If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
We live in a world that is becoming more personalized every day. Consumers have come to expect experiences that are tailored for them -- especially when it comes to engaging with brands. When you open your Uber app, it now suggests your home address; online shopping is increasingly personalized, and, of course, so is advertising. You expect to see ads that reflect your interests and buying patterns and, in fact, are more likely to engage with those ads.We have artificial intelligence (AI) to thank for our increasingly personalized world. As the demand for personalization increases, so too does the buzz around AI. AI is a term that is becoming ubiquitous -- and potentially overused -- as an umbrella term relating to any action a machine takes based on a set of rules in order to mimic human intelligence.
Artificial Intelligence is the need of the hour. This technology of today is neither an elementary school math nor a rocket science application. The understanding of AI not only allows business decision makers and enthusiasts to make advancements in technologies but also let them make processes better. Another term that is doing the rounds is artificial general intelligence (AGI) which encompasses human-level cognitive ability making automation think and work like a human mind. So how do you benefit from AI and the latest advancements that move around it?
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.
Machine learning and Artificial intelligence have taken over data centers by storm. As racks begin to fill with ASICs, FPGAs, GPUs, and supercomputers, the face of the hyper-scale server farm seems to change. These technologies are known to provide exceptional computing power to train machine learning systems. Machine learning is a process that involves tremendous amounts of data-crunching, which is a herculean task in itself. The ultimate goal of this tiring process is to create applications that are smart and also to improve services that are already in everyday use.
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.