ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Efficiency-Wage Theory and Law Firm Pay

Every first-year law student knows that Big Law pays $160,000 a year. In fact, this number is likely the biggest incentive for applying in the minds of most law-school hopefuls. Taking New York City as an example, a quick look at Vault’s salary data reveals that, indeed, the large majority of New York firms with available salary data pay first-year associates exactly$160,000

Efficiency-Wage Theory and Law Firm Pay

Every first-year law student knows that Big Law pays $160,000 a year. In fact, this number is likely the biggest incentive for applying in the minds of most law-school hopefuls. Taking New York City as an example, a quick look at Vault’s salary data reveals that, indeed, the large majority of New York firms with available salary data pay first-year associates exactly$160,000

Application of the Meijer theorem in calculation of three-loop massive vacuum Feynman integrals and beyond

We present an analytical method to calculate the three-loop massive Feynman integral in arbitrary dimensions. The method is based on the Mellin-Barnes representation of the Feynman integral. The Meijer theorem and its corollary are used to perform the integration over the Gamma functions, exponential functions, and hypergeometric functions. We also discuss the application of the method in other multi-loop Feynman integrals.Comment: 24 pages, 4 figure

Working memory capacity of ChatGPT: an empirical study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory

Synchronous charge extraction and voltage inversion (SCEVI): a new efficient vibration-based energy harvesting scheme

This paper presents a new interface technique called synchronous charge extraction and voltage inversion (SCEVI), which consists of a synchronous inductor and a buck-boost converter for vibration-based energy harvesting using piezoelectric elements. The theoretical calculation of the harvested power obtained by using such a technique are proposed and compared with the so-called Standard, SECE (Synchronous Electric Charge Extraction), Parallel-SSHI (Parallel Synchronized Switch Harvesting on Inductor) and Series-SSHI (Series Synchronized Switch Harvesting on Inductor) methods commonly used in piezoelectric vibration-powered generator considering both constant displacement amplitude and force amplitude. From the harvested power point of view, SCEVI and Parallel – SSHI techniques are the better ones and each has its own merits. But the harvested power of SCEVI is independent of the load connected to the generator and Parallel – SSHI depend on the load resistance. The harvested power of SECE is also independent of the load, but the further experimental results show that the proposed SCEVI interface technique dramatically increases the harvested power by almost up to 150 % compared with the SECE method under the same amplitude of displacement excitation

Working Memory Capacity of ChatGPT: An Empirical Study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT (gpt-3.5-turbo), a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT experiences significant declines in performance as n increases (which necessitates more information to be stored in working memory), suggesting a limit to the working memory capacity strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory and deepening our understanding of human working memory through AI models.Comment: 19 pages, 21 figures, 10 table

Working memory capacity of ChatGPT: an empirical study

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory