Artificial intelligence is ascendant in medicine--from AI eye doctors to chatbot therapists. As medical databases balloon in size and complexity, researchers are teaching computers to sift through and identify patterns, hinting at a future in which machine learning algorithms diagnose disease all on their own. Sometimes, algorithms pick up on early signs of disease that humans wouldn't even know to look for. Last week, researchers at the University of North Carolina and Washington University reported an AI that can identify autistic infants long before they present behavioral symptoms. It's a thrilling opportunity: Early detection gives autism neuroscience a big leg up, as researchers try to understand what goes wrong during development.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.

Back in 2007, when London was booming as the financial capital of the world, a new field called "algorithmic trading" was emerging. In essence, it is about leveraging Artificial Intelligence to place bets on financials markets faster than any human can. Like most PhD students doing AI, I was working with banks to help them build their trading algorithms, which back then represented about 3% of their activity. Fast forward to 2017, and this type of trading represents over 90% in some cases, almost completely replacing human traders in big banks. One of those victims turned out to be my own dad, a trader who worked passionately for over 40 years.

Based in the Big Apple itself (New York), Synechron is one of the fastest-growing digital, business consulting & technology services providers. Since they opened their doors in 2001, Synechron has been expanding at a rapid rate. They now operate in 18 countries around the the world, and has a marked presence in the US, Australia, Canada, UK, Japan, The Netherlands, Hong Kong, Singapore, UAE, Ireland, Germany, Switzerland, Luxembourg, Italy, France, and India. With the AI Summit London drawing ever closer (it's only one week away!), we spoke to Ben Musgrave, who is Synechron's Business Development Manager, in order to understand how one of the event's key sponsors is deploying AI today and how they plan to in the future. We started off our conversation with Musgrave – who'll be delivering a keynote speech at the AI Summit London – how they are currently involved in the AI-space.

Food delivery company DoorDash says personalized restaurant recommendations based on AI are seeing a significant lift in orders, compared to regular recommendations based on popularity. In an interview with VentureBeat, DoorDash product manager Jimmy Liu said customers who saw personalized recommendations on average "were over 25 percent more likely" to place an order versus people who saw the most popular restaurants in their area. We talked with Liu on the eve of the company's announcement today that it's rolling out these machine-learning based recommendations to all of its users, after testing it on increasing percentages of its customer base. Millions of users have already seen the recommendations, the company said. Liu said the 25 percent lift from recommendations came specifically from email campaigns.

This Deloitte Global report outlines a clear roadmap for you to deploy RPA within your organization. Our paper indicates that companies that are not already considering automation as a component of a broader worker ecosystem will miss significant opportunities for efficiency, quality enhancement, risk mitigation, innovation, and ultimately growth. This report, Automation is here to stay…but what about your workforce?, is the first in a series of upcoming reports looking at automation in Financial Services. Read this report to learn what automation has to offer, and how that might impact your business. Contact a Deloitte professional to get strategic advice on how you can prepare your workforce for launching your next automation initiative.

No one knows what the future holds, but many people are unable to cope with the uncertainty. However, researchers have discovered that the fear of the unknown may be linked to an unusual enlargement of a brain region that is responsible for decision making and motor control. The team believes the findings could help specialists predict those at risk of developing anxiety disorder or OCD later in life, allowing intervention to occur before symptoms arise. Researchers at Dartmouth College conducted MRI scans on 61 students following a survey that measured their ability to tolerate the uncertainty of future negative events. The team analyzed the scans and compared them with the intolerance of uncertainty scores, which showed the size of the striatum was linked with intolerance of uncertainty.

It's been quite an interesting journey for Factmata since we started in January and we're now about to launch a tool that puts factual context in the hands of the people. This will happen around the UK general election, and marks the completion of our Google Digital News Initiative (DNI) project. For 5 months, we've been working around the clock with a distributed team of NLP researchers, PhDs and scientists from around the world to build this, and now finishing off the final touches. As we prepare for launch, we wanted to tell the world about what's next and where we want to take Factmata in the future. Given our team's work in automated fact-checking in previous research, we are uniquely placed to build AI to solve the problem of online misinformation.

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Our Western diet is famously bad for the circulatory system, but for a long time, people thought the damage stopped there. Then around 10 years ago, Terry Davidson, a behavioral neuroscientist, wondered whether our modern eating habits might also affect our brains. To test it out, he fed lab rats a diet high in saturated fats and sugars. He found that the animals had problems learning various memory tasks for which they'd get rewards. Their difficulties were probably linked to changes in the way blood reaches a portion of the brain called the hippocampus.