The retail banking sector has been hit with numerous scams during the past few years. Cybercriminals are now also beginning to increasingly go after much larger corporate accounts by launching sophisticated malware and phishing attacks, according to Beate Zwijnenberg, chief information security officer at ING Group. Zwijnenberg recommends using advanced AI defense systems to identify potentially fraudulent transactions which may not be immediately recognizable by human analysts. Financial institutions across the globe have been spending a lot of money to deal with serious cybersecurity threats. They've been using static, rules-based verification processes to identify suspicious activity.
Today's billion-dollar unicorn start-ups have the advantage of building these capabilities into their process design at the outset. In contrast, businesses running on more-established processes may find themselves encumbered by inefficient legacy systems, rigid silos and fixed schedules. Most were designed and built decades ago to solve for specific needs, with a premium placed on consistency and simplicity rather than on speed, scale and agility. Fortunately, the evolution of technology has lifted a lot of the constraints facing seasoned enterprises, so the past doesn't have to dictate the future. Mature companies can incorporate today's rapidly advancing and increasingly accessible tech enablers to create Living Process.
ReverseAds announced the launch of its reverse-engineered search advertising solution that uses Big Data, A.I., and predictive modeling to help brands serve intuitive ads everywhere buyers go online after their initial search. ReverseAds addresses the need for predictive multi-channel ad campaigns that provide total visibility of the buyer's journey, allowing brands to move beyond underperforming search ads. This approach to digital advertising prioritizes ROI and CPA compared to the CPC bidding model provided by Google. With ReverseAds, considered purchase brands gain access to unprecedented amounts of intent data and a USPTO provisional patent-approved Assignment Algorithm. The algorithm uses predictive learning A.I. to determine which keywords will drive a business's highest total conversion.
Since the earliest days of the COVID-19 pandemic, one of the biggest challenges for health systems has been to gain an understanding of the community spread of this virus and to determine how likely is it that a person walking through the doors of a facility is at a higher risk of being COVID-19 positive. Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold. Parkland Health & Hospital System, the safety net health system for Dallas County, Texas, and PCCI, a Dallas-based non-profit with expertise in the practical applications of advanced data science and social determinants of health, had a better idea. Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals.
This comes out as a personal observation, but I'm sure that many of you will share the same feeling upon reading this post. I'm a data scientist, and I like my job because I think it covers various interdependent domains that make it rich and stimulating. However, I sometimes have to deal with people who don't exactly understand this role in the organization nor the field in general. This, quite frankly, is what makes things a little bit frustrating for me and also for a lot of people I know. Before you keep reading, I should mention that I don't aim to discourage anyone from aspiring to this role.
Recently one of my clients received a well-performed phishing attack with an "invoice", that like a lot of attachments was malware. Everything seemed to be legit except that the invoice ended in one of my honeypot inboxes. I usually deploy some email addresses, not in use active use by the company, that I monitor in order to catch attacks. The malware seems to be a trojan focused on stealing information. Furthermore being a fresh sample at the beginning is was only detected by six detection engines in VirusTotal, right now it detected by 18 over the 60 available on VirusTotal.
When I first encountered A/B testing, I immediately wanted to become the type of marketer who tested everything. The idea sounded fun to me. Like being a mad scientist running experiments to prove when my work was actually "working." Turns out though, there's always a long list of other things to do first… blog posts to write, campaigns to launch, and don't get me started on the meetings! A lot of marketers are just too darned busy to follow up and optimize the stuff they've already shipped.
In 1990, a fellow chess player and I attended one of Gary Kasparov's several matches in New York City. Kasparov had become the youngest ever World Chess Champion in 1985, at the age of 22. In 1997, Kasparov also became the first World Chess Champion to be defeated by a computer, losing to IBM's Deep Blue in a stunning six-game match. Computers have become so good at chess that phones are now banned at chess tournaments, and players are very carefully monitored for any sort of digital activity or connection. Today, however, there is a new kind of chess becoming competitively dominant. Called "Advanced Chess," or more colloquially "Centaur Chess," it involves teaming human beings with computers, and it has been actively supported by Kasparov.
He said: "It was a preventative health care system or model so it was built with all the good intentions, so was not to deny health care insurance to people. "But it was really for existing members to see if the company could target preventative health care practices to them. "Now, we all know that it's very biased because if you are black, and you have similar healthcare conditions as someone who is white, then unfortunately in the US, black people have less access to health care, so less is being spent on curing them from that disease. "So and that led to large amounts of bias and it was not something that couldn't be fixed." And the AI expert has warned how the bias inherent within AI can most likely never be eradicated.