Neural Information Processing Systems http://nips.cc/

CES 2025 was a fantastic show for companies making good on the promises of years past. At these events, we're used to seeing booth after booth of gadgets in concept and prototype phases, with vague details about final designs and release windows of "eventually." This year, however, the vibe was way more release-ready. A handful of high-profile projects that debuted at previous CESes are back with concrete plans and actual release dates, and many of these products are available right now. Remember Ballie, the rolling robot that Samsung debuted in 2020 and then brought to CES 2024?

Audio signals provide rich information for the robot interaction and object properties through contact. These information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ambiguous or incomplete. However, the usage of audio data in robot manipulation has been constrained to teleoperated demonstrations collected by either attaching a microphone to the robot or object, which significantly limits its usage in robot learning pipelines. In this work, we introduce ManiWAV: an 'ear-in-hand' data collection device to collect in-the-wild human demonstrations with synchronous audio and visual feedback, and a corresponding policy interface to learn robot manipulation policy directly from the demonstrations. We demonstrate the capabilities of our system through four contact-rich manipulation tasks that require either passively sensing the contact events and modes, or actively sensing the object surface materials and states. In addition, we show that our system can generalize to unseen in-the-wild environments, by learning from diverse in-the-wild human demonstrations. Project website: https://mani-wav.github.io/

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods.

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Among the components of the World Economic Forum's Great Resetare a drastically reduced population and the replacement of human labor with robots and artificial intelligence (AI). The question immediately comes to mind: can robots and AI really make all the stuff for the elites after they have gotten rid of the people? Because a plan has been formulated and described does not mean that it is possible to realize. The plan may contradict laws of logic or reality, or assume the existence of resources that do not exist. Podcaster and journalist James Delingpole, speaking to investigative journalist Whitney Webb on October 23, 2021, discussed this topic with his guest. One of the main pillars of that is automation and artificial intelligence.

Charles Simon, BSEE, MSCs, is the founder and CEO of Future AI: Technologies that Think. If you've recently eaten at a fast-food restaurant, driven on a toll road or checked out groceries at the supermarket, you may have noticed one thing they all have in common: Many people who used to help you have been replaced by machines. Most people regard this as simply the price of progress. As new technology is introduced, it displaces an older technology, along with the jobs it created. What is often overlooked is that the new technology, whatever it happens to be, typically brings with it new jobs for those with the smarts and the skills to take advantage of the new opportunities at hand.

Artificial Intelligence (AI) is here to stay, and many people are not happy. After all, it is hard to embrace something that could displace about 40 per cent of human jobs in the next 15 years. In an interview for CBS's 60 minutes, Kai-Fu Lee (a Chinese AI expert) also mentioned truck drivers, chauffeurs, waiters, and chefs as some of the professions that will be disrupted. That right there must have hit a nerve. However, everything is about to change because this article highlights some of the reasons you should not fear AI. And even if you were to ask the experts, they would unwaveringly confirm that regardless of all the noise, AI is here to benefit us all.

Artificial Intelligence (AI) will create as many jobs in the UK as it will displace over the next 20 years, a report has said. The analysis, by accountancy giant PwC, found AI would boost economic growth, creating new roles as others fell away. But it warned there would be "winners and losers" by industry sector, with many jobs likely to change. Opinion is split over AI's potential impact, with some warning it could leave many out of work in future. The pessimists argue AI is different to previous forms of technological change, because robots and algorithms will be able to do intellectual as well as routine physical tasks.

Successful firms will boost profits as a result, much of which will be reinvested either in those companies or in other businesses by shareholders receiving dividends and realising capital gains. To stay competitive, firms will ultimately have to pass most of these benefits on to consumers in the form of lower (quality-adjusted) prices, which will have the effect of increasing real income levels. This means that households can buy more with their money and, as a result, firms will need to hire additional workers to respond to the extra demand. We refer to this as the income effect, which offsets the displacement effect on jobs.