Understanding artificial intelligence and machine learning in digital business


The problem of learning and decision-making is at the core of human and artificial thought, which is why scientists introduced machine learning (ML) into artificial intelligence (AI). AI is a platform or solution that appears to be intelligent and can often exceed the performance of humans. It is a broad description of any device that mimics human or intellectual functions, such as mechanical movement, reasoning or problem solving. ML is a widely used AI concept that teaches machines to detect different patterns and adapt to new circumstances and can be both experience- and explanation-based. For instance, in robotics, ML plays a vital role by optimizing machine-based decision-making, which eventually increases a machine's efficiency by enabling a more organized way of performing a particular task.

The technology behind AI in PPC


I believe artificial intelligence (AI) will be a key driver of change in PPC in 2018 as it leads to more and better PPC intelligence. So far, I've discussed the roles humans will play when PPC management becomes nearly fully automated and six strategies agencies can take to future-proof their business. In this final post on the state of AI in PPC, I'll cover the technology of AI. AI has been around since 1956, and PPC has existed since the late 1990s. So why did it take until now for AI's role in paid search to become such a hot topic in our industry?

IoT in Action: The promise of AI-enabled IoT


The next technology revolution is underway. Advances in artificial intelligence (AI) and machine learning coupled with more robust and competent devices is driving transformation across industries and workstreams, from small farms in India to huge corporations in the United States. So what kinds of changes are underway and what does AI-enabled Internet of Things (IoT) offer businesses across industries? For a full discussion on this, I'd suggest attending the upcoming IoT in Action event in San Francisco on February 13 (more on that later). For this article, we'll look to Microsoft's Chief Storyteller, Steve Clayton, who touches on some key areas.

Artificial Intelligence to Sort Through ISR Data Glut


Inundated with more data than humans can analyze, the U.S. military and intelligence community are banking on machine learning and advanced computing technologies to separate the wheat from the chaff. The Defense Department operates more than 11,000 drones that collect hundreds of thousands of hours of video footage every year. "When it comes to intelligence, surveillance and reconnaissance, or ISR, we have more platforms and sensors than at any time in Department of Defense history," said Air Force Lt. Gen. John N.T. "Jack" Shanahan, director for defense intelligence (warfighter support) in the office of the undersecretary of defense for intelligence. "It's an avalanche of data that we are not capable of fully exploiting," he said at a technology conference in Washington, D.C., hosted by Nvidia, a Santa Clara, California-based artificial intelligence computing company. For example, the Pentagon has deployed a wide-area motion imagery sensor that can look at an entire city.

New Center Headquartered at Carnegie Mellon Will Build Smarter Networks To Connect Edge Devices to the Cloud - News - Carnegie Mellon University


Carnegie Mellon University will lead a $27.5 million Semiconductor Research Corporation (SRC) initiative to build more intelligence into computer networks. Researchers from six U.S. universities will collaborate in the CONIX Research Center headquartered at Carnegie Mellon. For the next five years, CONIX will create the architecture for networked computing that lies between edge devices and the cloud. The challenge is to build this substrate so that future applications that are crucial to IoT can be hosted with performance, security, robustness, and privacy guarantees. "The extent to which IoT will disrupt our future will depend on how well we build scalable and secure networks that connect us to a very large number of systems that can orchestrate our lives and communities.

A startup uses quantum computing to boost machine learning


A company in California just proved that an exotic and potentially game-changing kind of computer can be used to perform a common form of machine learning. The feat raises hopes that quantum computers, which exploit the logic-defying principles of quantum physics to perform certain types of calculations at ridiculous speeds, could have a big impact on the hottest area of the tech industry: artificial intelligence. Researchers at Rigetti Computing, a company based in Berkeley, California, used one of its prototype quantum chips--a superconducting device housed within an elaborate super-chilled setup--to run what's known as a clustering algorithm. Clustering is a machine-learning technique used to organize data into similar groups. Rigetti is also making the new quantum computer--which can handle 19 quantum bits, or qubits--available through its cloud computing platform, called Forest, today.

High Bandwidth Memory: The Great Awakening of AI


Artificial intelligence (AI) is fast becoming one of the most important areas of digital expansion in history. The CEO of Applied Materials recently stated that "the war" for AI leadership will be the "biggest battle of our lifetime."1 AI promises to transform almost every industry, including healthcare (diagnosis, treatments), automotive (autonomous driving), manufacturing (robot assembly), and retail (purchasing assistance). Although the field of AI has been around since the 1950s, it was not until very recently that computing power and the methods used in AI have reached a tipping point for major disruption and rapid advancement. Both of these areas have a tremendous need for much higher memory bandwidth.

Master machine learning -- and snag a great job -- with these key job skills


If you'd go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.

3 IoT Predictions for 2018 IoT For All


Commercial devices are rapidly evolving to be powerful enough to run full-fledged operating systems and complex algorithms. Edge Computing, which refers to the fact that part of the work happens right at the edge of the network where IoT connects the physical world to the Cloud, involves a great deal more than having computation and data processing on IoT devices. A fundamental aspect of this concept is the strong and seamless integration between IoT and the Cloud, bringing the physical and computation worlds closer together in a profound way. The next developmental wave of resource-efficient machine learning algorithms will advance the potential for Edge Devices to take over a growing percentage of computing requirements. Due to its ability to increase privacy and reduce latency, Edge Computing is expected to evolve greatly over the coming year.

Real Time Digital Image Processing of Agricultural Data


In my earlier articles, I had discussed about about application of Big data for gathering Insights on green revolution and witnessed about a research work on supply chain management using big data analytics on agriculture. Incrementally, got an opportunity to implement data science methodology (a game theory approach) to make the results of SCM as an incentive compatible one. However, in this article I am trying to discuss about a large scale digital image processing obtained using time-series photographs of agricultural fields and sensor data for parameters, that should be done parallely with the help of Big Data Analytics such that the result of this work can facilitate SCM process exponentially. We are focusing on using deep learning and machine learning techniques for identifying patterns for making predictins and decision making on large-scale stored / near real-time data sets. By this, we can identify the crop type, quality, maturity period for harvesting, early identification of bugs and diseases, soil quality attributes, early identification of need for soil nourishments etc., on a larger farms.