Over the weekend, experimenters discovered that OpenAI's new chatbot, ChatGPT, can hallucinate simulations of Linux shells and role-play dialing into a bulletin board system (BBS). The chatbot, based on a deep-learning AI model, uses its stored knowledge to simulate Linux with surprising results, including executing Python code and browsing virtual websites. Last week, OpenAI made ChatGPT freely available during a testing phase, which has led to people probing its capabilities and weaknesses in novel ways. I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show.

Relying on shortcuts for work isn't always a great idea – and that holds true for artificial intelligence (AI) with chest X-rays and COVID-19, as well. New research from the University of Washington (UW), shows that AI, like humans, has a tendency to lean on shortcuts for disease detection with these scans. If the tools are deployed clinically, investigators said, the result could be diagnostic errors that impact real patients. Rather than learning from actual medical pathology and clinically significant indicators, the team, led by Paul G. Allen School of Computer Science & Engineering doctoral students, Alex DeGrave, who is also a medical student in the UW Medical Scientist Training Program, and Joseph Janizek, also a UW medical student, showed that algorithms used during the pandemic relied on text markers and patient positioning specific to each dataset to predict whether someone was COVID-19-positive. The team published their results May 31 in Nature Machine Intelligence.

Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. But as University of Washington researchers discovered, AI models -- like humans -- have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings. In a new paper published May 31 in Nature Machine Intelligence, UW researchers examined multiple models recently put forward as potential tools for accurately detecting COVID-19 from chest radiography, otherwise known as chest X-rays.

An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximations for the Jacobian. This method quickly grows expensive with increasing numbers of parameters, such as found in deep learning. We propose the implementation of a modern physics engine, which can differentiate control parameters. This engine is implemented for both CPU and GPU. Firstly, this paper shows how such an engine speeds up the optimization process, even for small problems. Furthermore, it explains why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Finally, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.