"We could all do a better job of celebrating the women and underrepresented groups in science'" says Dr Jessica Wade today on International Day of Women and Girls in Science. Dr Wade is a physicist at Imperial College London well known for her work to raise the profile of under-represented groups in science, with hundreds of Wikipedia pages on female scientists she has written outside of her day job at the Centre for Plastic Electronics. There is a critical skills gap looming in the tech sector, and especially in data science and AI. Much more needs to be done to mobilize young people, especially poorly represented groups (including women) into Science, Technology, Engineering and Mathematics (STEM) careers. The U.K. Industrial Strategy has recognized the critical skills shortage that needs to be addressed to help the U.K. become a global leader in data, AI and other critical technologies for the future, and the need for education to address this skills shortage highlighted in the recent All Party Parliamentary Group on AI which will be looking at this as a key task area for 2019.

And one place that it's really starting to change things is e-commerce. Below you'll find some interesting stats and facts about how AI is growing in e-commerce and how it's changing the way we do things. From personalizing the shopping experience for customers to creating personal buying assistants, AI is something retailers can't ignore. We'll also take a look at some examples of how leading online stores have used AI to enrich the customer buying experience.

The best thing the technology community can do to help is to debunk the notion that only people who know how to code can work with AI. Instead, the world needs to focus on lowering the educational and psychological barriers to entry for computer-skill training and AI literacy – and that starts in grade school. In the United Kingdom, for instance, there is a clear digital skills gap and, unfortunately, stark gender disparity among student populations interested in computer science. In fact, only 12% of high school students in the country chose to take computer courses in 2017. On top of that, only 20% of those students were female.

Learning new things is a huge part of life -- we should always be striving to learn and grow. But it takes time, and time is precious. So how can you make the most of your time by speeding up the learning process? Thanks to neuroscience, we now have a better understanding of how we learn and the most effective ways our brains process and hold on to information. If you want to get a jump start on expanding your knowledge, here are 10 proven ways you can start learning faster today.

Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied. "A lot of these techniques are designed to always make a prediction," she said.

It was one of the most infamous companies in Japan, rocking the nation with a corporate scandal that ousted a prime minister and then nearly collapsing under a mountain of debt. Now, Recruit Holdings Co. is back, reinvented by a group of employees who quietly turned the magazine publisher and job placement firm into an internet giant that touches the lives of almost every consumer in the world's third-biggest economy. If Recruit were a U.S. company, it would be like having LinkedIn, Zillow, Yelp, eHarmony, Booking.com, "We are there, every time people choose to do things," said Masumi Minegishi, Recruit's 55-year-old chief executive officer. As the biggest internet companies compete for world domination with apps that track consumers and use artificial intelligence to crunch data and provide tailored services, Recruit is Japan's leading contender.

Will artificial intelligence make most people better off over the next decade, or will it redefine what free will means or what a human being is? A new report by the Pew Research Center has weighed in on the topic by conferring with some 979 experts, who have, in summary, predicted that networked AI "will amplify human effectiveness but also threaten human autonomy, agency and capabilities." When the experts were asked whether AI and related technology will by the year 2030 enhance human capacities or allow them to deteriorate, the majority (63 percent) said most people will be better off. The opportunities cited in "Artificial Intelligence and the Future of Humans" were far-ranging: "smart systems" built into cities, vehicles and buildings "will save time, money and lives," and AI-driven uses in healthcare will advance diagnosis and treatment of patients or help people who need daily aid to "live full and healthier lives." At the same time, as decision-making is turned over to "black box tools," people will sacrifice their "independence, privacy and power over choice" -- an outcome that will "deepen" as automation becomes "more prevalent and complex."

In today's hyper-fast cloud computing era, machine learning solutions drive exponential progress in improving systems. Machine learning's ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses. Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.

This week we speak to Alexander Rinke, co-founder of German technology company Celonis. When Alexander Rinke wanted some of the world's biggest companies to employ his small start-up business he came up with a novel approach - he would send their bosses handwritten letters. "We knew if we sent an email it could just be deleted," he says. "And if we sent out typed letters then their secretaries would open them, and bin them as junk mail. "But with a handwritten note, it seems more personal, it could have been a letter from a family member, or a friend." Alexander launched Celonis when he was 22 with two friends, Martin Klenk and Bastian Nominacher, in 2011 after they had finished maths and computer science degrees at the Technical University of Munich. Expanding on a project they had worked on as part of their courses, Celonis is a hi-tech data mining company that uses software and artificial intelligence to monitor the performance of businesses, to help them become more efficient and work better. In very simple terms, Celonis's software will monitor a company's computer system, and find out things such as which employees are being unproductive, which suppliers are too slow, and which production processes could be streamlined. The three friends were confident about what they could offer businesses, but they just needed to get themselves noticed. They worked like a treat, leading to meetings with some of the largest companies in Europe. Today, eight years later, Celonis's customers include BMW, Exxon-Mobile, General Motors, L'Oreal, Siemens, Uber and Vodafone. And after securing an additional $50m (£39m) of investment last year, Celonis says it is now valued at more than $1bn (£780m). Born and raised in Berlin, Alexander says he started his first company when he was just 15, supplying tutors to high school students. "It was great to get my first idea of how a business ran," he says. "But ultimately I knew it wouldn't last forever." Fast-forward to 2011 in Munich, and Alexander came up with the idea for Celonis when, as part of their studies, he, Martin and Bastian were helping a real world business improve its customer service. The three students found that the firm was taking about five days to come up with fixes to problems, and they thought there must be a quicker way. "We interviewed people in the company to try and understand why it took so long," says Alexander, who is now 29. "But we quickly realised that no-one was going to take the blame.

Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the parity learning problem.