Researchers developed the Human Guided Exploration (HuGE) approach that doesn’t rely on expertly designed reward functions for AI agents learning new tasks.
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HuGE doesn’t rely on expertly designed reward functions for AI agents learning new tasks, according to MIT News.
In this video I discuss Neuromorphic Computing and the Future of AI
#AI
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Google’s company-defining effort to catch up to ChatGPT creator OpenAI is turning out to be harder than expected.
Google representatives earlier this year told some cloud customers and business partners they would get access to the company’s new conversational AI, a large language model known as Gemini, by November. But the company recently told them not to expect it until the first quarter of next year, according to two people with direct knowledge. The delay comes at a bad time for Google, whose cloud sales growth has slowed while that of its bigger rival, Microsoft, has accelerated. Part of Microsoft’s success has come from selling OpenAI’s technology to its customers.
To teach an AI agent a new task, like how to open a kitchen cabinet, researchers often use reinforcement learning—a trial-and-error process where the agent is rewarded for taking actions that get it closer to the goal.
In many instances, a human expert must carefully design a reward function, which is an incentive mechanism that gives the agent motivation to explore. The human expert must iteratively update that reward function as the agent explores and tries different actions. This can be time-consuming, inefficient, and difficult to scale up, especially when the task is complex and involves many steps.
Researchers from MIT, Harvard University, and the University of Washington have developed a new reinforcement learning approach that doesn’t rely on an expertly designed reward function. Instead, it leverages crowdsourced feedback, gathered from many non-expert users, to guide the agent as it learns to reach its goal. The work has been published on the pre-print server arXiv.
A team of AI researchers from EquiLibre Technologies, Sony AI, Amii and Midjourney, working with Google’s DeepMind project, has developed an AI system called Student of Games (SoG) that is capable of both beating humans at a variety of games and learning to play new ones. In their paper published in the journal Science Advances, the group describes the new system and its capabilities.
Over the past half-century, computer scientists and engineers have developed the idea of machine learning and artificial intelligence, in which human-generated data is used to train computer systems. The technology has applications in a variety of scenarios, one of which is playing board and/or parlor games.
Teaching a computer to play a board game and then improving its capabilities to the degree that it can beat humans has become a milestone of sorts, demonstrating how far artificial intelligence has developed. In this new study, the research team has taken another step toward artificial general intelligence—in which a computer can carry out tasks deemed superhuman.
As an optimist, I believe it will be a catalyst for changes that will help all of us to learn faster and achieve more of our potential.
Focus on the data
Where do I start? Let me start by noting that, in all the conversations about artificial intelligence, very few people are talking about the data. Most people don’t recognize that AI is actually extremely stupid without data. Data is the fuel that shapes the intelligence of AI. Everyone seems to assume that more and more data will be available as AI evolves. But is that assumption valid?
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A new artificial intelligence benchmark called GAIA aims to evaluate whether chatbots like ChatGPT can demonstrate human-like reasoning and competence on everyday tasks.
Created by researchers from Meta, Hugging Face, AutoGPT and GenAI, the benchmark “proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency,” the researchers wrote in a paper published on arXiv.
An important #AI report for breast cancer leading to the potential of sparing chemotherapy for many. The 1st comprehensive analysis of both cancerous and non-cancerous tissue in hundreds of thousands of patient tissues->HiPS score https://nature.com/articles/s41591-023-02643-7
Deep learning enables comprehensive and interpretable scoring for breast cancer prognosis prediction, outperforming pathologists in multicenter validation and providing insight on prognostic biomarkers.
The CEO of Baidu, China’s largest search engine, believes developers should focus on building AI-powered apps on top of existing foundational models.
Companies hope to improve their operations using the new tools. But they want to establish rules that minimize any risks.
