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Artificial Intelligence and ML in Cybersecurity: Is it Worth the Hype?

#artificialintelligence

The world is going digital at a pace faster than the blink of an eye. Artificial intelligence (AI) and machine learning (ML) have been heralded as a means of digital technology that can solve a wide range of problems in different industries and applications. This also includes the realm of cybersecurity. Capgemini's Reinventing Cybersecurity with Artificial Intelligence Report, which was published last year, found that 61% of enterprises say they cannot detect breach attempts today without using AI technologies. In a similar survey by Webroot, it was observed that 89% of IT professionals believe their company could be doing more to defend against cyberattacks.


DARPA sets sights on making AI self-aware of complex time dimensions

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The Defense Advanced Research Projects Agency (DARPA) is setting its sights on developing an AI system with a detailed self-understanding of the time dimensions of its learned knowledge. DARPA's Time-Aware Machine Intelligence (TAMI) research program and incubator is looking to develop a new class of neural network architectures that incorporate an explicit time dimension as a fundamental building block for network knowledge representation," according to the TAMI program solicitation. The overall goal is to create an AI system that will be able to "think in and about time" when exercising its learned task knowledge in task performance. Current neural networks do not explicitly model the inherent time characteristics of their encoded knowledge. Consequently, state-of-the-art machine learning does not have the expressive capability to reason with encoded knowledge using time.


IoT Machine Learning and Artificial Intelligence Services to Reach US$10.6 Billion in 2026

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The value of data is increasing, and that value is stimulating the Internet of Things (IoT) Advanced Analytics Market, with the emergence of accessible out-of-the-box and off-the-shelf machine learning (ML) and artificial intelligence (AI) solutions. Vendors are now easing access to ML and AI toolsets by expanding availability through deployment options that include the edge, on-premises, cloud, Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Global tech market advisory firm, ABI Research, finds that the IoT ML and AI market will reach US$1.09 billion in 2020 and grow to US$10.6 billion in 2026. Edge ML/AI is more prevalent in manufacturing and industrial segments, where there is an immediate need to assess, transform and augment data as it is being generated through functions of quick pattern recognition, labeling, and protocol optimization. "The IoT Edge Advanced Analytics Market is essentially operationalized ML and AI products and services targeted at Operational Technology (OT) teams to understand and extract insights," explains Kateryna Dubrova, Research Analyst at ABI Research.


U.S. Navy to tap Google machine learning for repairs

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In a Thursday event unveiling a slew of new home devices ahead of the holidays, Amazon made clearer than ever its determination to flood America with cameras, microphones and the voice of Alexa, its AI assistant. The big picture: Updating popular products and expanding its range to car alarms and in-home drones, Amazon extended its lead in smart home devices and moved into new areas including cloud gaming and car security. The new offerings will also fuel criticism that the tech giant is helping equip a society built around surveillance.


Kids' brains may hold the secret to building better AI

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The mathematician and computer science pioneer Alan Turing hit on a promising direction for artificial intelligence research way back in 1950. "Instead of trying to produce a program to simulate the adult mind," he wrote, "why not rather try to produce one which simulates the child's?" Now AI researchers are finally putting Turing's ideas into action. They're realizing that by paying attention to how children process information, they can pick up valuable lessons about how to create machines that learn. DARPA, the Defense Department's advanced research agency, is embracing this approach.


Sentinel loads up with $1.35M in the deepfake detection arms race – TechCrunch

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Estonia-based Sentinel, which is developing a detection platform for identifying synthesized media (aka deepfakes), has closed a $1.35 million seed round from some seasoned angel investors -- including Jaan Tallinn (Skype), Taavet Hinrikus (TransferWise), Ragnar Sass & Martin Henk (Pipedrive) -- and Baltics early-stage VC firm, United Angels VC. The challenge of building tools to detect deepfakes has been likened to an arms race -- most recently by tech giant Microsoft, which earlier this month launched a detector tool in the hopes of helping pick up disinformation aimed at November's U.S. election. "The fact that [deepfakes are] generated by AI that can continue to learn makes it inevitable that they will beat conventional detection technology," it warned, before suggesting there's still short-term value in trying to debunk malicious fakes with "advanced detection technologies." Sentinel co-founder and CEO Johannes Tammekänd agrees on the arms race point -- which is why its approach to this "goal-post-shifting" problem entails offering multiple layers of defence, following a cybersecurity-style template. He says rival tools -- mentioning Microsoft's detector and another rival, Deeptrace, aka Sensity -- are, by contrast, only relying on "one fancy neural network that tries to detect defects," as he puts it.


ESA's Φ-Week: Digital Twin Earth, Quantum Computing and AI Take Center Stage

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Digital Twin Earth will help visualize, monitor, and forecast natural and human activity on the planet. The model will be able to monitor the health of the planet, perform simulations of Earth's interconnected system with human behavior, and support the field of sustainable development, therefore, reinforcing Europe's efforts for a better environment in order to respond to the urgent challenges and targets addressed by the Green Deal. ESA's 2020 Φ-week event kicked off this morning with a series of stimulating speeches on Digital Twin Earth, updates on Φ-sat-1, which was successfully launched into orbit earlier this month, and an exciting new initiative involving quantum computing. The third edition of the Φ-week event, which is entirely virtual, focuses on how Earth observation can contribute to the concept of Digital Twin Earth – a dynamic, digital replica of our planet which accurately mimics Earth's behavior. Constantly fed with Earth observation data, combined with in situ measurements and artificial intelligence, the Digital Twin Earth provides an accurate representation of the past, present, and future changes of our world.


Two UK-built shoebox-sized supercomputer satellites set for lift-off

Daily Mail - Science & tech

Two shoebox-sized supercomputer satellites, built in Scotland to monitor shipping movements from low-Earth orbit, are due for launch this afternoon. Each nanosatellite has an onboard supercomputer with machine learning algorithms that can provide'hyper-accurate predictions' of the locations of boats. The the so-called'Spire' satellites will calculate their arrival times at ports to help businesses and authorities manage busy docks, the UK Space Agency said. They will join a fleet of more than 100 objects in low Earth orbit that work together to track the whereabouts of ships and predict global ocean traffic. Two of the satellites will launch at lunchtime today and another couple will launch on an Indian PSLV rocket on November 1.


daviddao/awful-ai

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Infer Genetic Disease From Your Face - DeepGestalt can accurately identify some rare genetic disorders using a photograph of a patient's face. This could lead to payers and employers potentially analyzing facial images and discriminating against individuals who have pre-existing conditions or developing medical complications.


Programming Fairness in Algorithms

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"Being good is easy, what is difficult is being just." "We need to defend the interests of those whom we've never met and never will." Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g.