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) …
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.
As 5G networks continue to expand in cities and countries across the globe, key researchers have already started to lay the foundation for 6G deployments roughly a decade from now. This time, they say, the key selling point won't be faster phones or wireless home internet service, but rather a range of advanced industrial and scientific applications -- including wireless, real-time remote access to human brain-level AI computing. That's one of the more interesting takeaways from a new IEEE paper published by NYU Wireless's pioneering researcher Dr. Ted Rappaport and colleagues, focused on applications for 100 gigahertz (GHz) to 3 terahertz (THz) wireless spectrum. As prior cellular generations have continually expanded the use of radio spectrum from microwave frequencies up to millimeter wave frequencies, that "submillimeter wave" range is the last collection of seemingly safe, non-ionizing frequencies that can be used for communications before hitting optical, x-ray, gamma ray, and cosmic ray wavelengths. Dr. Rappaport's team says that while 5G networks should eventually be able to deliver 100Gbps speeds, signal densification technology doesn't yet exist to eclipse that rate -- even on today's millimeter wave bands, one of which offers access to bandwidth that's akin to a 500-lane highway.
The AI and analytics revolution has revolutionized nearly every corner of industry, helping businesses innovate, become more efficient and pioneer entirely new application areas and product lines. At the same time, the greatest beneficiaries of these advances have often been larger companies that can afford to hire the specialized expertise necessary to fully harness these new advances. In contrast, smaller and medium-sized businesses and those in non-traditional industries have struggled to integrate these technologies with their overtaxed technical staff focused more on the mundane IT issues of desktop upgrades and higher priority tasks like shoring up their cybersecurity. Cloud companies are moving rapidly to help these businesses through a wealth of new APIs and tools that don't require any deep learning or advanced analytics experience. The future of the cloud lies in analytics.
Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.
Graph Neural Network has become the new fashion in many graph-based learning problems. As the team behind this library, we want to share with you the new release of DGL (v0.3) that is much faster (up to 19x faster) and more scalable for training GNNs on large graphs (up to 8x larger). For whom have never heard of DGL or Graph Neural Network, maybe it is worth to take a look at this new trend of geometric deep learning. Checkout more about how a variety of models can be unified under the message passing framework and can be implemented in DGL (https://docs.dgl.ai/tutorials/models/index.html). Our project site: https://www.dgl.ai/ .
Using deep learning for image recognition allows a computer to learn from a training data set what the important "features" of the images are. By using a hierarchy of numerous artificial neurons, deep learning can automatically classify images with a high degree of accuracy. Thus, neural networks can recognize different species of cats, or models of cars or airplanes from images. Sometimes neural networks can exceed the performance of the human eye for certain applications.