Even though security solutions are becoming modern and robust, cyber threats are ever-evolving and always on the peak. The main reason for this is because the conventional methods to detect the malware are falling apart. Cybercriminals are regularly coming up with smarter ways to bypass the security programs and infect the network and systems with different kinds of malware. The thing is, currently, most antimalware or antivirus programs use the signature-based detection technique to catch the threats, which is ineffective in detecting the new threats. This is where Artificial Intelligence can come to rescue.
The retail banking sector has been hit with numerous scams during the past few years. Cybercriminals are now also beginning to increasingly go after much larger corporate accounts by launching sophisticated malware and phishing attacks, according to Beate Zwijnenberg, chief information security officer at ING Group. Zwijnenberg recommends using advanced AI defense systems to identify potentially fraudulent transactions which may not be immediately recognizable by human analysts. Financial institutions across the globe have been spending a lot of money to deal with serious cybersecurity threats. They've been using static, rules-based verification processes to identify suspicious activity.
We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
In the wrong hands, AI is proving dangerous. In an age where everything is becoming connected and data is regarded as a business's most valuable commodity, cybersecurity continues to diversify in a hyper-competitive marketplace. Set to hit a worth of US$248 billion by 2023, the prosperity of the sector is down to the constant growth and mutations of cyberthreats, which every year demands higher caliber weaponry with either better precision or a wider spread. Cybercrime, today, is where the money is. The tools to enact it are widely available even to non-technical individuals.
By now, it is obvious to everyone that widespread remote working is accelerating the trend of digitization in society that has been happening for decades. What takes longer for most people to identify are the derivative trends. One such trend is that increased reliance on online applications means that cybercrime is becoming even more lucrative. For many years now, online theft has vastly outstripped physical bank robberies. Willie Sutton said he robbed banks "because that's where the money is." If he applied that maxim even 10 years ago, he would definitely have become a cybercriminal, targeting the websites of banks, federal agencies, airlines, and retailers.
Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. A central task of TEVV for AI is estimating brittleness, where brittleness implies that the system functions well within some bounds and poorly outside of those bounds. This paper argues that neither of those criteria are certain of Deep Neural Networks. First, highly touted AI successes (eg. image classification and speech recognition) are orders of magnitude more failure-prone than are typically certified in critical systems even within design bounds (perfectly in-distribution sampling). Second, performance falls off only gradually as inputs become further Out-Of-Distribution (OOD). Enhanced emphasis is needed on designing systems that are resilient despite failure-prone AI components as well as on evaluating and improving OOD performance in order to get AI to where it can clear the challenging hurdles of TEVV and certification.
Brian Patrick Green is the director of Technology Ethics at the Markkula Center for Applied Ethics. This article is an update of an earlier article which can be found here . Artificial intelligence and machine learning technologies are rapidly transforming society and will continue to do so in the coming decades. This social transformation will have deep ethical impact, with these powerful new technologies both improving and disrupting human lives. AI, as the externalization of human intelligence, offers us in amplified form everything that humanity already is, both good and evil. At this crossroads in history we should think very carefully about how to make this transition, or we risk empowering the grimmer side of our nature, rather than the brighter. Why is AI ethics becoming a problem now?
Over the past two years, the development of Artificial Intelligence and the new techniques for using Big Data has become both faster and more widespread. According to the old definition, by Artificial Intelligence we mean teaching a machine to think like a man, while Big Data is such a large mass of data in terms of quantity, speed and variety that it has to enable specific technologies and methods to extrapolate data from news already learned and extract new data and links from the news which seem unrelated to one another. Just to make an example, each buyer wants a specific reward. Currently we also have the possibility of developing Generative Adversarial Networks (GANs), which create objects not existing in reality, but similar to reality, as well as faces that have never been seen before but are quite probable, and objects that do not exist but seem to work well. Not to mention the self-correcting systems based on concepts that are adapted by the machine itself, as well as programs that self-create themselves, starting from a small nucleus.
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Here we understand 'intelligence' as referring to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts.