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Three opportunities of Digital Transformation: AI, IoT and Blockchain

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Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).


7 Lessons I've Learnt From Deploying Machine Learning Models Using ONNX

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In this post, we will outline key learnings from a real-world example of running inference on a sci-kit learn model using the ONNX Runtime API in an AWS Lambda function. This is not a tutorial but rather a guide focusing on useful tips, points to consider, and quirks that may save you some head-scratching! The Open Neural Network Exchange (ONNX) format is a bit like dipping your french fries into a milkshake; it shouldn't work but it just does. ONNX allows us to build a model using all the training frameworks we know and love like PyTorch and TensorFlow and package it up in a format supported by many hardware architectures and operating systems. The ONNX Runtime is a simple API that is cross-platform and provides optimal performance to run inference on an ONNX model exactly where you need them: the cloud, mobile, an IoT device, you name it!


What is Artificial Intelligence? How does AI work, Types, Trends and Future of it?

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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.


Artificial intelligence

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Deep learning[133] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[134] Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification[135] and others. Deep learning often uses convolutional neural networks for many or all of its layers.


What Are Deep Learning Embedded Systems And Its Benefits - Onpassive

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In recent years, deep learning has been a driving force in advance of artificial intelligence. Deep learning is an approach to artificial intelligence in which a neural network – an interconnected group of simple processing units – is trained with data that are adjusted until it performs a task with maximum efficiency. In this article, we'll talk about deep learning embedded systems and how they can help your organization by improving efficiencies in processes ranging from manufacturing to customer experience. Deep learning is a subfield of machine learning that uses artificial neural networks to simulate how the brain learns. Neural networks are algorithms that use large amounts of data to understand patterns.


IKEA launches AI-powered design experience (no Swedish meatballs included)

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. For IKEA, the latest in digital transformation is all about home design driven by artificial intelligence (AI) – minus the home furnishing and decor retailer's famous Swedish meatballs. Today, it launched IKEA Kreativ, a design experience meant to bridge the ecommerce and in-store customer journeys, powered by the latest AI developments in spatial computing, machine learning and 3D mixed reality technologies. Available in-app and online, IKEA Kreativ's core technology was developed by Geomagical Labs, an IKEA retail company, which Ingka Group (the holding company that controls 367 stores of 422 IKEA stores) acquired in April 2020. IKEA Kreativ is the next step in IKEA's long journey towards digital transformation.


Sentient? Google LaMDA feels like a typical chat bot

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LaMDA is a software program that runs on Google TPU chips. Like the classic brain in a jar, some would argue the code and the circuits don't form a sentient entity because none of it engages in life. Google engineer Blake Lemoine caused controversy last week by releasing a document that he had circulated to colleagues in which Lemoine urged Google to consider that one of its deep learning AI programs, LaMDA, might be "sentient." Google replied by officially denying the likelihood of sentience in the program, and Lemoine was put on paid administrative leave by Google, according to an interview with Lemoine by Nitasha Tiku of The Washington Post. There has been a flood of responses to Lemoine's claim by AI scholars. University of Washington linguistics professor Emily Bender, a frequent critic of AI hype, told Tiku that Lemoine is projecting anthropocentric views onto the technology. "We now have machines that can mindlessly generate words, but we haven't learned how to stop imagining a mind behind them," Bender told Tiku. In an interview with MSNBC's Zeeshan Aleem, AI scholar Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, observed that the concept of sentience has not been rigorously explored. Mitchell concludes the program is not sentient, however, "by any reasonable meaning of that term, and the reason is because I understand pretty well how the system works."


Sentient? Google LaMDA feels like a typical chat bot

ZDNet

LaMDA is a software program that runs on Google TPU chips. Like the classic brain in a jar, some would argue the code and the circuits don't form a sentient entity because none of it engages in life. Google engineer Blake Lemoine caused controversy last week by releasing a document that he had circulated to colleagues in which Lemoine urged Google to consider that one of its deep learning AI programs, LaMDA, might be "sentient." Google replied by officially denying the likelihood of sentience in the program, and Lemoine was put on paid administrative leave by Google, according to an interview with Lemoine by Nitasha Tiku of The Washington Post. There has been a flood of responses to Lemoine's claim by AI scholars. University of Washington linguistics professor Emily Bender, a frequent critic of AI hype, told Tiku that Lemoine is projecting anthropocentric views onto the technology. "We now have machines that can mindlessly generate words, but we haven't learned how to stop imagining a mind behind them," Bender told Tiku. In an interview with MSNBC's Zeeshan Aleem, AI scholar Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute, observed that the concept of sentience has not been rigorously explored. Mitchell concludes the program is not sentient, however, "by any reasonable meaning of that term, and the reason is because I understand pretty well how the system works."


Build your first text-to-image searcher with TensorFlow Lite Model Maker

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An on-device embedding based search package is been introduced by Tensorflow which could be run on android, ios and web applications. It runs with help of the Edge ML technique. This on-device package could help the user to search images, text or audio in just a snap of time. In this article, we would learn the implementation of on-device text-to-image search with TensorflowLite. Following are the topics to be covered.


Artificial Intelligence Market 2022 Expected to Grow at a CAGR of 26.1% with Renowned Players by Till 2029 - Digital Journal

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For each of the aforementioned regions and countries, detailed analysis and data for annual revenue (demand and production) are available for 2020-2027. The breakdown of all regional markets by country and the key national markets by Technology, Component, and Industry Vertical over the forecast years are also included.