New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
In 2021, Nikhil Kamath, founder of Zerodha, defeated five-time world champion Vishwanathan Anand in chess with the help of computers (he confessed later on) at a celebrity fundraiser. The controversy sparked discussions around the use of AI in the game of chess. As India is all set to host the 44th edition of the Chess Olympiad in Mahabalipuram starting on July 28, let's look at how AI has impacted the game of chess. The earliest mention of technology in chess can be traced back to the 18th century when Austrian empress Maria Theresa commissioned a chess-playing machine. Many players competed against the'Mechanical Turk', thinking it was an automated machine.
Scientists in Japan were able to identify osteoporosis with high accuracy using AI to analyze routine dental x-rays. The researchers at Kagawa Prefectural Central Hospital, Okayama University, and Matsumoto Dental University used deep learning to construct an osteoporosis classifier from dental x-rays. The study entitled Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates was published in Scientific Reports on April 12, 2022. It's estimated that over 200 million people worldwide have osteoporosis. People with osteoporosis are at high risk for sudden bone fractures.
The centerpiece of the new approach is a neural network that can learn to view the world at different levels of detail. Ditching the need for pixel-perfect predictions, this network would focus only on those features in a scene that are relevant for the task at hand. LeCun pairs this core network with another, called the configurator, which determines what level of detail is required and tweaks the overall system accordingly. For LeCun, AGI is going to be a part of how we interact with future tech. His vision is colored by that of his employer, Meta, which is pushing a virtual-reality metaverse.
For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.
Figure 1: Overview of a local variational layer (left) and an attentive variational layer (right) proposed in this post. Attention blocks in the variational layer are responsible for capturing long-range statistical dependencies in the latent space of the hierarchy. Generative models are a class of machine learning models that are able to generate novel data samples such as fictional celebrity faces, digital artwork, and scenic images. Currently, the most powerful generative models are deep probabilistic models. This class of models uses deep neural networks to express statistical hypotheses about the data generation process, and combine them with latent variable models to augment the set of observed data with latent (unobserved) information in order to better characterize the procedure that generates the data of interest.
This is another specialization program offered by Coursera. This specialization program is for both computer science professionals and healthcare professionals. In this specialization program, you will learn how to identify the healthcare professional's problems that can be solved by machine learning. You will also learn the fundamentals of the U.S. healthcare system, the framework for successful and ethical medical data mining, the fundamentals of machine learning as it applies to medicine and healthcare, and much more. This specialization program has 5 courses. Let's see the details of the courses-
The conventional approach for improving the decision-making of deep reinforcement learning (RL) agents is to gradually amortize the useful information they gain from their experiences via gradient descent on training losses. This method however requires building increasingly large models to deal with increasingly complex environments and is difficult to adapt to novel situations. Although adding information sources can benefit agent performance, there is currently no end-to-end solution for enabling agents to attend to information outside their working memory to inform their actions. In the new paper Large-Scale Retrieval for Reinforcement Learning, a DeepMind research team introduces a novel approach that dramatically expands the information accessible to reinforcement learning (RL) agents, enabling them to attend to tens of millions of information pieces, incorporate new information without retraining, and learn in an end-to-end manner how to use this information in their decision making. In the work, the team trains a semiparametric model-based agent to predict future policies and values conditioned on future actions in a given state and adds a retrieval mechanism to enable the model to draw from information in a large-scale dataset to inform its predictions.
Though marketers are still in the early stages of experimenting with deepfakes and deepfake technology, these videos convey a more immersive marketing experience through storytelling. Deepfake technology is a type of "deep learning." Deep learning is a machine learning type that allows computers to learn tasks independently without being explicitly programmed. Deepfake technology also involves computer vision, allowing computers to recognize objects in images. For example, computer vision uses deep learning algorithms to identify objects in photos or videos.