We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant rates over long periods. Production of patents and publications doubles every ten years, by contrast with the growth of computing resources driven by Moore's Law, roughly a doubling every two years. We argue that the input of AI researchers is also crucial and its contribution can be objectively estimated. Consequently, we give a simple argument that explains the 5:1 relation between these two rates. We then discuss the application of this argument to different output measures and compare our analyses with predictions based on machine learning scaling laws proposed in existing literature. Our quantitative framework facilitates understanding, predicting, and modulating the development of these important technologies.

The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the information provided. That said, one of the simplest AI classification models, Logistic Regression (LR), has an unwieldy interpretation of its model weights, with greater difficulties when extending LR to generalised additive models. In this work, we show via a User Study that skilled participants are unable to reliably reproduce the action of small LR models given the trained parameters. As an antidote to this, we define Linearised Additive Models (LAMs), an optimal piecewise linear approximation that augments any trained additive model equipped with a sigmoid link function, requiring no retraining. We argue that LAMs are more interpretable than logistic models -- survey participants are shown to solve model reasoning tasks with LAMs much more accurately than with LR given the same information. Furthermore, we show that LAMs do not suffer from large performance penalties in terms of ROC-AUC and calibration with respect to their logistic counterparts on a broad suite of public financial modelling data.

Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language. However, they encounter difficulties when tasked with adapting to specialized domains such as accounting. To address this challenge, we introduce Kuaiji, a tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned using the Baichuan framework, which encompasses continuous pre-training and supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing large genuine accountant-client dialogues, Kuaiji exhibits exceptional accuracy and response speed. Our contributions encompass the creation of the first Chinese accounting dataset, the establishment of Kuaiji as a leading open-source Chinese accounting LLM, and the validation of its efficacy through real-world accounting scenarios.

A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent. The present work forgoes this assumption and considers the situation where the value of a reward decays proportionally to the time elapsed since it was obtained. Emphasizing the inflection point occurring at the time of payment, we use the term asset to refer to a reward that is currently in the possession of an agent. Adopting this language, we initiate the study of depreciating assets within the framework of infinite-horizon quantitative optimization. In particular, we propose a notion of asset depreciation, inspired by classical exponential discounting, where the value of an asset is scaled by a fixed discount factor at each time step after it is obtained by the agent. We formulate a Bellman-style equational characterization of optimality in this context and develop a model-free reinforcement learning approach to obtain optimal policies.

We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additive binary classification model equipped with a sigmoid link function. Moreover, they afford direct global and local attributions of additive components to the model output in probability space. We argue that LAMs and SubscaleHedge improve the interpretability of their base algorithms. Using rigorous null-hypothesis significance testing on a broad suite of financial modelling data, we show that our algorithms do not suffer from large performance penalties in terms of ROC-AUC and calibration.

SoftBank Group Corp. and video game-makers are emerging as rare beneficiaries of the weaker yen, which no longer offers the clear advantage to Japan's corporate sector that it did a decade ago. Automakers and electronics-makers including Sony Group Corp. once welcomed a softer yen to bolster their competitiveness abroad and inflate the value of their repatriated profits. But after shifting production overseas in recent years to secure growth and resilient supply chains, many of them see a mixed -- or mostly neutral -- effect from the yen's free fall to 20-year lows, according to industry executives and analysts. For some consumer-facing companies, including Uniqlo owner Fast Retailing Co., the latest slump in the yen is a negative factor, exacerbating the impact of surging raw materials costs and higher energy prices amid Russia's war in Ukraine. "It's no longer the case that the weak yen benefits many firms in the manufacturing sector," Morningstar Research analyst Kazunori Ito said.

Recent software and hardware democratized DL methods allowing scholars and practitioners to apply them in their fields. On the software side, recent frameworks as Tensorflow (Abadi et al., 2015) and PyTorch (Paszke et al., 2019) allowed to create complex DL models avoiding the need to write ad-hoc compilers as did by LeCun et al. (1990). On the hardware side, the decrease in the cost of the necessary hardware to train such models, allowed many people to build and deploy sophisticated Neural Networks with minimal costs (Zhang et al., 2018). The democratization of such powerful technologies allowed many fields to benefit from it aside from computer science. Some of those that benefitted the most are Economics (Nosratabadi et al., 2020), and Finance (Ozbayoglu et al., 2020). DL applications have piqued the interest of governments, who are concerned about possible social implications. It is well known that these models necessitate extra vigilance when it comes to training data in order to minimize biases of any kind, especially in high-stakes judgments (Rudin, 2019). To counter these side effects, the governments enacted several regulatory standards, and the jurisprudence started to elaborate on the right to explanation concept (Dexe et al., 2020). In this effort to build interpretable but DL grounded models, scholars have started developing post-hoc interpretation methods.

In recent years, bankruptcy forecasting has gained lot of attention from researchers as well as practitioners in the field of financial risk management. For bankruptcy prediction, various approaches proposed in the past and currently in practice relies on accounting ratios and using statistical modeling or machine learning methods. These models have had varying degrees of successes. Models such as Linear Discriminant Analysis or Artificial Neural Network employ discriminative classification techniques. They lack explicit provision to include prior expert knowledge. In this paper, we propose another route of generative modeling using Expert Bayesian framework. The biggest advantage of the proposed framework is an explicit inclusion of expert judgment in the modeling process. Also the proposed methodology provides a way to quantify uncertainty in prediction. As a result the model built using Bayesian framework is highly flexible, interpretable and intuitive in nature. The proposed approach is well suited for highly regulated or safety critical applications such as in finance or in medical diagnosis. In such cases accuracy in the prediction is not the only concern for decision makers. Decision makers and other stakeholders are also interested in uncertainty in the prediction as well as interpretability of the model. We empirically demonstrate these benefits of proposed framework on real world dataset using Stan, a probabilistic programming language. We found that the proposed model is either comparable or superior to the other existing methods. Also resulting model has much less False Positive Rate compared to many existing state of the art methods. The corresponding R code for the experiments is available at Github repository.

Workshop Grants Conference-AAAI-84, paid in 1985 (6.870) Misc. Income 130 Gross Profit: combined 944,383 Operating Expenses (256,698) Net Income 687,685 Fund Balance, beginning of year 874;634 Fund Balance, end of year $ 1.562:319 We have examined the balance sheet of the American Association for Artificial Intelligence as of December 31, 1985.

Conference-AAAI-84, paid in 1985 (6.870)