Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go agents have similar capabilities to humans, implicitly managing risks presented by their opponents. To further increase performance in these tasks, out-of-distribution evaluation can characterize a model's bias, what we view as a type of risk management. However, learning to identify and mitigate low-frequency, high-impact risks is at odds with the observational bias required to train machine learning models. StarCraft and Go are closed-world domains whose risks are known and mitigations well documented, ideal for learning through repetition. Adversarial filtering datasets provide difficult examples but are laborious to curate and static, both barriers to real-world risk management. Adversarial robustness focuses on model poisoning under the assumption there is an adversary with malicious intent, without considering naturally occurring adversarial examples. These methods are all important steps towards improving risk management but do so without considering open-worlds. We unify these open-world risk management challenges with two contributions. The first is our perception challenges, designed for agents with imperfect perceptions of their environment whose consequences have a high impact. Our second contribution are cognition challenges, designed for agents that must dynamically adjust their risk exposure as they identify new risks and learn new mitigations. Our goal with these challenges is to spur research into solutions that assess and improve the anticipatory thinking required by AI agents to manage risk in open-worlds and ultimately the real-world.

Deutsche Bank is looking to deploy artificial intelligence (AI) acceleration technology from Nvidia to power financial services applications. The bank hopes AI will improve its efforts to serve customers worldwide and enable it to build new data-driven products and services, increase efficiency and recruit tech talent. Using Nvidia AI Enterprise software, Deutsche Bank said its AI developers, data scientists and IT professionals would be able to build and run AI workflows in hosted on-premise datacentres as well as on Google Cloud, which the bank uses as its public cloud provider. The bank plans to use the latest version of Nvidia's enterprise AI tool – AI Enterprise 3.0. This introduces workflows for contact centre intelligent virtual assistants, audio transcription and digital fingerprinting for cyber security.

As AI spreads throughout the enterprise, organizations are having a difficult time balancing the benefits against the risks. AI is already baked into a range of tools, from IT infrastructure management to DevOps software to CRM suites, but most of those tools were adopted without an AI risk-mitigation strategy in place. Of course, it's important to remember that the list of potential AI benefits is every bit as long as the risks, which is why so many organizations skimp on risk assessments in the first place. Many organizations have already made serious breakthroughs that wouldn't have been possible without AI. For instance, AI is being deployed throughout the health-care industry for everything from robot-assisted surgery to reduced drug dosage errors to streamlined administrative workflows.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. While AI-driven solutions are quickly becoming a mainstream technology across industries, it has also become clear that deployment requires careful management to prevent unintentional damage. As is the case with most tools, AI has the potential to expose individuals and enterprises to an array of risks, risks that could have otherwise been mitigated through diligent assessment of potential consequences early on in the process. This is where "responsible AI" comes in -- that is, a governance framework that documents how a specific organization should address the ethical and legal challenges surrounding AI. A key motivation for responsible AI endeavors is resolving uncertainty about who is accountable if something goes wrong.

This week's blogs are inspired by our global connector Marisol Menendez Tell me about Responsive and what you guys are all about? We're an advice-centric FinTech that's focused on financial advisors with better decisions and actions, that helps them grow customer wealth and loyalty. We do that by performing behavioural analytics on data and providing the advisors with insights in real-time. For the time being, that's the model we believe in. At this stage, we think that's just too valuable a process to be left to automation when it comes to the complexities of life and the weight of these decisions.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Like fabric softener and football, banks -- or at least banks as physical spaces -- have been cited as yet another industry that's being killed by those murderous Millennials.

With time we adapt to technological advancement; and needless to say, Artificial Intelligence (AI) is the hot talk at the dinner table. With its increasing popularity, Artificial Intelligence will spur the next industrial revolution. The finance industry was one of the first to adopt AI, and according to IDC (International Data Corporation), banking will invest more than $5 billion in 2020 on AI systems. A major chunk of which will go towards automating threat intelligence and prevention systems as well as fraud analysis and investigation. Banking and financial services are utilizing AI to gain a competitive advantage in the market.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.

From the shiny corridors of BlackRock's Palo Alto laboratory, to the cramped shared workspaces of scientifically minded hedge fund start-ups, to the hallways of quantitative investing stalwarts such as Renaissance Technologies and Two Sigma, artificial intelligence (AI) is being adopted as the new temple of asset management. Even discretionary managers are starting to bring in data scientists and machine learning experts. Most attempts to apply AI so far have been in stock price forecasting. But risk managers are asking how the technology can be harnessed in their domain also. One area of exploration is the use of machine learning to replace traditional approaches to risk modelling.