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Industry Tech Outlook Magazine

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If intelligence and consciousness can indeed be reduced to series of mathematical models then carbon based human beings are a much better deployment vehicle than computers, their silica based counterparts. Carbon based systems have actually been perfected over millions of years through slow-but-steady Darwinian evolutionary approach, while their silica based counterparts have evolved over last 70 years by human beings themselves. Who will excel whom, and at what point of time, is the debate which has been raging since past several decades but never before it had been so cued towards artificial intelligence (AI). One way to think about AI is in terms of Descriptive, Predictive, and Prescriptive analytics, with the next step leading to Autonomous AI. Descriptive explains the data through visualization and basic statistics, predictive helps one predict future events, while prescriptive prescribes an action to a human as a response to a future event.


Perceptron

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Perceptron is one of the most fundamental concepts of deep learning which every data scientist is expected to master. It is a supervised learning algorithm specifically for binary classifiers. Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading. In this article, we will develop a solid intuition about Perceptron with the help of an example. Without any further delay, let's begin!


AI: Facebook's new algorithm was trained on one billion Instagram pics

ZDNet

Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet. Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet, in a breakthrough that, although still in its early stages, the team expects to generate a "revolution" in computer vision. Dubbed SEER (SElf-SupERvised), the model was fed one billion publicly available Instagram images, which had not previously been manually curated. But even without the labels and annotations that typically go into algorithm training, SEER was able to autonomously work its way through the dataset, learning as it was going, and eventually achieving top levels of accuracy on tasks such as object detection. The method, aptly named self-supervised learning, is already well-established in the field of AI: it consists of creating systems that can learn directly from the information they are given, without having to rely on carefully labeled datasets to teach them how to perform a task such as recognizing an object in a photo or translating a block of text.


Cracking Open Bitcoin with Artificial Intelligence

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In bitcoin mining, blocks, private keys, and public keys there can be found some connection to SHA256 mentioned somewhere. This makes SHA256 interesting to investigate. In this article we are going to focus on SHA256. We will dive into the code of SHA256, while also investigating the semantics of the cryptographic hash function. We will also break SHA256 down to its basic components and do some machine learning for fun.


Facebook's New AI Teaches Itself to See With Less Human Help

WIRED

Most artificial intelligence is still built on a foundation of human toil. Peer inside an AI algorithm and you'll find something constructed using data that was curated and labeled by an army of human workers. Now, Facebook has shown how some AI algorithms can learn to do useful work with far less human help. The company built an algorithm that learned to recognize objects in images with little help from labels. The Facebook algorithm, called Seer (for SElf-supERvised), fed on more than a billion images scraped from Instagram, deciding for itself which objects look alike.


Reinforcement learning and reasoning

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Reinforcement learning has seen a lot of progress in recent years. From DeepMind success with teaching machines how to play Atari games, then AlphaGo beating world champions in Go to recent OpenAI's progress on Dota 2, a multiplayer game where players divided into two teams compete with each other. The common thread is an artificial agent operating in a virtual world, where the prize is clear (e.g. On the other hand people are experimenting with AI agents operating in real-world. Each clip of Boston Dynamics gets a lot of press, showing robots performing amazing stunts, as you can see yourself here or here.


Exploring AI in the cultural heritage sector

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AI terminology can be complex, so let's clear up some definitions. While reading our posts you might see terms like'machine learning', 'deep learning', 'models' or'training'. Machine learning vs deep learning is a common area of confusion for those not familiar with AI techniques. Machine learning consists of a set of algorithms which automatically learn from data. Deep learning is a type of machine learning that excels in solving problems with high dimensionality (where the number of features is much greater than the number of observations). Deep learning uses a family of models inspired by the structure and functioning of the brain (artificial neural networks) that effectively learn to extract relevant features from the data.


From perceptrons to deep learning

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Have you ever wondered if it's possible to learn all there is to know about machine learning and deep learning from a book? Machine Learning--A Journey to Deep Learning, with Exercises and Answers is designed to give the self-taught student a solid foundation in machine learning with step-by-step solutions to the formative exercises and many concrete examples. By going through this text, readers should become able to apply and understand machine learning algorithms as well as create new ones. The statistical approach leads to the definition of regularization out of the example of regression. Building on regression, we develop the theory of perceptrons and logistic regression.


BrainChip Success in 2020 Advances Fields of on-Chip Learning

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BrainChip Holdings Ltd., a leading provider of ultra-low power, high-performance AI technology, ended the 2020 calendar year having made significant strides in the development of its technology backed by the launch of its Early Access Program (EAP), availability of Akida evaluation boards, new partnerships, and expansion of its executive leadership and global facilities. "This past year saw significant progress in the development of the Akida technology in terms of both market readiness and the increase in market possibilities that the solution will provide immediate impact in" The Company's EAP was launched in June targeting specific customers in a diverse set of end markets in order to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV), Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation. Among those joining the EAP include VORAGO Technologies in a collaboration intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. BrainChip is also collaborating with Tier-1 Automotive Supplier Valeo Corporation to develop neural network processing solutions for ADAS and AV.


BrainChip's Success in 2020 Advances Fields of On-Chip Learning and Ultra-Low Power Edge AI

#artificialintelligence

San Francisco, March 3, 2021 -- BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low-power, high-performance AI technology, ended the 2020 calendar year having made significant strides in the development of its technology backed by the launch of its Early Access Program (EAP), availability of Akida evaluation boards, new partnerships, and expansion of its executive leadership and global facilities. The Company's EAP was launched in June targeting specific customers in a diverse set of end markets in order to ensure availability of initial devices and evaluation systems for key applications. Multiple customers have committed to the advanced purchase of evaluation systems for a range of strategic Edge applications including Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV), Unmanned Aerial Vehicles (UAV), Edge vision systems and factory automation. Among those joining the EAP include VORAGO Technologies in a collaboration intended to support a Phase I NASA program for a neuromorphic processor that meets spaceflight requirements. BrainChip is also collaborating with Tier-1 Automotive Supplier Valeo Corporation to develop neural network processing solutions for ADAS and AV.