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) …
Generative Adversarial Networks (GANs) have been a hot topic in Deep Learning ever since their initial invention and publication at NIPS 2014. There's a good reason for it all: GANs can create new content based on only a small bit of guidance. It's that sort of creativity which makes them so powerful. Just to name a few there. With all of this, massive resources are being poured into GAN research to figure out both how they work and how to design the absolute best GAN networks.
Generative Adversarial Networks (GANs) are classified within the group of generative models. That means that they're able to produce, i.e generate completely new, "valid" data. By valid data, we mean that the network's output should be something that we would deem acceptable for our target. To illustrate, consider an example where we wish to generate some new images for training an image classification network. Of course, for such an application we want our training data to be as realistic as possible, perhaps quite similar in style to other image classification training data sets.
Energy-Based Models(EBM) is one of the most promising areas of deep learning that hasn't seen a tremendous level of adoption yet. Conceptually, EBMs are a form of generative modeling that learns the key characteristics of a target dataset and tries to generate similar datasets. While EBMs results appealing because of its simplicity they have experienced many challenges when applied in real world applications. Recently, AI-powerhouse OpenAI published a new research paper that explores a new technique to create EBM model that can scale across complex deep learning topologies. EBMs are typically used in one of the most complex problems of real world deep learning solutions: generating quality training datasets.
Bidirectional Generative Adversarial Networks, BiGANs(Jeff Donahue et al.), as the name suggests, is bidirectional in that the real data is encoded before being passed to the discriminator. The discriminator takes as input both the feature representations ( z and E(x)) and the fully representative data (G(z) and x), distinguishing which from which. The generator and encoder collaborate to fool the discriminator by approaching E(x) to z and G(z) to x .
This example shows how to train a generative adversarial network (GAN) to generate images. A generative adversarial network (GAN) is a type of deep learning network that can generate data with similar characteristics as the input training data. The generator - Given a vector or random values as input, this network generates data with the same structure as the training data. The discriminator - Given batches of data containing observations from both the training data, and generated data from the generator, this network attempts to classify the observations as "real" or "generated". Train the generator to generate data that "fools" the discriminator.
Machine learning can now emulate human behavior, thought processes, and strategies, to the point of human indistinguishability between humans and machines in certain contexts. Google's Duplex system makes reservations by conversing with humans over the phone. Here, learning algorithms captured subtle artifacts of spoken English to replicate human artifacts such as hesitations or pauses, thereby generating speech that is very conversational and lifelike. In another domain, Christie's announced this past summer that it was the first auction house to sell art generated by a neural network. This questions the necessary involvement of human forms of creativity as a prerequisite to producing art that is enjoyable to humans.
Memory management is now a really important topic in Machine Learning. Because of memory constraints, it is becoming quite common to train Deep Learning models using cloud tools such as Kaggle and Google Colab thanks to their free NVIDIA Graphical Processing Unit (GPU) support. Nonetheless, memory can still be a huge constraint in the cloud when working with large amounts of data. In my last article, I explained how to speed up Machine Learning workflow execution. This article aims instead to explain to you how to efficiently reduce memory usage when implementing Deep Learning models.
Generative Adversarial Networks (GANs) have been a hot topic in Deep Learning ever since their initial invention and publication at NIPS 2014. There's a good reason for it all: GANs are able to create totally new content based on only a small bit of guidance. It's that sort of creativity which makes them so powerful. Just to name a few there. With all of this, massive resources are being poured into GAN research to figure out both how they work and how to design the absolute best GAN networks.
Between the fake news potential of deepfakes, the fear of robots stealing jobs, and the occasional call for automated systems to have control of the nuclear button, A.I.'s public image could do with a PR makeover here in 2019. Could saving a few million lives help? That's something a new biotech pharmaceutical startup called Insilico Medicine may be able to help with. Combining genomics, big data analysis, and deep learning, the company -- which is based in Rockville in Johns Hopkins University's Emerging Technology Centers -- has been using artificial intelligence algorithms to potentially discover the next world-changing drug. Using two of the most exciting and popular A.I. techniques of the moment, it's found a way of discovering drug molecules not only far more cheaply than usual, but also much, much, much faster.