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) …
It shouldn't be surprising given the media spotlight on artificial intelligence, but AI will be all over the keynote and session schedule for this year's Spark Summit. The irony, of course, is that while Spark has become known as a workhorse for data engineering workloads, its original claim to fame was that it put machine learning on the same engine as SQL, streaming, and graph. But Spark has also had its share of impedance mismatch issues, such as making R and Python programs first-class citizens, or adapting to more compute-intensive processing of AI models. Of course, that hasn't stopped adventurous souls from breaking new ground. Hold those thoughts for a moment.
The aviation industry, especially the commercial aviation sector, is constantly striving to improve both the way it works and its customer satisfaction. To that end, it has begun using artificial intelligence. But AI can potentially go far beyond the current use cases. To make a long story short, AI can redefine how the aviation industry goes about its work. The global aviation industry has been growing exponentially.
Do we still need humans to power customer experiences? In its Digital CX Trends 2018 report (fee charged) released today, Forrester researchers found that "while AI, intelligent agents, and chatbots were central to the business conversation in 2017, most companies discovered they lack the design acumen and technical chops to seize the opportunities." This, researchers found, has led to widespread struggles with the basics and few leaders "innovating the way forward." It's fair to say not everyone is excited about artificial intelligence's invasion into customer experience, even those who profit from it. Yet many organizations are still turning to AI to power customer experiences.
Here we are going to see different type of Augmentations that can be applied to images. One the most basic Augmentations is to apply the flipping to image which can double the data (based on how you apply). Random flipping: With a 1 in 2 chance your image will be flipped horizontally or vertically. Alternatively you can also use tf.reverse for the same. Image will be rotated k times 90 degrees in counter-clockwise direction.
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research.
The explosion of user-generated content on the internet during the last decades has left the world of querying multimedia data with unprecedented challenges. There is a demand for this data to be processed and indexed in order to make it available for different types of queries, whilst ensuring acceptable response times.
Finance has always been interesting for statisticians, psychologists, data-miners, and other disciplines for many reasons such as its profitability, its chaos and the psychology behind it. To be honest, financial markets are very difficult to predict. This unpredictability is due to the fluctuations, which are a function of many parameters such as political decisions by governments, local and global news, and etc. Despite such complexity, there is still something predictable, and that is the "market psychology"! According to various researches and principles such as Elliott wave principle, financial markets are cyclic waves.
Over the past decade or so, "The Low-Fertility Trap," a hypothesis put forth by Wolfgang Lutz, Vegard Skirbekk and Maria Rita Testa, respectively Austrian, Norwegian and Italian scholars, has worried many countries facing the risks of an aging population. The theory suggests that when a country's birth rate is lower than 1.5, three self-reinforcing mechanisms -- demographic, sociological and economic -- can work, if unchecked, towards a downward spiral in its future fertility.