In cooperative bandits, a framework that captures essential features of collective sequential decision making, agents can minimize group regret, and thereby improve performance, by leveraging shared information. However, sharing information can be costly, which motivates developing policies that minimize group regret while also reducing the number of messages communicated by agents. Existing cooperative bandit algorithms obtain optimal performance when agents share information with their neighbors at \textit{every time step}, i.e., full communication. This requires $\Theta(T)$ number of messages, where $T$ is the time horizon of the decision making process. We propose \textit{ComEx}, a novel cost-effective communication protocol in which the group achieves the same order of performance as full communication while communicating only $O(\log T)$ number of messages. Our key step is developing a method to identify and only communicate the information crucial to achieving optimal performance. Further we propose novel algorithms for several benchmark cooperative bandit frameworks and show that our algorithms obtain \textit{state-of-the-art} performance while consistently incurring a significantly smaller communication cost than existing algorithms.

These are nascent efforts but one forecast suggests adoption is about to pick up. AI will be involved in 75% of venture capital investment decisions by 2025, up from less than 5% today, according to a recent Gartner Inc. forecast. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. AI's ability to recognize patterns in data and predict likely outcomes has raised hopes that it can play a bigger role in decision-making in fields such as finance and healthcare. Now, similar bets are being placed in venture capital.

Since Sweden's EQT Ventures embraced AI to drive the way it makes investments, the company has learned that reaping the benefits of algorithms is a journey full of detours that involve experimenting, fine-tuning, and adaption to achieve the promised efficiencies and insights. Following the firm's launch in 2016, a team there developed Motherbrain, an AI-driven system whose goal is to help EQT spot the hidden gems that no one else sees and back them early. So far, Motherbrain has directly led to investments in five startups out of the 50 the firm has made. That may seem like a disappointment. But according to Henrik Landgren, the EQT partner who took the lead on developing the system, the practical value so far has been the ability to make partners more productive by prioritizing which companies are worth spending time getting to know.

In a venture capital firm, you want different talents that will enrich the investing team, such as a person from industry, say, mixed with people from the finance world, and perhaps people with a legal or public policy background. You may even want an automaton that crunches numbers. "Motherbrain" is the name that Henrik Landgren, operating partner, and his colleagues at venture capital firm EQT Ventures have given to the computer program that they increasingly turn to in order to get an early read on potential investments. Motherbrain uses convolutional neural networks, or CNNs, the most popular form of machine learning, to review time-series data about companies to help guide where the firm should invest. The technology has seriously improved EQT Ventures's ability to scope out deals early in the pipeline, Landgren said in an interview with ZDNet.

At a meeting in Berlin with venture lead Ashley Lundström, Anydesk cofounder Philipp Weiser learned about Motherbrain, a machine learning system EQT Ventures built to find under-the-radar startups. "She told us we were among the first companies that were discovered by this software," says Weiser. In May, AnyDesk, which sells remote desktop software powered by a proprietary compression system, closed a funding round of $7.6 million with EQT Ventures. Whether the money EQT is putting into Anydesk will turn into a success story remains to be seen. But the firm has applied the Motherbrain algorithm to historical data and shown that it would have identified some of today's highest-flying tech companies as promising investment candidates before they became phenoms.