Kushal Kedia (left) and Prithwish Dan (right) are members of the development team behind RHyME, a system that allows robots to learn tasks by watching a single how-to video. Cornell researchers have developed a new robotic framework powered by artificial intelligence – called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) – that allows robots to learn tasks by watching a single how-to video. RHyME could fast-track the development and deployment of robotic systems by significantly reducing the time, energy and money needed to train them, the researchers said. "One of the annoying things about working with robots is collecting so much data on the robot doing different tasks," said Kushal Kedia, a doctoral student in the field of computer science and lead author of a corresponding paper on RHyME. "That's not how humans do tasks. We look at other people as inspiration."

As any adult who loves video games knows, there are simply too many of them – 19,000 games were released in 2024 on PC games storefront Steam alone, not counting all the playable delights on consoles and smartphones. Most of us have backlogs of unplayed classics that make us feel guilty about buying newer games. Finding things that are actually good, meanwhile, can feel totally impossible. At least 50% of the questions people send in for this newsletter are a variant of "Help, what should I play?" We do our best to help, but even though it's my job to know about games, I still don't have infinite time to play them.

Prof Antonio Toralba AI has been trying to figure out a way to learn from data without labels just like humans do. So far, self-supervised learning has been used for context prediciton, colorization, audio prediction, solving puzzle and more. Self-supervises systems learn by themselves by creating a pre-task which will help with the learning by itself. This is a system doesn’t require any training labels. Another way is learning by visual representations. Self-supervised methods generally involv

Saryu Nayyar is CEO of Gurucul, a provider of behavioral security analytics technology and a recognized expert in cyber risk management. Providing effective cybersecurity measures for your organization is like playing a very serious cat-and-mouse game. If you aren't familiar with the idiom, cat and mouse is an interaction in which the advantage continually shifts between the contestants. One moment, the cat appears ready to pounce on the mouse, and the next moment, the mouse dodges the advance. Then, the cat blocks the mouse's path but the mouse jukes and goes the other way.

This paper describes Netmarble's submission to WMT21 Automatic Post-Editing (APE) Shared Task for the English-German language pair. First, we propose a Curriculum Training Strategy in training stages. Facebook Fair's WMT19 news translation model was chosen to engage the large and powerful pre-trained neural networks. Then, we post-train the translation model with different levels of data at each training stages. As the training stages go on, we make the system learn to solve multiple tasks by adding extra information at different training stages gradually. We also show a way to utilize the additional data in large volume for APE tasks. For further improvement, we apply Multi-Task Learning Strategy with the Dynamic Weight Average during the fine-tuning stage. To fine-tune the APE corpus with limited data, we add some related subtasks to learn a unified representation. Finally, for better performance, we leverage external translations as augmented machine translation (MT) during the post-training and fine-tuning. As experimental results show, our APE system significantly improves the translations of provided MT results by -2.848 and +3.74 on the development dataset in terms of TER and BLEU, respectively. It also demonstrates its effectiveness on the test dataset with higher quality than the development dataset.

Dutch and Spanish computer scientists have discovered how systems that use artificial intelligence (AI) learn in practice. In many systems that are based on so-called'deep learning', it was not clear how that learning process actually took place. The researchers have now managed to figure out how an image recognition system learns about its environment. Then they simplified that learning system by forcing it to focus on less important information as well. AI systems for image recognition are of great importance for autonomous driving cars, for a start.

Lee Sedol, a world-class Go Champion, was flummoxed by the 37th move Deepmind's AlphaGo made in the second match of the famous 2016 series. So flummoxed that it took him nearly 15 minutes to formulate a response. The move was strange to other experienced Go players as well, with one commentator suggesting it was a mistake. In fact, it was a canonical example of an artificial intelligence algorithm learning something that seemed to go beyond just pattern recognition in data -- learning something strategic and even creative. Indeed, beyond just feeding the algorithm past examples of Go champions playing games, Deepmind developers trained AlphaGo by having it play many millions of matches against itself.

Our ecosystem is changing fast, and your ability as a leader to clearly distinguish between the powers of emerging technologies is important for the success of your business. Poor investments into new ventures can threaten your competitiveness and waste valuable resources. If you invest into an AI venture, but you treat it as a software venture, then you are doing it wrong. Despite the huge business potential of AI technologies, many AI ventures are poorly executed and miss significant business opportunities. There are many reasons for this poor execution, e.g., ill-prepared culture and strategy, insufficient access to talent, and poor data and infrastructure preparedness.

In recent years, artificial intelligence has been on the rise in the diagnosis of medical imaging. A doctor can look at an X-ray or biopsy to identify abnormalities, but this can increasingly also be done by an AI system by means of "deep learning" (see'Background: what is deep learning' below). Such a system learns to arrive at a diagnosis on its own, and in some cases it does this just as well or better than experienced doctors. The two major differences compared to a human doctor are, first, that AI is often not transparent in how it's analyzing the images, and, second, that these systems are quite "lazy." AI looks at what is needed for a particular diagnosis, and then stops.

"You can already see a material effect that deepfakes have had," said Nick Dufour, one of the Google engineers overseeing the company's deepfake research. "They have allowed people to claim that video evidence that would otherwise be very convincing is a fake." For decades, computer software has allowed people to manipulate photos and videos or create fake images from scratch. But it has been a slow, painstaking process usually reserved for experts trained in the vagaries of software like Adobe Photoshop or After Effects. Now, artificial intelligence technologies are streamlining the process, reducing the cost, time and skill needed to doctor digital images.