As the recently launched AI Monthly digest shows, significant improvements, breakthroughs and game-changers in machine learning and AI are months or even weeks away, not years. It is, therefore, worth the challenge to summarize and show the most significant AI trends that are likely to unfold in 2019, as machine learning technology becomes one of the most prominent driving forces in both business and society. According to a recent Deloitte study, 82% of companies that have already invested in AI have gained a financial return on their investment. For companies among all industries, the median return on investment from cognitive technologies is 17%. AI is transforming daily life and business operations in a way seen during previous industrial revolutions.

Wearable tech has been slowly gaining traction. The space has largely been dominated by fitness trackers like the ones made by Fitbit, along with watches that go beyond fitness into advanced health-tracking, productivity, and entertainment. Apple, which has become a market leader with its watch, accounts for a large part of that market – more than a quarter of all wearable devices shipped in 2018, according to IDC data. Wearables have been a growing business for the company, though it's hard to know by exactly how much since the company reports its wearable devices sales as part of a category that also includes home products and non-wearable accessories. Driving high: Police have a new device to tell if you're driving high Robots: Amazon's self-driving delivery robots are coming to California Going forward, however, Apple can expect those sales to continue to grow.

Disney's ESPN has chosen not to broadcast a recent video-game competition -- one that features gun violence -- in the wake of last weekend's mass shootings in Texas and Ohio, according to a person familiar with the plans. ESPN is delaying its planned Aug. 10 broadcast of a recent tournament for Apex Legends, a popular battle royale game made by publisher Electronic Arts Inc., the person said, asking not to be identified as the matter is internal. The decision comes in the wake of the two shootings that prompted politicians, including President Donald Trump, to say video games that glorify violence could be contributing to the country's shooting epidemic. ESPN2 will air the taped segment on three nights in October, according to the person. It will still be available this weekend on ESPN's digital channels, including its app.

After Mike Rufail graduated from college in 2001, he turned to video games as a cheaper alternative to nights out, buying an Xbox 360 paired with popular first-person shooter Call of Duty 2. "I realized I was one of the better players pretty quickly," says Rufail, 36. Rufail went on to become a professional esports athlete and, later, founder and CEO of Envy Gaming, an esports squad that competes in tournaments for games like Fortnite, Overwatch, and, yes, Call of Duty. "I was a fairly good athlete, and played sports my whole life," says Rufail, who retired from competitive play in 2013 to manage Envy's roster. "But I also had a love for gaming." But in the past few days, Rufail's profession has been criticized by politicians and pundits who argue that violent video games have at least in part fueled America's mass shooting epidemic.

As privacy regulations like GDPR and the California Consumer Privacy Act proliferate, more startups are looking to help companies comply. Enter Preclusio, a member of the Y Combinator Summer 2019 class, which has developed a machine learning-fueled solution to help companies adhere to these privacy regulations. "We have a platform that is deployed on-prem in our customer's environment, and helps them identify what data they're collecting, how they're using it, where it's being stored and how it should be protected. We help companies put together this broad view of their data, and then we continuously monitor their data infrastructure to ensure that this data continues to be protected," company co-founder and CEO Heather Wade told TechCrunch. She says that the company made a deliberate decision to keep the solution on-prem.

Like many emergency rooms in the United Kingdom, the A&E department at Salford Royal NHS Foundation Trust, Greater Manchester, faces high congestion. The Data Science team at the Northern Care Alliance (NCA) National Health Service (NHS) Group of hospitals is implementing support mechanisms to ease wait times, using machine learning and regression to better predict peak demand times and improve the flow of patients from intake to discharge. I recently spoke with Karim Webb, Data Science Manager and Robyn Hamilton, Data Scientist at the Northern Care Alliance NHS Group, regarding some exciting developments with the use of data science to ease wait times, support clinicians and provide better patient experiences at the hospital. Karim believes that his team is on the leading edge of this discipline within the UK healthcare economy. However, one of the biggest challenges the team faces is that, "… data science is still a relatively new discipline…so finding stakeholder engagement to drive it forward is a challenge."

I should have taken heed of the Socratic paradox that'all I know is that I do not know anything', as in January of 2016, I publicly expressed to the scientific and medical community that'There are certain things that a human brain does much better than any piece of technology – such as solving a crossword puzzle or playing the game Go.' In January of 2016, I was in lofty company, as the majority of the big brains of Artificial Intelligence (AI) felt that it would take at least 50 years for a computer to beat any human at Go. Three months later the Google DeepMind Alpha Go system did just that, when it beat not any average human Go player – but the world's 18-time world Go champion, Lee Sedol. This is a non-trivial occurrence. Because there are many tasks that are performed in healthcare each day by humans, that are well suited to be better performed by intelligent thinking machines. For example, the foundation of healthcare – the diagnosis, consists of pattern recognition and algorithms, both of which are superior strengths of machine over humans. My take away from this is that the changes are occurring much more quickly than I realised, not only in the development of AI, but in many other areas such as the global dispersion of high-speed connectivity, blockchain, plummeting costs of data storage, and tremendous improvements in biosensors of all shapes and sizes. The future that many felt was at least 50 years away, appears to already be behind us – and these powerful thinking machines will not stand alone, but will play a central role in our increasing global connectivity.

In a blockbuster $27 billion deal that's certain to threaten Bloomberg's financial-data empire, the London Stock Exchange struck an agreement this week to buy the data company Refinitiv. The tie-up highlights trading venues' desire to move beyond low-margin trading and clearing into the more lucrative business of selling data. LSE CEO David Schwimmer, who joined the stock-exchange group less than a year ago from Goldman Sachs, is driving the industry-changing transaction. Our banking reporter Dakin Campbell talked to Schwimmer's former colleagues and clients, who told him why Schwimmer is ideally placed to do the deal. If you aren't yet a subscriber to Wall Street Insider, you can sign up here.

Algorithms have taken a lot of heat recently for producing biased decisions. People are outraged over a recruiting algorithm Amazon developed that overlooked female job applicants. Likewise, they are outraged over predictive policing and predictive sentencing that disproportionately penalize people of color. Importantly, race and gender were not included as inputs into any of these algorithms. Should we be outraged by bias reflected in algorithmic output? But the way organizations respond to their algorithms determines whether they make strides in debiasing their decisions or further perpetuate their biased decision making.

Computer games have been linked with artificial intelligence (AI) since the first program was designed to play chess (Shannon 1950). The challenge to defeat human expert players in rule-based strategy games such as Chess, Poker and Go has greatly advanced the domain of AI research, affecting breakthroughs in e.g. In turn, such new AI methods have been used in computer games, for instance to enhance graphical realism, to generate levels, sceneries and storylines, to establish player profiles, to balance complexity or to add intelligent behaviours to non-playing characters (NPC; Yannakakis and Togelius 2015, 2018). Over the years, however, various authors (Champandard 2004; Bourassa and Massey 2012; Yannakakis 2012; Yannakakis and Togelius 2018) have pointed at the marginal penetration of academic game AI methods in industrial game production. This limited uptake has been attributed to 1) research projects largely focusing on advanced, but non-scalable projects of little commercial or practical value, and 2) game studios reluctant to adopt and include promising but risky AI techniques (such as neural networks) rather than established, fully scripted technologies in their games.