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How Machine Learning And IoT Can Be Beneficial For Business?

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Machine learning and IoT are one of the topmost trending topics. Moreover, Machine learning has been adopted by the top organizations for their IoT platforms, including Microsoft Azure, Google Cloud IoT edge, and Amazon AWS IoT. This blog post will cover enough information on Machine learning with IoT, including market size, benefits, and industry use cases. Machine learning was introduced in 1959 by an inventor named Arthur Samuel, working with IBM. Machine learning is part of Artificial Intelligence, which is mainly used to analyze the data with AI's help and identify patterns and make decisions with less human interference.


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!


Pinaki Laskar on LinkedIn: #AI #machinelearning #algorithms

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How can a mathematically-oriented machine truly learn things? Mathematical machines are either formal logical systems, operationalized as symbolic rules-based AI or expert systems, or statistical learning machines, dubbed as narrow/Weak AI, ML, DL, ANNs. Such machines follow blind and mindless mathematical and statistical algorithms, codes, models, programs, and solutions, transforming input data (as independent variables) into the output data (as dependent variables), dubbed as predictions, recommendations, decisions, etc. They are unable to real knowing or learning, as having no interactions with the world, its various domains, rules, laws, objects, events, or processes. Learning is the "acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences" via senses, experience, trial and error, intuition, study and research.


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.


iot machinelearning_2022-06-22_05-12-01.xlsx

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The graph represents a network of 1,368 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 June 2022 at 12:26 UTC. The requested start date was Wednesday, 22 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 19-hour, 59-minute period from Monday, 20 June 2022 at 04:01 UTC to Wednesday, 22 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Machine Learning With Google Cloud - AI Summary

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From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity." However, being acutely aware of the concerns around intelligent technology, the company's AI research division Google Brain recently published an AI Precision Safety whitepaper. Powerful Infrastructure Underpinning all of these projects, as well as the company's flagship Search, Translate and Youtube products is Google Cloud Platform, providing developers with the tools to build a range of programs from simple websites to complex, intelligent applications. As part of our AI in Business Festival, we spoke to Miles Ward, Global Head of Solutions at Google Cloud Platform, to find out more about the machine learning tools they offer to developers. From their DeepMind project beating champions of Alpha Go at their own game, to recent announcements Magneta and Springboard, not to mention driverless cars, its clear that AI and Machine Learning are central to Google's strategy across its vast portfolio. In a recent interview with Hollywood Reporter, Alphabet chairman Eric Schmidt played down the fears that surround advancements in AI: 'To be clear, we're not talking about consciousness, we're not talking about souls, we're not talking about independent creativity."