The financial industry has long been riddled with inequalities. These inequalities are closely aligned with social inequalities, which show bias against women and minorities. Access to services, approval for loans, and availability of products that fit their needs have all been problems in the past for these two groups. But now, along comes Artificial Intelligence (AI) to hopefully remove the human bias that's plagued the financial industry. FinTech promises a revolution and in many sectors, it's already begun.

Bias has crept into our machines and, unless we urge developers to change business as usual, it's there to stay. We've asked the computers to try and see the world as humans do, and they've responded by showing the potential to be as racist and ignorant as we are. Don't get me wrong, not all robots are bigots; but the existence of the ones that are is an area for concern. Tay, the Microsoft bot who learned to hate Jewish people and spew racial epithets, was the victim of its own desire to please. Its creators told it to learn from everyone it interacted with, and it learned to be a bigot.

Apple this week unveiled its new iPhone X, and with it, a host of security concerns. Apple this week unveiled its new iPhone X as part of the smartphone's 10th birthday, and with it comes a host of security concerns. One of the major features of the iPhone X (X for the roman numeral 10) is FaceID, a facial recognition feature for unlocking the phone by just looking at it. Apple has a solid track record on personal privacy when it comes to securing its devices, but FaceID raises major issues, such as whether the tool be used against an owner's will to gain access to their phone or what happens if a hacker steals your facial identity? A staff attorney with the American Civil Liberties Union argued that law enforcement could use someone's face against their will to unlock their phone, possibly without violating the person's' Fifth Amendment right against self-incrimination.

Preface: Lately we hear too many news about Artificial Intelligence (AI). Google, IBM, Apple, Microsoft etc. years ago announced "mobile" support. Today, mobile is obvious, and to differentiate, they claim to use "AI". The word "AI" for most people can be only the sci-fi movies AI, since we get too many AI movies too: Her, Ex Machina, … and even "Alien: Covenant" is about a rogue AI, the aliens are secondary. Companies went mobile for real: their services run in cell phones. But, we don't see sci-fi "AI" in any service like Alexa, Cortana, Siri etc.

NHTSA released their latest draft robocar regulations just a week after the U.S. House passed a new regulatory regime and the senate started working on its own. The proposed regulations preempt state regulation of vehicle design, and allow companies to apply for high volume exemptions from the standards that exist for human-driven cars. It's clear that the new approach will be quite different from the Obama-era one, much more hands-off. There are not a lot of things to like about the Trump administration but this could be one of them. The prior regulations reached 116 pages with much detail, though they were mostly listed as "voluntary."

The third edition of our Valencian Summer School in Machine Learning broke our records across the board. A big thank you to all our attendees who made this event one for the books! Over 200 BigMLers from 14 countries representing 100 companies and 41 academic organizations gathered in Valencia for two fun, intense days of Machine Learning training. The Valencian Summer School gave BigMLers a quick and practical introduction to Machine Learning, all the while enjoying beautiful ocean views from the elegant Veles e Vents at the Valencian harbor. Not a bad way to dive into the world of Machine Learning, eh?

It becomes increasingly popular to perform mediation analysis for complex data from sophisticated experimental studies. In this paper, we present Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The stable unit treatment assumption for causal mediation analysis is thus unrealistic for this type of time series data. To address this challenge, our framework integrates two types of models: causal mediation analysis across the variables and vector autoregressive models across the temporal observations. We further extend this framework to handle multilevel data to address individual variability and correlated errors between the mediator and the outcome variables. These models not only provide valid causal mediation for time series data but also model the causal dynamics across time. We show that the modeling parameters in our models are identifiable, and we develop computationally efficient methods to maximize the likelihood-based optimization criteria. Simulation studies show that our method reduces the estimation bias and improve statistical power, compared to existing approaches. On a real fMRI data set, our approach not only infers the causal effects of brain pathways but accurately captures the feedback effect of the outcome region on the mediator region.

February 16, 2017 Written by: Susan C. Daffron Today's businesses are capitalizing on cognitive capabilities to gain a competitive advantage. A recent IBM report surveyed cognitive adopters, and 58 percent said cognitive computing was an essential element of their organization's digital transformation. However, not everyone embracing cognitive is entirely sure what it can do for them. According to the study, 46 percent of early adopters struggle with a roadmap for adoption and only 7 percent report that they have a comprehensive, company-wide strategy.1 Once you understand the possibilities of cognitive technology, it can open up new opportunities for innovation. Here are three examples of organizations that use cognitive technology today to transform their businesses.

Big data can mean big business. But as Texas copes with the destruction of Hurricane Harvey, which ravaged the state late last month, and with Irma barreling over the Caribbean toward Florida, and Mexico shaken by the most powerful earthquake in 100 years, can mining vast amounts of data also help save lives from the fury of natural disasters? Find and rescue victims from rubble? Governments are looking to the same sophisticated analytics techniques that are predicting -- with fast-improving accuracy -- the paths and destruction potential of increasingly fearsome storms to better prepare for and tend to their constituents' needs during calamities. But whether such data-driven decision-making is actually making a difference is, in 2017, an open question.

Beyond the downloads, Uber said it should be allowed to argue that Waymo brought the lawsuit in bad faith to slow down a competitor. As evidence, Uber wants to tell jurors that Drummond failed to fully disclose Waymo's self-driving car plans while he served on Uber's board.