Among researchers studying how AI can be used to lie and manipulate the world, there's a feeling that 2017 has been the calm before the storm. The past few years have brought example after example from research labs of how AI can generate videos of politicians saying literally anything, or potentially trick self-driving cars into speeding past stop signs. But nobody, to the field's knowledge, has actually used the technology for malicious purposes. In an effort to get in front of this perceived threat, hundreds of AI researchers will gather today (Dec. Tim Hwang, director of ethics and governance at the Reed Hoffman-backed AI Initiative and co-organizer of the workshop, says he realized this area needed more attention after a 2016 research project called Face2Face showed that AI could be used to realistically imitate politicians like Donald Trump and Vladimir Putin.

I originally wrote and published a version of this article in September 2016. Since then, quite a bit has happened, further cementing my view that these changes are coming and that the implications will be even more substantial. I decided it was time to update this article with some additional ideas and a few changes.

Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions. Concretely, we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures, and a large number of existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits), indicating that fairness interventions might be more brittle than previously thought.

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions.

A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to explore the suitability of supervised machine learning as a preliminary design aid that provides guidance to engineers with regards to the statistically optimal bridge type to choose, ultimately improving the likelihood of optimized design, design standardization, and reduced maintenance costs. In order to devise this supervised learning system, data for over 600,000 bridges from the National Bridge Inventory database were analyzed. Key attributes for determining the bridge structure type were identified through three feature selection techniques. Potentially useful attributes like seismic intensity and historic data on the cost of materials (steel and concrete) were then added from the US Geological Survey (USGS) database and Engineering News Record. Decision tree, Bayes network and Support Vector Machines were used for predicting the bridge design type. Due to state-to-state variations in material availability, material costs, and design codes, supervised learning models based on the complete data set did not yield favorable results. Supervised learning models were then trained and tested using 10-fold cross validation on data for each state. Inclusion of seismic data improved the model performance noticeably. The data was then resampled to reduce the bias of the models towards more common design types, and the supervised learning models thus constructed showed further improvements in performance. The average recall and precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0% and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.

NEW DELHI: The ministry of electronics and IT has formed four committees to prepare a roadmap for the national artificial intelligence programme. The committees will be headed by IIT directors and experts from industry bodies such as Nasscom, union minister for electronics and IT Ravi Shankar Prasad said. The four committees will be for citizen centric services; data platforms; skilling, reskilling and R&D; and legal regulatory and cybersecurity. The ministry will promote high level research in these areas, the minister said, adding: "We must learn new dimensions of AI." MeitY will also assist the government machinery to implement technologies such as AI and 3D printing, he said. It is also looking at the blockchain technology and working on developing standards and regulations around it.

In 1976, philosopher Julian Jaynes issued the provocative theory that recent ancestors lacked self-awareness. Instead, they mistook their inner voices for outside sources–the voice of God, say, or the ghosts of their ancestors. Jaynes called his theory "bicameralism" (Westworld fans will recall an episode from the last season called "The Bicameral Mind") and, in his telling, it persisted in early humans until about 3,000 years ago. We are in a similar pre-conscious state now, but the voice we hear is not the other side of our brains. It's our digital self–a version of us that is quickly becoming inseparable from our physical self. I call this comingled digital and analog self our "Meta Me."

In the weeks before the launch of the MIT Intelligence Quest, an initiative that will advance the science and engineering of human and machine intelligence, School of Engineering graduates were asked: "What positive role can MIT play in the AI revolution?" Alumni urged MIT to energize the artificial intelligence community, including people in industry, academia, and the government, around a thoughtful strategy for the future. They wrote directly to Anantha P. Chandrakasan, dean of engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, who posed the question in a monthly newsletter, The Infinite.

"They are already using a combination of facial recognition technologies, AI, and a compulsory social ratings system – like an Uber for human beings – to try to remove or punish bad behaviour among its citizens" Speaking exclusively to Daily Star Online, Chris Middleton even claimed the communist government is using the technology to "punish bad behaviour". He said: "China is an interesting case in point. "They are already using a combination of facial recognition technologies, AI, and a compulsory social ratings system – like an Uber for human beings – to try to remove or punish bad behaviour among its citizens." His comments come after the Chinese government revealed plans to unroll a mass surveillance system to watch the country's citizens by the year 2020. Chris added: "Such a draconian, state-sanctioned system risks excluding some people completely from society, if they don't conform to the government's model of'a good person'.

The Boston City Council is holding a City Hall hearing Tuesday on Mayor Marty Walsh's proposed ordinance to track and regulate the rentals of bedrooms, apartments or entire houses for days or weeks at a time to visitors and tourists.