The impact of artificial intelligence is starting to be realized across a broad spectrum of industries. Typically, deep learning (DL) training is a centralized datacenter process and inferencing occurs in the field. To build an AI system, data is collected, run through data scientist training models based on deep learning (DL) frameworks — on the fastest accelerated computers in the world — with the output sent to the field for an “AI at the Edge” system to inference from this model in day-to-day decision making. Read More
Daily Archives: July 24, 2020
From Long-distance Entanglement to Building a Nationwide Quantum Internet
Today, many scientific experts recognize that building and scaling quantum-protected and enhanced communication networks are among the most important technological frontiers of the 21st century. The international research community perceives the construction of a first prototype global quantum network—the Quantum Internet—to be within reach over the next decade.
In February 2020, the U.S Department of Energy (DOE)’s Office of Advanced Scientific Computing Research hosted the Quantum Internet Blueprint workshop to define a potential roadmap toward building the first nationwide quantum Internet. Read More
What can I do here? A Theory of Affordances in Reinforcement Learning
Reinforcement learning algorithms usually assume that all actions are always available to anagent. However, both people and animals un-derstand the general link between the features of their environment and the actions that are feasible.Gibson (1977) coined the term “affordances” to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents. In this paper, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role in this case. On one hand, they allow faster planning, by reducing the number of actions available in any given situation. On the other hand, they facilitate more efficient and precise learning of transition models from data, especially when such models require function approximation. We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances and use it to estimate transition models that are simpler and generalize better. Read More
6 Steps to Get the Best Out of Your RPA Implementation
Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “the fastest growing segment of the global enterprise software market” (with a revenue growth of 63%).
Moreover, based on a Dave Vellante study conducted between April 2019 and 2020, RPA was the technology with the highest adoption rate, together with machine learning and artificial intelligence. Yet, RPA implementation has led to mixed results for companies across the world and across industries. Read More
COVID Mask Detection using Machine Learning
Machine Learning Courses
A thoughtful user on Github has put together a tremendous list of courses for AI and machine learning. He covers everything from introductory to advanced lectures. Read More
A Deep Dive into Reinforcement Learning
Let’s take a deep dive into reinforcement learning. In this article, we will tackle a concrete problem with modern libraries such as TensorFlow, TensorBoard, Keras, and OpenAI gym. You will see how to implement one of the fundamental algorithms called deep Q-learning to learn its inner workings. Regarding the hardware, the whole code will work on a typical PC and use all found CPU cores (this is handled out of the box by TensorFlow). Read More
Legal Risks of Adversarial Machine Learning Research
Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Corporations and governments are paying attention. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US is putting security and safety of AI systems as a top priority when defining AI regulation, with the EU releasing a complete set of non-binding6checklists as part of its Trustworthy AI initiative.
Research in this field is booming. Read More
Post-quantum cryptography program enters ‘selection round’
The race to protect sensitive electronic information against the threat of quantum computers has entered the home stretch.
After spending more than three years examining new approaches to encryption and data protection that could defeat an assault from a quantum computer, the National Institute of Standards and Technology (NIST) has winnowed the 69 submissions it initially received down to a final group of 15. NIST has now begun the third round of public review. This “selection round” will help the agency decide on the small subset of these algorithms that will form the core of the first post-quantum cryptography standard. Read More
Researchers Want to Protect Your Selfies From Facial Recognition
‘Fawkes’ may be the most advanced system yet for fooling facial recognition tech like Clearview AI—until the algorithms catch up.
Researchers have created what may be the most advanced system yet for tricking top-of-the-line facial recognition algorithms, subtly modifying images to make faces and other objects unrecognizable to machines. Read More
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