From disrupting elections to targeted ransomware to privacy regulations to deepfakes and malevolent AI, 141 cybersecurity predictions for 2020 did not exhaust the subject so here are additional 42 from senior cybersecurity executives.
Read more here
Extra! Extra! 42 Additional 2020 Cybersecurity Predictions
Aging and Automation
The “under-appreciated” workforce — experienced workers with long tenures at their companies, aged 50 and above — are estimated to have contributed $7.6 trillion to U.S. economic activity in 2015, set to jump to over $13.5 trillion by 2032, according to new report by Mercer and Oliver Wyman with Marsh & McLennan Advantage on aging and automation. Yet, those employees also face the threat of having their work replaced by machines, with older workers in the U.S. doing jobs that are on average 52% automatable. However, a rapidly aging population and falling birthrate means retraining this workforce is vital for the success of many companies, argues the report
Robot Augmentation
You may have heard “AI” explained as “augmented intelligence,” i.e.., robots supporting or enhancing human intelligence. Now, researchers at Amazon are experimenting with robot augmentation:
“…researchers at Amazon’s Alexa AI division developed a framework that endows agents with the ability to ask for help in certain situations. Using what’s called a model-confusion-based method, the agents ask questions based on their level of confusion as determined by a predefined confidence threshold, which the researchers claim boosts the agents’ success by at least 15%.”
Cybersecurity 2020 Predictions
Read more here
Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is “a bit like Alice in Wonderland” where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen’s observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice—or ten times—as fast as that.
The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that’s hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you “wish to fly on commercial aircrafts or access federal facilities” in the U.S., you must have a REAL ID compliant card.
Read more here
Translating Between Computer Science and Statistics
Terence Parr: “I am a computer scientist retooling as a machine learning droid and have found the nomenclature used by statisticians to be peculiar to say the least, so I thought I’d put this document together. It’s meant as good-natured teasing of my friends who are statisticians, but it might actually be useful to other computer scientists. I look forward to a corresponding document written by the statisticians about computer science terms!” (Statisticians say the darndest things)
I know of at least one corresponding document, published in 1994 with the rise of Neural Networks or what I have called Statistics on Steroids (SOS), which are responsible, to a large extent, to the success of today’s “AI” or Deep Learning, an advanced version of machine learning.
In Neural Networks and Statistical Models (1994), Warren Sarle explained to his worried and confused fellow statisticians that the ominous-sounding artificial neural networks
are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software… like many statistical methods, [artificial neural networks] are capable of processing vast amounts of data and making predictions that are sometimes surprisingly accurate; this does not make them “intelligent” in the usual sense of the word. Artificial neural networks “learn” in much the same way that many statistical algorithms do estimation, but usually much more slowly than statistical algorithms. If artificial neural networks are intelligent, then many statistical methods must also be considered intelligent.
Sarle provided his colleagues with a handy dictionary translating the terms used by “neural engineers” to the language of statisticians (e.g., “features” are “variables”). In anticipation of today’s “data science” and predictions of algorithms replacing statisticians (and even scientists), Sarle reassured them that no “black box” can substitute for human intelligence:
Neural engineers want their networks to be black boxes requiring no human intervention—data in, predictions out. The marketing hype claims that neural networks can be used with no experience and automatically learn whatever is required; this, of course, is nonsense. Doing a simple linear regression requires a nontrivial amount of statistical expertise.
See here for a discussion of the larger historical context and A Very Short History of Data Science
Posted in AI, Data Science, deep learning, Statistics
Leave a comment
Apple Pay to Become the Most Popular Digital Wallet Worldwide in 2020 with 227 Million Users
You will find more infographics at Statista
The global number of digital wallet users could double by 2020, according to Juniper Research
The Internet of Things (IoT) by the Numbers
IDC estimates that there will be 41.6 billion connected IoT devices, or “things,” generating 79.4 zettabytes (ZB) of data in 2025. Gartner forecasts that the enterprise and automotive IoT market will grow to 5.8 billion endpoints in 2020, a 21% increase from 2019. IDC predicts that by 2023, over 50% of new enterprise IT infrastructure deployed will be at the edge rather than corporate data centers, up from less than 10% today; by 2024, the number of apps at the edge will increase 800%.
The Digital Transformation of the Oil and Gas Industry
The oil and gas industry today faces four key challenges: volatile commodities, workforce retirements, low carbon economy, and shifting geopolitics. Culminating in its new era of operational efficiency, digital transformation serves as the answer for cost reduction and revenue increment.
As the industry struggles to successfully scale up digital across global operations, we provide a framework for identifying applications that hold potential for enterprise-wide deployment to avoid scale-up failures. From our analysis, we conclude that while the majority of applications do offer ease of scale-up, not all of them produce high financial gains. A handful of applications instead contribute to other value drivers, such as sustainability.
Moving forward, as the digital transformation of the industry matures, we anticipate more operator-operator digital collaborations, creation of data marketplaces, inception of “tech” oil companies, and disruption of traditional service companies by major software giants.