Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types, harboring inherently different characteristics, e.g., difficulty level. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. Our method quickly and effectively adapts the meta-learned programmer to new questions based on the most similar questions retrieved from the training data. The meta-learned policy is then used to learn a good programming policy, utilizing the trial trajectories and their rewards for similar questions in the support set. Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set. We have released our code at https://github.com/DevinJake/MRL-CQA.

Frequency spectrum has played a significant role in learning unique and discriminating features for object recognition. Both low and high frequency information present in images have been extracted and learnt by a host of representation learning techniques, including deep learning. Inspired by this observation, we introduce a novel class of adversarial attacks, namely `WaveTransform', that creates adversarial noise corresponding to low-frequency and high-frequency subbands, separately (or in combination). The frequency subbands are analyzed using wavelet decomposition; the subbands are corrupted and then used to construct an adversarial example. Experiments are performed using multiple databases and CNN models to establish the effectiveness of the proposed WaveTransform attack and analyze the importance of a particular frequency component. The robustness of the proposed attack is also evaluated through its transferability and resiliency against a recent adversarial defense algorithm. Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.

The Department of Justice recently sued Google for allegedly monopolizing the market for search engines. The Department's complaint alleges that Google took numerous actions well before 2010 that formed part of the claimed antitrust violations. I have no comment about the merits. What I do want to call attention to, however, are the dates: a lawsuit beginning in 2020 to try to correct the market consequences of actions that began more than 10 years ago. The revolution that some scholars call "regulating by robot" is already underway.

Rock The Vote's voting house in Minecraft allows players to vote on a variety of real-world issues. Rock The Vote's voting house in Minecraft allows players to vote on a variety of real-world issues. At the top of a hill sits a large white building with columns and draped with American flags. It resembles the Capitol building in Washington, D.C., except for a key difference. It's built out of Minecraft blocks.

In 1953, US senators grilled General Motors CEO Charles "Engine Charlie" Wilson about his large GM shareholdings: Would they cloud his decision making if he became the US secretary of defense and the interests of General Motors and the United States diverged? Wilson said that he would always put US interests first but that he could not imagine such a divergence taking place, because, "for years I thought what was good for our country was good for General Motors, and vice versa." Although Wilson was confirmed, his remarks raised eyebrows due to widespread skepticism about the alignment of corporate and societal interests. The skepticism of the 1950s looks quaint when compared with today's concerns about whether business leaders will harness the power of artificial intelligence (AI) and workplace automation to pad their own pockets and those of shareholders--not to mention hurting society by causing unemployment, infringing upon privacy, creating safety and security risks, or worse. But is it possible that what is good for society can also be good for business--and vice versa?

The fake news: A new weekly satire show from the creators of South Park is using deepfakes, or AI-synthesized media, to poke fun at some of the most important topics of our time. Called Sassy Justice, the show is hosted by the character Fred Sassy, a reporter for the local news station in Cheyenne, Wyoming, who sports a deepfaked face of president Trump, though a completely different voice, hair style, and persona. Meta commentary: The first episode, released on YouTube on October 26, took on the topic of deepfakes themselves, with Fred Sassy warning his faithful viewers that they shouldn't believe everything they see. The satirical twist is that all the footage shown as real is, of course, deepfaked, while all the footage labeled fake is either real or played by puppets. The episode features a wide range of highly convincing deepfakes representing people including former vice president Al Gore, Facebook CEO Mark Zuckerberg, and president Trump's son-in-law Jared Kushner, whose face is deepfaked onto a child.

We can safely defer the discussion about whether artificial intelligence will eventually take over board functions. We cannot, however, defer the discussion about how boards will oversee AI -- a discussion that's relevant whether organizations are developing AI systems or buying AI-powered software. With the technology in increasingly widespread use, it's time for every board to develop a proactive approach for overseeing how AI operates within the context of an organization's overall mission and risk management. According to McKinsey's 2019 global AI survey, although AI adoption is increasing rapidly, overseeing and mitigating its risks remain unresolved and urgent tasks: Just 41% of respondents said that their organizations "comprehensively identify and prioritize" the risks associated with AI deployment. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations. Board members recognize that this task is on their agendas: According to the 2019 National Association of Corporate Directors (NACD) Blue Ribbon Commission report, Fit for the Future: An Urgent Imperative for Board Leadership, 86% of board members "fully expect to deepen their engagement with management on new drivers of growth and risk in the next five years."1

The National Oceanic and Atmospheric Administration plans to improve weather forecasting by more effectively using satellite and environmental data in a series of pilots with Google Cloud. Google entered into a three-year other transaction authority (OTA) agreement with the National Environmental Satellite, Data, and Information Service (NESDIS) to explore machine learning and artificial intelligence applications for not only weather but environmental monitoring, climate research and technical innovation, it announced Tuesday. Together NESDIS and Google will develop small-scale ML and AI systems and use results from those pilots to build full-scale prototypes for operationalization across NOAA. "Strengthening NOAA's data processing through the use of big data, artificial intelligence, machine learning, and other advanced analytical approaches is critical for maintaining and enhancing the performance of our systems in support of public safety and the economy," said Neil Jacobs, acting NOAA administrator, in the announcement. "I am excited to utilize new authorities granted to NOAA to pursue cutting-edge technologies that will enhance our mission and better protect lives and property."

Humans have been teaming up with machines throughout history to achieve goals, be it by using simple machines to move materials or complex machines to travel in space. But advances in artificial intelligence today bring possibilities for even more sophisticated teamwork--true human-machine teams that cooperate to solve complex problems. Much of the development of these human-machine teams focuses on the machine, tackling the technology challenges of training AI algorithms to perform their role in a mission effectively. But less focus, MIT Lincoln Laboratory researchers say, has been given to the human side of the team. What if the machine works perfectly, but the human is struggling?