Your Friend Asked You a Question. Your friend came to you because they respect your knowledge and opinion, and outsourcing the answer to a machine is lazy and rude. Back in the 2010s, a website called Let Me Google That For You gained a notable amount of popularity for serving a single purpose: snark. The site lets you generate a custom link that you can send somebody who asks you a question. When they click the link, it plays an animation of the process of typing a question into Google.

The rise of generative AI, has driven significant advancements in high-risk sectors like healthcare and finance. The Retrieval-Augmented Generation (RAG) architecture, combining language models (LLMs) with search engines, is particularly notable for its ability to generate responses from document corpora. Despite its potential, the reliability of RAG systems in critical contexts remains a concern, with issues such as hallucinations persisting. This study evaluates a RAG system used in due diligence for an investment fund. We propose a robust evaluation protocol combining human annotations and LLM-Judge annotations to identify system failures, like hallucinations, off-topic, failed citations, and abstentions. Inspired by the Prediction Powered Inference (PPI) method, we achieve precise performance measurements with statistical guarantees. We provide a comprehensive dataset for further analysis. Our contributions aim to enhance the reliability and scalability of RAG systems evaluation protocols in industrial applications.

This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.

AI has several notable advantages over the human mind, especially when it comes to the ability to retain and recall vast quantities of information and to make decisions and give advice unaffected by biases and emotions. An analysis of the relevant sections of the Companies Act led us to conclude in the previous article that, as the law stands, there is nothing to prevent a company from legislating, in its memorandum of incorporation or a shareholders' agreement, that an AI system must be consulted before a final decision is made by the board. However, the directors cannot abrogate their duty to become fully informed about corporate affairs, apply their own minds and exercise their discretion in the best interests of the company. Given the nature of AI, this issue must also be addressed if a company requires the directors to rely on AI in making decisions. Since well before the advent of the latest generation of AI, company boards have been relying on information collected, stored and processed electronically when making decisions.

SESAMm, a leader in natural language processing (NLP), a field of artificial intelligence, announced the close of a Series B2 funding round of €35 million (USD 37 million) to accelerate its ambitious growth and global expansion plans. "Since we started working with SESAMm as investors and clients over two years ago, we've been impressed with both the company's growth and the advanced analytics that have supported our deal sourcing, diligence, and portfolio company value creation efforts" Securing this funding will enable SESAMm to further expand into U.S. and Asian markets, support technology development to generate AI-powered ESG and sentiment analytics, and hire key talent across sustainability, technology, sales, and marketing. The Series B2 round was co-led by Elaia, a deep tech VC firm, and Opera Tech Ventures, the venture capital arm of BNP Paribas (BNP). Other participating companies include asset manager Unigestion, Raiffeisen Bank International's (RBI) venture capital entity Elevator Ventures, AFG Partners, CEGEE Capital, and historical backers, including Carlyle (CG) and New Alpha Asset Management, who participated in the previous Series B1 round. This latest round brings the total funding raised to €50 million.

Sesamm, a French startup that helps financial firms and corporates adhere to their ESG goals by using natural language processing (NLP) to generate insights from digital content, has raised €35 million ($37 million) in a round of funding to expand internationally. Despite a growing backlash against ESG efforts from some politicians and vocal executives, companies are still cognizant of the reputational and commercial risks of ignoring their environmental, social, and corporate governance (ESG) responsibilities -- this applies both to their internal practices and those of third-parties they do business with. With that in mind, Sesamm enables businesses to track textual data from across the web -- including news portals, NGO reports, and social networks -- and convert this into actionable insights. Founded out of Paris in 2014, Sesamm has amassed a fairly impressive roster of clients from across the financial realm specifically, including U.S. investment giant Carlyle Group, French corporate and investment bank Natixis, Japanese multinational insurance holding company Tokio Marine, and U.K.-based asset management firm Unigestion. Companies can access Sesamm's flagship product, TextReveal, via several conduits, including an API that brings Sesamm's NLP engine into their own systems.

Industries and processes across the globe are embracing new technologies to increase efficiency and deliver faster and accurate outcomes. Artificial Intelligence (AI) and Machine Learning (ML) have recently taken the world by storm with their advancements in delivering impactful and insightful results. The legal industry is not any different. The changing customer needs and technology […]

Incorporating two of the most hyped technologies sounds promising, does n't it? But are there factual benefits of combining artificial intelligence( AI) and blockchain technology results? Will this combination be an effective business decision or just an instigative trial? Last time's hype over cryptocurrency made the blockchain a buzzword. But disruptors have been experimenting with the technology for several times.

Less than 24 hours into its fundraising, the Dash 2 Trade crypto trading intelligence platform has already raised more than $300,000 in the first stage of its presale. Positioning itself as the'Bloomberg Terminal for crypto' – but minus the $2,000 a month subscription, Dash 2 Trade is bringing the functionality and feature-set of a fully professional analytics and intelligence suite to the ordinary crypto trader. At the core of the Dash 2 Trade platform is its powerful and innovative dashboard. It's been in development for several months and is already at the minimum viable product stage, and the polished version will be ready to launch at the end of the presale. The Dash 2 Trade dashboard brings together all the tools, metrics, signals and indicators to turbo-charge trading performance.

The transformation of finance over the past few years has been profound, as big data, artificial intelligence (AI), and cryptocurrency have merged to create a highly complex industry that continues to rapidly evolve. For Antoinette Schoar (Stewart C. Myers-Horn Family Professor of Finance; Professor, Finance), finding meaningful patterns within this structure is both a passion and a calling. Schoar's work reveals the extraordinary opportunities that are hidden inside this shifting mosaic: new finance models enable more entrepreneurs to launch companies, venture capitalists can lower their risk and increase their chance of success, and variations in cryptocurrency valuations by country reflect the value of trust in traditional institutions. When used wisely, data and AI can also help remove bias from hiring and other critical processes. Along with these opportunities, however, come new challenges that will require society to engage in difficult conversations--and the outcomes will shape the future of finance.