Breakthroughs, discoveries, and DIY tips sent every weekday. Think back to some of your core memories -meeting a spouse, getting a job you really wanted, or finding out someone you loved had died. Some are pretty easy to recall, with vivid details that seem as fresh as the moment itself . Other memories might feel more ambiguous and faded, while the most stubborn ones don't come up at all . A study published today in the journal found that mundane memories get extra sticking power in the brain if they are connected to a significant event.

The person staring back from the computer screen may not actually exist, thanks to artificial intelligence (AI) capable of generating convincing but ultimately fake images of human faces. Now this same technology may power the next wave of innovations in materials design, according to Penn State scientists. "We hear a lot about deepfakes in the news today -- AI that can generate realistic images of human faces that don't correspond to real people," said Wesley Reinhart, assistant professor of materials science and engineering and Institute for Computational and Data Sciences faculty co-hire, at Penn State. "That's exactly the same technology we used in our research. The scientists trained a generative adversarial network (GAN) to create novel refractory high-entropy alloys, materials that can withstand ultra-high temperatures while maintaining their strength and that are used in technology from turbine blades to rockets. "There are a lot of rules about what makes an image of a human face or what makes an alloy, and it would be really difficult for you to know what all those rules are or to write them down by hand," Reinhart said. "The whole principle of this GAN is you have two neural networks that basically compete in order to learn what those rules are, and then generate examples that follow the rules." The team combed through hundreds of published examples of alloys to create a training dataset. The network features a generator that creates new compositions and a critic that tries to discern whether they look realistic compared to the training dataset. If the generator is successful, it is able to make alloys that the critic believes are real, and as this adversarial game continues over many iterations, the model improves, the scientists said. After this training, the scientists asked the model to focus on creating alloy compositions with specific properties that would be ideal for use in turbine blades. "Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand," said Zi-Kui Liu, Dorothy Pate Enright Professor of Materials Science and Engineering at Penn State. It's really what we are missing in our computational community in materials science in general."

Continental calls for universities to focus more on practical requirements. In order for Germany to catch up in the area of artificial intelligence (AI), Continental is calling for a fundamental shift in the training of AI specialists in universities. "AI is a key success factor for Germany as a business location. Without a fundamental rethink, Germanywill fall behind the global front runners when it comes to technology," explained Ariane Reinhart, Continental Executive Board member for Human Relations. "In order for the economy to have a sufficient number of AI graduates available, universities must focus their training more on practical requirements. Otherwise, our national economy risks losing ground on other leading economies when it comes to technology."

A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill can be learned given a task space that defines all the possible achievable goals. In this work, we focus on tasks defined in terms of final states (goals), and we face on the challenge where the agent aims to autonomously acquire a parameterized skill to manipulate an initially unknown environment. In this case, the task space is not known a priori and the agent has to autonomously discover it. The agent may posit as a task space its whole sensory space (i.e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space. However, the space of achievable goals may be a very tiny subspace in relation to the whole sensory space, thus directly using the sensor space as task space exposes the agent to the curse of dimensionality and makes existing autonomous skill acquisition algorithms inefficient. In this work we present an algorithm that actively discovers the manifold of the achievable goals within the sensor space. We validate the algorithm by employing it in multiple different simulated scenarios where the agent actions achieve different types of goals: moving a redundant arm, pushing an object, and changing the color of an object.

For shoppers who absolutely love a good deal but hate hunting through racks, ecommerce company thredUP has a new offering just for them. The secondhand retailer has launched Goody Boxes–a non-subscription, no commitment box filled with discounted items for purchase. Over time, thredUP's Goody Boxes will learn your tastes, using a machine learning algorithm to figure out what you like and dislike based on what you keep and what you send back, essentially taking the rack hunting out of shopping. Here's how it works: For a deposit of $20, the company will send a box filled with 10 to 20 secondhand items to customers to try on. Consumers can keep what they like and send back any items they don't want.

Digital music service Spotify recently acquired machine learning startup Niland to improve its recommendation and personalization technologies. In other words, Spotify wants to better connect its users to music they will like. The heart of this concept is nothing new. Netflix and Amazon, too, use machine learning – a type of artificial intelligence (AI) in which machines learn when exposed to new data without being programmed – to suggest content and products their respective users might like. And while this ability to tap into AI – machines that perform smart, human-like tasks – to analyze internal data is increasingly common, it's a bit more complicated when it comes to using AI to capture consumer attention externally, like, say, in search.