You could become your own worst enemy. CEOs and other managers are increasingly under the microscope as some investors use artificial intelligence to learn and analyze their language patterns and tone, opening up a new frontier of opportunities to slip up. In late 2020, according to language pattern software specialist Evan Schnidman, some executives in the IT industry were playing down the possibility of semiconductor chip shortages while discussing supply-chain disruptions. All was fine, they said. Yet the tone of their speech showed high levels of uncertainty, according to an algorithmic analysis designed to spot hidden clues in -- ideally unscripted -- spoken words. "We found that IT sector executives' tone was inconsistent with the positive textual sentiment of their remarks," said Schnidman, who advises two fintech companies behind the analysis.

Remedying this weakness is a key challenge in the quest for building intelligent agents that can learn continually when deployed in the real world, where their experiences are not necessarily i.i.d. and their resources may be limited. In my PhD, I have studied catastrophic forgetting in the context of deep reinforcement learning, where changes to the distribution of an agent's experiences arise from multiple sources and occur unpredictably over the course of learning. Inspired partially by the processes of synaptic consolidation and systems consolidation in the brain, I will present two methods that harness multi-timescale processes to mitigate catastrophic forgetting in an RL setting. Bio: Christos is currently pursuing a PhD on the topic of Continual Reinforcement Learning at Imperial College London, co-supervised by Claudia Clopath (Bioengineering) and Murray Shanahan (Computing). He graduated with a BA in Applied Mathematics from Harvard and worked as a trader at Brevan Howard for several years, before leaving to pursue MScs in Computing and Informatics at Imperial College and Edinburgh University respectively, driven by an interest in computational neuroscience and machine learning. In April, he will start a job as a Research Scientist at DeepMind.

Man Group's Man GLG unit has named former Florin Court Capital executive William Ferreira as the company's new head of machine learning. In his new role, Ferreira will be responsible for developing Man GLG's machine learning capabilities, providing the firm's portfolio managers with tools and techniques through which to support their analysis and decision-making processes, the company said in a statement. He will also work directly with Man GLG's teams on the application and interpretation of machine learning techniques in relation to topics such as analyzing news and social media, market events and announcements, and the visualization of complex data. Man AHL has been actively researching machine learning techniques and applying them within its client trading programs for several years, the statement continued, while the firm benefits from its collaboration with University of Oxford's Oxford-Man Institute, which focuses on cutting-edge research into machine learning techniques and data analytics. Before joining Man GLG, Ferreira was a senior quantitative researcher with London-based Florin Court Capital, and beforehand worked as technology manager for Man AHL from 2011 to 2014.

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modeling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, U.K. hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.

Hedge funds have been trying to teach computers to think like traders for years. Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

Hedge funds have been trying to teach computers to think like traders for years. Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modelling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, UK hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.

This article was originally published on International Business Times. Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modeling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet.

Anthony Ledford and his colleagues at Man AHL investment fund spent three painstaking years building a machine-learning model to do something mere mortals often can't: find fresh ideas in an avalanche of data. But even Ledford, chief scientist at the $19 billion Man AHL in London, rolls his eyes when he hears people say that machine learning, a type of artificial intelligence, is going to transform hedge funds tomorrow. To Ledford, a lot of the buzz smacks of hype. The technology is more robust than its predecessors but hardly revolutionary. "There is some real science here, but it's not the way it's been portrayed," said Ledford, who holds a doctorate in mathematics.

Anthony Ledford and his colleagues at Man AHL spent three painstaking years building a machine-learning model to do something mere mortals often can't: find fresh ideas in an avalanche of data. But even Ledford, chief scientist at the $19 billion Man AHL in London, rolls his eyes when he hears people say that machine learning, a type of artificial intelligence, is going to transform hedge funds tomorrow. To Ledford, a lot of the buzz smacks of hype. The technology is more robust than its predecessors but hardly revolutionary. "There is some real science here, but it's not the way it's been portrayed," said Ledford, who holds a Ph.D. in mathematics.