Writing about a subject enriches writers' understanding of that subject. This cognitive benefit of writing -- known as constructive learning -- is essential to how students learn in various disciplines. However, does this benefit persist when students write with generative AI writing assistants? Prior research suggests the answer varies based on the type of AI, e.g., auto-complete systems tend to hinder ideation, while assistants that pose Socratic questions facilitate it. This paper adds an additional perspective. Through a case study, we demonstrate that the impact of genAI on students' idea development depends not only on the AI but also on the students and, crucially, their interactions in between. Students who proactively explored ideas gained new ideas from writing, regardless of whether they used auto-complete or Socratic AI assistants. Those who engaged in prolonged, mindless copyediting developed few ideas even with a Socratic AI. These findings suggest opportunities in designing AI writing assistants, not merely by creating more thought-provoking AI, but also by fostering more thought-provoking writer-AI interactions.

The paper proposes a statistical test for particular non-linear effects in a linear mixed model (LMM). The problem of testing non-linear effects is relevant, especially in the natural sciences. The experimental validation has its flaws, but may be considered acceptable for a conference paper. The method consists of multiple parts: 1) The main new idea introduced in the paper is to introduce a kernel parameter (garotte) that interpolates between a null model and the desired alternative model and to perform a score test on this parameter. This elegant new idea is combined with several established steps to obtain the final testing procedure: 2) Defining a score statistic and deriving an approximate null distribution for the statistic based on the Satterthwaite approximation.

ACM Fellow Daniela Rus has been dreaming of robots since she was a child, imagining mechanical shoes to help her jump higher. As director of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), Rus has done pioneering work in modular robots, soft robotics, novel neural networks, and more. Her talk on the future of robotics and AI was featured at a recent TED conference, and this year she released a pair of books for the general public, including The Mind's Mirror: Risk and Reward in the Age of AI. Throughout her career, Rus has maintained a dual focus on improving both the bodies and the brains of intelligent machines. This traces back to her Ph.D. thesis, when she discovered the algorithms she'd developed for dexterous manipulation were too advanced for the robotic hands of the day.

The rapid advancement of large language models (LLMs) has revolutionized natural language processing (NLP). While these models excel at understanding and generating human-like text, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference for deep neural networks. It leverages network modularity to create sub-models with varying computational loads, sorting them based on computation/accuracy characteristics in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any pretraining and by only replacing standard Supervised Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT) at the same costs. Our approach boosts model efficiency, eliminating the need for multiple models for various scenarios during inference. We show that using this approach, we are able to unlock the potential of intermediate layers of transformers in generating the target output. Our sub-models remain integral components of the original model, minimizing storage requirements and transition costs between different computational/latency budgets. By applying this approach on LLaMa 2 13B for tuning on the Stanford Alpaca dataset and comparing it to normal tuning and early exit via PandaLM benchmark, we show that Sorted Fine-Tuning can deliver models twice as fast as the original model while maintaining or exceeding performance.

Google's new chatbot Bard made a mistake this week, answering a question incorrectly in a product demo on Twitter. It's a measure of our high expectations of AI that this simple error wiped out $100 billion of Google's share price in a matter of hours. Investors are also underwhelmed by the company's plans to deploy the artificial intelligence in its products. You can understand why Google felt the need to accelerate the response to ChatGPT, which is backed by it's arch-rival Microsoft. Since its launch in November, ChatGPT has passed a bar exam, picked stocks in line with Warren Buffet's strategy, and produced usable computer code.

Generative AI like ChatGPT is truly exciting, and it's easy to be seduced by the technology's potential to produce, well, almost any sort of output. The opportunity in generative AI is enormous but requires careful analysis of where the best applications lie. Healthcare, in particular, requires this assessment – this isn't an industry known for fast change, and the risks of inappropriately deploying new technology can be huge. For instance, consider the hype around IBM's Watson Health a few years ago; this AI was going to figure out complex cancers! It didn't, and it was sold off cheaply in parts last year. This includes what people need to stop doing in order to start embracing the new solution.

Our mission is to ensure that artificial general intelligence--AI systems that are generally smarter than humans--benefits all of humanity. If AGI is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility. AGI has the potential to give everyone incredible new capabilities; we can imagine a world where all of us have access to help with almost any cognitive task, providing a great force multiplier for human ingenuity and creativity. On the other hand, AGI would also come with serious risk of misuse, drastic accidents, and societal disruption. Because the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right.[1]

Despite perceptions, artificial intelligence doesn't have a shred of creativity within it. It's all statistical algorithms, slurping data from sources created by humans somewhere along the line. It will never be a source of innovation, but will serve to augment human innovation. "AI will not develop fundamentally new ideas on its own; however, there are ways in which AI can support humans in doing so," according to a recent study out of the Gottlieb Duttweiler Institute. In this context, AI serves as a liberating force.

Radix Trading is a proprietary firm focused on quantitative research and scientific trading. We're one of the most active liquidity providers on electronic exchanges globally, and have leveraged a culture of open, collaborative innovation to scale the reach of our ideas and pace of iteration, without having to scale our headcount (currently, we're around 125 people across Chicago, New York, and Amsterdam). In our industry, the vast majority of ideas will fail. So, since inception, we've focused on continuous enhancement of our automated research platform and cutting-edge technology, allowing us to fail faster than the day prior, glean insights from each idea, and leverage individual contributions to the fullest across our entire organization. We're led by Ben Blander and Michael Rauchman, who played key roles in the rise of electronic trading, but both recognized a major gap in the industry -- a true focus on research processes coupled with an open organizational structure that fosters collaboration.

The rapid emergence of increasingly sophisticated'AI ' programs such as CHATgpt will profoundly impact our world in many ways. That will inevitably include Innovation, especially the front end. But will it ultimately help or hurt us? Better access to information should be a huge benefit, and my intuition was to dive in and take full advantage. I still think it has enormous upside, but I also think it needs to be treated with care.