Genus AI, a London-based artificial intelligence company, has raised $1m in Seed funding. The round was led by early-stage technology investment firm Picus Capital. Andy Chung at AngelList; Charlie Songhurst, investor in Onfido; Matt Robinson, founder of GoCardless and Nested; also invested. Genus AI will use the money to expand its technology team in London and boost its sales operations in the US – the startup's primary market. "We believe in using technology to understand people and the world around us.
One of the major challenges facing engineers as they develop more agile robots is helping them move through space while avoiding collisions, especially in a dynamic environment. RealTime Robotics, a Boston-based startup announced an $11.7 million Series A investment to help solve this problem. SPARX Asset Management led the round with participation from some strategic investors including Mitsubishi Electric Corporation, Hyundai Motor Company and Omron Ventures. Existing investors Toyota AI Ventures, Scrum Ventures and the Duke Angel Network also pitched in. Today's investment is actually the culmination of a couple of investments over this year that the company is announcing today, and brings the total raised to $12.9 million.
Kneron, Inc., a leading on-device edge artificial intelligence (AI) company based in San Diego, California, was named by CB Insights to the fourth annual AI 100 ranking, showcasing the 100 most promising private AI companies in the world. "To be listed on the AI 100 is an honor," stated Albert Liu, Kneron's Cofounder and CEO. "It represents our determination to expand AI inferencing from the cloud to the edge so that private user data can be more secure, and edge AI devices and applications can be more ubiquitous in our everyday lives. We're excited and inspired to see our work being recognized by CB Insights." Kneron's on-device edge AI solutions include AI chips and AI software models that enhance smart devices with AI functions without the constant need to be connected to a cloud-based AI service because the AI inferencing happens where the data is collected.
Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.