Reinforcement learning (RL) is the branch of machine learning that is concerned with making sequences of decisions. It considers an agent situated in an environment: each timestep, the agent takes an action, and it receives an observation and reward. An RL algorithm seeks to maximize the agent's total reward, given a previously unknown environment, through a trial-and-error learning process. The key idea of policy gradients is to push up the probabilities of actions that lead to higher return, and push down the probabilities of actions that lead to lower return, until you arrive at the optimal policy. Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions.

In neuroscience and medical research, artificial intelligence (AI) deep learning is increasingly being used as a potential biomarker for brain health. A new study by the Max Planck Institute for Human Cognitive and Brain Sciences demonstrate how an AI algorithm can estimate biological age with high accuracy based on brain scan images. "This bias-free computational approach yields insights into the global nature of brain aging as well as pathomechanisms," wrote the researchers. Artificial intelligence trained on magnetic resonance imaging (MRI) that estimate brain age in comparison to chronological age is emerging as a potential new form of a digital biomarker for cognitive decline, lifestyle cardiovascular risk factors, Alzheimer's disease (AD), dementia, and hypertension according to the researchers who authored the study. The Max Planck Institute researchers used ensembles of convolutional neural networks (CNNs) with Layer-wise Relevance Propagation (LRP) in order to discover which brain features are involved in brain aging.

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While many of these interactions happen through direct contact, like the concert-goers' nudging, some interactions can transmit through the material the objects are on or in -- these are known as indirect interactions. For example, a bridge with pedestrians on it can transmit vibrations, like in the famous Millennium Bridge "wobbly bridge" instance. While the results of direct interactions (like nudging) are of increasing interest and study, and the results of indirect interactions through mechanisms like vision are well-studied, researchers are still learning about indirect mechanical interactions (for example, how two rolling balls might influence each other's movement on a trampoline by indenting the trampoline's surface with their weight, thus exerting mechanical forces without touching). Physicists are using small wheeled robots to better understand these indirect mechanical interactions, how they play a role in active matter, and how we can control them. Their findings, "Field-mediated locomotor dynamics on highly deformable surfaces" are recently published in the The Proceedings of the National Academy of Sciences (PNAS).

Text-to-image art generators work through a process called deep learning, in which algorithms make predictions and complete tasks in a process that mimics the human brain's neurons. In the case of AI-generated art, the generators pull from a database of existing pictures and illustrations to put together a discrete piece based on a user's prompt. Pinkney explained that his own creation is adapted from an open-source deep-learning model called Stable Diffusion, which already has vast data sets of information. Text-to-Pokémon works by matching Stable Diffusion's data sets to a data set of 850 Pokémon images from a previous university-run research project, which Pinkney filed using an automated caption system to categorize each image with a text description.

During our conversation, he spoke about the company's use of AI and big data, how it impacts the customer experience, and the opportunities (and, at times, challenges) it brings to the industry. Schreiber: When Lemonade was founded six years ago, part of our mission was to digitize insurance end-to-end, leveraging technology, data, and artificial intelligence to create a more delightful, affordable, and precise approach. Whether that's renting their first apartment or buying a home, this opens up Lemonade to two separate opportunities with both Renters and Homeowners insurance, and our use of AI makes it possible for either audience to get a quote and obtain a policy in a matter of minutes. Simply put, by using big data and AI individuals would be charged a rate directly proportional to the risk they pose, creating a constant ratio across an insurer's customer base. Last year we brought on Tulsee Doshi as our AI Ethics and Fairness Advisor to help ensure both Lemonade and the future of insurance is one where opportunity is available, equitably, to everyone--a passion we both share.

In order to find the best weights/biases (W/b) parameters of a model during the learning phase, an optimization algorithm is used. The most used one is "gradient descent", which is an iterative optimization algorithm that operates over…

Back in my day, you turned devices off when you didn't need to use them. But in the age of Amazon, your "smart" home and its ecosystem of gadgets will ideally always be turned on. That vision even has a cool-sounding name: ambient intelligence. While I'm not anywhere near old enough to have truly earned my rose-tinted, geezer mindset, I am wrinkled just enough to remember a time before every device was connected to the internet; before the internet of things. So when I look at the direction Amazon is taking its ever-expanding hardware business and the welcoming, futuristic-sounding spin it's using to sell this to consumers, well, I just can't kick this dire feeling in my gut.

Graphcore CTO and co-founder Simon Knowles delivered a keynote talk during the IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) on June 15, 2022. In his keynote, Simon provided an overview of the current state of AI and outlined the path to achieving human-scale (or even superhuman) artificial intelligence with current or near-future technology. For more on the Good Computer, read Graphcore announces roadmap to Ultra-Intelligence AI supercomputer.

The European Parliament's co-rapporteurs Dragoş Tudorache and Brando Benifei circulated two new batches of compromise amendments, seen by EURACTIV, on Wednesday (28 September), ahead of the technical discussion with the other political groups on Friday. These latest batches introduce significant changes to the regulation's scope, subject matter and obligations for high-risk AI systems concerning risk management, data governance and technical documentation. A new article has been added to extend the regulation's scope to AI system operators in specific metaverse environments that meet several cumulative conditions. These criteria are that the metaverse requires an authenticated avatar, is built for interaction on a large scale, allows social interactions similar to the real world, engages in real-world financial transactions and entails health or fundamental rights risks. The scope has been expanded from AI providers to any economic operators placing an AI system on the market or putting it into service.