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) …
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.
In terms of sheer speed and precision, delta robots are some of the most impressive to watch. They're also some of the most useful, for the same reasons--you can see them doing pick-and-place tasks in factories of all kinds, far faster than humans can. The delta robots that we're familiar with are mostly designed as human-replacement devices, but as it turns out, scaling them down makes them even more impressive. In Robert Wood's Microrobotics Lab at Harvard, researcher Hayley McClintock has designed one of the tiniest delta robots ever. Called milliDelta, it may be small, but it's one of the fastest moving and most precise robots we've ever seen.
You probably did not hear it here first. Spark has been making waves in big data for a while now, and 2017 has not disappointed anyone who has bet on its meteoric rise. That was a pretty safe bet actually, as interpreting market signals, speaking with pundits and monitoring data all pointed to the same direction.
You can't really give a conference keynote in 2017 without staking some sort of claim on AI, so Databricks was smart to keep its credibility, and let its geeky co-founder and CTO Matei Zaharia officially open the Spark Summit Europe 2017 yesterday. Instead, Zaharia spoke about streaming data and deep learning -- the engineers and developers in the room ate it up. The conference, organized by Databricks, creators of Apache Spark, brought more than 1200 enthusiasts to Dublin, Ireland this week to learn about what new features and functions will be added to the open source project. The short answer, according to Zaharia is cost based optimization, Python and R improvements, Kubernetes support and more. Databricks CEO and co-founder Ali Ghodsi spoke to them.
Databricks, the inventor and commercial distributor of the Apache Spark processing platform, has announced a system called Delta, which it believes will appeal to CIOs as a data lake, a data warehouse and a "streaming ingest system". It is said to eliminate the need for extract, transform and load (ETL) processes. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.