U.S. DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program recently conducted its third experiment to assess the performance of off-road unmanned vehicles. These test runs, conducted March 12–27, included the first with completely uninhabited RACER Fleet Vehicles (RFVs), with a safety operator overseeing in a supporting chase vehicle. The goal of the RACER program is to demonstrate autonomous movement of combat-scale vehicles in complex, mission-relevant off-road environments that are significantly more unpredictable than on-road conditions. The multiple courses were in the challenging and unforgiving terrain of the Mojave Desert at the U.S. Army’s National Training Center (NTC) in Ft. Irwin, California. As at the previous events, teams from Carnegie Mellon University, NASA’s Jet Propulsion Laboratory, and the University of Washington participated. This completed the project’s first phase.

“We provided the performers RACER fleet vehicles with common performance, sensing, and compute. This enables us to evaluate the performance of the performer team autonomy software in similar environments and compare it to human performance,” said Young. “During this latest experiment, we continued to push vehicle limits in perceiving the environments to greater distances, enabling further increase in speeds and better adaptation to newly encountered environmental conditions that will continue into RACER’s next phase.”

“At Experiment Three, we successfully demonstrated significant improvements in our off-road speeds while simultaneously reducing any interaction with the vehicle during test runs. We were also honored to have representatives from the Army and Marine Corps at the experiment to facilitate transition of technologies developed in RACER to future service unmanned initiatives and concepts,” said Stuart Young, RACER program manager in DARPA’s Tactical Technology Office.

Chat gpt 4 is wonderful but one thing it is lacking is sentience which could do all work for millions of years so essentially we would not need find all discoveries and all things by ourselves.

Richards said he originally wanted an AI agent to automatically email him daily AI news. But, as Motherboard, he realized in the process that existing LLMs struggle with “tasks that require long-term planning,” or are “unable to autonomously refine their approaches based on real-time feedback.” That understanding inspired him to create Auto-GPT, which, he said, “can apply GPT4’s reasoning to broader, more complex problems that require long-term planning and multiple steps.” (Richards didn’t respond for a request for an interview with Fast Company.)

Auto-GPT can do a lot of things ChatGPT can’t do.

If people are worried that Chat-GPT could be taking their jobs, they haven’t seen Auto-GPT yet.

Auto-GPT is an AI chatbot similar to ChatGPT and others. It is based on the GPT-4 language model of OpenAI, the same LLM that powers the ChatGPT. But, as the name implies, “Autonomous Artificial Intelligence Chat Generative Pre-trained Transformer,” a step further, but what exactly is it? Let us go through what Auto-GPT is and how it works.

What is Auto-GPT

Essentially, Auto-GPT is a chatbot. You ask it the questions it answers smartly. But, unlike ChatGPT and other GPT-based chatbots, which need a prompt every single time, Auto-GPT can automate the whole task, so you do not need to prompt it. Once given a task, Auto-GPT will figure out the steps on its own to reach the goal.