Large Language Models (LLMs) achieve superior performance through training-time scaling, and test-time scaling further enhances their capabilities by conducting effective reasoning during inference. However, as the scale of reasoning increases, existing test-time scaling methods suffer from accumulated historical information, which not only wastes computational resources but also interferes with effective reasoning. To address this issue, we observe that complex reasoning progress is often achieved by solving a sequence of independent subquestions, each being self-contained and verifiable. These subquestions are essentially atomic questions, relying primarily on their current state rather than accumulated history, similar to the memoryless transitions in a Markov process. Based on this observation, we propose Atom of Thoughts (AoT), where each state transition in the reasoning process consists of decomposing the current question into a dependency-based directed acyclic graph and contracting its subquestions, forming a new atomic question state. This iterative decomposition-contraction process continues until reaching directly solvable atomic questions, naturally realizing Markov transitions between question states. Furthermore, these atomic questions can be seamlessly integrated into existing test-time scaling methods, enabling AoT to serve as a plug-in enhancement for improving reasoning capabilities. Experiments across six benchmarks demonstrate the effectiveness of AoT both as a standalone framework and a plug-in enhancement. Notably, on HotpotQA, when applied to gpt-4o-mini, AoT achieves an 80.6% F1 score, surpassing o3-mini by 3.4% and DeepSeek-R1 by 10.6%. The code will be available at https://github.com/qixucen/atom.

Getty Images claims that Stability AI, the creator of Stable Diffusion, used images obtained from Getty Images to train their algorithms without obtaining proper licensing. This definition applies to photographs, art, and images that the artists allege have been infringed. First, let's discuss how the above-mentioned artificial intelligence models work. In general, deep neural networks and machine learning models are trained in a method that has its similarities to humans learning. Programmers do not instruct the algorithm to specifically do what it does, or in this case, do not get the algorithms to copy specific elements from original pictures when constructing a new image.

When Ben Wu, an engineer in China, wanted to install Facebook's open-source AI framework PyTorch in 2017, he visited its online community on GitHub and asked for some pointers. Soumith Chintala, a Facebook AI research engineer based in New York, showed him how he could download it quickly. PyTorch has become a foundational component of AI technology, thanks in large part to knowledge-sharing exchanges like the one between Wu and Chintala that happen every day. And although it's become increasingly corporatized, the borderless, open-source software movement has risen above geopolitical tensions between China and the U.S., which have centered on concerns over China's use of AI to carry out repressive surveillance, its plans to transfer civilian tech for military applications, and Chinese government espionage and intellectual property theft. "I'm definitely surprised at how much [of the] general global considerations you would have from a business angle don't really come in when you're talking about open-source collaboration, especially with AI," Chintala told Protocol in September when Facebook parent company Meta handed over PyTorch to the nonprofit open-source software consortium Linux Foundation.

Move over selfie, artificial intelligence (AI) might just be creating your world-class portrait. His art practice follows two main paths. On one hand, Tresset presents theatrical installations in which robotic agents are actors. He also crafts the computational systems driving the robots so that their behavior can be perceived as artistic, expressive and even obsessive. These systems are influenced by research into actual human behavior--more specifically, how humans make marks or draw, how humans depict other humans, how humans perceive artwork, and how humans relate to robots.

Anaconda, Inc., provider of the world's most popular data science platform, announced that Kevin Goldsmith has joined its team as Chief Technology Officer. In his role, Goldsmith will oversee innovation for Anaconda's current open-source and commercial offerings, as well as developing new solutions to bring data science practitioners together with innovators, vendors, and thought leaders in the industry. "Anaconda makes a real impact in the lives of data scientists around the world every day, and I'm excited to be part of the future of the company as it continues to expand that impact," said Goldsmith. "Millions of data science practitioners rely on Anaconda's solutions, and I'm proud to be part of a team that's obsessed with increasing the value it delivers to a growing community." Goldsmith brings more than 28 years of experience in software development and engineering management to the team.

