As natural language becomes the default interface for human-AI interaction, there is a critical need for LMs to appropriately communicate uncertainties in downstream applications. In this work, we investigate how LMs incorporate confidence about their responses via natural language and how downstream users behave in response to LM-articulated uncertainties. We examine publicly deployed models and find that LMs are unable to express uncertainties when answering questions even when they produce incorrect responses. LMs can be explicitly prompted to express confidences, but tend to be overconfident, resulting in high error rates (on average 47%) among confident responses. We test the risks of LM overconfidence by running human experiments and show that users rely heavily on LM generations, whether or not they are marked by certainty. Lastly, we investigate the preference-annotated datasets used in RLHF alignment and find that humans have a bias against texts with uncertainty. Our work highlights a new set of safety harms facing human-LM interactions and proposes design recommendations and mitigating strategies moving forward.

Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press) [Minsky, Marvin, Papert, Seymour A., Bottou, Leon] on Amazon.com. *FREE* shipping on qualifying offers. Perceptrons, Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou: An Introduction to Computational Geometry (The MIT Press)

Starting last fall, Blake Lemoine began asking a computer about its feelings. An engineer for Google's Responsible AI group, Lemoine was tasked with testing one of the company's AI systems, the Language Model for Dialogue Applications, or LaMDA, to make sure it didn't start spitting out hate speech. But as Lemoine spent time with the program, their conversations turned to questions about religion, emotion, and the program's understanding of its own existence. Lemoine: Are there experiences you have that you can't find a close word for? Sometimes I experience new feelings that I cannot explain perfectly in your language.

Whenever I have had the displeasure of interacting with an obtuse online customer service bot or an automated phone service, I have come away with the conclusion that whatever "intelligence" I have just encountered was most certainly artificial and not particularly smart, and definitely not human. However, this likely would not have been the case with Google's experimental LaMDA (Language Model for Dialogue Applications). Recently, an engineer at the tech giant's Responsible AI organisation carried the chatbot to global headlines after claiming that he reached the conclusion that it is not merely a highly sophisticated computer algorithm and it possesses sentience – ie, the capacity to experience feelings and sensations. To prove his point, Blake Lemoine also published the transcript of conversations he and another colleague had with LaMDA. In response, the engineer has been suspended and put on paid leave for allegedly breaching Google's confidentiality policies.

This video will help you leverage the power of TensorFlow to perform advanced image processing. TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. This video will help you leverage the power of TensorFlow to perform advanced image processing. This course is a continuation of the Intro to Computer Vision course, building on top of the skills learned in that course. In this course, you'll dive deeper as we cover more advanced computer vision concepts.

Technology is progressively reshaping the domestic environment as we know it, enhancing home security and the overall ambient quality through smart connected devices. However, demographic shift and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare. Elderly monitoring is only one of the possible service applications an intelligent robotic platform can handle for collective wellbeing. In this paper, we present Marvin, a novel assistive robot we developed with a modular layer-based architecture, merging a flexible mechanical design with state-of-the-art Artificial Intelligence for perception and vocal control. With respect to previous works on robotic assistants, we propose an omnidirectional platform provided with four mecanum wheels, which enable autonomous navigation in conjunction with efficient obstacle avoidance in cluttered environments. Moreover, we design a controllable positioning device to extend the visual range of sensors and to improve the access to the user interface for telepresence and connectivity. Lightweight deep learning solutions for visual perception, person pose classification and vocal command completely run on the embedded hardware of the robot, avoiding privacy issues arising from private data collection on cloud services.

In this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments hich contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.

If we are honest, this time has not really ended. Thanks to the new partnership between the MySolutions Group and Imnoo AG, this could soon change. Request, offer, calculation... and the order is lost If you want to have a CNC milled part produced, you have to ask for a quotation. The supplier will then take a few days to get back to you. You first have to calculate and check the technical and economic framework data.

They say "behind every good man, there is a woman," but in the world of blockbuster movies, the best allies to have by your side are often of the robotic persuasion. Always ready to dig you out of a rough spot, or march gung-ho into a battle, the movie robot sidekick has become a staple in modern sci-fi and action/adventure. Sure, there have been some bad-ass solo robots over the years like Optimus Prime, Ava of Ex-Machina fame, and even Robocop (although, technically he's a cyborg), but we're here celebrating the sidekick. The robots that make the best partners in crime. Whether it's intergalactic co-pilots, shape-shifting planetary protectors, or time-travelling androids, join us as we count down the 10 best movie robot sidekicks.

Maker Faire has always been not only a chance to see things made by passionate community members but also an opportunity to experience cutting edge technology as it is explored by the brilliant people around you. I can remember going to Maker Faires and seeing the evolution of the cheap desktop 3d printer first hand. Now, we have the opportunity to see similar growing and exploring in the areas of machine learning and artificial intelligence. Get yourself to World Maker Faire New York September 22nd-23rd, and you'll have a chance to see people making some incredible things. Here are just a few examples of the AI based projects that we expect to have there.