Technology Engineering World's largest steam locomotive heads out on tour Union Pacific's Big Boy No. 4014 will travel coast-to-coast in commemoration of the semiquincentennial. Breakthroughs, discoveries, and DIY tips sent six days a week. The world's largest operating steam locomotive is hitting the road--or tracks . Union Pacific's Big Boy No. 4014 is heading out on its first coast-to-coast steam tour to celebrate the United States' 250th anniversary . The first leg begins on March 29, when Big Boy and other historical passenger cars from Union Pacific's Heritage Fleet will travel from the locomotive's home base in Cheyenne, Wyoming, west towards California.

Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image. These tags, marked with alphanumerics, can be indexed via text tokens for easy reference. Despite the extraordinary performance from GPT-4V, we observe that other Multimodal Large Language Models (MLLMs) struggle to understand these visual tags. To promote the learning of SoM prompting for open-source models, we propose a new learning paradigm: "list items one by one," which asks the model to enumerate and describe all visual tags placed on the image following the alphanumeric orders of tags. By integrating our curated dataset with other visual instruction tuning datasets, we are able to equip existing MLLMs with the SoM prompting ability. Furthermore, we evaluate our finetuned SoM models on five MLLM benchmarks. We find that this new dataset, even in a relatively small size (10k-30k images with tags), significantly enhances visual reasoning capabilities and reduces hallucinations for MLLMs. Perhaps surprisingly, these improvements persist even when the visual tags are omitted from input images during inference. This suggests the potential of "list items one by one" as a new paradigm for training MLLMs, which strengthens the object-text alignment through the use of visual tags in the training stage. Finally, we conduct analyses by probing trained models to understand the working mechanism of SoM. Our code and data are available at \url{https://github.com/zzxslp/SoM-LLaVA}.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?

In 1829, an event took place that unleashed a technological revolution. At the Rainhill Trials a group of steam locomotives squared off to determine which one could win a series of tests of speed, strength and reliability. The winning machine, Rocket, not only blew away its competition at the trials, it also set the direction for steam locomotive development for the following century. What does all this have to do with GPT-3, the transformer language model that OpenAI made available in a limited beta starting in June? Some reviewers have heralded GPT-3 as the first glimpse of artificial general intelligence, while others are calling it a massive lookup table.

Christof Koch is a neuroscientist distinguished by his rock-solid scientific work and romantic yearning to understand consciousness. He recently closed an essay by wondering: "What is it about the brain, the most complex piece of active matter in the known universe, that turns its activity into the feeling of life itself?" No coincidence with that phrasing--The Feeling of Life Itself is his latest book. He argues that consciousness is produced by the brain but that it's also more widespread in nature than we might suppose. His essay described new experimental work, from Stanford neuroscientist Kieran Fox and his colleagues, that explored the effects of electrically stimulating the brain, which revealed an ordering principle.

In 1829, an event took place that unleashed a technological revolution. At the Rainhill Trials a group of steam locomotives squared off to determine which one could win a series of tests of speed, strength and reliability. The winning machine, Rocket, not only blew away its competition at the trials, it also set the direction for steam locomotive development for the following century. What does all this have to do with GPT-3, the transformer language model that OpenAI made available in a limited beta starting in June? Some reviewers have heralded GPT-3 as the first glimpse of artificial general intelligence, while others are calling it a massive lookup table.

Artificial Intelligence is already impacting Manufacturing, Retail, Marketing, Healthcare, Food industries and more. Today we will take an in-depth look at another industry, that with proper AI expertise from development companies could be disrupted. Transportation is an industry that helps humanity with moving people their belongings from one location to the other. While doing that, this industry had experienced countless twists, turns, breakthroughs, and setbacks to get to the place where it is now. The year 1787 was the defining one for this industry because steamboat was introduced and changed everything.

The transportation url is actually starting to use Artificial Intelligence (AI) in mission critical tasks (for instance, self driving automobiles carrying passengers) in which the reliability as well as security of an AI system will be below question coming from the common public. Major issues of the transportation market as capability troubles, environmental pollution, reliability, safety, and wasted energy are actually providing ample opportunity (and potential for higher ROI) for AI innovation. For the benefit of this post,' transportation' is going to include all systems which move luggage as well as folks. We explore each of the applications and the future of their technology roadmap in more detail below. The compatibility of AI to transportation apps is actually a relatively natural match.

What does the world look like to AI? Researchers have puzzled over this for decades, but in recent years, the question has become more pressing. Machine vision systems are being deployed in more and more areas of life, from health care to self-driving cars, but "seeing" through the eyes of a machine -- understanding why it classified that person as a pedestrian but that one as a signpost -- is still a challenge. Our inability to do so could have serious, even fatal, consequences. Some would say it already has due to the deaths involving self-driving cars. New research from Google and nonprofit lab OpenAI hopes to further pry open the black box of AI vision by mapping the visual data these systems use to understand the world.

The world's largest robot has been unveiled and it is a completely autonomous railway system. AutoHaul has been developed by a mining firm and is being used to transport iron ore from mines to shipping ports 500 miles away (800 km) in Western Australia. This journey can be completed in just 40 hours, including the loading and dumping of the ferrous cargo. Its deployment is the end result of a project which has so far cost $940 million (£740 million). Rio Tinto, the corporation that built the infrastructure and hardware for the locomotive, says this could be the first step in transforming the firm's 1,000-mile (1,700-kilometre) network connecting 16 iron ore mines and two ports.