Big data is changing how tennis stars train and play; but the key to success is taking all that information and turning it into something players can use to win. Craig O'Shannessy, official strategy analyst for both the ATP Tour and all-time great Novak Djokovic, says that the smart use of data when preparing can have a significant impact on a match. O'Shannessy explains to ZDNet at the ATP Tour Finals in London how he uses a range of tools to give Djokovic that data-led advantage. These tools include the Infosys Tennis Platform, which is being used for the first time in 2019 across the ATP Tour, which is the worldwide top-tier tennis tour for men organised by the Association of Tennis Professionals. The platform includes a portal that gives players and coaches access to advanced analytics and match video.

In a brightly coloured shipping container in east London, Rubens Filho is asking me to pick a card. "Any card," he says, fanning the pack out face down. "And don't worry, you can show me. I pull out the seven of spades, and show it to him; he gets me to sign my name on it with a marker pen. Then he slides it back into the middle of the pack, puts the cards back into their box and puts the box on the table in front of us. "Now," he says with a grin, "the magic begins." Filho is 51, tall, handsome and infectiously enthusiastic about the power of magic tricks and illusions. Born in Brazil, he's been a keen magician since adolescence. He came to Britain in 2012 to work in advertising, before, in 2015, setting up Abracademy, a startup dedicated to bringing magic – and in particular the skills needed to perform it – to the rest of us. "I think magic has a such a positive twist," he says. "It brings this soft approach that's hard to explain, this role of creating something beautiful." But he is also fascinated by the relationship between magic and neuroscience and psychology, and set up Abracademy Labs, an offshoot of Abracademy, to explore this connection. "Magic has lived in the'glitches' of the brain for a long time," he says. "How you see things, how you form beliefs, how you experience wonder.

Automation has left nations driving on Robotic Process Automation (RPA) and Artificial Intelligence (AI) based technologies globally. According to an academic study conducted by Goldsmiths (University of London) in collaboration with the enterprise software provider Automation Anywhere, 71% of Indian respondents said that their employees used RPA and AI-based augmentation in its full potential, the highest percentage for any of the four markets surveyed. The findings were published by the Augmented Human Enterprise where 66% of Indians surveyed said they are empowered to take risks to embrace automation and RPA while 77% added that their organization prioritized employee development. The study pointed out that India is big on employee engagement too, with an impressive 84% saying employee listening is a priority among Indian enterprises. Mihir Shukla, the CEO at Automation Anywhere asserted that "Think of the human body breathing. It's a complex and critical mechanism but automated so our brains are freed to power everything else we do. I think for many organizations, all they can do is'breathe.' It's so important, it's all the employees can focus on," If the breathing is automated in the organization, then employees have the times to focus on other strategic issues and opportunities within the organization, thus leading to a greater return on investment.

Dr. Rebecca Fiebrink is a Lecturer in Computing at Goldsmiths, University of London. She creates new technologies for digital music and art, and she designs new ways for humans to interact with computers in creative practice. Much of her current research combines techniques from human-computer interaction, machine learning, and signal processing to allow people to apply machine learning more effectively to new problems, such as the design of new digital musical instruments and gestural interfaces for gaming and health. She is also involved in projects developing rich interactive technologies for digital humanities scholarship, and in designing new approaches to integrating the arts into computer science teaching and outreach. Rebecca is the developer of the Wekinator system for interactive machine learning.

Every day, there are new headlines about AI and automation, predicting everything from the end of work to the creation of hyper-productive cyborg employees. The subject generates a lot of debate and a lot of hype, stoked along the way by fears for the future of humans and their jobs. What's often missing in this debate are reliable facts and insights. The reciprocal relationship between work design and employee performance is heavily influenced by the technology we use. But the scope of this collaboration and the opportunity for impact on both performance and our experience of work are still largely unknown.