Traditional computers operate based on data that is encoded in a binary system. Essentially, each bit of data is represented in zeroes and ones only — no more, no less than the two forms. Hence, the binary computing system. However, there is a new generation of computers emerging on the horizon called quantum computing and it’s taking computing systems beyond the normal binary.
… One of the areas where quantum computing is more lucrative and promising is artificial intelligence. As AI operates on the analysis of large datasets, the margin of error and inaccuracy in the process of learning has significant room for improvement — and quantum computing may well allow us to improve the algorithm’s ability to learn and interpret. Read More
Monthly Archives: October 2020
NtechLab face biometrics piloted in ten more Russian cities’ public security systems
NtechLab, which provides the face biometric technology used in Moscow’s massive public security system, has launched pilot projects in ten other cities, though Kommersant (as translated by Google) reports that funding challenges could slow or prevent an operational rollout. Read More
The Edge is the new center: Edge computing enables emerging technologies (IoT, 5G and AI) for the new data decade
For decades now, most data-driven innovation has taken place in centralized glass-walled rooms, data centers and mega clouds. The gravity these facilities create pulls data inward for processing, and then the resulting value is pushed back out.
Today, the world is changing, as a new digital future takes shape. We are entering an era in which the bulk of new data will be processed at the Edge, outside of corporate and cloud data centers. Read More
Deep Learning with CIFAR-10
Image Classification using CNN
Neural Networks are the programmable patterns that helps to solve complex problems and bring the best achievable output. Deep Learning as we all know is a step ahead of Machine Learning, and it helps to train the Neural Networks for getting the solution of questions unanswered and or improving the solution!
In this article, we will be implementing a Deep Learning Model using CIFAR-10 dataset. Read More
Microsoft’s new Lobe app lets anyone train AI models
Unsupervised NLP : Methods and Intuitions behind working with unstructured texts
tldr; this is a primer in the domain of unsupervised techniques in NLP and their applications. It begins with the intuition behind word vectors, their use and advancements. This evolves to the centerstage discussion about the language models in detail — introduction, active use in industry and possible applications for different use-cases. Read More
Google, Cambridge, DeepMind & Alan Turing Institute’s ‘Performer’ Transformer Slashes Compute Costs
It’s no coincidence that Transformer neural network architecture is gaining popularity across so many machine learning research fields. Best known for natural language processing (NLP) tasks, Transformers not only enabled OpenAI’s 175 billion parameter language model GPT-3 to deliver SOTA performance, the power- and potential-packed architecture also helped DeepMind’s AlphaStar bot defeat professional StarCraft players. Researchers have now introduced a way to make Transformers more compute-efficient, scalable and accessible. Read More
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer,based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task,our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. Read More
American Republic vs CCP Documentary | Crossroads with Joshua Philipp
Microsoft, MITRE Release Adversarial Machine Learning Threat Matrix
Microsoft and MITRE, in collaboration with a dozen other organizations, have developed a framework designed to help identify, respond to, and remediate attacks targeting machine learning (ML) systems.
Many companies today do not have the necessary tools to secure machine learning systems. …The Adversarial ML Threat Matrix, which Microsoft has released in collaboration with MITRE, among others, is an industry-focused open framework that aims to address this issue. Read More
Rick's Cafe AI 