Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers — particularly in, but not limited to, artificial intelligence — and explain why they matter.
The market-driven platform work builds on Salesforce’s AI Economist, an open source research environment for understanding how AI could improve economic policy. In fact, some of the researchers behind the AI Economist were involved in the new work, which was detailed in a study originally published in March.
As the coauthors explained to TechCrunch via email, the goal was to investigate two-sided marketplaces like Amazon, DoorDash, Uber and TaskRabbit that enjoy larger market power due to surging demand and supply. Using reinforcement learning — a type of AI system that learns to solve a multi-level problem by trial and error — the researchers trained a system to understand the impact of interactions between platforms (e.g., Lyft) and consumers (e.g., riders).
“We use reinforcement learning to reason about how a platform would operate under different design objectives … [Our] simulator enables evaluating reinforcement learning policies in diverse settings under different objectives and model assumptions,” the coauthors told TechCrunch via email. “We explored a total of 15 different market settings — i.e., a combination of market structure, buyer knowledge about sellers, [economic] shock intensity and design objective.”
Using their AI system, the researchers arrived at the conclusion that a platform designed to maximize revenue tends to raise fees and extract more profits from buyers and sellers during economic shocks at the expense of social welfare. When platform fees are fixed (e.g., due to regulation), they found a platform’s revenue-maximizing incentive generally aligns with the welfare considerations of the overall economy.
The findings might not be Earth-shattering, but the coauthors believe the system — which they plan to open source — could provide a foundation for either a business or policymaker to analyze a platform economy under different conditions, designs and regulatory considerations. “We adopt reinforcement learning as a methodology to describe strategic operations of platform businesses that optimize their pricing and matching in response to changes in the environment, either the economic shock or some regulation” they added. “This may give new insights about platform economies that go beyond this work or those that can be generated analytically.”
Traditionally, computer vision algorithms learn to recognize objects (e.g., trees, cars, tumors, etc.) by being shown many examples of the objects that have been labeled by humans. STEGO does away with this time-consuming, labor-intensive workflow by instead applying a class label to each pixel in the image. The system isn’t perfect — it sometimes confuses grits with pasta, for example — but STEGO can successfully segment out things like roads, people and street signs, the researchers say.
When they tested it with a group of users, the researchers said that those who tried Opal were “more efficient” at creating featured images for articles, creating over two times more “usable” results than users without. It’s not difficult to imagine a tool like Opal eventually making its way into content management systems like WordPress, perhaps as a plugin or extension.
“Given an article text, Opal guides users through a structured search for visual concepts and provides pipelines allowing users to illustrate based on an article’s tone, subjects and intended illustration style,” the coauthors wrote. “[Opal] generates diverse sets of editorial illustrations, graphic assets and concept ideas.”