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Google Cloud Rolls Out Visual Inspection AI for Manufacturing

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The World of Reality, Causality and Real Artificial Intelligence: Exposing the Great Unknown Unknowns

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"All men by nature desire to know." - Aristotle "He who does not know what the world is does not know where he is." - Marcus Aurelius "If I have seen further, it is by standing on the shoulders of giants." "The universe is a giant causal machine. The world is "at the bottom" governed by causal algorithms. Our bodies are causal machines. Our brains and minds are causal AI computers". The 3 biggest unknown unknowns are described and analyzed in terms of human intelligence and machine intelligence. A deep understanding of reality and its causality is to revolutionize the world, its science and technology, AI machines including. The content is the intro of Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE). It is all a power set of {known, unknown; known unknown}, known knowns, known unknowns, unknown knowns, and unknown unknowns, like as the material universe's material parts: about 4.6% of baryonic matter, about 26.8% of dark matter, and about 68.3% of dark energy. There are a big number of sciences, all sorts and kinds, hard sciences and soft sciences. But what we are still missing is the science of all sciences, the Science of the World as a Whole, thus making it the biggest unknown unknowns. It is what man/AI does not know what it does not know, neither understand, nor aware of its scope and scale, sense and extent. "the universe consists of objects having various qualities and standing in various relationships" (Whitehead, Russell), "the world is the totality of states of affairs" (D. "World of physical objects and events, including, in particular, biological beings; World of mental objects and events; World of objective contents of thought" (K. How the world is still an unknown unknown one could see from the most popular lexical ontology, WordNet,see supplement. The construct of the world is typically missing its essential meaning, "the world as a whole", the world of reality, the ultimate totality of all worlds, universes, and realities, beings, things, and entities, the unified totalities. The world or reality or being or existence is "all that is, has been and will be". Of which the physical universe and cosmos is a key part, as "the totality of space and times and matter and energy, with all causative fundamental interactions".


Cognitive Explainable Artificial Intelligence (AI) breakthroughs in Machine Learning (ML) for US Air Force: 3D Image Recognition using few training samples on CPU (without GPU)

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Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI) software startup, has made AI and Machine Learning (ML) breakthroughs: ZAC has achieved 3D Image Recognition using only a few training samples, and using only an average laptop with low power CPU, for both training and recognition, for the US Air Force (USAF). This is in sharp contrast to the other algorithms in industry that require thousands to billions of samples, being trained on large GPU servers. "ZAC requires much less computing power and much less electrical power to run, which is great for mobile and edge computing, as well as environment, with less Carbon footprint," emphasized Dr. Saied Tadayon, CTO of ZAC. ZAC is the first to demonstrate the novel and superior algorithms Cognition-based Explainable-AI (XAI), where various attributes and details of 3D (three dimensional) objects are recognized from any view or angle. "You cannot do this task with the other algorithms, such as Deep Convolutional Neural Networks (CNN) or ResNets, even with an extremely large number of training samples, on GPU servers. That's basically hitting the limitations of CNNs or Neural Nets, which all other companies are using now," said Dr. Bijan Tadayon, CEO of ZAC.


Growing Demand of Machine Learning Market by 2027

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Machine learning is a subset of artificial intelligence. The concept has evolved from computational learning and pattern recognition in artificial intelligence. It explores the construction and study of algorithms and carries out forecasts on data. Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.


Stock Forecast Based On a Predictive Algorithm

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This stock forecast is part of the Stocks Under 5 Dollars Package, as one of I Know First's algorithmic trading tools. Package Name: Stocks Under $5 Recommended Positions: Long Forecast Length: 3 Months (3/28/21 – 6/28/21) I Know First Average: 27.44% In this 3 Months forecast for the Stocks Under $5 Package, there were many high performing trades and the algorithm correctly predicted 10 out 10 trades. SB was the highest-earning trade with a return of 50.38% in 3 Months. Other notable stocks were EDN and ENOB with a return of 41.71% and 35.0%.


Google's Visual Inspection AI to Inspect the Manufacturing Units

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The Google Cloud in 2019, identified six sectors contributing to its growth. These are public, healthcare, financial services, retail, media, businesses, and manufacturing. The cost of quality control and inspection continues to be high in the manufacturing sector. With increasing demand, it is difficult for humans to inspect the defects in computer chips and other products manually. To tackle this problem Google Cloud has announced visual inspection AI.


The power of two

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MIT's Hockfield Court is bordered on the west by the ultramodern Stata Center, with its reflective, silver alcoves that jut off at odd angles, and on the east by Building 68, which is a simple, window-lined, cement rectangle. At first glance, Bonnie Berger's mathematics lab in the Stata Center and Joey Davis's biology lab in Building 68 are as different as the buildings that house them. And yet, a recent collaboration between these two labs shows how their disciplines complement each other. The partnership started when Ellen Zhong, a graduate student from the Computational and Systems Biology (CSB) Program, decided to use a computational pattern-recognition tool called a neural network to study the shapes of molecular machines. Three years later, Zhong's project is letting scientists see patterns that run beneath the surface of their data, and deepening their understanding of the molecules that shape life.


All You Need To Know About Google's Visual Inspection AI

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In 2019, Google Cloud identified six sectors as vital components of its growth: public, healthcare, financial services, retail, media, and manufacturing. Within manufacturing, the cost of quality control and inspection continues to be among the highest. The American Society for Quality estimates that the price of quality may be as high as 15 to 20 percent of annual sales revenues for many organisations. Additionally, the rapid increase in production volumes makes it difficult for humans to manually inspect defects in computer chips and other products. To combat this, Google Cloud has recently announced an approach, backed by artificial intelligence (AI), for visual inspection.


All You Need To Know About Google's Visual Inspection AI

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… to general-purpose machine learning (ML) models, such as superior computer vision technology, shorter time-to-value and high scalability. Through …


Google Visual Inspection AI Augments AutoML To Detect Defects In Manufacturing

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Google launched Visual Inspection AI, a new service to identify production defects in manufacturing units. The service uses the state-of-the-art computer vision models developed by the AI research teams at Google. Vertex AI AutoML Vision, an integral part of the managed AI platform, delivers similar capabilities. Customers can upload images and classify them based on labels before initiating a training job. AutoML Vision generates a fully-trained model hosted in the cloud or deployed at the edge for performing inference. Visual Inspection AI takes AutoML Vision to the next level through its domain knowledge of the manufacturing industry.