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).

We introduce a new notion of generalization -- Distributional Generalization -- which roughly states that outputs of a classifier at train and test time are close *as distributions*, as opposed to close in just their average error. For example, if we mislabel 30% of dogs as cats in the train set of CIFAR-10, then a ResNet trained to interpolation will in fact mislabel roughly 30% of dogs as cats on the *test set* as well, while leaving other classes unaffected. This behavior is not captured by classical generalization, which would only consider the average error and not the distribution of errors over the input domain. Our formal conjectures, which are much more general than this example, characterize the form of distributional generalization that can be expected in terms of problem parameters: model architecture, training procedure, number of samples, and data distribution. We give empirical evidence for these conjectures across a variety of domains in machine learning, including neural networks, kernel machines, and decision trees. Our results thus advance our empirical understanding of interpolating classifiers.

Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defense. To do so, honeybees use an alarm pheromone that recruits nearby bees into mass stinging of the perceived threat. This alarm pheromone is carried directly on the stinger, hence its concentration builds up during the course of the attack. Here, we investigate how individual bees react to different alarm pheromone concentrations, and how this evolved response-pattern leads to better coordination at the group level. We first present an individual dose-response curve to the alarm pheromone, obtained experimentally. Second, we apply Projective Simulation to model each bee as an artificial learning agent that relies on the pheromone concentration to decide whether to sting or not. If the emergent collective performance benefits the colony, the individual reactions that led to it are enhanced via reinforcement learning, thus emulating natural selection. Predators are modeled in a realistic way so that the effect of factors such as their resistance, their killing rate or their frequency of attacks can be studied. We are able to reproduce the experimentally measured response-pattern of real bees, and to identify the main selection pressures that shaped it. Finally, we apply the model to a case study: by tuning the parameters to represent the environmental conditions of European or African bees, we can predict the difference in aggressiveness observed between these two subspecies.

Caterpillar's autonomous driving technology, which can be bolted on to existing machines, is helping the U.S. heavy equipment maker mitigate the heavy impact of the coronavirus crisis on sales of its traditional workhorses. With both small and large customers looking to protect their operations from future disruptions, demand has surged for machines that don't require human operators on board. Sales of Caterpillar's autonomous technology for mining operations have been growing at a double-digit percentage clip this year compared with 2019, according to previously unreported internal company data shared with Reuters. Fred Rio, worldwide product manager at Caterpillar's construction digital & technology division, told Reuters that a remote-control technology, which allows users to operate machines from several miles away, would be available for construction sites in January. The company is also working with space agencies to use satellite technology to allow an operator sitting in the United States to remotely communicate with machines on job sites in, say, Africa or elsewhere in the world, he said.

CHICAGO (Reuters) - Question: How can a company like Caterpillar CAT.N try to counter a slump in sales of bulldozers and trucks during a pandemic that has made every human a potential disease vector? Caterpillar's autonomous driving technology, which can be bolted on to existing machines, is helping the U.S. heavy equipment maker mitigate the heavy impact of the coronavirus crisis on sales of its traditional workhorses. With both small and large customers looking to protect their operations from future disruptions, demand has surged for machines that don't require human operators on board. Sales of Caterpillar's autonomous technology for mining operations have been growing at a double-digit percentage clip this year compared with 2019, according to previously unreported internal company data shared with Reuters. By contrast, sales of its yellow bulldozers, mining trucks and other equipment have been falling for the past nine months, a trend that's also hit its main rivals including Japan's Komatsu Ltd 6301.T and American player Deere & Co DE.N .

The Machine Learning for Mobile Robot Navigation in the Wild Symposium will consist of invited talks, technical presentations, spotlight posters, robot demonstrations, industry spotlights, breakout sessions, and interactive panel discussions. All contributions should be submitted electronically via AAAI EasyChair site.

Every lion has its own unique roar, one that lets the'kings of the jungle' recognise each other and could be used to track population movements, a study has found. Researchers from Oxford used machine learning to analyse the roars of various lions -- picking out the distinguishing frequency that can be used to tell them apart. According to the experts, lions' calls are usually issued in a set -- with one or two soft moans followed by several loud, full-throated roars and finishing with grunts. Previous research had suggested that lions could distinguish their peer's roars from each other -- allowing them to identify distant friends and hostile neighbours. However, it had not previously been clear what aspects of the calls' structure allowed them to discriminate between those made by different individuals.

The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules. We have previously shown that the algorithm can be further simplified and made more biologically plausible by (i) introducing a learnable set of backwards weights, which overcomes the weight-transport problem, and (ii) avoiding the computation of nonlinear derivatives at each neuron. However, tthe efficacy of these simplifications has, so far, only been tested on simple multi-layer-perceptron (MLP) networks. Here, we show that these simplifications still maintain performance using more complex CNN architectures and challenging datasets, which have proven difficult for other biologically-plausible schemes to scale to. We also investigate whether another biologically implausible assumption of the original AR algorithm - the frozen feedforward pass - can be relaxed without damaging performance. The backpropagation of error algorithm (backprop) has been the engine driving the successes of modern machine learning with deep neural networks.