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The Difference Between Artificial Intelligence and Machine Learning

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Most common people tend to use the terms like artificial intelligence and machine learning as synonymous and they do not know the difference. However, these two terms are actually two different concepts even though machine learning is actually a part of artificial intelligence. It can be said that artificial intelligence is a vast area of topics where machine learning consists of a small part. Here are the major differences between them. Artificial intelligence is a field of computer science that makes a computer system that can mimic human intelligence.


Is DeepMind's new reinforcement learning system a step toward general AI?

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All the sessions from Transform 2021 are available on-demand now. This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. One of the key challenges of deep reinforcement learning models -- the kind of AI systems that have mastered Go, StarCraft 2, and other games -- is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the "first steps to train an agent capable of playing many different games without needing human interaction data," according to a blog post about their new "open-ended learning" initiative. Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges.


Artificial Intelligence Is Key To Preserving America's Superpower Status

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Here's What You Need to Know: AI technology is fast evolving. The national security establishment is racing to adopt artificial intelligence in nearly every aspect of operations, from processing payroll to processing disparate battlefield information into a cohesive whole, such as in the Pentagon's Joint All Domain Command and Control effort to network otherwise separated operational "nodes" to one another in warfare to optimize and streamline attack. However, training AI systems to recognize the things they are meant to recognize requires vast, even seemingly limitless volumes of annotated data. As promising AI is, an AI system is only as effective as its training data. At the moment, there seem to be few barriers to AI and its promise for the future, yet an actual AI-system is only as effective as its database.


MATLAB and Simulink Training

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Get started quickly with the basics of MATLAB. Get started quickly with the basics of Simulink. Get started quickly using deep learning methods to perform image recognition. Master the basics of creating intelligent controllers that learn from experience. An interactive introduction to signal processing methods for spectral analysis.


What is Machine Learning and How does it work? Technology Gyan – LN-TECHINFO

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Machine learning as I have already mentioned that it is a type of application of artificial intelligence (AI) which provides the ability to systems so that they can automatically learn and improve themselves if needed. To do this, they use their own experience and not explicitly programmed. Machine learning always focuses on the development of computer programs so that it can access the data and later use it for its own learning. In this learning begins with observations of data, for example direct experience, or instruction, to find patterns in data and make it easier to make better decisions in the future. Machine learning algorithms are often divided into some categories.


Hands-on Anomaly Detection with Variational Autoencoders

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Anomaly detection is one of those domains in which machine learning has made such an impact that today it almost goes without saying that anomaly detection systems must be based on some form of automatic pattern learning algorithm rather than on a set of rules or descriptive statistics (though many reliable anomaly detection systems operate using such methods very successfully and efficiently). Indeed, a variety of ML approaches to anomaly detection have become increasingly popular over the past decade or so. Some approaches, such as One-Class SVM, try to identify the "normal" area or plane in the dimensional space in which the data is spread out and then mark as anomalous any sample that lies outside that area. Other approaches attempt to estimate the parameters of a distribution (or a mixture of distributions) that represent the training data and then designate as anomalous any sample that seems considerably less likely under it. Each approach has its own assumptions and weaknesses that need to be taken into account, and this is partly why it is important to test and fit the anomaly detection algorithm to the particular domain.


How TensorFlow.js Became a Performance Bottleneck for My App

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In the wee hours of the morning, I started my first day of preparing a presentation about identifying and fixing memory leaks. But my plan is different. Instead of implementing a dummy app for the demo where I explicitly leak memory, I want to use a real-world case study so that the challenge would be as close as possible to what we face as software developers in our daily work. I have started analyzing websites like der-sack.de As I tried to create a new order and edit existing ones, I found that, to my amazement, the app was sluggish and the user experience was very slow and terrible.


How China Is Transforming Its Economy Through Lifelong Learning

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I highly recommend reading the McKinsey Global Institute's new report, "Reskilling China: Transforming The World's Largest Workforce Into Lifelong Learners", which focuses on the country's biggest employment challenge, re-training its workforce and the adoption of practices such as lifelong learning to address the growing digital transformation of its productive fabric. How to transform the country that has become the factory of the world, where manual assembly was the cheapest due to its low labor costs, into an artificial intelligence giant, with the largest public blockchain infrastructure in the world, a digital currency in an advanced stage of development that will see an end to cash payments, along with the world's largest 5G network? Xi Jinping's state capitalism is transforming the Asian giant: the possibility of drawing up and maintaining long-term strategies thanks to political stability is driving change at an unprecedented rate, that includes autonomous driving and digital healthcare, advanced retail or even livestock farming. No matter where you look: the modernization and robotization of Chinese assembly factories has led to enormous reductions in the size of their workforces, which, moreover, immediately correspond not only to an increase in their production capacity, but also to a drastic reduction in the number of errors. And the COVID-19 pandemic, far from slowing the process, has accelerated it even further.


DeepMind & Google Use Neural Networks to Solve Mixed Integer Programs

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Linear programming is used to maximize or minimize a linear objective function subject to one or more constraints, while mixed integer programming (MIP) adds one additional condition: that at least one of the variables can only take on integer values. MIP has found broad use in operational research and practical applications such as capacity planning and resource allocation. In the new paper Solving Mixed Integer Programs Using Neural Networks, a team from DeepMind and Google Research leverages neural networks to automatically construct effective heuristics from a dataset of MIP instances. The novel approach significantly outperforms classical MIP solver techniques, demonstrating especially impressive improvements on the state-of-the-art SCIP (Solving Constraint Integer Programs) 7.0.1 solver. A compelling use case for the proposed techniques is when applications have to solve a large set of instances of the same high-level semantic problem with different problem parameters.


Pinaki Laskar on LinkedIn: #machinelearning #artificialintelligence #neuralnetworks

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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 What is the Mother Idea of All Ideas, Concepts, Rules, Laws or Algorithms? All in all, the idea of all ideas, the rule of all rules, or the law of all laws, the algorithms of all algorithms, or the Mother Discovery is certainly the concept of causality and causation as the real, circular, or reversible causality with the interactive causation, governing the causal order of the world and all its regions, domains and fields. The Six Layer Causal Hierarchy defines the Ladder of Reality, Causality and Mentality, Science and Technology, Human Intelligence and Non-Human Intelligence (MI or AI). The Causal World [levels of causation] is a basis for all real world constructs, as power, force and interactions, agents and substances, states and conditions and situations, events, actions and changes, processes and relations; causality and causation, causal models, causal systems, causal processes, causal mechanisms, causal patterns, causal data or information, causal codes, programs, algorithms, causal analysis, causal reasoning, causal inference, or causal graphs (path diagrams, causal Bayesian networks or DAGs). CAUSALITY AND GLOBAL ARTIFICIAL INTELLIGENCE We need a unifying model of reality/world in terms of causality/actuality, mentality/intelligence and computing/data /virtuality/cyberspace, where neural networks are brain-encoded causal networks.