This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm.

The ability to automatically identify industry sector coverage in articles on legal developments, or any kind of news articles for that matter, can bring plentiful of benefits both to the readers and the content creators themselves. By having articles tagged based on industry coverage, readers from all around the world would be able to get to legal news that are specific to their region and professional industry. Simultaneously, writers would benefit from understanding which industries potentially lack coverage or which industries readers are currently mostly interested in and thus, they would focus their writing efforts towards more inclusive and relevant legal news coverage. In this paper, a Machine Learning-powered industry analysis approach which combined Natural Language Processing (NLP) with Statistical and Machine Learning (ML) techniques was investigated. A dataset consisting of over 1,700 annotated legal articles was created for the identification of six industry sectors. Text and legal based features were extracted from the text. Both traditional ML methods (e.g. gradient boosting machine algorithms, and decision-tree based algorithms) and deep neural network (e.g. transformer models) were applied for performance comparison of predictive models. The system achieved promising results with area under the receiver operating characteristic curve scores above 0.90 and F-scores above 0.81 with respect to the six industry sectors. The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way. Traditional ML methods perform better than deep neural networks when only a small and domain-specific training data is available for the study.

"How did you go bankrupt?" "Two ways," answered Mike, "Gradually, then suddenly." The very same story describes how major industry disruption usually happens--gradually, and then suddenly. For board members and other industry leaders, being on the right side of such disruption typically requires looking years ahead. But with the release of ChatGPT in November, 2022, OpenAI "suddenly" and shockingly threatened to overthrow Google's hitherto total dominance of internet search.

Forecasts from Persistence Market Research indicate that by the end of the forecast period in 2027, the worldwide deep learning market would be worth US$ 261,113.0 This indicates a 49.0% compound annual growth rate that was seen over the anticipated period. This development can be ascribed to the demand for improved processing hardware, an increase in global R&D activity in particular industries, and the quick global adoption of cloud-based technologies. A recent research from Persistence Market Research offers a complete review of the worldwide deep learning market. In-depth analysis of the deep learning concept and the performance of the global deep learning market across significant end-use industry sectors throughout seven significant geographies are provided in this study.

One in four global technology leaders believe up to 75 per cent of jobs across the global economy will be augmented by AI-driven software in 2023, and the vast majority of tech bosses are also planning to make moves in the Metaverse next year. These results were uncovered in The Impact of Technology in 2023 and Beyond: an IEEE Global Study a survey which questioned 350 CIOs, CTOs, IT directors and other technology leaders in the US, UK, China, India and Brazil. Respondents worked at organisations with over 1,000 employees in multiple industry sectors including banking and financial services, consumer goods, education, electronics, engineering, energy, government, healthcare, insurance, retail, technology and telecommunications. The study covered the most important technologies in 2023 and future technology trends. Global technology leaders surveyed said cloud computing (selected by 40 per cent), 5G (38 per cent), Metaverse (37 per cent), electric vehicles (35 per cent), and the Industrial Internet of Things (33 per cent) will be the most important areas of technology next year.

Interpretable machine learning plays a key role in healthcare because it is challenging in understanding feature importance in deep learning model predictions. We propose a novel framework that uses deep learning to study feature sensitivity for model predictions. This work combines sensitivity analysis with heterogeneous time-series deep learning model prediction, which corresponds to the interpretations of spatio-temporal features. We forecast county-level COVID-19 infection using the Temporal Fusion Transformer. We then use the sensitivity analysis extending Morris Method to see how sensitive the outputs are with respect to perturbation to our static and dynamic input features. The significance of the work is grounded in a real-world COVID-19 infection prediction with highly non-stationary, finely granular, and heterogeneous data. 1) Our model can capture the detailed daily changes of temporal and spatial model behaviors and achieves high prediction performance compared to a PyTorch baseline. 2) By analyzing the Morris sensitivity indices and attention patterns, we decipher the meaning of feature importance with observational population and dynamic model changes. 3) We have collected 2.5 years of socioeconomic and health features over 3142 US counties, such as observed cases and deaths, and a number of static (age distribution, health disparity, and industry) and dynamic features (vaccination, disease spread, transmissible cases, and social distancing). Using the proposed framework, we conduct extensive experiments and show our model can learn complex interactions and perform predictions for daily infection at the county level. Being able to model the disease infection with a hybrid prediction and description accuracy measurement with Morris index at the county level is a central idea that sheds light on individual feature interpretation via sensitivity analysis.

Anand Rao: AI is impacting every industry sector and every functional area. It is a general purpose technology that will have a profound influence in the next 10-20 years on how we interact with each other and how individuals, businesses, and governments make decisions. In some cases they are also moving from automating and transforming today's businesses to disrupting our current business models. We are already seeing fundamental ways in which AI has changed our behaviours – from searching for content, to summarising and synthesising what we read, see, or hear, to even creating new forms of art, music, and literature. As a result, businesses that ignore AI are doing so at their own peril.

Oversight of AI is the board's job, regardless of the subject matter complexity. One of the most consequential challenges confronting corporate governance in the near term will be its ability to exercise informed oversight over the application of artificial intelligence (AI) within its organization. It will be a challenge that will arise regardless of the industry sector in which the company operates, and regardless of how it applies AI in that operation. The essence of the challenge is the rapidly emerging conflict between the perceived societal and commercial benefits arising from AI implementation, and the perceived societal and institutional risks arising from its use. The need to address the challenge is urgent; the competing interests of benefit and risk are hurtling at each other at hypersonic speed.

Oversight of AI is the board's job, regardless of the subject matter complexity. One of the most consequential challenges confronting corporate governance in the near term will be its ability to exercise informed oversight over the application of artificial intelligence ("AI") within its organization. It will be a challenge that will arise regardless of the industry sector in which the company operates, and regardless of how it applies AI in that operation. The essence of the challenge is the rapidly emerging conflict between the perceived societal and commercial benefits arising from AI implementation, and the perceived societal and institutional risks arising from its use. The need to address the challenge is urgent; the competing interests of benefit and risk are hurtling at each other at hypersonic speed.

Artificial Intelligence (AI) and Machine Learning (ML), Cloud Computing, and 5G will be the most important technologies in 2022, according to a new study conducted by IEEE called'The Impact of Technology in 2022 and Beyond.' The report includes a survey of 350 CTOs, CIOs, and IT Directors, and other technology leaders at organizations with over 1,000 employees across multiple industry sectors, including banking and financial services, consumer goods, education, electronics, engineering, energy, government, healthcare, insurance, retail, technology, and telecommunications. Findings reveal the key technologies that will not only impact industries in 2022 but will dominate the next decade's digital transformation strategies. According to the IEEE study, technology leaders agree that Artificial Intelligence (AI) and Machine Learning (ML) will drive the majority of innovation across nearly every sector in the next 1 to 5 years. Other technologies driving innovation from 2022 onwards include Cloud Computing, 5G, Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (XR).