New Loihi AI chip and a new 49-qubit quantum chip

Intel’s first “neuromorphic” chip Loihi, designed to mimic the way a human brain learns and understands is now “fully functional”. In the fast developing world of AI chips Loihi is Intel’s effort, an area where Nvidia and start-ups like Graphcore are also attempting to stake a claim. In order to increase the efficiency of the system, the idea is that the processes involved in AI will be more complex and require more computing power and some of that can be moved to the chip.

As of other AI systems it “learns” over time and gets smarter. But it doesn’t require masses of training data to learn a process. Initial applications are most likely to be in robots and self-driving cars. The chip itself is conceived as modeled on the human brain or at least how we know it to work with pulses and spikes based around synapses, with different learning functions were taken by different parts of the chip.

The 49-qubit chip has improved thermal performance and reduced radio frequency interference, scalable interconnect for more signals to pass in and out of the chip and design to scale for quantum integrated circuitry.

Major tracks summoned are Artificial Intelligence, Artificial Neural Networks, Cognitive Computing, Bioinformatics, Autonomous Robots, Natural Language Processing, Computational Creativity, Self-Organizing Neural Network, Deep Learning, Ubiquitous Computing, Parallel Processing, Support Vector Machines, Cloud Computing.

For further more updates on the availing research proficiency, do visit: https://neuralnetworks.conferenceseries.com/abstract-submission.php

For details about the webpage, go through the link provided; PS: https://neuralnetworks.conferenceseries.com/

Comments

Does Machines Perceive Human Emotions?

Researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. In the growing research field of “affective computing”, robots and computers are being developed to analyze facial expressions, interpret our emotions and respond accordingly. Applications include, for instance, monitoring an individual’s health and well-being, gauging student interest in classrooms, helping diagnose signs of certain diseases, and developing helpful robot companions . A challenge, however, is people express emotions quite differently, depending on many factors. General differences can be seen between cultures, genders, and age groups. But other differences are even more fine-grained: The time of day, how much you slept, or even your level of familiarity with a conversation partner leads to subtle variations in the way you express, say, happiness or sadness in a given moment. Human brains instinctively catch these dev...

Neural Networks and Deep Learning

Neural Networks and Deep Learning have grown widely over the last few years. By using neural network architecture, softwares of AI can go through and check millions of images to find the right tone to fit any image. This method could be used to colorize still frames of white and black movies, surveillance footage or any number of images. Because neural networks can derive data from any number of resources with access to millions of sounds and videos, it can make predictive judgments. Neural network architecture can now synthesize audio to fill in the blank spots of a silent video. Neural network architecture can perform translations of text without preprocessing the sequence so that the algorithm can learn word relationships. The network then processes these relationships through its image mapping technology to create a contextual solution to a translation issue. By getting access to a wide variety of images and learning the context of each one, neural network architecture can...

Artificial Neural Networks can Detect Human Ambiguity

Artificial Neural Networks (ANNs) computational model based on the structure and functions of biological neural networks, it became a strong tool for researching artificial intelligence and information analysis and are utilised in robotics, social sciences and neuroscience for classification, prediction and pattern recognition. A global scientific team which incorporates scientists from Russia has created an artificial neural network that detects human ambiguity. They assist to classify neural signals, observe pathological activity of the brain (for example, with epilepsy), and neurodegenerative diseases. ANNs have three layers that are interconnected. The primary layer consists of input neurons. Those neurons send information on to the second layer that successively sends the output neurons to the third layer. Training an artificial neural network involves selecting from allowed models for which there are several associated algorithms. In this analysis, the scientist...

8th Global Summit on Artificial Intelligence and Neural Networks

Search This Blog