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) …
This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.
Understanding an emotion isn't as simple as noticing a smile-- but we still look to facial movements for everything from navigating everyday social interactions to the development of emotionally attuned artificial intelligence. According to a July 2019 study from researchers at Northeastern and the California Institute of Technology, facial expressions only reflect the surface of emotions: The culture, situation, and specific individual around a facial expression add nuance to the way a feeling is conveyed. For example, the researchers note that Olympic athletes who won medals only smiled when they knew they were being watched by an audience. While they were waiting behind the podium or facing away from people, they didn't smile (but were probably still happy). These results reinforce the idea that facial expressions aren't always reliable indicators of emotion.
Nvidia CEO Jensen Huang said AI would drive long-term demand because it is the "single most powerful force of our time." Nvidia reported earnings and revenues that beat analysts' expectations as demand for graphics and artificial intelligence chips picked up in the second fiscal quarter. Huang also said his company's near-term growth will come from gaming and a couple of variants of the company's artificial intelligence chip business: inferencing and AI at the edge. During a conference call with analysts, Huang said artificial intelligence is the "single most powerful force of our time" and that there are more than 4,000 AI startups working with the company -- as compared to 2,000 AI startups in April 2017. In an interview with VentureBeat, Huang said the actual number of AI startups Nvidia is tracking is closer to 4,500.
Martin Spano is the author of Artificial Intelligence in a Nutshell, a book that explores the mystified subject of artificial intelligence (AI) with simple, non-technical language. Spano's passion for AI began after he watched 2001: A Space Odyssey, but he insists this ever-changing technology is not just the subject for sci-fi novels and movies; artificial intelligence is present in our everyday lives. Alex Krizhevsky was born in Ukraine but lived most of his life in Canada. After finishing his undergraduate studies, he continued as a postgraduate under the supervision of Geoffrey Hinton, legendary computer scientist and cognitive psychologist, one of the foremost advocates of using artificial neural networks for artificial intelligence. Krizhevsky stumbled upon an algorithm by Hinton that used graphics cards instead of processors for its execution.
If you ask any group of data science students about the types of machine learning algorithms, they will answer without hesitation: supervised and unsupervised. However, if we ask that same group to list different types of unsupervised learning, we are likely to get an answer like clustering but not much more. While supervised methods lead the current wave of innovation in areas such as deep learning, there is very little doubt that the future of artificial intelligence(AI) will transition towards more unsupervised forms of learning. In recent years, we have seen a lot of progress on several new forms of unsupervised learning methods that expand way beyond traditional clustering or principal component analysis(PCA) techniques. Today, I would like to explore some of the most prominent new schools of thought in the unsupervised space and their role in the future of AI.
The beach offers a wide open playscape where children are fuelled by curiosity. Whether at the beach or elsewhere outdoors, it helps to take a moment to see the world through the lens of a child who is discovering the world anew, and slow down to be present. Part of what happens through children's play is the exhilaration of making choices. These choices, and their consequences, are part of the child's emerging sense of agency and identity. Children's inquisitive minds crave opportunities that allow them to become designers, builders, mathematicians and innovators of their world.
In March 2017, I joined the MathWorks Student Competitions team to focus on supporting university-level robotics competitions. The competition I spend most time with is RoboCup, which is great because RoboCup contains a variety of leagues and skill levels that keeps me sharp with almost everything going on in the field. Today I will talk about my experience in this role, and what it's been like returning to robotics and academia after more than 5 years away from the field. Let me start with a personal history lesson about my experience in robotics. I am a mechanical engineer with a background in controls, dynamics, and systems.
Generative adversarial networks, or GANs, are effective at generating high-quality synthetic images. A limitation of GANs is that the are only capable of generating relatively small images, such as 64 64 pixels. The Progressive Growing GAN is an extension to the GAN training procedure that involves training a GAN to generate very small images, such as 4 4, and incrementally increasing the size of the generated images to 8 8, 16 16, until the desired output size is met. This has allowed the progressive GAN to generate photorealistic synthetic faces with 1024 1024 pixel resolution. The key innovation of the progressive growing GAN is the two-phase training procedure that involves the fading-in of new blocks to support higher-resolution images followed by fine-tuning. In this tutorial, you will discover how to implement and train a progressive growing generative adversarial network for generating celebrity faces. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. Photo by Alessandro Caproni, some rights reserved. GANs are effective at generating crisp synthetic images, although are typically limited in the size of the images that can be generated.
The progressive growing generative adversarial network is an approach for training a deep convolutional neural network model for generating synthetic images. It is an extension of the more traditional GAN architecture that involves incrementally growing the size of the generated image during training, starting with a very small image, such as a 4 4 pixels. This allows the stable training and growth of GAN models capable of generating very large high-quality images, such as images of synthetic celebrity faces with the size of 1024 1024 pixels. In this tutorial, you will discover how to develop progressive growing generative adversarial network models from scratch with Keras. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. How to Implement Progressive Growing GAN Models in Keras Photo by Diogo Santos Silva, some rights reserved.
Progressive Growing GAN is an extension to the GAN training process that allows for the stable training of generator models that can output large high-quality images. It involves starting with a very small image and incrementally adding blocks of layers that increase the output size of the generator model and the input size of the discriminator model until the desired image size is achieved. This approach has proven effective at generating high-quality synthetic faces that are startlingly realistic. In this post, you will discover the progressive growing generative adversarial network for generating large images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.