Sam: I laugh at the stupid Prophecizers who are so certain of technological Singularity, for ex: Kurzweil. He is a fraud or worst, an inductivist idiot. Just plot a line of past progresses against time, and extend that "exponential" line to the future. And voila you have got Artificial General Intelligence and the fountain of youth. Chris: So you think AGI is a myth that will never happen?

But what are the challenges for AI in Finance this year? We caught up with experts from JP Morgan & Chase, UBS, University of Greenwich, Cornell University, and Fidelity Investments to find out about the top challenges that AI in Finance will face in 2023. These leading experts will be joining us at the AI in Finance Summit New York on April 19-20, 2023, and AI in Finance Summit London on April 25-26, 2023, where they will be discussing the challenges of AI in Finance in more detail and how to overcome them. Early Bird ticket sale for AI in Finance Summit New York ends on Friday, February 24, so secure your place today to save $500. Early Bird ticket sale for AI in Finance Summit London ends on Friday, March 3, so secure your place today to save £500.

In a special award session, the best papers of the conference were announced. The AAAI-2023 outstanding paper award went to Joar Skalse and Alessandro Abate for their work Misspecification in Inverse Reinforcement Learning. The AAAI-2023 outstanding student paper award was given to Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation, authored by Yulu Gan, Yan Bai, Yihang Lou, Xianzheng Ma, Renrui Zhang, Nian Shi, and Lin Luo. There were also 12 distinguished paper award winners, the details of which can be found here. As well as these best paper awards, a number of prestigious AAAI awards were presented at the conference. These included the AAAI Award for Artificial Intelligence for the Benefit of Humanity, which was won by Tuomas Sandholm. You can find out more about this prize, and the others awarded, here. There will be plenty more content to come as we continue to cover the conference, and hear from participants about their work. You can find our conference coverage here, and this collection will be updated as soon as we add new content.

As some newsroom roles go the way of the dinosaurs, brand new jobs are being born. This interview is part of an occasional series of Q&As with people who are the first to hold their title in their newsroom. Madhumita Murgia describes herself as an accidental tech journalist. As a biology student, Murgia studied non-human intelligence in a gray parrot named Alex before she ever focused on intelligence of the artificial variety. Now, as the Financial Times' first-ever artificial intelligence editor, Murgia has been tasked with leading coverage on the rapidly evolving field and providing advice and expertise to other FT reporters as they "increasingly encounter stories about how AI is upending industries around the world."

AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks. These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as "co-creators." Yet, working effectively with such systems requires different skills than working with complex CAD tools alone. To date, we know little about how engineering designers learn to work with AI-based design tools. In this study, we observed trained designers as they learned to work with two AI-based tools on a realistic design task. We find that designers face many challenges in learning to effectively co-create with current systems, including challenges in understanding and adjusting AI outputs and in communicating their design goals. Based on our findings, we highlight several design opportunities to better support designer-AI co-creation.

We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics.Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. The additional easy-to-use toolkit that consists of short text entity linking, query generation, and helpful knowledge classification is also released to enable diverse applications. All the source code is available on Github.

Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption that a reward only depends on the immediate state and action. However, a reward sometimes depends on the history of states and actions, which may result in the decision process in a non-Markovian environment. In such environments, agents receive rewards via temporally-extended behaviors sparsely, and the learned policies may be similar. This leads the agents acquired with similar policies generally overfit to the given task and can not quickly adapt to perturbations of environments. To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation. Specifically, we first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies. Finally, we prove that if the dispersion matrix is positive definite, the dispersed embeddings can effectively enlarge the disagreements across policies, yielding a diverse expression for the original policy embedding distribution. Experimental results show that this dispersion scheme can obtain more expressive diverse policies, which then derive more robust performance than recent learning baselines under various learning environments.

I was recently recommended to watch an interview with Eliezer Yudkowsky created by YouTubers and all-round crypto smart guys David and Ryan from "Bankless", a crypto and blockchain education company. Here's the link if you have a spare two hours, it's an equally scary and fascinating watch: I used to obsessively watch Bankless videos back in 2021 during the last crypto/NFT boom, but since the bear market of 2022 set in, I kind of lost some of my mojo for crypto. Anyhow, this video, was a dramatic departure from the Bankless crew's regular weekly roundup of the crypto markets, where they get deep into the weeds of the latest developments in the space. Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a subscriber.

Every time a new application of AI is announced, I feel a short-lived rush of excitement -- followed soon after by a knot in my stomach. This is because I know the technology, more often than not, hasn't been designed with equity in mind. One system, ChatGPT, has reached 100 million unique users just two months after its launch. The text-based tool engages users in interactive, friendly, AI-generated exchanges with a chatbot that has been developed to speak authoritatively on any subject it's prompted to address. In an interview with Michael Barbaro on the The Daily podcast from the New York Times, tech reporter Kevin Roose described how an app similar to ChatGPT, Bing's AI chatbot, which also is built on OpenAI's GPT-3 language model, responded to his request for a suggestion on a side dish to accompany French onion soup for Valentine's Day dinner with his wife.

Tesla has applied for a set of patents that are set to significantly improve virtualization, recognition, and Full Self Driving overall. Tesla has worked tirelessly to improve full self-driving technology in the first two months of the year. Most recently, Tesla pushed its most significant improvement to employees, v11.3. Still, with new patented technology, the software is set to continue to improve dramatically this year. The two patents, focusing on virtualization and machine learning, appeared in the U.S. Patent Office database late last week.