The CEO of Instagram has defended the company's decision not to take down a deepfaked video of Mark Zuckerberg two weeks after the doctored video was reported. Adam Mosseri told CBS' Gayle King - in his first US television interview since taking over the platform last year - that the company hasn't yet formulated an official policy on AI-altered video called'deepfakes', and until then taking action would be'inappropriate.' Mosseri said, 'I don't feel good about it,' but said there is no rush to remove the video, in part because'the damage is done.' Mosseri's comments about deepfakes come as a response to King's questioning about a faked video of Facebook CEO Mark Zuckerberg taken from an actual interview with CBSN in 2017. The doctored video features a fairly convincing Zuckerberg next to a superimposed CBSN logo talking about how Facebook wields power over its users.

Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.

Three years ago, if you told me that one day I would use python to analyze AI policy and make Guido van Rossum chuckle, I would think you are crazy. Three years later at PyCon 2019 in Cleveland, that's exactly what happened. I was by no means a tech person. I was trained as an economist (read: stats nerd), but somehow for the past three years I've been writing analysis on deep-tech fields including AI and 5G. What I hope to achieve with this post is not #humblebrag (ok, maybe a little happy dance) but to share with you all the struggles I had and am still experiencing on a daily basis and to reassure a fellow researcher somewhere feeling that he/she is faking it all the time, you are not alone.

As technology goes more and more towards Artificial Intelligence branches such as Machine Learning and Deep Learning technologies, data and information are getting more and more endangered by Fake News and tampered materials. Deep Fakes seems to be the most "promising" and dangerous example of this kind. Shortly, it allows creating a tampered video content by replace its behavior. Say, like replacing an actor's face with someone's else, as it happened to Gal Gadot (read more). Turns out there are out thousands of "faked" videos out in the network.

What is the future of work in the investment industry? How do we, as providers of human capital prepare for the evolution of this profession? What does it mean for employers seeking to engage and motivate staff over the long term? It is widely accepted that, like most other industries, the financial services industry is in a state of flux. Unlike other episodes of change in recent decades, the current context is often characterised as an "industrial revolution" creating continual disruption of a structural nature. On that backdrop, the prudent approach is to prepare, not predict.

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Using accepted hiring models and the concept of structured interviews, a best practice in human resources, we cast tiered hiring as a combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting. The goal is to select a subset of arms (in our case, applicants) with some combinatorial structure. We present new algorithms in both the probably approximately correct (PAC) and fixed-budget settings that select a near-optimal cohort with provable guarantees. We show on real data from one of the largest USbased computer science graduate programs that our algorithms make better hiring decisions or use less budget than the status quo. '... nothing we do is more important than hiring and developing people. At the end of the day, you bet on people, not on strategies." - Lawrence Bossidy, The CEO as Coach (1995)

This paper reveals a trap for artificial general intelligence (AGI) theorists who use economists' standard method of discounting. This trap is implicitly and falsely assuming that a rational AGI would have timeconsistent preferences. An agent with time-inconsistent preferences knows that its future self will disagree with its current self concerning intertemporal decision making. Such an agent cannot automatically trust its future self to carry out plans that its current self considers optimal. Economists have long used utility functions to model how rational agents behave (see Mas-Colell et al., 1995).

What do you think are the top three policy and governance issues that face AI/ML currently? For me the biggest governance issue facing AI/ML ethics is the gap between principles and practice. The hard problem the industry faces is turning good intentions into demonstrably good behaviour. In the last 2.5 years there has been a gold rush of new ethical principles in AI. Since Jan 2017 at least 22 sets of ethical principles have been published, including principles from Google, IBM, Microsoft and Intel.

Fairness can be measured at group levels or at the individual level. Do you wish to ensure that on average you don't discriminate against a protected group or apply the protection to each and every individual? For example, you may hire female job applicants with the same average probability that you hire male applicants. That would achieve group fairness. However, you may be biased by giving junior roles with higher probability to females and senior jobs with higher probability to males. Achieving individual fairness is more difficult than achieving group fairness.

RALEIGH – One of the most perplexing challenges facing businesses wanting to explore Artificial Intelligence as a tool is "How do I build a team?" Rett Crocker CEO and CTO of UDU, an artificial intelligence firm in Raleigh, has plenty of advice. In an interview with Alexander Ferguson, CEO of YourLocalStudio, for its UpTech series focusing on AI, Crocker explains his reasoning in this excerpt of an interview published June 20. "First off, I wouldn't build anything from scratch anymore. There's just almost no point in that because of all of the existing tools that are out there, that are open source" Crocker says.