducted the recently published study with colleagues at the University of Virginia's MOB Lab in collaboration with the Virginia Transportation Research Council. Bridge surveys are important for protecting threatened and endangered bat species.

The UK's biggest police force is set to significantly expand its facial recognition capabilities before the end of this year. New technology will enable London's Metropolitan Police to process historic images from CCTV feeds, social media and other sources in a bid to track down suspects. But critics warn the technology has "eye-watering possibilities for abuse" and may entrench discriminatory policing. In a little-publicised decision made at the end of August, the Mayor of London's office approved a proposal allowing the Met to boost its surveillance technology. The proposal says that in the coming months the Met will start using Retrospective Facial Recognition (RFR), as part of a £3 million, four-year deal with Japanese tech firm NEC Corporation.