Performing real-world tasks such as cooking meals or assembling furniture requires an agent to determine long-term strategies. This is often formulated as aplanning problem.

The Download: Kenya's Great Carbon Valley, and the AI terms that were everywhere in 2025 Welcome to Kenya's Great Carbon Valley: a bold new gamble to fight climate change In June last year, startup Octavia Carbon began running a high-stakes test in the small town of Gilgil in south-central Kenya. It's harnessing some of the excess energy generated by vast clouds of steam under the Earth's surface to power prototypes of a machine that promises to remove carbon dioxide from the air in a manner that the company says is efficient, affordable, and--crucially--scalable. The company's long-term vision is undoubtedly ambitious--it wants to prove that direct air capture (DAC), as the process is known, can be a powerful tool to help the world keep temperatures from rising to ever more dangerous levels. But DAC is also a controversial technology, unproven at scale and wildly expensive to operate. On top of that, Kenya's Maasai people have plenty of reasons to distrust energy companies. This article is also part of the Big Story series: 's most important, ambitious reporting.

Abstract--This paper presents a soft robot finger capable of adaptive-twist deformation to grasp objects by wrapping them. For a soft hand to grasp and pick-up one object from densely contained multiple objects, a soft finger requires the adaptive-twist deformation function in both in-plane and out-of-plane directions. The function allows the finger to be inserted deeply into a limited gap among objects. Once inserted, the soft finger requires appropriate control of grasping force normal to contact surface, thereby maintaining the twisted deformation. In this paper, we refer to this type of grasping as grasping by wrapping. T o achieve these two functions by a single actuation source, we propose a variable stiffness mechanism that can adaptively change the stiffness as the pressure is higher . We conduct a finite element analysis (FEA) on the proposed mechanism and determine its design parameter based on the FEA result. Using the developed soft finger, we report basic experimental results and demonstrations on grasping various objects. There is great demand for task automation across industries, especially in the agricultural and food industries, because of constantly shrinking work-age population.

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We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series of straightforward questions, it uncovers the significant limitations of well-regarded models to perform tasks that humans manage with ease. It also highlights the potential of prompt engineering to mitigate some errors and underscores the necessity for better training methodologies. Our findings stress the importance of grounding LLMs with human reasoning and common sense, emphasising the need for human-in-the-loop for enterprise applications. We hope this work paves the way for future research to enhance the usefulness and reliability of new models.

Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore the novel field of incorporating privacy, security, and access control constraints with robot task planning approaches. We report preliminary results on the classical symbolic approach, deep-learned neural networks, and modern ideas using large language models as knowledge base. From analyzing their trade-offs, we conclude that a hybrid approach is necessary, and thereby present a new use case for the emerging field of neuro-symbolic artificial intelligence.

Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world. We tease these factors apart by leveraging counterfactual conditionals, which force language models to predict unusual consequences based on hypothetical propositions. We introduce a set of tests from psycholinguistic experiments, as well as larger-scale controlled datasets, to probe counterfactual predictions from five pre-trained language models. We find that models are consistently able to override real-world knowledge in counterfactual scenarios, and that this effect is more robust in case of stronger baseline world knowledge -- however, we also find that for most models this effect appears largely to be driven by simple lexical cues. When we mitigate effects of both world knowledge and lexical cues to test knowledge of linguistic nuances of counterfactuals, we find that only GPT-3 shows sensitivity to these nuances, though this sensitivity is also non-trivially impacted by lexical associative factors.

It seems that AI-created images have finally made it to the mainstream. On John Oliver's Last Week Tonight, the comedian/commentator dedicated an entire monologue and even a skit to AI programs such as DALL-E. These programs take inputted text data and create images based on the information provided. John Oliver spoke at length about not only the programs that he discovered but also what he found when searching through the images created by users using the program. In one instance, he looked up fellow British comedian, James Corden, who found himself the inspiration of a couple of images, mostly quite comical nature.

Eating a bowl of noodles has never been easy for me. Now I don't blame the chopsticks (yet to learn how to use'em) but my aversion towards the cabbage in the noodles. Sorting through those yummy strands, I neatly pick out the shreds of cabbage before gobbling the entire lot. How did I differentiate a strip of cabbage from a thread of noodle? Would have never given it a thought, if not for the growing importance of imitating the model of the human neurons in the technological space. In an attempt to replicate the much-marveled human intelligence, voluminous efforts are taken to turn machines into rational mortal-like beings.