There are many reasons England did not reach the World Cup final this year. There are myriad factors which contributed to Croatia stopping football coming home. I could probably spend a while talking about Harry Kane's missed opportunity, Modric's masterclass or general fatigue setting in during Extra Time. Instead, working at a technology company, I spoke with our AI Department Skunkworx and they took the opportunity to look at things from a different perspective. After using their machine-learning tools to analyse data from every world cup game ever, they presented me with multiple patterns.

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related.

AI, machine learning, and deep learning - these terms overlap and are easily confused, so let's start with some short definitions. AI means getting a computer to mimic human behavior in some way. Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems. Those descriptions are correct, but they are a little concise.

In April 2018, the Nobel Prize Inspiration Initiative and 3M hosted the lecture, Climate Change: Science and Policy with Dr. Mario Molina. Molina won the Nobel Prize for Chemistry for his scientific discovery of the chemistry of the stratospheric ozone layer and its susceptibility to human-made activities. He co-authored research in 1974 in Nature magazine on the threat to the ozone layer from chlorofluorocarbon (CFC) gasses being used in spray cans. Molina has also served on the United States President's Council of Advisors on Science and Technology from 1994 to 2000 and again in 2010-2016. "Science doesn't tell you what to do. Science isn't either good or bad so you can not give Nobel prizes in science to good people, you do that in principle for the science," said Molina.

How do you get every single girl a full 12 years of quality education? That's the question at the heart of the Malala Fund, the organisation set up by Malala Yousafzai, the young Nobel Prize winner. And she wants to provide this education in parts of the world where it can't be taken for granted. Luckily, she has a powerful ally. In January, Apple revealed a tie-up with Malala Fund as part of the initial goal of getting 100,000 girls into education in Afghanistan, Pakistan, Lebanon, Turkey and Nigeria. But today it has been announced that the collaboration is expanding to Latin America. This expansion means grants will be offered to advocates in Brazil, who will join the Malala Fund's network of so-called Gulmakai Champions.

India is facing an acute shortage of skilled healthcare professionals and hospital beds, and the situation can change if doctors embrace Artificial Intelligence (AI)-driven devices and wearables/apps at hospitals, a new report said on 10 July. There are 29 skilled healthcare professionals for 10,000 people -- way below the global average of 109 and the lowest score across all 16 countries surveyed, according to the first edition of India's Future Health Index (FHI) by Royal Philips, a global leader in healthcare technology. People walk past the Philips headquarters in Barueri, Brazil. Another barrier is the low number of hospital beds 7 per 10,000 in India in comparison to 38 per 10,000 on average globally, the findings showed. "While top hospitals and clinics in metro cities may boast of having cutting-edge technologies, semi-urban and rural areas are yet to fully leverage the potential of digital healthcare," the survey noted.

Strategically placed recycled cell phones are combating deforestation by sending notifications to rangers when chainsaw noises are recorded. Algorithms similar to those used by Homeland Security are being developed to provide effective routes for ranger patrols in their battle against poaching. And drones are delivering sylvatic plague vaccines to prairie dog populations in an effort to save the Black Footed Ferret, a highly endangered predator of prairie dogs, from extinction. What happens when wildlife biologists join forces with computer scientists? A new era in wildlife conservation is born!

Willis Towers Watson, a leading global advisory, broking and solutions company, and Relativity6, Inc., a machine learning and artificial intelligence (AI) insurance-technology company, today announced that Willis Towers Watson has selected the Relativity6 platform to predict and optimise customer retention and win-back. Brent Lehmann, General Manager Affinity & Commercial Australasia said the partnership with such an innovative technology company will help to ensure Willis Towers Watson remains competitive in the marketplace. "Relativity6's product offerings are a good fit to accomplish our strategic objectives across the organisation, so we are very excited to partner with them to take full advantage of the data that we have accumulated within our core systems in Australia." Alan Ringvald, Chief Executive Officer at Relativity 6, commented: "We are honoured to partner with such a distinguished organization. We believe that our solution will enable Willis Towers Watson to better serve their customers and ultimately drive significant top line revenue growth. We've engaged with top-tier insurers in the U.S. and Latin America, and this is a fantastic opportunity to expand our footprint with a truly global insurance broking brand."

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets. Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type

Traffic hell is alive and well in Los Angeles. In 2017, Angelenos were stuck on the road for 102 hours each (more than four full days), costing the city $19.2 billion, according to INRIX's annual global traffic scorecard. Traffic is almost as bad--and costly--in Moscow, Sao Paulo, and London. But this is the 21st century! Can't AI fix these problems by optimizing traffic flow?