- Chimpanzees have their own unique set of local traditions and cultures, but these are slowly eroded with human intervention and observation. In the short amount of years we have studied animals through intervention, we have taken away the diversity of their behavior. Ed Yong writes a great number of articles on this topic which are equally fascinating. I’ve always been interested in anthropological studies through extrapolation from our primate counterparts.
- A compilation of tips for product managers towards successful integration. This is so relevant to my previous job, where deployments happened monthly. Integrations start with learning the API of the system, building and testing codes for the integration, and performing regression tests everytime the vendor updates. Of the tips, I found: prioritizing long-term roadmaps, documentations, and supporting users’/customer’s big-picture plans and goals to be the most pertinent. Although they are not particularly ‘agile’ (have to plan long-term and document extensively), I feel the pain of non-sustainable integrations that require continuously patching and bug catching. Will relook this if and when I go back to product management.
- Applications of AI in customer experience is becoming the new standard. This article breaks down the ways in which AI could be utilized. Nothing new to me, since they are what I do at work every day! But worth noting for anyone new to tech applications in customer experience. Data unification and analyses allows extraction of customer insights. These enable tailored recommendations based on purchase behavior; personalized customer support (chatbots, virtual assistants) and user experience based on individual customer behavior and contextual factors. Beyond that, the article mentions anticipating customer obstacles and automating routine processes for greater efficiency. Personally I have not worked on the latter, but it is worth digging into.
- Transposit raises $12.2 million in Series A. It is an API composition platform that serves as a universal translation layer for developers to compose their APIs. It uses relational databases, with a Java backend translating SQL into optimized execution (whatever that means). It’s also able to handle pagination, authentication, and retries in the background.
- Research on image generation AI models found a solution to bypass ground-truth labels. It first uses a semantic extractor to extract feature representations, then perform cluster analysis on the data. Training is done with a GAN (two part neural network consisting of generators and discriminators) to infer labels than rely on hand annotated labels. This is paired with co-training – combining unsupervised, self-supervised, and semi-supervised methods. We’re moving towards unsupervised AI and away from data tagging and labeling reliant AI training – seen here too.
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