"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).
In Star Wars: The Empire Strikes Back, Luke Skywalker is rescued from the frozen wastes of Hoth after a near-fatal encounter, luckily to be returned to a medical facility filled with advanced robotics and futuristic technology that treat his wounds and quickly bring him back to health. The healthcare industry could be headed toward yet another high-tech makeover (even as it continues to adapt to the advent of electronic health records systems and other healthcare IT products) as artificial intelligence (AI) improves. Could AI applications become the new normal across virtually every sector of the healthcare industry? Many experts believe it is inevitable and coming sooner than you might expect. AI could be simply defined as computers and computer software that are capable of intelligent behavior, such as analysis and learning.
Well, suppose on a normal day you are playing football in a nearby ground. So let's try to build a solution that changes our scenario from former to latter I can't do that yet. I am relatively new to AI. I can't build and code super complex projects yet, but I'm well on my way. I built a sign language recognizer, training it using the MNIST sign language database.
Event You know that you could achieve great things if only you had time to get to grips with TensorFlow, or mine a vast pile of text, or simply introduce machine-learning into your existing workflow. That's why at our artificial-intelligence conference MCubed, which runs from September 30 to October 2, we have a quartet of all-day workshops that will take you deep into key technologies, and show you how to apply them in your own organisation. Prof Mark Whitehorn and Kate Kilgour will dive deep into machine learning and neural networks, from perceptrons through convolutional neural networks (CNNs) and autoencoders to generative adversarial networks. If you want to get more specific, Oliver Zeigermann returns to MCubed with his workshop on Deep Learning with TensorFlow 2. This session will cover Neural Networks, CNNs and recurrent neural networks, using TensorFlow 2, and Python, to show you how to develop and train your own neural networks. One problem many of us face is making sense of a mountain of text.
In recent years, researchers have proposed a wide variety of hardware implementations for feed-forward artificial neural networks. These implementations include three key components: a dot-product engine that can compute convolution and fully-connected layer operations, memory elements to store intermediate inter and intra-layer results, and other components that can compute non-linear activation functions. Dot-product engines, which are essentially high-efficiency accelerators, have so far been successfully implemented in hardware in many different ways. In a study published last year, researchers at the University of Notre Dame in Indiana used dot-product circuits to design a cellular neural network (CeNN)-based accelerator for convolutional neural networks (CNNs). The same team, in collaboration with other researchers at the University of Minnesota, has now developed a CeNN cell based on spintronic (i.e., spin electronic) elements with high energy efficiency.
Adobe, along with researchers from the University of California, Berkeley, have trained artificial intelligence (AI) to detect facial manipulation in images edited using the Photoshop software. At a time when deepfake visual content is getting commoner and more deceptive, the decision is also intended to make image forensics understandable for everyone. "This new research is part of a broader effort across Adobe to better detect image, video, audio and document manipulations," the company wrote in a blog post on Friday. As part of the programme, the team trained a convolutional neural network (CNN) to spot changes in images made with Photoshop's "Face Away Liquify" feature, which was intentionally designed to change facial features like eyes and mouth. On testing, it was found that while human eyes were able to judge the altered face 53 percent of the time, the the trained neural network tool achieved results as high as 99 percent.
Throughout this article, I will discuss some of the more complex aspects of convolutional neural networks and how they related to specific tasks such as object detection and facial recognition. This article is a natural extension to my article titled: Simple Introductions to Neural Networks. I recommend looking at this before tackling the rest of this article if you are not well-versed in the idea and function of convolutional neural networks. Due to the excessive length of the original article, I have decided to leave out several topics related to object detection and facial recognition systems, as well as some of the more esoteric network architectures and practices currently being trialed in the research literature. I will likely discuss these in a future article related more specifically to the application of deep learning for computer vision.
I also did some experimentation with GRUs and LSTMs in NLP context, where I saw LSTMs performing better than GRUs, while they need more training time. Honestly, I never tried complete variable length sequences, because of the restriction, that each batch must be the same length and some layers are not usable if you have variable sequences. I don't think the difference will be huge, at least in my data. I experimented with different sequence lengths (100, 200, 250, 400, 500), and 400 and 500 have not performed better then 250. I did indeed achieve a noticeable performance improvement with embeddings, instead of one hot encoding.
When mundane objects such as cords, keys and cloths are fed into a live webcam, a machine-learning algorithm'sees' brilliant colours and images such as seascapes and flowers instead. The London-based, Turkish-born visual artist Memo Akten applies algorithms to the webcam feed as a way to reflect on the technology and, by extension, on ourselves. Each instalment in his Learning to See series features a pre-trained deep-neural network'trying to make sense of what it sees, in context of what it's seen before'. In Gloomy Sunday, the algorithm draws from tens of thousands of images scraped from the Google Arts Project, an extensive collection of super-high-resolution images of notable artworks. Set to the voice of the avant-garde singer Diamanda Galás, the resulting video has unexpected pathos, prompting reflection on how our minds construct images based on prior inputs, and not on precise recreations of the outside world.
The model, Global Automated Target Recognition (GATR), runs in the cloud, using Maxar Technologies' Geospatial Big Data platform (GBDX) to access Maxar's 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPUs enable GATR to scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training. The tool teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system then scales quickly to scan large areas, such as entire countries. GATR uses common deep learning techniques found in the commercial sector and can identify airplanes, ships,, buildings, seaports, etc. "There's more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process," says Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions.
With a little help from AI, you can now create a Bob Ross-style landscape in seconds. In March, researchers from NVIDIA unveiled GauGAN, a system that uses AI to transform images scribbled onto a Microsoft Paint-like canvas into photorealistic landscapes -- just choose a label such as "water," "tree," or "mountain" the same way you'd normally choose a color, and the AI takes care of the rest. At the time, they described GauGAN as a "smart paintbrush" -- and now, they've released an online beta demo so you can try it out for yourself. The level of detail included in NVIDIA's system is remarkable. Draw a vertical line with a circle at the top using the "tree" label, for example, and the AI knows to make the bottom part the trunk and the top part the leaves.