We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset). Current encoder-decoder architectures do not take the poset structure of semantics into account properly, thus suffering from poor compositional generalization ability. In this paper, we propose a novel hierarchical poset decoding paradigm for compositional generalization in language. Intuitively: (1) the proposed paradigm enforces partial permutation invariance in semantics, thus avoiding overfitting to bias ordering information; (2) the hierarchical mechanism allows to capture high-level structures of posets. We evaluate our proposed decoder on Compositional Freebase Questions (CFQ), a large and realistic natural language question answering dataset that is specifically designed to measure compositional generalization. Results show that it outperforms current decoders.

Though it was held this year in October instead of September, Apple's "Hi, Speed" media event was a largely typical iPhone launch party, opening with the expansion of its Siri-powered line of HomePod speakers ("Hi"), and concluding with the long-awaited addition of 5G cellular connectivity to the iPhone lineup ("Speed"). Some companies might have tread cautiously on these topics -- Siri and 5G have both been dogged by complaints -- but Apple didn't hold anything back, using a seemingly endless parade of spokespeople to hype the new devices ahead of preorders. The 5G iPhone 12 family, it promised, will "blast past fast," while the $99 HomePod mini will become a hub to "control your smart home," bringing "intelligent assistant" access to the lowest price yet for any Siri device. Having covered Apple for a long time, I'm not surprised that its latest pitches were all sunshine and roses, but I couldn't help but feel that it was making big promises that could come back to bite the company and its partners. As of October 2020, the only thing less likely to thrill someone than a Siri speaker is typical U.S. 5G network performance, which despite boasts of 1-4Gbps downloads has seen average speeds that are barely better than 4G/LTE. Siri and 5G are both theoretically moving targets -- they're services that could improve at any time and in any region without advance notice -- but prior to this event, neither has delivered on its transformative potential.

SAN FRANCISCO, CA, USA, October 14, 2020 – Gorilla Corporation, a leader in global partner marketing and strategy, today announced the expansion of their corporate social responsibility (CSR) program, by including Tortora Brayda Partnership Excellence. Gorilla Corporation, with a long-standing commitment to their corporate social responsibility program, will allocate further sponsorship to the non-profit organization Tortora Brayda Partnership Excellence. Tortora Brayda focuses on engaging business, cultural, government, and thought leaders worldwide to reshape and develop collaboration and partnering practices with a view to improving and securing business and economies. "This official partnership with Gorilla Corporation is powerful for our Think Tank, Gorilla's reputation as a leader in partner marketing internationally, is relevant and strategic. Their contribution and support are pivotal for us," said Carlo Tortora Brayda, founder and chairperson of Tortora Brayda Partnership Excellence.

In Milan, Italy, STIIMA, the National Research Council's Institute for Smart Industrial Technology Systems for Advanced Manufacturing, and the Polytechnic University of Milan have set up a joint experimental "re-manufacturing" and "de-manufacturing" facility. While still at a pilot experimental level, this is an excellent example of the enormous potential of artificial intelligence in the circular economy. This is because there are no similar plants in the world capable of managing electronic waste, understanding what the items are, dismantling them and recovering their useful or valuable components. For this reason, millions of tonnes of old TVs, monitors, broken PCs, telephones, and electrical appliances of every type, are piling up at waste sites, from where they are often taken to fuel an illegal and extremely polluting market. Its real size is difficult to estimate, but according to UNEP, the United Nations Environmental Protection agency, the global market for electronic waste is worth more than 62 billion dollars and only 20% of it is officially recycled.

Artificial Intelligence is the new buzzword that no one can go without. The reasons are numerous; AI has given as self-driven cars, fancy robots that have close to human intelligence, and many many more. Experts predict that AI will significantly improve the lives of humans in years to come. Even now, we are enjoying some of the benefits of this awesome technology. Artificial Intelligence is the act of giving machines the ability to perform human-level tasks without explicit programming.

Just like any other day, we start our morning with a coffee and a snack to go from our favorite bakery. Later on the same day, we check out our mail where we find letters, newspapers, magazines, and possibly a package that just arrived. Finally at night, after a rough week, we decide to go out to have drinks with friends. Sounds like a pretty uneventful day, right? Except that we produced lots of trash in the form of plastic, glass, paper, ad more.

Want to grow food sustainably on a global scale? You're going to need more than a few tractors and plows. Enter Alphabet's X lab moonshot Mineral--a "computational agriculture" project. "Alongside experts in the field--literally and figuratively--we've been developing and testing a range of software and hardware prototypes based on breakthroughs in artificial intelligence, simulation, sensors, robotics, and more," Elliott Grant, project lead at X, wrote in a blog post. "From strawberry fields in California to soybean fields in Illinois, we've been learning about crops from sprout to harvest," he continued.

A free introductory session is offered to anyone new to online learning. Artificial Intelligence and The Environmental Crisis: Can technology really save the world? Based on his latest book of the same title, Dr Keith Skene explores the history of artificial intelligence, its contributions to humanity and the dangers it may pose. We'll encounter many interesting characters, key events and controversial moments associated with this rapidly developing field. We'll then explore what human intelligence is and how other forms of intelligence (including plant, animal, bacterial and ecosystem intelligence) offer alternative ways of thinking.

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we cause these prerequisites and goals, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people). This model rests on two key properties of the vulnerability of the user and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (which detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We then present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.

Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models. While this leaderboard paradigm has been remarkably successful, a historical focus on performance-based evaluation has been at the expense of other qualities that the NLP community values in models, such as compactness, fairness, and energy efficiency. In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory. We frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them. With this framing, we formalize how leaderboards -- in their current form -- can be poor proxies for the NLP community at large. For example, a highly inefficient model would provide less utility to practitioners but not to a leaderboard, since it is a cost that only the former must bear. To allow practitioners to better estimate a model's utility to them, we advocate for more transparency on leaderboards, such as the reporting of statistics that are of practical concern (e.g., model size, energy efficiency, and inference latency).