We develop a general duality between neural networks and compositional kernel Hilbert spaces. We introduce the notion of a computation skeleton, an acyclic graph that succinctly describes both a family of neural networks and a kernel space. Random neural networks are generated from a skeleton through node replication followed by sampling from a normal distribution to assign weights. The kernel space consists of functions that arise by compositions, averaging, and non-linear transformations governed by the skeleton's graph topology and activation functions. We prove that random networks induce representations which approximate the kernel space. In particular, it follows that random weight initialization often yields a favorable starting point for optimization despite the worst-case intractability of training neural networks.

In some cases, ChatGPT's outputs went beyond those of the human participants, including personalised insults and explicit threats. In some cases, ChatGPT's outputs went beyond those of the human participants, including personalised insults and explicit threats. 'I'll key your car': ChatGPT can become abusive when fed real-life arguments, study finds ChatGPT can escalate into abusive and even threatening language when drawn into prolonged, human-style conflict, according to a new study. Researchers tested how large language models (LLMs) responded to sustained hostility by feeding ChatGPT exchanges from real-life arguments and tracking how its behaviour changed over time. One expert not connected with the study described it as "one of the most interesting ever done into AI language and pragmatics Dr Vittorio Tantucci, who co-authored the research paper with Prof Jonathan Culpeper at Lancaster University, said their research found AI mirrored the dynamics of real-world disputes.

Tim Cook's Legacy Is Turning Apple Into a Subscription The soon-to-exit Apple CEO went all in on services. Now, the incoming CEO, John Ternus, will need to embrace the AI era. Tim Cook's tenure as CEO at Apple, which is coming to a close September 1, will likely be defined by operational efficiency and financial growth, ushering Apple into its trillion-dollar era. But his most significant achievement might be in doubling down on Apple's services business, which includes iCloud, the App Store, Apple Music, Apple TV+, News+, and more. It's the subscription layer on top of iOS, and almost all of the service apps are tightly integrated with Messages, the glue that keeps people stuck to their iPhones .

Some seemingly simple sequences of multiplication and addition grow so quickly that they question the very foundations of mathematics. Did you hear the one about the man who invented chess and got himself executed? Legend has it that a man called Sessa, who lived in India long ago, developed the rules for the game and presented them to a king. The king was delighted and offered the man his pick of reward. Sessa asked for a supposedly humble quantity of rice.

Alan Winfield is Professor of Robot Ethics at the University of the West of England (UWE), Visiting Professor at the University of York, and Associate Fellow of the Cambridge Centre for the Future of Intelligence. Alan co-founded the Bristol Robotics Laboratory, where his research is focussed on the science, engineering and ethics of cognitive robotics. Alan is an advocate for robot ethics; he chairs the advisory board of the Responsible Technology Institute at the University of Oxford and has co-drafted new standards on ethical risk assessment and transparency. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Claire chatted to Vikas Enti from Reframe Systems about using robotics and automation to build climate-resilient, high-performance homes. Vikas Enti is the co-founder and CEO of Reframe Systems, a physical AI company rethinking how homes are built through automation and localized fabrication. He previously spent more than a decade at Amazon Robotics, where he helped scale advanced robotics systems across global logistics networks. Today, he is applying those same principles of systems design and repeatable production to address the housing shortage. Vikas focuses on building climate-resilient, high-performance homes faster and more predictably than traditional methods.

What if training a robot to handle dirty, dangerous work on the factory floor was as simple as showing it how? Czech startup RoboTwin is doing exactly that, helping factory workers teach robots new skills by demonstration. Instead of writing complex code, workers perform the job once and RoboTwin's technology turns those movements into a robot programme - opening the door to automation for smaller manufacturers. Founded in Prague in 2021, RoboTwin builds handheld devices and no-code software that capture human movements and translate them into instructions for industrial robots. The aim is to make automation faster, simpler and more accessible to manufacturers that do not have specialist robotics programmers.

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by "seeing" through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot's ability to reliably grasp and manipulate objects that are blocked from view. This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

Simona Aracri is a researcher in the Institute of Marine Engineering at the National Research Council of Italy. Previously, she was a Post Doctoral Research Associate at the University of Edinburgh, working on the award winning project ORCA Hub and focusing on offshore robotic sensors. Her research uses innovative sensors and robotic platforms to push the boundaries of observational oceanography and environmental monitoring. She has spent more than 6 months at sea on oceanographic sampling campaigns, in the Mediterranean Sea, Pacific Ocean and the North Sea. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

If the goal of a robot is to perform a function, then minimizing the possibility of failure is a top priority when it comes to robotic design. But this minimization is at odds with the robotic raison d'être: systems with multiple units, or agents, can perform more diverse functions, but they also have more different parts that can potentially fail. Researchers led by Jamie Paik, head of the Reconfigurable Robotics Laboratory ( RRL) in EPFL's School of Engineering, have not only circumvented this problem, but flipped it: they have designed a modular robot that actually lowers its odds of failure by sharing resources among its individual agents. "For the first time, we have found a way to reverse the trend of increasing odds of failure with increasing function," Paik explains. "We introduce local resource sharing as a new paradigm in robotics, reducing the failure rate with a larger number of modules."