Linq

Techstars has invested in 186 companies through 16 programs in the past month, bringing our total portfolio to 4,400+ companies.🚀 Fundraising News: ◽ Twelve Labs (Techstars 2021) raises $50M in Series A funding to fuel the ongoing development of its industry-leading foundation models dedicated to all aspects of video. ◽ Alexi (Techstars 2018) has closed $15M CAD ($11M USD) in Series A financing as it looks to expand its team this year. ◽ Linq (Techstars 2023) raises $6.6M to use AI to make research easier for financial analysts. ◽ Pipedream Labs (Techstars 2021) raises $13M to pioneer the underground food delivery revolution. ◽ innerlogic (Techstars 2024), an AI-based culture transformation company, closes $1.3M plus in an oversubscribed seed round. 🔗 See what we were up to in June on the blog: http://tsta.rs/yThW50StV9x #Funding #Techstars #Investments #VC

🚀 Congratulations to the below startups on their recent funding announcements! 📹 Twelve Labs (Techstars 2021), the startup making searching videos easier and faster, has just raised $50M Series A.  → https://lnkd.in/gtBSGmtP 🖥️ Alexi (Techstars 2018), a legaltech automating routine tasks for lawyers with AI, has closed an $11M Series A financing round. → https://lnkd.in/g6X32hkw 💰 Linq (Techstars 2023), an AI agent that can automate a variety of tasks required for financial analysis and research, has raised a $6.6M funding round. → https://lnkd.in/gpKcajyJ 🌱 Alō Index (Techstars 2023), a platform designed to connect corporate travel buyers with hotels that match their ESG needs, raised a $1.9M pre-seed funding round.  → https://lnkd.in/epRQVSsq Jordan F. | Kerty Levy | Ryan Kuder #Funding #StartUp #Fundraising #PreSeed

Exciting News! Our paper, "Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses," has been accepted at the ICLR 2024 Workshop on Reliable and Responsible Foundation Models. We believe that our finding is crucial for enhancing the trustworthiness of LLM applications on specific industries such as Finance. 🔍 About Re-Ex: Re-Ex addresses a critical challenge in deploying LLMs—hallucination, where models generate incorrect information—through: 1. Identifying Errors: Using external tools to pinpoint inaccuracies in LLM responses. 2. Explaining Errors: Prompting the LLM to explain inaccuracies using the identified evidence. 3. Revising Responses: Adjusting the initial responses to enhance accuracy and efficiency, significantly reducing token count and inference time. 📊 Benefits & Industry Impact: Our method, Re-Ex, not only improves accuracy and efficiency but is also essential for industries aiming to integrate LLMs seamlessly into their operations. The ability to reduce errors and streamline processing makes LLMs more dependable and practical for sectors like finance, healthcare, and legal, where precision is subsequently crucial. 🤝 Acknowledgements: A sincere thank you to all co-authors and collaborators for their dedication and contributions to this amazing research. Jy-yong Sohn Junseong Kim Juyeon Kim Jeongeun Lee Yoonho Chang 🏢 At Linq, we aim to provide financial research insights through our AI agent, 'Marvin'. Learn more about our work: https://lnkd.in/gncSs7AB! #Linq #ICLR2024 #GenAI #trustworthiness #LLM

Insights from the Dubai FinTech Summit 2024: Enhancing AI with Domain Expertise As we showcased our AI solution, Marvin, at the Dubai Fintech Summit, I reflected on our journey with generative AI over the past few months and the lessons it taught us. 1️⃣ The feasibility of workflow automation using LLMs became apparent as we rolled out retrieval-augmented generation solutions across industries such as legal finance, insurance, healthcare, and more. Since last year, we introduced retrieval-augmented generation as an enterprise solution in fields like legal finance, insurance, and healthcare. This application demonstrated that LLMs could effectively analyze "unstructured data", and automate a wide range of specialized tasks. Our initial proof of concept validated the potential for basic task automation using AI technologies. 2️⃣ However, our collaboration with major US enterprises taught us valuable lessons about the limitations of applying LLMs applications across different domains via partnership models. Despite our dedicated efforts and continuous development cycles, the feedback loop proved challenging. It took over a month to gather feedback to refine our products. This experience underscored the necessity of domain-specific knowledge to navigate and overcome these barriers effectively. 3️⃣ These led us to strategically place domain experts within our team so that the iteration cycle becomes within a day. This decision has been instrumental in developing 'Marvin', a service tailored for financial research, which was well-received at the summit. The presence of domain experts not only enhances our product development but also ensures our solutions are fine-tuned to meet the sharp demands of the financial sector. We left the summit inspired and more committed than ever to push the boundaries of what AI can achieve in finance. Thank you to everyone who engaged with us and shared their insights and special thanks to the Mayor of Seoul for visiting our booth and to our domain expert, Hojun Choi, for his invaluable contributions. Many more to come. #DubaiFintechSummit #GenerativeAI #FinancialResearch #Marvin