We don't start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. With recent, significant advances in deep learning, "there's been an interesting development in how one might use non-text information -- for example, images, audio, or other grounding information -- to tackle practical tasks involving language" says Kim, because "when humans are performing language processing tasks, we're doing so within a grounded, situated world." The pairing of hallucinated images and text during inference, the team postulated, imitates that process, providing context for improved performance over current state-of-the-art techniques, which utilize text-only data. To do this, the team used an encoder-decoder architecture with two transformers, a type of neural network model that's suited for sequence-dependent data, like language, that can pay attention key words and semantics of a sentence. Moreover, Kim and Panda note, a technique like VALHALLA is still a black box, with the assumption that hallucinated images are providing helpful information, and the team plans to investigate what and how the model is learning in order to validate their methods.

The advent of general adversarial networks (GANs) has led to increased popularity and adoption of artificial intelligence in the art world. It has been quite a few years since researchers have been trying to infuse the artistic skills into AI and there have been many interesting developments since then. Artists such as Mario Klingemann, Anna Ridler and many others have been at the forefront of this new-age GAN-powered art. Not only is AI creating breathtaking artwork but it is also being sold at auctions for hefty amounts. For instance, Canadian-Mexican artist Rafael Lozano-Hemmer has already made around $600,000 for an AI artwork.

We live in a world where technology is rapidly taking over our lives, like literally. Getting up in the morning we have our handheld devices at disposal, helping us, guiding us and informing us about our new and'upgraded' lives. The Internet has made connectivity seamless across various platforms. The ideal of the world becoming a global village has in actuality been achieved. A huge contribution to these advancements goes to the rapid developments in the IoT sector.

The past year has been rich in events, discoveries and developments in AI. It is hard to sort through the noise to see if the signal is there and, if it is, what is the signal saying. This post attempts to get you exactly that: I'll try to extract some of the patterns in the AI landscape over the past year. And, if we are lucky, we'll see how some of the trends extend into the near future. Make no mistake: this is an opinion piece. I am not trying to establish some comprehensive record of accomplishments for the year. I am merely trying to outline some of these trends. Another caveat: this review is US-centric. A lot of interesting things are happening, say, in China, but I, unfortunately, am not familiar with that exciting ecosystem.

The water crisis has become one of the major concerns across the globe. A report suggests that the US alone wastes 7 billion gallons of drinking water per day. As only less than one percent of earth surface water is suitable for human consumption, it becomes crucial that we save water so that our future generations survive. The unchecked use of water and extreme weather conditions have worsened the situation and in no time there will be a fresh water shortage, irregularities in supply and demand, groundwater shrinkage, among other challenges. To help overcome water crisis, organizations have started using artificial intelligence to efficiently stop this wastage.

I'm still driving my own car and visit a human doctor when I feel sick. I still haven't surrendered my job to a lifeless robot, and I don't think Alexa or Siri is my best friend. And no, we haven't manufactured our AI-powered robot overlords yet. Nonetheless, just like last year, this year saw some interesting developments in the field of artificial intelligence. As I watched the landscape, I can describe the developments as a shift from hype and craze to reality checks and more focus on the social and political repercussions of this fast moving domain.

The use of artificial intelligence (AI) in the form of artificial neural networks -- in particular, deep neural networks (DNNs) -- is poised to experience exponential growth in a wide variety of embedded systems, but who is going to define, create, and train these little scamps? Before we plunge into the fray with gusto and abandon, it's worth noting that many people think of DNNs only in the context of computer/machine/embedded vision applications. In reality, however, these little rascals are applicable to a wide variety of tasks (see Deep learning hits a sweet note). There are several steps involved in creating a DNN. The first is to define and implement the network architecture and topology.