.

Recent Comments

    artificial intelligence tagged posts

    AI Systems are Already Skilled at Deceiving and Manipulating Humans, study shows

    May 12, 2024, Categories  Physics, Technology/Electronics

    Many artificial intelligence (AI) systems have already learned how to deceive humans, even systems that have been trained to be helpful and honest. In a review article published in the journal Patterns on May 10, researchers describe the risks of deception by AI systems and call for governments to develop strong regulations to address this issue as soon as possible.

    “AI developers do not have a confident understanding of what causes undesirable AI behaviors like deception,” says first author Peter S. Park, an AI existential safety postdoctoral fellow at MIT. “But generally speaking, we think AI deception arises because a deception-based strategy turned out to be the best way to perform well at the given AI’s training task. Deception helps them achieve their goals.”

    Park and coll...

    Read More

    Q&A: How to Train AI when you Don’t Have Enough Data

    March 30, 2024, Categories  Physics

    Artificial intelligence excels at sorting through information and detecting patterns or trends. But these machine learning algorithms need to be trained with large amounts of data first.

    As researchers explore potential applications for AI, they have found scenarios where AI could be really useful—such as analyzing X-ray image data to look for evidence of rare conditions or detecting a rare fish species caught on a commercial fishing boat—but there’s not enough data to accurately train the algorithms.

    Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues. For example, Hwang and his team developed a method that teaches AI to monitor how many distinct poses a baby can achieve throughout the day...

    Read More

    New Study uses Machine Learning to Bridge the Reality Gap in Quantum Devices

    January 11, 2024, Categories  Physics

    New study uses machine learning to bridge the reality gap in quantum devices
    (a) Device geometry including the gate electrodes (labeled G1–G8), donor ion plane, and an example disorder potential experienced by confined electrons. Typical flow of current from source to drain is indicated by the white arrow. (b) Schematic of the disorder inference process. Colors indicate the following: red for experimentally controllable variables, green for quantities relevant to the electrostatic model, blue for experimental device, and yellow for machine learning methods. Dashed arrows represent the process of generating training data for the deep learning approximation and are not part of the disorder inference process. Credit: Physical Review X (2024). DOI: 10.1103/PhysRevX.14.011001

    A study led by the University of Oxford has used the power of machine learning to ove...

    Read More

    DeepMind achieves Giant Leap in Sorting Speed

    June 13, 2023, Categories  Physics, Technology/Electronics

    Fundamentally different algorithms discovered by AlphaDev. a, A flow diagram of the variable sort 4 (VarSort4) human benchmark algorithm. In this algorithm, a sequence of unsorted numbers are input into the algorithm. If the sequence length is four, three or two numbers, then the corresponding sort 4, sort 3 or sort 2 sorting network is called that sorts the resulting sequence. The result is then returned and output by the function. b, The VarSort4 algorithm discovered by AlphaDev. This algorithm also receives sequences of length four, three or two numbers as input. In this case, if the length is two, then it calls the sort 2 sorting network and returns. If the length is three then it calls sort 3 to sort the first three numbers and returns...
    Read More