The first episode of Sesame Street in 1969 included a segment called "One of These Things Is Not Like the Other." Viewers were asked to consider a poster that displayed three 2s and one W, and to decide--while singing along to the game's eponymous jingle--which symbol didn't belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between "same" and "different" was enough.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. In 2019, Facebook open-sourced AI Habitat, a simulator that can train AI systems embodying things like a home robot to operate in environments meant to mimic real-world settings, like apartments and offices. Today Facebook announced that it's extended the capabilities of Habitat to make it "orders of magnitude" faster than other 3D simulators available, allowing researchers to perform more complex tasks in simulation, like setting the table and stocking the fridge. Coinciding with this, Facebook collaborated with 3D space capture company Matterport to open-source what it claims is the largest dataset of indoor 3D scans to date.

In 2017-18, only six per cent of Australian soft plastic waste was recycled. The rest added to the growing mountain of plastic in landfills around the country. The biggest problem is the lack of an automatic solution to sort soft plastic waste from co-mingled recycling. Vucetic, an Engineers Australia Fellow, explained that this is because soft plastics like bread bags and cling wrap get tangled in machinery, causing equipment failures and contaminating other waste streams. Sydney-based recycling provider iQRenew invited Vucetic's team at the University of Sydney's Centre for IoT and Telecommunications to see the problem first hand, and potentially help them automate their processes.

Explaining Traditional Engineering Models It is a well-known fact that models of physical phenomena that are generated through mathematical equations can be explained. This is one of the main reasons behind the expectation of engineers and scientists that any potential model of the physical phenomena should be explainable. Explainability of the traditional models of physical phenomena is achieved through the solutions of the mathematical equations that are used to build the models. Explanations of such models are achieved through analytical solutions (for reasonably simple mathematical equations) or numerical solutions (for complex mathematical equations) of the mathematical equations. Solutions of the mathematical equations provide the opportunities to get answers to almost any question that might be asked from the model of the physical phenomena. Solutions of the mathematical equations are used to explain why and how certain results are generated by the model. It allows examination and explanation of the influence and effect of all the involved parameters (variables) on one another and on the model's results (output parameters).

Rescuers deployed sonar and camera equipment early on as officials scoured the rubble for survivors. Heavy machinery was brought in to remove some bits of the pancaked building materials. Yet, nearly 150 people remain unaccounted for. And officials still have a tedious mission ahead as teams try to avoid falling debris and other unforeseen obstacles.

New machine learning algorithms could soon help firefighters forecast dangerous flashover ignition events using sensor data from burning buildings. Called P-Flash, the system was developed by Thomas Cleary and colleagues at the National Institute of Standards and Technology (NIST) in the US and Hong Kong Polytechnic University. Trained using data from thousands of simulated fires, the model can predict some flashovers in housefires up to 30 s before they occur. Flashovers are among the most hazardous threats faced by firefighters. At high temperatures, all exposed combustible material in a room can be ignited simultaneously, releasing a huge amount of energy. To avoid danger, while maximizing the amount of time spent searching a fire for victims, it is critical for firefighters to predict these events as far in advance as possible.

Tsukuba, Japan - Depression is a worldwide problem, with serious consequences for individual health and the economy, and rapid and effective screening tools are thus urgently needed to counteract its increasing prevalence. Now, researchers from Japan have found that artificial intelligence (AI) can be used to detect signs of depression. In a study published this month in BMJ Open, researchers from University of Tsukuba have revealed that an AI system using machine learning could predict psychological distress among workers, which is a risk factor for depression. Although many questionnaires exist that screen for mental health conditions, individuals may be hesitant to answer truthfully questions about subjective mood due to social stigma regarding mental health. However, a machine learning system could be used to screen depression/psychological distress without such data, something the researchers at University of Tsukuba aimed to address.

Alexa wants to help your child learn how to read. With Amazon's new Reading Sidekick, kids can say "Alexa, let's read," to an Amazon Kids-enabled Echo device or the Amazon Kids app on a tablet and the artificial intelligence-powered assistant will take turns reading with them. An Amazon Kids subscription ($2.99 monthly) is required. Kids can choose from hundreds of physical and digital books that are supported, with more being added monthly. After asking Alexa to read with them, the AI assistant will ask how much do they want to read: a little, a lot, or taking turns.

In 2016, Columbus, Ohio, beat out 77 other small and midsize US cities for a pot of $50 million that was meant to reshape its future. The Department of Transportation's Smart City Challenge was the first competition of its kind, conceived as a down payment to jump-start one city's adaptation to the new technologies that were suddenly everywhere. Ride-hail companies like Uber and Lyft were ascendant, car-sharing companies like Car2Go were raising their national profile, and autonomous vehicles seemed to be right around the corner. "Our proposed approach is revolutionary," the city wrote in its winning grant proposal, which pledged to focus on projects to help the city's most underserved neighborhoods. It laid out plans to experiment with Wi-Fi-enabled kiosks to help residents plan trips, apps to pay bus and ride-hail fares and find parking spots, autonomous shuttles, and sensor-connected trucks.

Voice assistants equipped with speech-to-text technology have seen a major boost in performance and usage, thanks to the new powerful machine learning methods based on deep neural networks. These methods follow a supervised learning approach, requiring large amounts of paired speech-text data to train the best performing speech-to-text transcription models. After collecting large amounts of relevant and diverse spoken utterances, the complex and intensive task of annotating and labelling of the collected speech data awaits. To get a feel for a typical scenario, let's look at some estimates. On average a typical user query, for example "Do you have the Christmas edition with Santa?", would last for about 3 seconds.