CART loss given by Equation 5. The reason for the name "theoretical cuts" is that by the strong law of large numbers CART loss We first remark that in [32] their proof of Lemma 1 in fact proves more than its statement: Lemma 1. P ( Y y | X) is bounded, we may omit the truncation operators appearing in the original statements.) B.2.1 The approximation error goes to 0 (uniformly in y) B.2.2 The estimation error goes to 0 (uniformly in y) Theorem 4, we have that for all "> 0, " The classical conformalized prediction algorithm transforms a point prediction algorithm into an algorithm that outputs prediction intervals. Even with this adjustment, it took considerably longer than the other methods. In Section 6.1, we use We have re-run the tabular experiments from Section 6.1 five times to get confidence intervals.

We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires domain knowledge in addition to a deep understanding of the underlying problem and data structure. The model is provided with a dataset description in a prompt and asked to generate code transforming it. The evaluation score is derived from the improvement achieved by an XGBoost model fit on the modified dataset compared to the original data. By an extensive evaluation of state-of-the-art models and comparison to well-established benchmarks, we demonstrate that the FeatEng of our proposal can cheaply and efficiently assess the broad capabilities of LLMs, in contrast to the existing methods. The reference implementation is available at https://github.com/FeatEng/FeatEng. The rapid evolution of LLMs has significantly expanded their capabilities in processing and generating human-like text. As these models become increasingly sophisticated, defining what constitutes a meaningful benchmark is becoming harder and harder, as it is much easier to distinguish between bad and good models than between good and better. Today, the limitations of LLMs are predominantly assessed using benchmarks focused on language understanding, world knowledge, code generation, or mathematical reasoning in separation. This setup, however, overlooks some critical capabilities that can be measured in scenarios requiring inregration of skills and verification of their instrumental value in complex, real-world problems. We argue that well-designed LLM benchmarks should embody the following qualities, each reflecting a fundamental aspect of problem-solving ability: 1. Practical Usability. We demand that tasks are grounded in real-world problems where solutions have high functional value. This ensures that improvements in the observed performance translates into tangible benefits, aligning with the pragmatist view on the instrumental value of knowledge and truth, meaning that the validity of an idea depends on its practical utility in achieving desired outcomes (James, 1907). We would value LLM's knowledge for its role in enabling reasoning, decision-making, and problem-solving. The benchmark should be designed to evaluate not only the breadth of a model's knowledge base but also, more importantly, its capacity to dynamically and effectively apply this knowledge within various functional contexts, similarly to how functionalism frames it (Block, 1980). We opt for assessing models concerning their ability to seamlessly combine various competencies, in contrast to measuring them in separation.

Assistive robots have attracted significant attention due to their potential to enhance the quality of life for vulnerable individuals like the elderly. The convergence of computer vision, large language models, and robotics has introduced the `visuolinguomotor' mode for assistive robots, where visuals and linguistics are incorporated into assistive robots to enable proactive and interactive assistance. This raises the question: \textit{In circumstances where visuals become unreliable or unavailable, can we rely solely on language to control robots, i.e., the viability of the `linguomotor` mode for assistive robots?} This work takes the initial steps to answer this question by: 1) evaluating the responses of assistive robots to language prompts of varying granularities; and 2) exploring the necessity and feasibility of controlling the robot on-the-fly. We have designed and conducted experiments on a Sawyer cobot to support our arguments. A Turtlebot robot case is designed to demonstrate the adaptation of the solution to scenarios where assistive robots need to maneuver to assist. Codes will be released on GitHub soon to benefit the community.

Michael Gartenberg has covered the personal technology beat for more than two decades at places like Gartner, Jupiter Research and Altimeter Group. Most recently, he spent a few years at Apple as Sr. Director of Worldwide Product Marketing. I'm afraid I can't do that – HAL 900 Apple is the new Blackberry is the latest twist on "Apple is DOOMED". Apple may be missing out on Artificial Intelligence and/or Machine Learning. Alexa, Facebooks "bots", and Google's new Assistant, clearly, there's a huge sea change that's happening, and Apple is going to be left out.