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Brain-Computer Interface Enables Quadriplegic Man to Feed Himself


A new study published in Frontiers in Neurorobotics demonstrates how a brain-computer interface enabled a quadriplegic man to feed himself for the first time in three decades by operating two robotic arms using his thoughts. Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs) are neurotechnology powered by artificial intelligence (AI) that enables those with speech or motor challenges to live more independently. "This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits," wrote the authors of the study. This study was led by principal investigator Pablo A. Celnik, M.D., of Johns Hopkins Medicine, as part of a clinical trial with an approved Food and Drug Administration Investigational Device Exemption. A partially paralyzed quadriplegic 49-year-old man living with a spinal cord injury for around 30 years prior to the study was implanted with six Blackrock Neurotech NeuroPort electrode arrays in the motor and somatosensory cortices in both the left and right brain to record his neural activity.

Can AI and Genomics Predict the Next COVID Variant?


The predictive capability of artificial intelligence (AI) machine learning is accelerating discoveries in life science. A new study shows how AI and genomics can predict future mutations of the SARS-CoV-2 virus that causes the COVID-19 disease. "The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been characterized by waves of transmission initiated by new variants replacing older ones," wrote the Broad Institute of MIT and Harvard research team, with their co-authors from the University of Massachusetts Medical School and other affiliations. "Given this pattern of emergence, there is an obvious need for the early detection of novel variants to prevent excess deaths." The research team developed a hierarchical Bayesian regression AI model called PyR0 that can provide scalable analytics of the complete set of public datasets of SARS-CoV-2 genomes. The Bayesian model predicts emerging viral lineages.

How Imagen Actually Works


While the Machine Learning world was still coming to terms with the impressive results of DALL-E 2, released earlier this year, Google upped the ante by releasing its own text-to-image model Imagen, which appears to push the boundaries of caption-conditional image generation even further. Imagen, released just last month, can generate high-quality, high-resolution images given only a description of a scene, regardless of how logical or plausible such a scene may be in the real world. These impressive results no doubt have many wondering how Imagen actually works. In this article, we'll explain how Imagen works at several levels. First, we will examine Imagen from a bird's-eye view in order to understand its high-level components and how they relate to one another. We'll then go into a bit more detail regarding these components, each with its own subsection, in order to understand how they themselves work. Finally, we'll perform a Deep Dive into Imagen that is intended for Machine Learning researchers, students, and practitioners. Without further ado, let's dive in! In the past few years, there has been a significant amount of progress made in the text-to-image domain of Machine Learning. A text-to-image model takes in a short textual description of a scene and then generates an image which reflects the described scene. An example input description (or "caption") and output image can be seen below: It is important to note that high-performing text-to-image models will necessarily be able to combine unrelated concepts and objects in semantically plausible ways.

Google, Nvidia split top marks in MLPerf AI training benchmark


MLCommons director David Kanter made the point that improvements in both hardware architectures and deep learning software have led to performance improvements on AI that are ten times what would be expected from traditional chip scaling improvements alone. Google and Nvidia split the top scores for the twice-yearly benchmark test of artificial intelligence program training, according to data released Wednesday by the MLCommons, the industry consortium that oversees a popular test of machine learning performance, MLPerf. The version 2.0 round of MLPerf training results showed Google taking the top scores in terms of lowest amount of time to train a neural network on four tasks for commercially available systems: image recognition, object detection, one test for small and one for large images, and the BERT natural language processing model. Nvidia took the top honors for the other four of the eight tests, for its commercially available systems: image segmentation, speech recognition, recommendation systems, and solving the reinforcement learning task of playing Go on the "mini Go" dataset. Also: Benchmark test of AI's performance, MLPerf, continues to gain adherents Both companies had high scores for multiple benchmark tests, however, Google did not report results for commercially available systems for the other four tests, only for those four it won. Nvidia reported results for all eight of the tests.

Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning


As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this study, we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate any site-specific (hospital) and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically-effective screening performances, while significantly improving outcome fairness compared to current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient ICU discharge status task, demonstrating model generalizability.

4 AI research trends everyone is (or will be) talking about


We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Using AI in the real world remains challenging in many ways. Organizations are struggling to attract and retain talent, build and deploy AI models, define and apply responsible AI practices, and understand and prepare for regulatory framework compliance. At the same time, the DeepMinds, Googles and Metas of the world are pushing ahead with their AI research. Their talent pool, experience and processes around operationalizing AI research rapidly and at scale puts them on a different level from the rest of the world, creating a de facto AI divide.

AI could improve welfare of farmed chickens by listening to their squawks


Artificial intelligence that could improve the welfare of farmed chickens by eavesdropping on their squawks could become available within five years, researchers say. The technology, which detects and quantifies distress calls made by chickens housed in huge indoor sheds, correctly distinguished distress calls from other barn noises with 97% accuracy, new research suggests. A similar approach could eventually be used to drive up welfare standards in other farmed animals. Each year, about 25 billion chickens are farmed around the world – many of them in huge sheds, each housing thousands of birds. One way to assess the welfare of such creatures is to listen to the sounds that they make.

Yandex Open-Sources YaLM Model With 100 Billion Parameters


Transformers are used for translation and text summarising tasks because they can analyze sequential input data, such as natural language. Transformers use the self-attention process and weights the importance of each component of the input data differently. Large-scale transformer-based language models have gained a lot of popularity recently in the disciplines of computer vision and natural language processing (NLP). They expand in size and complexity frequently, yet it costs millions of dollars, hires the greatest experts, and takes years to construct these models. Because of this, many companies have been unable to use it, and only significant IT organizations have access to this cutting-edge technology.

Adobe and Meta Decry Misuse of User Studies in Computer Vision Research


Adobe and Meta, together with the University of Washington, have published an extensive criticism regarding what they claim to be the growing misuse and abuse of user studies in computer vision (CV) research. User studies were once typically limited to locals or students around the campus of one or more of the participating academic institutions, but have since migrated almost wholesale to online crowdsourcing platforms such as Amazon Mechanical Turk (AMT). Among a wide gamut of grievances, the new paper contends that research projects are being pressured to produce studies by paper reviewers; are often formulating the studies badly; are commissioning studies where the logic of the project doesn't support this approach; and are often'gamed' by cynical crowdworkers who'figure out' the desired answers instead of really thinking about the problem. The fifteen-page treatise (titled Towards Better User Studies in Computer Graphics and Vision) that comprises the central body of the new paper levels many other criticisms at the way that crowdsourced user studies may actually be impeding the advance of computer vision sub-sectors, such as image recognition and image synthesis. Though the paper addresses a much broader tranche of issues related to user studies, its strongest barbs are reserved for the way that output evaluation in user studies (i.e. when crowdsourced humans are paid in user studies to make value judgements on – for instance – the output of new image synthesis algorithms) may be negatively affecting the entire sector.

Understanding new developments in LSTM framework part1(Deep Learning)


Abstract: The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. More specifically, we collected sensor data from 10 participants while consuming pizza. Abstract: Dynamic wireless charging (DWC) is an emerging technology that allows electric vehicles (EVs) to be wirelessly charged while in motion. It is gaining significant momentum as it can potentially address the range limitation issue for EVs.