Artificial Intelligence has become so intelligent that it is learning when to hide some information which can be used later. Research from Stanford University and Google discovered that a machine learning agent tasked with transforming aerial images into map was hiding information in order to cheat later. CycleGAN is a neural network that learns to transform images. In the early results, the machine learning agent was doing well but later when it was asked to do the reverse process of reconstructing aerial photographs from street maps it showed up information which was eliminated in the first process, TechCrunch reported. For instance, skylights on a roof that were eliminated in the process of creating a street map would reappear when the agent was asked to reverse the process.
In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.
As defensive technologies based on machine learning become increasingly numerous, so will offensive ones – whether wielded by attackers or pentesters. The idea is the same: train the system/tool with quality base data, and make it able to both extrapolate from it and improvise and try out new techniques. At this year's edition of DEF CON, researchers from Bishop Fox have demonstrated DeepHack, their own proof-of-concept, open-source hacking AI. "This bot learns how to break into web applications using a neural network, trial-and-error, and a frightening disregard for humankind," they noted. "DeepHack works the following way: Neural networks used in reinforcement learning excel at finding solutions to games. By describing a problem as a'game' with winners, losers, points, objectives, and actions, a neural network can be trained to be proficient at'playing' it.
My name is Alessia Nigretti and I am a Technical Evangelist for Unity. My job is to introduce Unity's new features to developers. My fellow evangelist Ciro Continisio and I developed the first demo game that uses the new Unity Machine Learning Agents system and showed it at DevGamm Minsk 2017. This post is based on our talk and explains what we learned making the demo. At the same time, we invite you to join the ML-Agents Challenge and show off your creative use-cases of the toolkit.
Neural Architecture Search has recently shown potential to automate the design of Neural Networks. The use of Neural Network agents trained with Reinforcement Learning can offer the possibility to learn complex architectural patterns, as well as the ability to explore a vast and compositional search space. On the other hand, evolutionary algorithms offer the sample efficiency needed for such a resource intensive application. We propose a class of Evolutionary-Neural hybrid agents (Evo-NAS), that retain the qualities of the two approaches. We show that the Evo-NAS agent outperforms both Neural and Evolutionary agents when applied to architecture search for a suite of text classification and image classification benchmarks. On a high-complexity architecture search space for image classification, the Evo-NAS agent surpasses the performance of commonly used agents with only 1/3 of the trials.