Figure 1: The overall workflow of the proposed algorithm.

This chapter explores the convergence of two major developments: the rise of environmental, social, and governance (ESG) investing and the exponential growth of artificial intelligence (AI) technology. The increased demand for diverse ESG instruments, such as green and ESG-linked loans, will be aligned with the rapid growth of the global AI market, which is expected to be worth $1,394.30 billion by 2029. AI can assist in identifying and pricing climate risks, setting more ambitious ESG goals, and advancing sustainable finance decisions. However, delegating sustainable finance decisions to AI poses serious risks, and new principles and rules for AI and ESG investing are necessary to mitigate these risks. This chapter highlights the challenges associated with norm-setting initiatives and stresses the need for the fine-tuning of the principles of legitimacy, oversight and verification, transparency, and explainability. Finally, the chapter contends that integrating AI into ESG non-financial reporting necessitates a heightened sense of responsibility and the establishment of fundamental guiding principles within the spheres of AI and ESG investing.

This study investigates the effectiveness of Large Language Models (LLMs) in interpreting existing literature through a systematic review of the relationship between Environmental, Social, and Governance (ESG) factors and financial performance. The primary objective is to assess how LLMs can replicate a systematic review on a corpus of ESG-focused papers. We compiled and hand-coded a database of 88 relevant papers published from March 2020 to May 2024. Additionally, we used a set of 238 papers from a previous systematic review of ESG literature from January 2015 to February 2020. We evaluated two current state-of-the-art LLMs, Meta AI's Llama 3 8B and OpenAI's GPT-4o, on the accuracy of their interpretations relative to human-made classifications on both sets of papers. We then compared these results to a "Custom GPT" and a fine-tuned GPT-4o Mini model using the corpus of 238 papers as training data. The fine-tuned GPT-4o Mini model outperformed the base LLMs by 28.3% on average in overall accuracy on prompt 1. At the same time, the "Custom GPT" showed a 3.0% and 15.7% improvement on average in overall accuracy on prompts 2 and 3, respectively. Our findings reveal promising results for investors and agencies to leverage LLMs to summarize complex evidence related to ESG investing, thereby enabling quicker decision-making and a more efficient market.

Randomized Smoothing (RS) is currently a scalable certified defense method providing robustness certification against adversarial examples. Although significant progress has been achieved in providing defenses against $\ell_p$ adversaries, the interaction between the smoothing distribution and the robustness certification still remains vague. In this work, we comprehensively study the effect of two families of distributions, named Exponential Standard Gaussian (ESG) and Exponential General Gaussian (EGG) distributions, on Randomized Smoothing and Double Sampling Randomized Smoothing (DSRS). We derive an analytic formula for ESG's certified radius, which converges to the origin formula of RS as the dimension $d$ increases. Additionally, we prove that EGG can provide tighter constant factors than DSRS in providing $\Omega(\sqrt{d})$ lower bounds of $\ell_2$ certified radius, and thus further addresses the curse of dimensionality in RS. Our experiments on real-world datasets confirm our theoretical analysis of the ESG distributions, that they provide almost the same certification under different exponents $\eta$ for both RS and DSRS. In addition, EGG brings a significant improvement to the DSRS certification, but the mechanism can be different when the classifier properties are different. Compared to the primitive DSRS, the increase in certified accuracy provided by EGG is prominent, up to 6.4% on ImageNet.

In this paper we explore the challenges of measuring sentiment in relation to Environmental, Social and Governance (ESG) social media. ESG has grown in importance in recent years with a surge in interest from the financial sector and the performance of many businesses has become based in part on their ESG related reputations. The use of sentiment analysis to measure ESG related reputation has developed and with it interest in the use of machines to do so. The era of digital media has created an explosion of new media sources, driven by the growth of social media platforms. This growing data environment has become an excellent source for behavioural insight studies across many disciplines that includes politics, healthcare and market research. Our study seeks to compare human performance with the cutting edge in machine performance in the measurement of ESG related sentiment. To this end researchers classify the sentiment of 150 tweets and a reliability measure is made. A gold standard data set is then established based on the consensus of 3 researchers and this data set is then used to measure the performance of different machine approaches: one based on the VADER dictionary approach to sentiment classification and then multiple language model approaches, including Llama2, T5, Mistral, Mixtral, FINBERT, GPT3.5 and GPT4.

Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention.

Clinician must write a lengthy summary each time a patient is discharged from the hospital. This task is time-consuming due to the sheer number of unique clinical concepts covered in the admission. Identifying and covering salient entities is vital for the summary to be clinically useful. We fine-tune open-source LLMs (Mistral-7B-Instruct and Zephyr-7B-\b{eta}) on the task and find that they generate incomplete and unfaithful summaries. To increase entity coverage, we train a smaller, encoder-only model to predict salient entities, which are treated as content-plans to guide the LLM. To encourage the LLM to focus on specific mentions in the source notes, we propose SPEER: Sentence-level Planning via Embedded Entity Retrieval. Specifically, we mark each salient entity span with special "{{ }}" boundary tags and instruct the LLM to retrieve marked spans before generating each sentence. Sentence-level planning acts as a form of state tracking in that the model is explicitly recording the entities it uses. We fine-tune Mistral and Zephyr variants on a large-scale, diverse dataset of ~167k in-patient hospital admissions and evaluate on 3 datasets. SPEER shows gains in both coverage and faithfulness metrics over non-guided and guided baselines.

IT services providers that are expanding their portfolios to target environmental, social and governance (ESG) opportunities are making a wise move as many enterprises require assistance developing and implementing ESG-related initiatives, says GlobalData. The leading data and analytics company notes that these companies must continue to adapt to shifting market dynamics to stay ahead of the curve. According to a recent GlobalData survey, 34% of respondents indicate that their company has made adjustments to its ESG initiatives in the last 12 months. Rena Bhattacharyya, Service Director for Enterprise Technology and Services at GlobalData, comments: "For the most part, IT service providers are focusing on the environmental aspect of ESG by offering services and solutions related to sustainability such as carbon emissions assessments and advice on methods for reducing carbon footprints. "Additionally, providers are helping customers implement circularity with strategies targeting reuse, reduce, and recycle initiatives. IT services providers are also embedding the sustainability conversation into the sale of complementary solutions, such as procurement and supply chain-related products, or smart city and fleet management solutions."

It's safe to say that Pinterest doesn't exist without machine learning and A.I.,

The high motivations for building the real superintelligence (RSI) Technology Platform are plain and clear: 1. To have the most powerful human-machine superintelligent technology platform for solving the most complex global problems humanity has ever faced, environmental, geopolitical, social, economic, humanitarian, and technological. The RSI as a digital synergy of human and machine is emerging as the summit of all human knowledge: Mythology Religion Philosophy Science & Technology Computing Machines the Internet/WWW Emerging Technologies NAI/ML/DL BCI Human Intelligence Digital Superintelligence Global Human-AI Superintelligence (RSI). The only way to reach the point of Technological Singularity is via real superintelligence (RSI), relying on the the comprehensive and consistent world model machine, integrating causal, mathematical, scientific, conceptual, statistic and probabilistic models, algorithms and techniques. It is all supported by exponential emerging technologies.