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
Monthly Archives: July 2020
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
Implicit Neural Representations with Periodic Activation Functions
Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal’s spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks,dubbed sinusoidal representation networks or SIRENs, are ideally suited for representing complex natural signals and their derivatives. We analyze SIREN activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how SIRENs can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine SIRENs with hypernetworks to learn priors over the space of SIRENf unctions. Please see theproject website for a video overview of the proposed method and all applications. Read More
Deepfakes Are Becoming the Hot New Corporate Training Tool
This month, advertising giant WPP will send unusual corporate training videos to tens of thousands of employees worldwide. A presenter will speak in the recipient’s language and address them by name, while explaining some basic concepts in artificial intelligence. The videos themselves will be powerful demonstrations of what AI can do: The face, and the words it speaks, will be synthesized by software.
WPP doesn’t bill them as such, but its synthetic training videos might be called deepfakes, a loose term applied to images or videos generated using AI that look real. Read More
Ingestion of ethanol just prior to sleep onset impairs memory for procedural but not declarative tasks
Study objectives: The aim of Experiment 1 was to determine if moderate ethanol consumption at bedtime would result in memory loss for recently learned cognitive procedural and declarative tasks. The aim of Experiment 2 was to establish that the memory loss due to alcohol consumption at bedtime was due to the effect of alcohol on sleep states. Read More
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