Last week’s look at AI and ML news includes: Elon Musk Launches xAI to Build ‘Maximum Curious’ AGI, Alibaba Launches Tongyi Wanxian for Image Generation from Prompts, and Computer Vision Helps Amazon Workers Prevent Damaged Products from Getting Shipped
Elon Musk Launches xAI to Build ‘Maximum Curious’ AGI
Last week, Elon Musk, the CEO of Tesla and owner of Twitter, launched a new artificial intelligence (AI) company called xAI to “understand the true nature of the universe.”
The xAI team consists of 12 people, including Musk himself, with impressive experience in organizations such as OpenAI, DeepMind, Google Research, Microsoft Research, and Tesla. The company is also advised by Dan Hendrycks, the director of the nonprofit Center for AI Safety. Despite working closely with X Corp, the parent company of Twitter, and Tesla, the newly established xAI will be independent.
The xAI team hosted a Twitter Spaces chat to discuss their plans for the company. Elon Musk said the goal of xAI is to build a good AGI (artificial general intelligence) and confirmed the purpose of understanding the universe. Musk said that the safest way is to build an AGI that is “maximum curious” and “truth curious” and to try and minimize the error between what humans think is true and what is actually true.
xAI will compete with major tech companies that have introduced their own generative AI tools. OpenAI’s GPT-4, the latest version of ChatGPT, was launched in March 2023. Microsoft introduced Bing, which utilizes ChatGPT, and Google released AI Bard as their respective AI chatbot offerings. Alibaba, the Chinese giant, also unveiled its own ChatGPT competitor that supports both Chinese and English languages.
Musk has said that it will take time until xAI is relevant on the scale of Microsoft AI or Google’s DeepMind AI.
Alibaba Launches Tongyi Wanxian To Enable Image Generation from Prompts
Chinese technology giant Alibaba has launched Tongyi Wanxiang, a generative AI tool to produce images from prompts. In addition to the Tongyi Wanxiang image generator, which roughly translates as “truth from tens of thousands of pictures,” presented on Friday, Alibaba Cloud has also rolled out ModelScopeGPT, an AI tool for developers. Initially available to enterprise customers in beta form, Tongyi Wanxiang allows users to input prompts in Chinese or English, and the AI tool will generate images in various styles, including sketches and 3D cartoons.
Text-to-image generation example by Tongyi Wanxiang:
Prompt: Immersive, captivating, grayscale coloring, featuring a tiger in the tranquil mandala forest. The image is composed of lines and brushstrokes.
Alibaba’s cloud division has made the tool available for beta testing to enterprise customers in China. Alibaba’s image generator will compete with OpenAI’s DALL-E and Midjourney Inc’s Midjourney, U.S.-based rivals that have gained a large following worldwide. Alibaba Cloud emerged from a massive overhaul announced in March that split the Chinese tech major into six units. In April, it launched a ChatGPT-like text generator Tongyi Qianwen.
However, there are already existing AI text-to-image generation services available. OpenAI’s DALL-E and Stable Diffusion are two well-known examples. Tongyi Wanxiang aims to make high-quality generative AI imagery more accessible, enabling innovative AI art and creative expressions across sectors such as e-commerce, gaming, design, and advertising.
Computer Vision Helps Amazon Workers Prevent Damaged Products from Getting Shipped
Earlier last week, Amazon posted some details on its blog about its successful use of computer vision to identify defective products before they are shipped. The company is automating product inspection to gain more efficiency over manual processes. A team of scientists at Amazon Fulfillment Technologies in Berlin is developing advanced AI capabilities to detect irregularities and flag defective items.
Damaged products are rare, making training data collection challenging. The team has trained a machine learning model using reference images to compare products with how they should look. This approach uses computer vision to scan each item passing through the warehouse, allowing the AI to make subjective decisions about damage, similar to the way humans do.
Amazon states that the AI system is three times more effective than manual identification of damaged products. Due to its success, plans are being made to implement the system in other Amazon facilities, with the goal of scanning over 40 million customer products monthly. The technology will help ensure that customers receive undamaged gifts during the holiday season.
While the machine learning model will continue to improve as it learns from more data, the collaboration between humans and machines is key. If the AI makes errors, feedback from operations employees helps improve the model’s decision-making. The technology is expected to streamline tasks, manage costs, and free up employees to focus on other important responsibilities.
Looking ahead, Amazon plans to expand the AI system’s capabilities beyond damage detection, potentially identifying when and where damage occurred. By using AI in conjunction with human expertise, Amazon aims to deliver products in better condition and improve overall efficiency in fulfillment centers.