On a visit to China this summer, I was asked by a local journalist if I thought their country's education and training system was ready for artificial intelligence (AI) and the fourth industrial revolution (4IR). This got me thinking about what it means for any country – let's say the UK – to be AI-ready in educational terms. In this country, there is increasing discussion and debate around the use of technology, including AI, in teaching and learning. Most teachers know the technology exists, but perhaps not necessarily how it can help them in their everyday work. Need to know: What is the fourth industrial revolution?

The G7 Summit is done. It is done for the year after a weekend of personality clashes, fires, uninvited guests and meagre discussions covering issues like inequality, climate change, digital economy, nuclear proliferation and the future of African development. Rejigging the format, host nation France invited democracies that have regional influence, which made India a participant alongside Australia, South Africa and Chile. Indian Prime Minister Narendra Modi addressed the G7 on two key issues - climate change and digital transformations - which testify to India's importance in tackling these issues. For countries like India, whose economy is deeply entwined with globalisation and its effects, G7 talks on matters like digital economy, climate change, oceans and biodiversity and inequality serve as an opportunity to take stock on domestic policies covering these issues and how to retool them.

How artificial intelligence is changing the workplace is starting to get the attention of congressional lawmakers at a time when some employment attorneys are sounding alarms about the need for legislation. The House Education and Labor Committee plans to hold hearings on machine learning's impact on workers and their jobs after Congress returns from recess in September. However, while a hearing is usually a precursor to legislation, employers using AI-based tools and tech companies developing those programs probably don't need to worry about new bills anytime soon. The focus on Capitol Hill remains on trying to understand what the effect of artificial intelligence on workers could be. The rise of algorithms and machine learning technology is already changing the way people work.

The following paper presents a method of comparing two sets of vectors. The method can be applied in all tasks, where it is necessary to measure the closeness of two objects presented as sets of vectors. It may be applicable when we compare the meanings of two sentences as part of the problem of paraphrasing. This is the problem of measuring semantic similarity of two sentences (group of words). The existing methods are not sensible for the word order or syntactic connections in the considered sentences. The method appears to be advantageous because it neither presents a group of words as one scalar value, nor does it try to show the closeness through an aggregation vector, which is mean for the set of vectors. Instead of that we measure the cosine of the angle as the mean for the first group vectors projections (the context) on one side and each vector of the second group on the other side. The similarity of two sentences defined by these means does not lose any semantic characteristics and takes account of the words traits. The method was verified on the comparison of sentence pairs in Russian.

The 2008-09 financial collapse led to a Federal Reserve directive that banks with consolidated assets over $50 billion have additional risk assessment frameworks and budgetary oversight in place. To assess a bank's financial foundation, the Federal Reserve oversees a number of scenarios (company-run stress tests). Referred to as the Comprehensive Capital Analysis and Review (CCAR) process, these tests are meant to measure the sources and use of capital under baseline as well as stressed economic and financial conditions to ensure capital adequacy in all market environments. As Ayasdi reports, Citi consistently struggled to pass its annual stress test, failing two of the first three stress tests. The bank was in need of a way to rapidly create accurate, defensible models that would prove to the Federal Reserve that they could adequately forecast revenues and the capital reserve required to absorb losses under stressed economic conditions.

BEIRUT – Lebanese army gunners opened fired at two of three Israeli reconnaissance drones Wednesday after they entered Lebanese airspace, a security official and the state news agency said, amid heightened tensions between the two countries. The incident occurred in the village of Adeisseh, a few kilometers (miles) from the Israeli border. The security official told The Associated Press that the drones left Lebanese airspace after being fired on and that none of them was shot down. The official spoke on condition of anonymity in line with regulations. The incident came just days after an alleged Israeli drone crashed in a Hezbollah stronghold in southern Beirut, landing on the militant group's Beirut media office while another exploded midair and crashed nearby. Hassan Nasrallah, leader of the Iran-backed Hezbollah, called it an act of aggression and vowed to retaliate and confront Israeli drones in Lebanese skies from now on.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI highlighted the role of AI in cybersecurity defense and in scoring standardized tests, the relationships between data migrating to the cloud and data modernization, lax security standards for IoT devices, and that the U.S. still leads the global race for AI domination but that China is making more rapid progress. Written essays on standardized tests in 18 states in the U.S. are currently graded by natural language processing (NLP) software ("automated essay scoring engines") with only a small percentage of students' essays--it varies between 5% to 20%--randomly selected for a human grader to double check the machine's work [VICE] Based on a 100-point scale, the U.S. leads the global AI race with 44.2 points, followed by China with 32.3 points and the European Union with 23.5 points; The U.S. came out on top in talent, research, development, and hardware, while China led in adoption and data. And if you're someone who has a great idea but you have no data, that's going to be a big roadblock for you, and you're going to have to find some collaborators or partners who have access to the data you need"--Ben Wilson AI quote of the week: "The risk isn't that machines will get smarter.

The incidence of construction workers' deaths in Israel has increased recently in 30% (annually). According to Workers' Hotline statistics, Israel is in third place among developed nations in terms of fatal construction accidents – 2.5 times that of the EU nations per capita. Despite their declarations of measures to be taken to deal with these accidents, it seems the authorities can't find effective solutions to the problem. Besides special training programs, legislation, increased enforcement and penalization, they should consider getting the startup nation involved in introducing advanced technologies to increase safety at construction sites and prevent these unnecessary accidents. The digital revolution we're in the midst of facilitates the application of technologies including autonomous equipment, drones and sensors that can monitor the safety of equipment, cranes and elevators, and even monitor wind speed and precipitation at the construction site.

The world famous Deep Learning Summit is returning to London for the 5th year, this time joined by the AI Assistant Summit and the AI in Retail and Advertising Summit. One pass provides access to all three events, plus the Deep Dive track. Confirmed speakers from Google, ASOS, DeepMind, Facebook AI Research, NASA, Amazon and more will come together to share the most cutting edge work in their space, bridging the gap between the latest technological research advancements and real world applications in business and society. Across the two day event, 90 speakers and 600 attendees will explore topics such as neural networks, machine learning, deep learning algorithms, speech recognition, robotics, personalised recommendation systems, image retrieval, reinforcement learning and more. Presentations from across the board touch on business case studies and research progressions, and the majority of attendees will spend time in all session rooms to expand their knowledge and find out how to apply AI in novel ways.

The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) has released a plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI). The plan recommends that the federal government "commit to deeper, consistent, long-term engagement" in activities to help the United States speed the pace of reliable, robust and trustworthy AI technology development. "The federal government can help the U.S. maintain its leadership in AI by working closely with our experts in industry and academia, investing in research, and engaging with the international standards community," said Under Secretary of Commerce for Standards and Technology and NIST Director Walter G. Copan. "This plan provides a path to ensure the federal government supports AI standards that are flexible and inclusive--and suited for a world of rapidly changing technologies and applications." A February 2019 Executive Order directed NIST to develop a plan that would, among other objectives, "ensure that technical standards minimize vulnerability to attacks from malicious actors and reflect Federal priorities for innovation, public trust, and public confidence in systems that use AI technologies; and develop international standards to promote and protect those priorities."