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) …
Machine learning is highly pervasive today so much so that we use it a dozen times a day without even realizing. Machine learning involves getting computers to learn, think, and act on their own without human interference. As described by Google, "Machine learning is the future." With an increasing number of humans becoming addicted to their machines, the future of machine learning looks very bright. We are indeed witnesses to a new revolution which is taking over the world owing to its immense potential.
Sure, world is crying out loud that big-data's biggest problem will be resources. Demand has skyrocketed and everyone in the world is going into tailspin in meeting that demands. Companies are going frantic and overspending to hire data scientists to secure themselves from any upcoming shortfall. This is nothing but a sign that world needs our robot algorithm friends to pacify some demand and increase credibility to new paradigms. Who could forget Steve Balmer's famous quote comparing Big Data as a Machine Learning problem.
Machine Learning Basics: In the stock market, you learn every day. There is no person trading in the stock market can claim to predict the price movement correctly every single time. The fluctuation and volatility always keep the trader interested. They apply many tool and techniques to rightly predict the stock price movement and accordingly take the position. On some occasions, they would be right and on some occasions, they would be wrong.
This blog post is an overview of quantum machine learning written by the author of the paper Bayesian deep learning on a quantum computer. In it, we explore the application of machine learning in the quantum computing space. The authors of this paper hope that the results of the experiment help influence the future development of quantum machine learning. With no shortage of research problems, education programs, and demand for talent, machine learning is one of the hottest topics in technology today. Parallel to the success of learning algorithms, the development of quantum computing hardware has accelerated over the last few years.
This is the era of Machine Learning (ML) as increasing the power of computation in computer technologies, and there is a lot of data to manage for each sector in the world. We have lots of data (symptoms, cures) for diseases, economic data (share market, trading) and there is also confidential data that could be used to decide on a business, agriculture, or even in a presidential decision for a country. These are the enormous use of ML, except that there are various small sectors where machine learning is performing well for doing human tasks. Machine Learning is a significant branch of Artificial Intelligence (AI). The idea of machine learning is to generate a decision from several earlier examples for a new instance of the same kind of problem or cluster them in several groups.
Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.
As machine learning applications increase, the threats of data poisoning also rise, making it imperative for organizations to secure their machine learning data against fraudulent manipulation. FREMONT, CA: Machine learning (ML) and artificial intelligence (AI) models are algorithms designed to make decisions and perform actions based on the data they ingest. However, like all data-based systems, they are not invincible to attacks. One of the most common threats to ML is ML poisoning, which involves feeding fraudulent or misleading data into the ML algorithms. Business data is based on data, and any errors in the data can lead to bad decisions.
Clinicians who order common diagnostic chest x-rays for patients have been sitting on a goldmine of unused prognostic information. The radiographs, used since the 19th century to detect specific abnormalities, could soon be repurposed to identify long-term mortality risk -- with a little help from machine learning. Using data from two large randomized trials, researchers have developed a convolutional neural network, called CXR-risk, that stratifies participants by all-cause mortality risk. They trained the artificial intelligence (AI) system with 85,000 x-rays and follow-up data from more than 40,000 individuals. Extracting information from single chest radiographs, the system found a graded association between risk score and mortality.
A new algorithm could enable quicker and more accurate detection of severe weather. When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms. However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations -- especially smaller-scale ones -- in real time. Now, there is a computer model that can help recognize severe storms more quickly and accurately, thanks to a team of researchers partially funded by the National Science Foundation. The researchers from Penn State, AccuWeather, Inc. and the University of Almería in Spain developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects from satellite images rotational movements in clouds that might have otherwise gone unnoticed.
Meanwhile, companies are installing more and more sensors hoping to improve efficiency and cut costs. However, machine learning consultants from InData Labs say that without proper data management and analysis strategy, these technologies are just creating more noise and filling up more servers without actually being used to their potential. Is there a way to convert simple sensor recordings into actionable industrial insights? The simple answer is yes, and it lies in machine learning (ML). The scope of ML is to mimic the way the human brain processes inputs to generate logical responses.