Two hundred students, industry professionals, and academic leaders convened at the Microsoft NERD Center in Cambridge, Massachusetts for the second annual Women in Data Science (WiDS) conference on March 5. The conference grew from 150 participants last year, and highlighted local strength in academics and health care. "The WiDS conference highlighted female leadership in data science in the Boston area," said Caroline Uhler, a member of the WiDS steering committee who is an IDSS core faculty member and assistant professor of electrical engineering and computer science (EECS) at MIT. "This event is particularly important to encourage more female scientists in related areas to join this emerging area that has such broad societal impact." Regina Barzilay, Delta Electronics Professor of EECS, gave the first presentation on how data science and machine learning approaches are improving cancer research. Barzilay said her experiences as a breast cancer survivor motivates her work.

We're honored to have Suhas Uliyar as our next speaker! Suhas is Oracle's Vice President of Bots, A.I., and Mobile Product Management. As you can see, it's quite a mouthful. We're all excited for him to share the chatbot initiatives that Oracle has been stacking up the past year, including the partnerships with Slack and Chatbox and how they complement the Oracle Mobile Cloud.

Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparable robustness measure is the minimum perturbation needed to craft an adversarial example. To this end, Foolbox provides reference implementations of most published adversarial attack methods alongside some new ones, all of which perform internal hyperparameter tuning to find the minimum adversarial perturbation. Additionally, Foolbox interfaces with most popular deep learning frameworks such as PyTorch, Keras, TensorFlow, Theano and MXNet and allows different adversarial criteria such as targeted misclassification and top-k misclassification as well as different distance measures. The code is licensed under the MIT license and is openly available at https://github.com/bethgelab/foolbox . The most up-to-date documentation can be found at http://foolbox.readthedocs.io .

This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies several desirable properties of measures of association, including R\'enyi's properties, the data processing inequality (DPI), and consequently self-equitability. Synthetic data simulations reveal that the statistical power of CIM compares favorably to other state-of-the-art measures of association that are proven to satisfy the DPI. Simulation results with real-world data reveal the CIM's unique ability to detect the monotonicity structure among stochastic signals to find interesting dependencies in large datasets. Additionally, simulations show that the CIM shows favorable performance to estimators of mutual information when discovering Markov network structure.

The National Transportation Safety Board is opening an investigation into the fatal accident involving one of Uber's self-driving cars in Tempe, Arizona. NTSB sending team to investigate Uber crash in Tempe, Arizona. Uber's self-driving car accident that resulted in a woman's death raises a number of questions about insurance and liability. Although the car was in self-driving mode, there was a safety driver behind the wheel who theoretically should have been able to intervene. Uber has since halted its self-driving car tests in Arizona, Pittsburgh and California.

Facebook said in a statement on Friday that it had learned in 2015 that a Cambridge University psychology professor had lied to the company and violated its policies by passing data to Cambridge Analytica from a psychology testing app he had built. Facebook said it suspended the firms and researchers involved.

The US Federal Trade Commission (FTC), Washington's consumer watchdog, has filed a lawsuit against two businesses it accuses of operating cryptocurrency pyramid schemes. The FTC is taking action against Bitcoin Funding Team and My7Network over what it defines as "chain referral" scams, in which participants pay upfront entry fees in order to be able to recommend others to follow suit. The companies allegedly promised customers who made an initial investment of just $100 (£71) that they could earn an $80,000 (£56,938) monthly income from doing so - although payouts seldom amounted to anything like that. The two businesses defrauded an estimated 30,000 people worldwide between them, the lawsuit alleges. "Bitcoin Funding Team's structure, which created a continual chain of recruitment and recruitment-related payments, ensured that few participants would obtain the results depicted or projected by the defendants," the FTC's complaint reads.

Downing Street expressed its concern for the Facebook data breach that affected tens of millions of people involving the analytics company that worked with Donald Trump's campaign team. No 10 weighed in on the row as almost $20bn (£14bn) was wiped off the social network company's market cap in the first few minutes of trading on the Nasdaq stock exchange, where Facebook opened down more than 3%. After less than two hours trading, the company's losses had multiplied to almost $30bn. Theresa May's spokesman said she backed an investigation by the information commissioner, which was prompted by a whistleblower who told the Observer how Cambridge Analytica harvested millions of Facebook profiles to influence voters through "psychographic" targeting. The European parliament president, Antonio Tajani, also said on Monday that the institution would "investigate fully".

Many people see the potential benefits of artificial intelligence technologies used for government services – but many also aren't convinced governments will use AI tech responsibly, according to a new survey from Accenture. The online survey of more than 6,000 citizens from US, Australia, the UK, Singapore, France and Germany found that more than half (54%) of citizens said they are willing to use AI services delivered by government, with even more expressing willingness when presented with the potential benefits derived from artificial intelligence. For instance, three-quarters (74%) of respondents said they would be willing to use artificial intelligence if it would increase pension or retirement income (such as by improving their personal investment strategy and/or pension scheme), and two-thirds (66%) said they would use a chatbot if it would guarantee faster processing of a tax refund or social service benefits. However, that doesn't mean citizens aren't worried about the government using artificial intelligence responsibly – two-thirds (66%) of respondents indicated a lack of confidence in government's ethical and responsible use of AI. Specifically, only one-third (34%) said they're "confident/very confident" that government would be ethical and responsible in its use of AI; fewer than one in three (29%) said they are "not at all confident" in government using AI ethically and responsibly, and slightly more than one-third (37%) said they are neutral on the point. The survey also determined that regardless of where they lived, citizens have concerns about the use of artificial intelligence in government, including in areas of job security and personal data security.

March 14, 2018 – An Oak Ridge National Laboratory method to improve the energy efficiency of scientific artificial intelligence is showing early promise in efforts to parse insights from volumes of cancer data. Researchers are realizing the potential of deep learning to rapidly advance science, but "training" the underlying neural networks with large volumes of data to tackle the task at hand can require large amounts of energy. These networks also require complex connectivity and enormous amounts of storage, both of which further reduce their energy efficiency and potential in real-world applications. To address this issue, ORNL's Mohammed Alawad, Hong-Jun Yoon, and Georgia Tourassi developed a novel method for the development of energy-efficient deep neural networks capable of solving complex science problems. They presented their research at the 2017 IEEE Conference on Big Data in Boston. The researchers demonstrated that by converting deep learning neural networks (DNNs) to "deep spiking" neural networks (DSNNs), they can improve the energy efficiency of network design and realization.