"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Medical imaging is the process of capturing the structure of an inner organ or tissue. These images can assist medical staff with diagnostics, treatment, and monitoring of patients. It can also prevent any unnecessary invasive procedures. The global AI healthcare market is expected to grow from 4.9 billion USD in 2020 to 45.2 billion USD by 2026. This rapid growth rate can be explained by the many advantages AI has to offer.
Before being an exaltation to Luddites (the English workers from the 19th century who actually destroyed textile machinery as a form of protest) or to some sort of technophobic movement, the provocative pun contained in the title of this article carries a methodological proposal, in the field of critical theory of information, to build a diagnosis about the algorithmic filtering of information, which reveals itself to be a structural characteristic of the new regime of information that brings challenges to human emancipation. Our analysis starts from the concept of mediation to problematize the belief, widespread in much of contemporary society, that the use of machine learning and deep learning techniques for algorithmic filtering of big data will provide answers and solutions to all our questions and problems. We will argue that the algorithmic mediation of information on the internet, which is responsible for deciding which information we will have access to and which will remain invisible, is operated according to the economic interests of the companies that control the platforms we visit on the internet, acting as obstacle to the prospects of informational diversity and autonomy that are fundamental in free and democratic societies.
It's been almost one year since the Covid-19 pandemic started. Data scientists worldwide have been analyzing data gathered during the pandemic to inform policies. As we have seen, policymaking has not been straight forward. During this time of social isolation, it's been a great opportunity for policymakers to figure out the right approach to making sense of the data to gain flexibility in community-based policy decisions. On Nov 17th, 2020, XPrize and Cognizant announced their Pandemic Response Challenge.
To obtain fast and accurate inference on edge devices, a model has to be optimized for real-time inference. Fine-tuned state-of-the-art models like VGG16/19, ResNet50 have 138 million and 23 million parameters respectively and inference is often expensive on resource-constrained devices. Previously I've talked about one model compression technique called "Knowledge Distillation" using a smaller student network to mimic the performance of a larger teacher network (Both student and teacher network has different network architecture). Today, the focus will be on "Pruning" one model compression technique that allows us to compress the model to a smaller size with zero or marginal loss of accuracy. In short, pruning eliminates the weights with low magnitude (That does not contribute much to the final model performance).
MCUNet embeds deep learning neural networks on the off-shelf microcontrollers to reduce memory usage. Artificial Intelligence is a technology which is getting heavily researched on a routine basis. Researchers all around the world are working to make the application and implementation of AI faster and better. Over the years, humans have encountered instances where AI led to potential breakthroughs. Be it in the early detection of heart diseases or in discovering historical events, AI has come far since its inception.
The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. They'd say Artificial Intelligence is a terminator like-figure that can act and think on its own. An AI researcher would say that it's a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. AI courses at Great Learning provide you with an overview of the current implementation scenario in various industries. With an in-depth introduction to artificial intelligence, you can easily master the basics for a better future in the course.
Machine learning is mostly about building good function approximations from data. And, when it comes to good approximations, deep learning algorithms have a great following as they are founded on principles of universal approximation. The adoption of these deep neural networks has been high in the past couple of years. But, as these systems scale, new challenges surface. It can be misclassification of an unexamined vulnerability to adversarial attack.
Deep learning neural networks are artificial intelligence systems that are being used for increasingly important decisions. Deep learning neural networks are used for tasks as varied as autonomous driving to diagnosing medical conditions. This type of network excels at recognizing patterns in large and complex datasets to help with decision-making. One big challenge is determining if the neural network is correct. Researchers at MIT and Harvard University have developed a quick way for a neural network to churn through data and provide a prediction along with the neural network's confidence level in its answer.
Colaboratory, or Colab for short, is a Google Research product, which allows developers to write and execute Python code through their browser. Google Colab is an excellent tool for deep learning tasks. It is a hosted Jupyter notebook that requires no setup and has an excellent free version, which gives free access to Google computing resources such as GPUs and TPUs. Since Google Colab is built on top of vanilla Jupyter Notebook, which is built on top of Python kernel, let's look at these technologies before diving into why we should and how we can use Google Colab. There are several tools used in Python interactive programming environments.
Now we are moving into the world of'edge computing', in which data is processed close to its source, cutting out the need for it to be sent to the cloud. But computing isn't the only thing taking place on'the edge' – now, AI is being brought to the source of the data as well, allowing'Edge AI' to bring about new standards of speed and intelligence. So, what is Edge AI, what kinds of benefits will it offer, and how will it empower solutions going forward? Currently, the heavy computing capacity required to run deep learning models necessitates that the majority of AI processes be carried out in the cloud. However, running AI in the cloud has its disadvantages, including the fact that it requires an internet connection, and that performance can be impacted by bandwidth and latency limitations.