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.
Is artificial intelligence the new alchemy? That is, are the powerful algorithms that control so much of our lives -- from internet searches to social media feeds -- the modern equivalent of turning lead into gold? Moreover: Would that be such a bad thing? According to the prominent AI researcher Ali Rahimi and others, today's fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions.
Recently, a team led by clinicians at Beth Israel Deaconess Medical Center and Harvard Medical School demonstrated that an artificial intelligence (AI)-based computer vision system can enhance screening accuracy of colon cancer. Tyler M Berzin, a gastroenterologist from Beth Israel Deaconess Medical Center, discusses how AI-based computer-vision algorithms can assist physicians. Let us examine how this is accomplished. According to Tyler, this would be a real-time application of artificial intelligence, which is also rather unique. In clinical medicine, the majority of examples of AI applications occur after the initial patient engagement, for example, during the subsequent evaluation of the X-ray.
For something so effortless and automatic, vision is a tough job for the brain. It's remarkable that we can transform electromagnetic radiation--light--into a meaningful world of objects and scenes. After all, light focused into an eye is merely a stream of photons with different wave properties, projecting continuously on our retinas, a layer of cells on the backside of our eyes. Before it's transduced by our eyes, light has no brightness or color, which are properties of animal perception. Our retinas transform this energy into electrical impulses that propagate within our nervous system. Somehow this comes out as a world: skies, children, art, auroras, and occasionally ghosts and UFOs.
In this blog post I will show how to use a low-code app in MATLAB, the Deep Network Designer, for two different tasks and design paradigms: creating a network from scratch vs. using transfer learning. The process of building deep learning (DL) solutions follows a standard workflow that starts from the problem definition and continues with the steps of collecting and preparing the data, selecting a suitable neural network architecture for the job, training and fine-tuning the network, and eventually deploying the model (Figure 1). The selection of a suitable neural network architecture usually follows the best practices for the application at hand, e.g., the use convolutional neural networks (CNNs or ConvNets) for image classification or recurrent neural networks (RNNs) with long short-term memory (LSTM) cells for text and sequence data types of applications. Transfer learning is an incredibly easy, quick, and popular method for building DL solutions in some domains, such as image classification – using neural network architectures pretrained on ImageNet (a large dataset of more than 1 million images in more than 1,000 categories). Essentially, it consists of using a deep neural network that has been pre-trained in a large dataset of similar nature to the problem you are trying to solve. This is usually accomplished by retraining some of its layers (while freezing the others).
Deep learning networks have gained immense popularity in the past few years. The'attention mechanism' is integrated with the deep learning networks to improve their performance. Adding attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization and similar applications. This tutorial shows how to add a custom attention layer to a network built using a recurrent neural network. We'll illustrate an end to end application of time series forecasting using a very simple dataset.
Bio: Antonio Bicchi is a scientist interested in robotics and intelligent machines. After graduating in Pisa and receiving a Ph.D. from the University of Bologna, he spent a few years at the MIT AI Lab of Cambridge before becoming Professor in Robotics at the University of Pisa. In 2009 he founded the Soft Robotics Laboratory at the Italian Institute of Technology in Genoa. Since 2013 he is Adjunct Professor at Arizona State University, Tempe, AZ. He has coordinated many international projects, including four grants from the European Research Council (ERC).
TensorFlow is an undisputed leader among the various libraries used for deep learning-powered applications. The official website describes TensorFlow as an open-source platform that consists of a comprehensive, flexible ecosystem of tools, libraries, and community resources that allow developers to build and deploy machine learning and deep learning applications. According to Payscale, a machine learning engineer with deep learning skills earns an average annual salary of $112,331 in the US. With experience, such professionals can earn even more and even entry-level professionals can command high salaries. Learning TensorFlow will make you capable of designing and deploying deep learning models and validating the same in front of employers. The Brain team of Google created an open-source machine learning library in 2015 called TensorFlow. TensorFlow is the combination of two words, Tensor -- representation of data for multi-dimensional array and Flow -- the series of operations performed on the Tensor. It is a low-level toolkit used for performing complicated and complex mathematics.
Riverside Research is seeking a Research Scientist with a general background in machine learning and artificial intelligence and a focus on signal processing to join a dynamic, growth-focused Artificial Intelligence and Machine Learning Lab. The Lab performs research and development focused on providing solutions to the Defense and Intelligence Communities. As a key member of our Open Innovation Center, the research scientist will execute as well as assist in growing opportunities with government research organizations (e.g. DARPA, IARPA, service labs, etc.), perform on our corporate-wide Independent Research & Development (IR&D) efforts in artificial intelligence and machine learning, manage existing R&D contracts, and transition technology into our other business units. The Research Scientist will work with team members located in the Dayton OH, Washington DC, New York City, and Boston office locations while reporting to the Director of the Artificial Intelligence and Machine Learning Lab of the Open Innovation Center Business Unit.