Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.
Capturing big data is easy. What's difficult is to corral, tag, govern, and utilize it. NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud. Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.
Good connectivity between different pieces of equipment on the shop floor is important for their communication with each other, which eventually enable smart decision-making. This is one of the aspects of the fourth Industrial revolution, which is all set to re-map manufacturing businesses to deliver higher operational efficiency, better business outcomes and customer satisfaction through digital transformation. The digital landscape is continuously evolving for the manufacturing industry as businesses are adapting to the change and even anticipating changes before they occur. Fast-changing customer expectations and technological improvements that have brought a paradigm shift in other industries has begun to show the similar results in the manufacturing sector as well. Smart manufacturing is giving rise to smart factories Smart manufacturing is more than just automation, as it enables learning and adapting to the ever changing market conditions, delivering higher efficiency in quality control, than that performed by Quality Inspectors.
These are some of the outcomes that AI developers fear will come from their work, according to a new report issued today by the Deloitte AI Institute and the U.S. Chamber of Commerce. Titled "Investing in trustworthy AI," the 82-page report from Deloitte and the Chamber Technology Engagement Center sought to identify the concerns that technology experts have when it comes to the adoption of AI, as well as highlight the impact that government investment in AI can have on the emerging technology. Algorithmic bias and a lack of humans in decision loops are concerns for about two-thirds of the 250 people who participated in the survey. Another 60% identified "rogue or unanticipated behavior" of autonomous agents as a threat, while 56% said the lack of explainability of algorithms was a concern. "Perceived, and actual, discrimination by AI systems undermines the confidence individuals have in whether they are being given a fair opportunity when AI is involved," the report stated.
Artificial intelligence (AI) is getting real in the marketing suite. When asked where they planned to invest this year, marketers ranked AI as their #1 priority, according to our most recent State of Marketing Report. AI adoption is surging: 84% of marketers reported they use AI somewhere in their acquisition and retention engines, up almost three times over just two years ago. What are these intrepid marketers doing with AI? Reported uses are expanding rapidly, from enhanced personalization to improved segmentation, insight discovery, predictive modeling, and process automation. Advertising technology also rode the wave of big data-driven AI adoption, as programmatic platforms revolutionized the process of buying and selling digital ads.
Satish Pala is the Chief Technology Officer (CTO) of Indium Software. His specialized skills include project management, requirements analysis, business intelligence, data warehousing, SDLC as well as solutions architecture. Satish is popularly known as an eclectic manager to work within the industry. He is one of the most diligent and technically strong performers with a humble demeanor. Indium Software is a technology solutions company providing deep expertise in digital and QA services to its global customers.
Lately, I have been on a quest to learn as much as possible about node embedding techniques. The goal of node embedding is to encode nodes so that the similarity in the embedding space approximates similarity in the original network. In layman's terms, we encode each node to a fixed size vector that preserves the similarity of the original network. Node embeddings are helpful when you want to capture network information in a fixed-size vector and use it in a downstream Machine Learning workflow. I have come across the Karate Club package in my search for the implementation of various node embedding models.
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New Delhi [India], July 21 (ANI / PNN): According to the World Economic Forum, 133 million new jobs will be created in the field of artificial intelligence (AI) by 2022. Job demand and growth is projected in three key areas: data analysts and data scientists, AImachine learning specialists (including AI software engineers), and big data specialists. At the peak of decision-intelligence companies, use software that embeds AI within organizations across sales, marketing, planning, and supply chains to transform decision-making. The company has grown rapidly in the last 12 months, expanding its teams in Jaipur (India) and the United Kingdom, as well as opening new offices in the United States and Pune (India). As a result, Peak is creating 150 new jobs worldwide this year, including roles in data science and AI software engineering.
All the sessions from Transform 2021 are available on-demand now. Dremio today launched a cloud service that creates a data lake based on an in-memory SQL engine that launches queries against data stored in an object-based storage system. The goal is to make it easier for organizations to take advantage of the data lake, dubbed Dremio Cloud, without having to employ an internal IT team to manage it, said Tomer Shiran, chief product officer for Dremio. An organization can now start accessing Dremio Cloud in as little as five minutes, he said. Based on Dremio's existing SQL Lakehouse platform, the Dremio Cloud service runs on the Amazon Web Services (AWS) public cloud.