Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX. Initial experiments reveal that RT-1-X does not generalize zero-shot to the unseen type of robot. However, fine-tuning of the RT-1-X model by demonstration allows the robot to learn a pickup task which was part of the foundation model (but learned for another type of robot). When the robot is presented with an object that is included in the foundation model but not in the fine-tuning dataset, it demonstrates that only the skill, but not the object-specific knowledge, has been transferred.

Last week, Apple held its Worldwide Developers Conference, the annual event that is often used to showcase the company's most significant innovations. Much of the presentation this year was devoted to A.I., or, as the company is branding it, Apple Intelligence. Whereas Google and Microsoft have leaped headlong into A.I. with their Gemini and OpenAI products, respectively, Apple is so far taking a narrower approach. The A.I. model it is unveiling on iPhone hardware is relatively weak. A.I. models are measured on their number of "parameters," or the variables adjusted during the training process; while OpenAI's GPT-4 has more than one and a half trillion parameters, Apple's model has three billion.

Art is an expression of oneself and is often used to convey a message, tell a story or evoke emotions. The advancement of technology has given artists more tools to enhance their creations, and one such tool is Stable Diffusion Animation. This innovative technology is a game-changer, allowing artists to bring their static artwork to life with fluid, mesmerizing animations that add an extra dimension to their work. In this article, we will discuss what Stable Diffusion Animation is, how it works, and the benefits of incorporating it into your artwork. Stable Diffusion Animation is a technique that enables artists to create dynamic animations from static images.

Emerging technologies such as AI, ML and IoT have been driving the technology market as well as the geospatial industry strongly. These technologies have provided data processing and analysis on fingertips of the people. For instance, mapping out an area earlier used to be a challenge and required a great deal of effort – from surveying and fetching the data to various compliances and government permissions. Whereas now, one can just fly a drone and through AI and ML applications, an analytical dataset becomes instantly available. With the new geospatial policy in India, emerging technologies will get a boost and play a major role in creating applications that will transform the geospatial sector in entirety. Hence, these evolving technologies are revolutionizing the industry and there is a scope for many new opportunities in this industry.

It was only a matter of time before the culture wars came to AI. Since the release of ChatGPT in late 2022, Elon Musk has railed on Twitter against what he has called "Woke AI." He has specifically criticized ChatGPT's developer, OpenAI, for the features designed to prevent the chatbot from parroting racism and sexism. Now, the billionaire is courting AI researchers with a proposal to start a new AI company to rival the developer of ChatGPT, the tech news site The Information reported on Wednesday. "The danger of training AI to be woke--in other words, lie--is deadly," Musk tweeted in December.

The problem of underrepresentation of women studying STEM subjects is well known and is being faced by several nations across the world. The field of computer science is no exception to this deteriorating gender ratio, nor is the Indian case. The male:female population ratio in India is 1.06, but the ratio of females making it to engineering institutions is lower, at 1.79.1 In absolute numbers, India produces around 1.5 million engineers from its 6,000 engineering institutions across the country.2 When it comes to employ-ability, 4.03% of male engineering students are employable by IT product firms, while only 2.54% of females are employable by these firms, and 16.67% of males as against 15.49% of females are employable by IT Services organizations. If we shift our focus to the employability of the graduates of top engineering institutions in the country--Indian Institutes of Technology (IITs), National Institutes of Technology (NITs), and other leading engineering educational institutions including International Institutes of Information Technology (IIITs)--employability among fresh graduates in IT product roles increases to 22.67%, and in IT Services roles, it is 36.29%.1

In this article, you will learn about conditional GAN (Generative Adversarial Network) and will be able to build one from scratch. After that you will be able to apply the cGAN model on a fashion products dataset for converting sketches of products to color images. If you would like to understand what are GANs [1] you can check out our previous tutorials on Latent Spaces. If we would like to generate one set of images while giving a different set of images as an input to a GAN model, this problem is called Image Translation. A classic GAN architecture doesn't take into account class labels therefore, we require a modified version of GAN.

In this article, you will learn about conditional GAN (Generative Adversarial Network) and will be able to build one from scratch. After that you will be able to apply the cGAN model on a fashion products dataset for converting sketches of products to color images. If you would like to understand what are GANs [1] you can check out our previous tutorials on Latent Spaces. If we would like to generate one set of images while giving a different set of images as an input to a GAN model, this problem is called Image Translation. A classic GAN architecture doesn't take into account class labels therefore, we require a modified version of GAN.

Just how much will AI influence tomorrow's consumer economy? Will it know much we like avocado? When you talk to the average person about harnessing AI, many don't consider these questions. They are prone to offload fears of Skynet rather than contemplate how this tech will be used in the real world, as in our sushi bar example. Those with a little more subject matter knowledge may point to purely digital applications, such as social media platforms identifying terrorist content sans human intervention, or drug companies using machine learning to sift through mountains of health data for tomorrow's cures.

By the time Aelita Andre turned three, she had more art-world accolades than many professional artists. She started painting at nine months old, and galleries were showing her work when she was just two. Now 14 years old, the Australian abstract artist is still going strong; she just closed her most recent solo show in South Korea this month. Most children are innately creative and curious. But some are obsessively so and as adults end up transforming their field--or the world.