Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language representations. Among existing approaches, a majority of current VLA systems employ parallel two finger grippers as their default end effectors. However, such grippers face inherent limitations in handling certain real world tasks such as wiping glass surfaces or opening drawers without handles due to insufficient contact area or lack of adhesion. To overcome these challenges, we present a low cost, integrated hardware design that combines a mechanical two finger gripper with a vacuum suction unit, enabling dual mode manipulation within a single end effector. Our system supports flexible switching or synergistic use of both modalities, expanding the range of feasible tasks. We validate the efficiency and practicality of our design within two state of the art VLA frameworks: DexVLA and Pi0. Experimental results demonstrate that with the proposed hybrid end effector, robots can successfully perform multiple complex tasks that are infeasible for conventional two finger grippers alone. All hardware designs and controlling systems will be released.

Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can alter their propensity to deceive. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Former Pentagon policy analyst Paul Scharre has recalled the story in his upcoming book Four Battlegrounds: Power in the Age of Artificial Intelligence. In the book, which will be released on February 28, Scharre recounts how the U.S. Army was testing AI monitoring systems and decided to use the Marines to help build the algorithms that the security cameras would use. They then attempted to put the AI system to the test and see if the squad of Marines could find new ways to avoid detection and evade the cameras. To train the AI, the security cameras, which were developed by Defense Advanced Research Projects Agency's (DARPA) Squad X program, required data in the form of a squad of Marines spending six days walking around in front of them. After six days spent training the algorithm, the Marines decided to put the AI security cameras to the test.

It wasn't that long ago that talking to computers was the preserve of movies and science fiction. Slowly, voice recognition improved, and these days it's getting to be pretty usable. The technology has moved beyond basic keywords, and can now parse sentences in natural language. The device is built around Google's AIY Voice Kit, which consists of a Raspberry Pi with some additional hardware and software to enable it to process voice queries. This allows WhatIsThat to respond to users asking questions by taking a photo, and then identifying what it sees in the frame.

The Cambridgeshire farm used to test Amazon's new delivery drones has become a heavily guarded site that's shrouded in paranoia and secrecy. A group of photographers visiting the testing ground has filmed the moment they were turned away from by security guards, who they say were equipped with ear pieces, binoculars and radios. One of the photographers described the confrontation as'hostile' and said that it was like'some kind of cult'. In the footage capturing the exchange last Thursday, one security guard, who was wearing a pair of binoculars around his neck, asked the photographers to leave while standing next to a private property sign. He refused to confirm or deny whether he was security or answer any questions.

For the past few years, the drumbeat of think pieces about automation taking your job–yes,your job–has gotten both louder and more incessant. Smart people like the folks at Oxford Martin and Gartner forecast more and more jobs being gobbled up by our mechanical overlords and President Obama made a passing reference in his otherwise upbeat final State of the Union address. But technological unemployment has been around forever; you can actually go back to the ancient Greeks here or the famous example of English luddites throwing their shoes into weaving machines they felt were destroying the textile industry (fun fact: those shoes were called "sabots" and yes, that's where the word "sabotage" comes from). The point is, societies have forever dealt with technological unemployment. But even as automation continues–and make no mistake, it absolutely will–don't buy into the Chicken Littles who say your job–yes, your job–is next. Automation isn't coming to take your job.