Tesla (TSLA) will raise the price of its top-end driver assistance system – Full Self-Driving (FSD) – according to a tweet from CEO Elon Musk. Musk said in a tweet on Sunday, "After wide release of FSD Beta 10.69.2, price of FSD will rise to $15k in North America on September 5th." After wide release of FSD Beta 10.69.2, price of FSD will rise to $15k in North America on September 5th. Current price will be honored for orders made before Sept 5th, but delivered later. The cost of the FSD will increase $3,000 on Sept. 5, up from its previous $12,000 price tag.

Earlier this month, Meta (the corporation formerly known as Facebook) released an AI chatbot with the innocuous name Blenderbot that anyone in the US can talk with. Immediately, users all over the country started posting the AI's takes condemning Facebook, while pointing out that, as has often been the case with language models like this one, it's really easy to get the AI to spread racist stereotypes and conspiracy theories. When I played with Blenderbot, I definitely saw my share of bizarre AI-generated conspiracy theories, like one about how big government is suppressing the true Bible, plus plenty of horrifying moral claims. But that wasn't what surprised me. We know language models, even advanced ones, still struggle with bias and truthfulness.

Earlier this month, Meta (the corporation formerly known as Facebook) released an AI chatbot with the innocuous name Blenderbot that anyone in the US can talk with. Immediately, users all over the country started posting the AI's takes condemning Facebook, while pointing out that, as has often been the case with language models like this one, it's really easy to get the AI to spread racist stereotypes and conspiracy theories. When I played with Blenderbot, I definitely saw my share of bizarre AI-generated conspiracy theories, like one about how big government is suppressing the true Bible, plus plenty of horrifying moral claims. But that wasn't what surprised me. We know language models, even advanced ones, still struggle with bias and truthfulness.

This is the third in a series of posts about how international competition could reshape the U.S. economy. As you might expect from the picture at the top of this post, I'm a little ambivalent when it comes to breathless reports that America is falling behind its rivals technologically. That picture is from the movie Dr. Strangelove, which (among other things) lampooned America's Cold War obsession with the "missile gap". On one hand, in a geopolitical contest such as the one we now find ourselves in with Russia and China, we need to prioritize which battles to concentrate resources on -- a point made very convincingly by Hal Brands and Michael Beckley's new book Danger Zone. But on the other hand, our obsession with the "missile gap" gave us the space race and the moon landings and all the technological spinoffs from those, plus a boost to our semiconductor industry.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! I have been thinking about death lately. Possibly because I recently had a month-long bout of Covid-19. And, I read a recent story about the passing of the actor Ed Asner, famous for his role as Lou Grant in "The Mary Tyler Moore Show."

Ayanna howard, roboticist, ACM Athena Lecturer, and dean of The Ohio State University College of Engineering, is optimistic about the ability of robots to help people. She understands the challenges that must be addressed for that to happen, and has worked throughout her career not just to advance the technical state of the art, but to quantify and overcome issues including trust and bias in artificial intelligence (AI). Here, she talks about self-driving cars, accessible coding, and how to incorporate different perspectives into hardware and software design. The pandemic heightened public interest in robots--suddenly, we all want robot cleaners and robot grocery deliverers and so on. How is that impacting the robotics community?

This article was originally an episode of MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Mateusz Opala about leveraging unlabeled image data with self-supervised learning or pseudo-labeling. But, if you prefer a written version, here it is! Sabine: With us today, we have Mateusz Opala, who is going to be answering questions about leveraging unlabeled image data with self-supervised learning or pseudo-labeling. Sabine: It's great to have you. Mateusz has held a number of leading machine learning positions at companies like Netguru and Brainly. So, Mateusz, you have a background in computer science, but how did you get more into the machine learning side of things? Mateusz: It all started during my sophomore year at university. One of my professors told me that Andrew Ng was doing his first iteration of the famous course on machine learning on Coursera. I kind of started from there, then did a bachelor thesis on deep unsupervised learning and went to Siemens to work in deep learning, and then all my positions were strictly about machine learning. Sabine: You've been on that path ever since? I worked for some time before as a backend engineer. But for most of the time in my career, I was a machine learning engineer/data scientist. Sabine: Mateusz, to warm you up.

The security issue of mobile robots has attracted considerable attention in recent years. In this paper, we propose an intelligent physical attack to trap mobile robots into a preset position by learning the obstacle-avoidance mechanism from external observation. The salient novelty of our work lies in revealing the possibility that physical-based attacks with intelligent and advanced design can present real threats, while without prior knowledge of the system dynamics or access to the internal system. This kind of attack cannot be handled by countermeasures in traditional cyberspace security. To practice, the cornerstone of the proposed attack is to actively explore the complex interaction characteristic of the victim robot with the environment, and learn the obstacle-avoidance knowledge exhibited in the limited observations of its behaviors. Then, we propose shortest-path and hands-off attack algorithms to find efficient attack paths from the tremendous motion space, achieving the driving-to-trap goal with low costs in terms of path length and activity period, respectively. The convergence of the algorithms is proved and the attack performance bounds are further derived. Extensive simulations and real-life experiments illustrate the effectiveness of the proposed attack, beckoning future investigation for the new physical threats and defense on robotic systems.

Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on social media. The research behind Instant NeRF is being honored as a best paper at SIGGRAPH -- which runs Aug. 8-11 in Vancouver and online -- for its contribution to the future of computer graphics research. One of just five papers selected for this award, it's among 17 papers and workshops with NVIDIA authors that are being presented at the conference, covering topics spanning neural rendering, 3D simulation, holography and more. NVIDIA recently held an Instant NeRF sweepstakes, asking developers to share 3D scenes created with the software for a chance to win a high-end NVIDIA GPU. Hundreds participated, posting 3D scenes of landmarks like Stonehenge, their backyards and even their pets.

QuantumBlack, AI by McKinsey recently sat down with Selim Turki, head of data and AI at Careem, to discuss the latest trends in advanced analytics and artificial intelligence. Far from a dry discussion of theory, the conversation coalesced around several fascinating use cases in which Careem is using AI to make a difference in people's lives. We discussed how AI is being leveraged to improve customer and driver security through targeted facial-recognition checks to ensure drivers (captains) are who they say they are. We also discussed how AI is being used to provide customers with the most accurate and up-to-date estimated times of arrival (ETAs) by factoring in a host of conditions, including local weather conditions, prayer times, and even iftar times during Ramadan. Along the way, we discussed what it means to be an "AI first" company and the outlook for AI tech--and talent--in the region.