Self Study


The impact of self-learning software now and in the foreseeable future

#artificialintelligence

We've spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived. Now, when major tech companies give their annual keynotes, they can't help but pepper the narrative with phrases like "machine learning." What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw? The future is here -- and it's equal parts exciting and terrifying.


Teaching Self-Learning Machines to Forget

#artificialintelligence

Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming. Artificial intelligence (AI) and, in particular, machine learning technologies, which date back to the 1950s, use a different approach.


CYBER MONDAY 1 1 Top 40 Stock Picks for December 2017 Based On Self-Learning AI Algorithm The Forecast For MU, NVDA, BABA and AAPL

#artificialintelligence

It has almost become regular news for this to occur, and these rapid pendulum swings are looking more and more normal for gold's trading pattern. Of course, as consistently reported, this trading pattern has been heavily dependent on geopolitical tensions. So much so, in fact, that analysts are calling this type of uncertainty the new normal. A recent report by Citi analysts stated, "Event-driven bids for gold seem to be occurring more frequently and may be the new normal […] In short, even as the rates and forex channel dominate the outlook for gold pricing, the yellow metal is increasingly being used by investors as a policy and tail risk hedge". This closely ties in to what was discussed in previous articles: gold has become something of a paper trading commodity, such that it it heavily reactive to events and bases itself in very reactive movement.


Self-learning AI emulates the human brain

#artificialintelligence

The research was led by Marco Zorzi at the University of Padova and funded with a starting grant from the European Research Centre (ERC). The project – GENMOD – demonstrated that it is possible to build an artificial neural network that observes the world and generates its own internal representation based on sensory data. For example, the network was able by itself to develop approximate number sense, the ability to determine basic numerical qualities, such as greater or lesser, without actually understanding the numbers themselves, just like human babies and some animals. "We have shown that generative learning in a probabilistic framework can be a crucial step forward for developing more plausible neural network models of human cognition," Zorzi says. Tests on visual numerosity show the network's capabilities, and offer insight into how the ability to judge the amount of objects in a set emerges in humans and animals without any pre-existing knowledge of numbers or arithmetic.


Self learning chip promises to accelerate artificial learning Robotics Research

#artificialintelligence

Fully asynchronous neuromorphic many core mesh that supports a wide range of sparse, hierarchical and recurrent neural network topologies with each neuron capable of communicating with thousands of other neurons. Each neuromorphic core includes a learning engine that can be programmed to adapt network parameters during operation, supporting supervised, unsupervised, reinforcement and other learning paradigms. Fully asynchronous neuromorphic many core mesh that supports a wide range of sparse, hierarchical and recurrent neural network topologies with each neuron capable of communicating with thousands of other neurons. Each neuromorphic core includes a learning engine that can be programmed to adapt network parameters during operation, supporting supervised, unsupervised, reinforcement and other learning paradigms.


Intel announces self-learning AI chip Loihi ZDNet

#artificialintelligence

Intel has announced a neuromorphic artificial intelligence (AI) test chip named Loihi, which it said is aimed at mimicking brain functions by learning from data gained from its environment. The chip has fabrication on Intel's 14nm process tech; 130,000 neurons; 139 million synapses; a fully asynchronous neuromorphic many-core mesh supporting sparse, hierarchical, and recurrent neural network topologies, with neurons capable of communicating with each other; a programmable learning engine for each neuromorphic core; and development and testing of several algorithms for path planning, sparse coding, dictionary learning, constraint satisfaction, and dynamic pattern learning and adaptation. Intel researchers have shown a learning rate 1 million times improved with typical spiking neural sets, he claimed, with 1,000 times more energy efficiency than typical computing used for training systems. CTO of Intel Artificial Intelligence Product Group Amir Khosrowshahi -- who co-founded Nervana Systems, which was purchased by the chip giant in August last year as the central part of Intel's plans for AI -- had in April told ZDNet that the industry needs new architecture for neural networks.


Intel announces self-learning AI chip Loihi

ZDNet

Intel has announced a neuromorphic artificial intelligence (AI) test chip named Loihi, which it said is aimed at mimicking brain functions by learning from data gained from its environment. The chip has fabrication on Intel's 14nm process tech; 130,000 neurons; 139 million synapses; a fully asynchronous neuromorphic many-core mesh supporting sparse, hierarchical, and recurrent neural network topologies, with neurons capable of communicating with each other; a programmable learning engine for each neuromorphic core; and development and testing of several algorithms for path planning, sparse coding, dictionary learning, constraint satisfaction, and dynamic pattern learning and adaptation. Intel researchers have shown a learning rate 1 million times improved with typical spiking neural sets, he claimed, with 1,000 times more energy efficiency than typical computing used for training systems. CTO of Intel Artificial Intelligence Product Group Amir Khosrowshahi -- who co-founded Nervana Systems, which was purchased by the chip giant in August last year as the central part of Intel's plans for AI -- had in April told ZDNet that the industry needs new architecture for neural networks.


Intel's New Self-Learning Chip Promises to Accelerate Artificial Intelligence Intel Newsroom

#artificialintelligence

The Loihi research test chip includes digital circuits that mimic the brain's basic mechanics, making machine learning faster and more efficient while requiring lower compute power. Compared to technologies such as convolutional neural networks and deep learning neural networks, the Loihi test chip uses many fewer resources on the same task. The self-learning capabilities prototyped by this test chip have enormous potential to improve automotive and industrial applications as well as personal robotics – any application that would benefit from autonomous operation and continuous learning in an unstructured environment. Today, we at Intel are applying our strength in driving Moore's Law and manufacturing leadership to bring to market a broad range of products -- Intel Xeon processors, Intel Nervana technology, Intel Movidius technology and Intel FPGAs -- that address the unique requirements of AI workloads from the edge to the data center and cloud.


Intel introduces an experimental 'self-learning' chip to make robots smarter

Mashable

Called the "Intel Loihi test chip," the processor is what Intel calls a "neuromorphic chip," meaning it's designed to learn from its environment. "The Intel Loihi research test chip includes digital circuits that mimic the brain's basic mechanics, making machine learning faster and more efficient while requiring lower compute power," Michael Mayberry, managing director of Intel Labs, wrote in a statement. This could help computers self-organize and make decisions ... "This could help computers self-organize and make decisions based on patterns and associations." But Intel's approach is different in that the Loihi test chip is designed to work and learn locally on whatever machine it's inside of.