Three key ways artificial intelligence is changing what it means to compute.
Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on.
Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing. Read More
Monthly Archives: October 2021
Intel’s ControlFlag Debugging Tool Uses AI To Clean Up Code And It’s Now Open Source
In 2020 a study showed the IT industry spent an estimated $2 trillion in software development associated with debugging code. The study also showed that 50 percent of IT budgets were allocated to debugging code alone. Intel hopes to change those numbers by making its ControlFlag tool open-source.
ControlFlag is an AI-powered tool created by Intel to detect bugs within computer code using advanced self-supervised machine learning (ML). The software developed last year was able to locate hundreds of confirmed software defects in proprietary, production-quality software systems in just a few analyses of source code repositories. Its machine learning techniques enable it to find coding anomalies, reduce time spent debugging and improving the quality and security of systems autonomously. Read More
Moving the U.S. Government Towards Zero Trust Cybersecurity Principles
Federal Zero Trust Strategy
The Office of Management and Budget (OMB) is releasing a draft Federal Zero Trust Strategy in support of Executive Order 14028, “Improving the Nation’s Cybersecurity”, to adapt civilian agencies’ enterprise security architecture to be based on zero trust principles.
The goal of this strategy is to accelerate agencies towards a shared baseline of early zero trust maturity. Moving to a zero trust architecture will be a multi-year journey for agencies, and the federal government will learn and adjust as new technologies and practices emerge. Read More
A new brain-inspired intelligent system can drive a car using only 19 control neurons!
Credit card PINs can be guessed even when covering the ATM pad
Researchers have proven it’s possible to train a special-purpose deep-learning algorithm that can guess 4-digit card PINs 41% of the time, even if the victim is covering the pad with their hands.
… By using three tries, which is typically the maximum allowed number of attempts before the card is withheld, the researchers reconstructed the correct sequence for 5-digit PINs 30% of the time, and reached 41% for 4-digit PINs. Read More
Big Tech & Their Favourite Deep Learning Techniques
Every week, the top AI labs globally — Google, Facebook, Microsoft, Apple, etc. — release tons of new research work, tools, datasets, models, libraries and frameworks in artificial intelligence (AI) and machine learning (ML).
Interestingly, they all seem to have picked a particular school of thought in deep learning. With time, this pattern is becoming more and more clear. Read More
How Organizations Make Sense of Big Data and Artificial Intelligence Strategy
Artificial intelligence (AI) helps organizations to make timely and accurate decisions from data in almost every field of study.
The volume of data keeps growing. Statista believes that 59 Zettabytes were produced in 2020 and that 74 Zettabytes will be produced in 2021.
A Zettabyte is a trillion gigabytes! Read More
Facebook is researching AI systems that see, hear, and remember everything you do
Facebook is pouring a lot of time and money into augmented reality, including building its own AR glasses with Ray-Ban. Right now, these gadgets can only record and share imagery, but what does the company think such devices will be used for in the future?
A new research project led by Facebook’s AI team suggests the scope of the company’s ambitions. It imagines AI systems that are constantly analyzing peoples’ lives using first-person video; recording what they see, do, and hear in order to help them with everyday tasks. Facebook’s researchers have outlined a series of skills it wants these systems to develop, including “episodic memory” (answering questions like “where did I leave my keys?”) and “audio-visual diarization” (remembering who said what when). Read More
This Person (Probably) Exists. IdentityMembership Attacks Against GAN GeneratedFaces.
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website http://thispersondoesnotexist.com, taunts users with GAN generated images that seem too real to believe. On the otherhand, GANs do leak information about their training data, as evidenced by membership attacks recently demonstrated in the literature. In this work, we challenge the assumption that GAN faces really are novel creations, by constructing a successful membership attack of a new kind. Unlike previous works, our attack can accurately discern samples sharing the same identity as training samples without being the same samples. We demonstrate the interest of our attack across several popular face datasets and GAN training procedures. Notably, we show that even in the presence of significant dataset diversity, an over represented person can pose a privacy concern. Read More
The Race For AI: Which Tech Giants Are Snapping Up Artificial Intelligence Startups
The usual suspects are leading the race for AI: tech giants like Facebook, Amazon, Microsoft, Google, and Apple (FAMGA) have all been aggressively acquiring AI startups for the last decade.
Among FAMGA, Apple leads the way. With 29 total AI acquisitions since 2010, the company has made nearly twice as many acquisitions as second-place Google (the frontrunner from 2012 to 2016), with 15 acquisitions.
Apple and Google are followed by Microsoft with 13 acquisitions, Facebook with 12, and Amazon with 7. Read More
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