The world's fish stocks are in decline and our increasing demand for seafood may be one of the main drivers. But the true extent of the problem is hard to estimate, especially when fishing occurs in the high seas, which lie beyond national jurisdiction and are hard to monitor. Conservation planners face growing pressures to combat illegal, unregulated and unreported (IUU) fishing, the value of which has been estimated at US$10-23.5 billion annually. This is an important cost for society as a whole, but also for the major high seas fishing countries such as China and Taiwan that subsidize their fleets and may have low labour costs. Artificial intelligence (AI) could address this global environmental concern -- and satisfy the need of seafood retailers and consumers to know if what they're selling and eating is sustainable.

We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our context-free rule probabilities are modulated by a per-sentence continuous latent variable, which induces marginal dependencies beyond the traditional context-free assumptions. Inference in this grammar is performed by collapsed variational inference, in which an amortized variational posterior is placed on the continuous variable, and the latent trees are marginalized with dynamic programming. Experiments on English and Chinese show the effectiveness of our approach compared to recent state-of-the-art methods for grammar induction from words with neural language models.

Law firms, anticipating more cases involving artificial intelligence, are bulking up their ranks with AI experts in an effort to secure new business as the technology becomes ubiquitous across industries. Among the issues firms are handling: rights to the datasets used for training machine-learning algorithms, ownership of the algorithms created from third-party data, and liability for property damage or personal injury caused by an autonomous system.

Over the past few years, a new trend has emerged in the business world, with companies looking to improve operational efficiency using the latest technological advances. Software developers are now pairing automation and artificial intelligence, which has caused a paradigm shift in many industries. It is within this framework that Jerry Ting built Evisort, a software looking to completely automate all processes related to sorting and pulling up information from a company's legal documents. Once he had the idea, he approached his co-founders about the feasibility of automation for legal paperwork. Jerry Ting, Amine Anoun and Jake Sussman co-founded Evisort in 2016.

As artificial intelligence re-writes business models, how will its application and adoption revolutionize business and commerce further? From the production and marketing era to the relationship and intelligence era, business models have been evolving over the centuries. Over the years, the rise of artificial intelligence (AI) has fundamentally transformed the very meaning of ideas, innovation, and inventions. As a result, business models are evolving further. As we witness businesses across industries undergo a profound and dramatic shift in the relative balance of intelligence power, AI applications and adoption are offering each business entity as many new opportunities as it does challenges.

Should your Roomba need a W-2? Probably not, but it's an amusing thought when debating the more serious topic of whether or not a robot should have to pay taxes -- and how to do it. During the June MIT Technology Review EmTech Next event, two experts argued both sides of the question before an audience at the MIT Media Lab in Cambridge, Massachusetts. Ryan Abbott, professor of law and health sciences at the University of Surrey, argued in favor of taxing robots, while Ryan Avent, economics columnist for The Economist, argued against the idea. Both agreed there needs to be a shift in tax burden from labor to capital. Avent, however, carried the most audience votes by the end of the debate. Here are some highlights from each of the men's arguments.

In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensuring that AI systems are developed responsibly may therefore require preventing and solving collective action problems between companies. We note that there are several key factors that improve the prospects for cooperation in collective action problems. We use this to identify strategies to improve the prospects for industry cooperation on the responsible development of AI.

A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The What-If Tool lets practitioners test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data. It also lets practitioners measure systems according to multiple ML fairness metrics. We describe the design of the tool, and report on real-life usage at different organizations.

Employers are using artificial intelligence (AI) in recruiting chatbots, in video interviews to assess job candidates' body language or word choices, or to extract themes from engagement survey responses. But how companies are using AI to benefit the employee experience, support compliance efforts and ease administrative workloads is not as well-known. "So much of the buzz about AI has been for its'sexy' uses around sourcing and screening in recruiting, but there are a growing number of other applications of value to HR to be aware of," said Jeanne Meister, founding partner of Future Workplace, an HR advisory and research firm in New York City. One example of AI's expanding utility: using it to audit employees' expense reports, to ensure they comply with company policy and avoid wasteful spending. AppZen in Sunnyvale, Calif., uses AI to read and extract information from receipts to catch duplicates, out-of-policy spending, incorrect amounts or suspicious merchants.

We attempt to make two arguments in this essay. First, through a case study of a mobile phone based voice-media service we have been running in rural central India for more than six years, we describe several implementation complexities we had to navigate towards realizing our intended vision of bringing social development through technology. Most of these complexities arose in the interface of our technology with society, and we argue that even other technology providers can create similar processes to manage this socio-technological interface and ensure intended outcomes from their technology use. We then build our second argument about how to ensure that the organizations behind both market driven technologies and those technologies that are adopted by the state, pay due attention towards responsibly managing the socio-technological interface of their innovations. We advocate for the technology engineers and researchers who work within these organizations, to take up the responsibility and ensure that their labour leads to making the world a better place especially for the poor and marginalized. We outline possible governance structures that can give more voice to the technology developers to push their organizations towards ensuring that responsible outcomes emerge from their technology. We note that the examples we use to build our arguments are limited to contemporary information and communication technology (ICT) platforms used directly by end-users to share content with one another, and hence our argument may not generalize to other ICTs in a straightforward manner.