If you think smart world-class companies don't face challenges when using machine learning to automate credit decisions, just ask Apple and Goldman Sachs. Based on a flurry of angry tweets and high-profile accusations, the New York Department of Financial Services launched an investigation into potential gender discrimination by algorithms that evaluate Apple Card applicants. Whether real or perceived, gender bias can damage the reputation of tech darlings like Apple, even if their credit decisioning process is wholly managed by someone else -- in this case, Goldman. It doesn't help when one of the accusers is a former Apple co-founder. Enova Decisions understands how to build accurate credit models -- without bias.

Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together--or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

On a sturdy workbench in seismologist Chris Marone's lab on the fifth floor of the geosciences building at Pennsylvania State University (Penn State) sits a large steel-framed machine with thick hydraulic pistons that force metal blocks and plates to grind past each other under extreme pressure. When the device is running, Marone sometimes closes the door to the lab so the loud bangs of "laboratory earthquakes" do not disrupt people across the hall. Lately, however, it has been the quieter sounds emanating from the machine that have caused a disruption in the field of seismology. In a recent spate of studies, researchers applied machine learning to acoustic emission data from Marone's earthquake machine, as well as from natural faults. The work led to the discovery of a new relationship between a fault's acoustic emissions and its physical characteristics, including its frictional state, its displacement rate, and the timing and magnitude of its next failure.

On this week's episode of Insurtech Insider, host Sarah Kocianski is joined by some wonderful guests to take a deep dive into the world of technology and insurance! According to McKinsey & Company in New York, by 2030, AI will inform every major decision an insurance company makes. So, what are the benefits of automation and how can it be used to help drive the industry forward? Subscribe so you never miss an episode, leave a review on iTunes and every other podcast app. Check out our brand new documentary 11:YEARS - the Rise of UK Fintech now.

The graph represents a network of 2,416 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 26 November 2019 at 20:47 UTC. The requested start date was Monday, 25 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 4-hour, 58-minute period from Tuesday, 19 November 2019 at 20:02 UTC to Monday, 25 November 2019 at 01:01 UTC.

Your supermarket and department store know who you are, what you've bought and where you're shopping next. That may sound invasive, but if it allows you to check out faster and get the products you want cheaper, is it a fair trade-off? Ethan Chernofsky, VP of marketing at Placer.ai, one of the Israeli companies vying to change the brick-and-mortar shopping experience, says yes. Big stores like Target and Walmart already had access to this kind of data through less technical means such as reviewing Mastercard sales reports, Chernofsky tells ISRAEL21c. Emerging technologies including artificial intelligence and machine learning will have a strong "democratizing influence that levels the playing field."

Chronic diseases are on the rise, with a threefold increase in the number of those with cancer in the last 40 years (1). In the face of mounting pressure in healthcare, all available technologies are being leveraged to deliver innovations that will provide sustainable long-term solutions. Hospitals are producing up to 50 petabytes (10 15) of data annually (2). With this in mind, image-analysis tools have been developed using AI that assist clinicians in the diagnoses of such conditions and provide increased precision for administering treatments such as radiotherapy. Recent developments in imaging are able to show tissues in three dimensions, allowing these specially trained AI algorithms to analyse them AI and imaging is a particularly important innovation for cancer treatment as it enables the precise location, type and stage of tumour to be identified (3).

Throughout the course of a day it's pretty common to speak to a number of colleagues and clients, yet we now have the ability to converse with an unlikely but familiar source: Salesforce's Einstein. Many Salesforce users will already have some experience with this artificial intelligence component, as it's been a central feature in Salesforce's cloud services, but Einstein now has become even more sophisticated. Unveiled at Dreamforce last week as one of the latest innovations, AI and voice-based functionality are now integrated into Einstein to give users a new way to access valuable information with voice control. Marc Benioff's opening keynote included a demo of Einstein's new voice capability, which was given an even greater focus during the Salesforce Einstein keynote led by Marco Casalaina, VP of Product Management. He shared a stat from Fortune that 7 out of 10 businesses using AI say they've achieved little to nothing from their efforts and investments.

It is the year 2020. You are sitting with your laptop at the kitchen table of your home with your best friend. You've been sitting there a lot lately, tweaking the software for a startup you are working on together. A minute ago, you were both cheering, but something has changed the mood. You are looking down on the keyboard of your laptop, slowly moving your fingers to grace the keys you were pressing frantically a few minutes ago.

Organizations today recognize the critical importance of diversity. They address it by changing internal practices and establishing chief diversity officers to enable equal opportunities and to strive for greater inclusion so that teams with a wealth of cultures, beliefs, experiences and skills can make their companies even stronger. The realization that diverse teams achieve better outcomes than homogenous ones was further reinforced by a McKinsey study that found that the most ethnically and racially diverse companies had a better chance of outperforming their peers. Those companies had a 33% great probability of achieving above-average returns. Whether it's pricing stocks or determining guilt or innocence in a trial, a diverse group is more likely to examine the facts and be objective and accurate.