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The Future of AI and Four New Demands on ERP Systems - IAA - Industrial Automation

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ERP systems need to lose their cumbersome heritage and open up to third-party applications, in order to help businesses benefit from technological innovations more quickly. Artificial intelligence (AI) will have a significant impact on companies and their business models over the next five years--85 percent of CEOs surveyed in PwC's 22nd Annual Global CEO Survey are convinced of this. But with only 33 percent having dipped their toe into AI for'limited uses', and fewer than one in ten using it on a wide scale, the range of applications has been limited so far. However, this is soon set to change. Despite the use of AI being a distant dream for many businesses, the current maturity of intelligent technologies and the expectations of enterprise resource planning (ERP) systems in particular--to support innovations--have fundamentally changed business demands.


The Problem With Including AI In School Curriculum

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One of the main reasons to integrate AI in the current school curriculum is to make the upcoming generation familiar with technology. The Government of India and the educational board have been pushing for more artificial intelligence to be integrated into the education system, not from the perspective of enhancing it, but also with the intention of making young minds more aware and skilled when it comes to artificial intelligence. Today, children are curious about the smart conversational devices and AI used in applications like Siri and Alexa; some of them even wonder how Netflix gives them precise recommendations. Gradually, they will grow curious and try to learn what algorithms are, what a neural network is, and how they work. The Government of India and the educational board have been taking measures to make the existing school curriculum more AI-centric with a firm belief that the students will learn about AI, have fun and also take India forward.


AI With Grove Zero and Codecraft (Scratch 3.0)

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The neural network models used in the above application are all run locally in your browser, which has a few distinct advantages as compared to sending the data to the cloud for processing: smaller latency and better privacy. A number of neural networks are used in Cognitive services - Sound Classification for speech commands(, Face Landmark Detection, Face Expression Recognition and Age estimation. There are multiple ways you can build on these examples to make even more fun and exciting applications! If you decide to give it a try,be it with Grove Zero or just using Stage mode, do share in the comments below.



Army Seeks AI Ground Truth

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Deep neural networks are being mustered by U.S. military researchers to marshal new technology forces on the Internet of Battlefield Things. U.S. Army and industry researchers said this week they have developed a "confidence metric" for assessing the reliability of AI and machine learning algorithms used in deep neural networks. The metric seeks to boost reliability by limiting predictions based strictly on the system's training. The goal is to develop AI-based systems that are less prone to deception when presented with information beyond their training. SRI International has been working since 2018 with the Army Research Laboratory as part of the service's Internet of Battlefield of Things Collaborative Research Alliance.


Frontier Development Lab Is Going Virtual: Now Accepting Applications For Paid Research Opportunities - SpaceWatch.Global

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The Frontier Development Lab (FDL) Europe applies AI technologies to science to push the frontiers of research and develop new tools to help solve some of the biggest challenges that humanity faces. These range from the effects of climate change to predicting space weather, from improving disaster response, to identifying meteorites that could hold the key to the history of our universe. FDL brings researchers from the cutting-edge of AI and data science, and teams them up with their counterparts from the space sector for an intensive eight-week research sprint, based on a range of challenge areas. The results far exceed what any individual could develop in the same time period, or even in years of individual research. A key aspect of our success is the careful formation of small interdisciplinary teams focused on tackling specific challenges.


Edge computing environments: what you need to know

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The saying goes: "If you're not on the edge, you're taking up too much space". And compute itself is now moving to the edge, forcing datacentre operators to wring the last drops of productivity from their infrastructure, ahead of a future supporting multi-sensor internet of things (IoT) devices over 5G for machine learning, and even artificial intelligence (AI). Jennifer Cooke, research director of cloud-to-edge datacentre trends at IDC, says datacentre operators need to start thinking about how many systems they will need to roll out, and the people they will need to support them. "Cost becomes the prohibitive factor," she says. Edge will take different forms.


Artificial Intelligence (AI) Applications in 2020

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Let's take a detailed look. This is the most common form of AI that you'd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.This is the only kind of Artificial Intelligence that exists today. They're able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters. AGI is still a theoretical concept. It's defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.


Observability for Data Engineering

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Observability is a fast-growing concept in the Ops community that caught fire in recent years, led by major monitoring/logging companies and thought leaders like Datadog, Splunk, New Relic, and Sumo Logic. It's described as Monitoring 2.0 but is really much more than that. Observability allows engineers to understand if a system works like it is supposed to work, based on a deep understanding of its internal state and context of where it operates. It is the capability of monitoring and analyzing event logs, along with KPIs and other data, that yields actionable insights. An observability platform aggregates data in the three main formats (logs, metrics, and traces), processes it into events and KPI measurements, and uses that data to drive actionable insights into system security and performance.


Online Pie & AI: Real-world AI Applications in Medicine

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AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. To help make this transformation possible worldwide, you need to gain practical experience applying machine learning to concrete problems in medicine. We've gathered experts in the AI and medicine field to share their career advice and what they're working on. We'll also be celebrating the launch of our new AI For Medicine Specialization!