Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we systematically investigate which models provide the best representations for a few-shot image classification task when pretrained on the Imagenet dataset. We test their representations when used as the starting point for different few-shot classification algorithms. We observe that models trained on a supervised classification task have higher performance than models trained in an unsupervised manner even when transferred to out-of-distribution datasets. Models trained with adversarial robustness transfer better, while having slightly lower accuracy than supervised models.

The CP 2002 paper entitled "Breaking Row and Column Symmetries in Matrix Models" by Flener et al. [6] describes some of the first work for identifying and analyzing row and column symmetry in mat rix models and for efficiently and effectively dealing with such symmetry u sing static symmetry-breaking ordering constraints. This commentary provides a retrospective on that work and highlights some of the subsequent work on the topic.

While AI techniques have found many successful applications in autonomous systems, many of them permit behaviours that are difficult to interpret and may lead to uncertain results. We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems. We give a new formulation of Goal Task Network (GTN) that is tailored for our model checking based framework. We then provide a systematic method that models GTNs in the model checker Process Analysis Toolkit (PAT). We present our planning and goal reasoning system as a framework called Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in an uncertain environment. Finally, we demonstrate the proposed ideas in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.

UPS has become the first drone delivery service to receive full approval from the Federal Aviation Administration. The company's program, called Flight Forward, is operated in partnership with Matternet, which provides drone logistics networking company in Mountain View, California. Previously, UPS's pilots were only allowed to fly the drones within line of sight, but the FAA approval means they'll be able to significantly expand their delivery range. 'This is history in the making, and we aren't done yet,' said David Abney, UPS chief executive officer in a statement. UPS's Flight Forward drone delivery program is the first to earn full approval by the FAA (pictured one of the drones they will use in the program) The program's currently deployed in Raleigh, North Carolina, where UPS's drones have made more than 1,000 flights carrying deliveries around the WakeMed Health & Hospitals campus.

Cyber Security Cooperative Research Centre (CSCRC), together with Data61, the innovation arm of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and cybersecurity startup Penten, have announced a joint research project that will focus on developing artificial intelligence (AI) enabled cybersecurity defence mechanisms. Under the arrangement announced at D61 Live on Wednesday, Penten will have access to Data61's AI research, which it will use to extend on its existing work to build AI-enabled technology such as "cyber traps" and "decoys". According to Penten CEO Matthew Wilson, using AI will help speed up the creation of cyber traps and make them more realistic. "Our solutions use artificial intelligence to learn the patterns of activity and content from surrounding computers and data. We then use this information to create realistic and believable mimics. This means we can deliver suitable content extremely efficiently, tailored to a customer environment and with minimal effort on the part of the defender," he said.

In the latest regulatory boost for expanded commercial drone services, the company also intends to gradually phase in routine night flights and heavier cargo limits--areas now generally off-limits to most operators. Under the Federal Aviation Administration's announcement Tuesday, the company's Flight Forward unit obtained an immediate green light to ship medical products and specimens in North Carolina across various hospital campuses. But the broad approval for an entire fleet of future drones and pilots on the ground--going beyond what the FAA approved previously--opens the door for many other types of longer-range applications spanning rural and suburban areas. The FAA approval doesn't apply to urban areas. Calling it a major step to enhance services for health-care customers and ultimately an array of other industries, the company said the FAA's approval "has no limits on the size or scope of operations."

A look at some clever uses of AI in travel and how it is already helping travel marketers overcome some of the biggest problems they're currently facing in the industry. Earlier this year, Sojern's State of the Industry: The 2019 Report on Travel Advertising, billed as the largest-ever survey of global travel marketers, revealed what industry insiders consider to be the greatest challenges for modern travel brands. Delivering personalised offers in real-time topped the list of challenges (46%) with ROI and profitability (45%), targeting travellers at specific points along the path to purchase (45%), keeping with the industry's fast-paced changes (45%) and using customer data effectively (44%) all closely followed. Artificial intelligence is already solving a lot of these and in this article, we take a look of some of these solutions. The generational shift from Babyboomers to Millennials has been particularly challenging, compounded by rapid advances in technology.

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One Georgia school district plans to spend $16.5 million to install artificial intelligence-powered surveillance cameras in its roughly 100 buildings in coming years. Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications.

Scientists can now weigh wild whales using remote-controlled drones hovering above them. In the past it has only been possible to measure the weight of free-living whales if they were dead and out of the water or stranded on a beach. But researchers have released aerial footage of them flying a drone over a mother and baby southern right whale, near Argentina, to work out how much they weigh. They can do this by taking photographs to work out the length, width and height of the animal. This can then be compared to past measurements of the density of that particular species of whale's body, to estimate its volume and weight.