This position paper contends that modern AI research must adopt an antifragile perspective on safety -- one in which the system's capacity to guarantee long-term AI safety such as handling rare or out-of-distribution (OOD) events expands over time. Conventional static benchmarks and single-shot robustness tests overlook the reality that environments evolve and that models, if left unchallenged, can drift into maladaptation (e.g., reward hacking, over-optimization, or atrophy of broader capabilities). We argue that an antifragile approach -- Rather than striving to rapidly reduce current uncertainties, the emphasis is on leveraging those uncertainties to better prepare for potentially greater, more unpredictable uncertainties in the future -- is pivotal for the long-term reliability of open-ended ML systems. In this position paper, we first identify key limitations of static testing, including scenario diversity, reward hacking, and over-alignment. We then explore the potential of antifragile solutions to manage rare events. Crucially, we advocate for a fundamental recalibration of the methods used to measure, benchmark, and continually improve AI safety over the long term, complementing existing robustness approaches by providing ethical and practical guidelines towards fostering an antifragile AI safety community.

Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a comprehensive definition of black swan events. To this end, this paper challenges that the standard view is incomplete and claims that high-risk, statistically rare events can also occur in unchanging environments due to human misperception of their value and likelihood, which we call as spatial black swan event. We first carefully categorize black swan events, focusing on spatial black swan events, and mathematically formalize the definition of black swan events. We hope these definitions can pave the way for the development of algorithms to prevent such events by rationally correcting human perception.

With increasing complexity of modern communication systems, machine learning algorithms have become a focal point of research. However, performance demands have tightened in parallel to complexity. For some of the key applications targeted by future wireless, such as the medical field, strict and reliable performance guarantees are essential, but vanilla machine learning methods have been shown to struggle with these types of requirements. Therefore, the question is raised whether these methods can be extended to better deal with the demands imposed by such applications. In this paper, we look at a combinatorial resource allocation challenge with rare, significant events which must be handled properly. We propose to treat this as a multi-task learning problem, select two methods from this domain, Elastic Weight Consolidation and Gradient Episodic Memory, and integrate them into a vanilla actor-critic scheduler. We compare their performance in dealing with Black Swan Events with the state-of-the-art of augmenting the training data distribution and report that the multi-task approach proves highly effective.

A survey published by KPMG today suggests that a large number of organizations have increased their investments in AI during the pandemic to the point that executives are now concerned about moving too fast. In fact, most of the survey respondents cited a definite need for increased AI regulation. The survey covered 950 business decision-makers and/or IT decision-makers with at least a moderate amount of AI knowledge at companies with more than $1 billion in revenue. It finds AI technologies are most likely to be moderately to fully employed in industrial manufacturing (93%), financial services (84%), technology (83%), retail (81%), life sciences (77%), health care (67%), and government (61%) sectors. Survey respondents all cited the pandemic as a factor that drove increased adoption of AI in the last year, though the degree varied by sector from industrial manufacturing (72%) to technology (57%), retail (53%), government (44%), financial services (42%), and health care and life sciences (37%). Many respondents also noted that AI technology is moving too fast for their comfort in industrial manufacturing (55%), technology (49%), retail (49%), life sciences (47%), financial services (37%), government (37%), and health care (35%) sectors.

… are ensuring that our team in India is future-ready and is constantly leveraging newer technologies including machine learning, artificial intelligence, …

AI and analytics trends are shaping the companies' operations, embedding data-driven decision making, to deliver promised business resilience. Global businesses are quickly evolving into something that they could have never anticipated before COVID-19. Enterprises are realizing the importance of building intelligence-based capabilities to prepare for, sense, and respond to upcoming disruption. They are, today reassessing their use of AI and analytics to ensure that the businesses are resilient enough to endure further shock from such future black swan events. The ability to collect, organize, analyze, and react to data will be the new differentiator in business operation.

How does Amazon help Alexa understand what people mean and not just what they say? And, we couldn't be talking about Alexa, smart home tech, and AI at a better time. During this week's Amazon Devices event, the company made a host of smart home announcements, including a new batch of Echo smart speakers, which will include Amazon's new custom AZ1 Neural Edge processor. In August this year, I had a chance to speak with Evan Welbourne, senior manager of applied science for Alexa Smart Home at Amazon, about everything from how the company is using AI and ML to improve Alexa's understanding of what people say, Amazon's approach to data privacy, the unique ways people are interacting with Alexa around COVID-19, and where he sees the future of voice and smart tech going in the future. The following is an transcript of our conversation edited for readability. Bill Detwiler: So before we talk about maybe IoT, we talk about Alexa, and kind of what's happening with the COVID pandemic, as people are working more from home, and as they may have questions that they're asking about Alexa, about the pandemic, let's talk about kind of just your role there at Amazon, and what you're doing with Alexa, especially with AI and ML. So I lead machine learning for Alexa Smart Home. And what that sort of means generally is that we try to find ways to use machine learning to make Smart Home more useful and easier to use for everybody that uses smart home. It's always a challenge because we've got the early adopters who are tech savvy, they've been using smart home for years, and that's kind of one customer segment. But we've also got the people who are brand new to smart home these days, people who have no background in smart home, they're just unboxing their first light, they may not be that tech savvy.

As covid-19 disrupted the world in March, online retail giant Amazon struggled to respond to the sudden shift caused by the pandemic. Household items like bottled water and toilet paper, which never ran out of stock, suddenly became in short supply. One- and two-day deliveries were delayed for several days. Though Amazon CEO Jeff Bezos would go on to make $24 billion during the pandemic, initially, the company struggled with adjusting its logistics, transportation, supply chain, purchasing, and third-party seller processes to prioritize stocking and delivering higher-priority items. Under normal circumstances, Amazon's complicated logistics are mostly handled by artificial intelligence algorithms.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. At some point, every one of us has had the feeling that online applications like YouTube and Amazon and Spotify seem to know us better than ourselves, recommending content that we like even before we say it. At the heart of these platforms' success are artificial intelligence algorithms--or more precisely, machine learning models--that can find intricate patterns in huge sets of data. Corporations in different sectors leverage the power of machine learning along with the availability of big data and compute resources to bring remarkable enhancement to all sorts of operations, including content recommendation, inventory management, sales forecasting, and fraud detection. Yet, despite their seemingly magical behavior, current AI algorithms are very efficient statistical engines that can predict outcomes as long as they don't deviate too much from the norm.

The Black Death in the 1300s broke the long-ingrained feudal system in Europe and replaced it with the more modern employment contract. A mere three centuries later, a deep economic recession -- thanks to the 100-year war between England and France -- kick-started a major innovation drive that radically improved agricultural productivity. Fast forward to more recent times, the SARS pandemic of 2002-2004 catalyzed the meteoric growth of a then-small ecommerce company called Ali Baba and helped establish it at the forefront of retail in Asia. This growth was fueled by underlying anxiety around traveling and human contact, similar to what we see today with Covid-19. The financial crises of 2008 also produced its own disruptive side effects.