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Blog: Newsletter Recommendations for AI Business Operations


Newsletter Recommendations for AI Business Operations

Monday through Friday, I wake up at 5:59 a.m. Although I’m not someone who needs much sleep to function optimally, regularly working until 1 a.m. makes waking up early M-F a struggle. As a solution, I blast Pandora’s house music station right at 5:59 through my whole apartment, which makes it really difficult to sleep. My Roomba also is programmed to start cleaning at that same time, so there’s a lot of really annoying stimuli all for the purpose of ensuring I have enough time to stay ahead on industry-news prior to hitting Equinox across the street at 7 a.m.

Although my drive to stay informed is rooted in curiosity, it’s also extremely important for my role as chief operating officer of an industry-leading AI firm. Anyone that knows me can vouch for my ability to contribute to nearly any discussion on technology and recall all sorts of facts, industry trends, news and metrics. Usually, my contributions are at a very high level, but it gives me a way to build a connection with almost anyone I meet (professionally, that is — I’ve learned that bringing up new developments with TensorFlow.JS and face rec on the edge and joblessness from AI/robot overlordz tends to freak people out in social settings).

I’ve probably subscribed and unsubscribed to over 50 business, tech, finance and AI newsletters in the past 5 years. I don’t follow a lot of political issues outside of those that affect my industry, as I just don’t have time. When I read through the newsletters in the AM, I typically just read the headlines and summaries and bookmark the linked article for later in the day. Somehow, there isn’t as much overlap in specific topics, articles and opinions, despite the similarity in subject matter. I do subscribe to the Financial Times (print version), but other than that, most of these newsletters curate the best articles from all over the web and from multiple sources.

This post will be broken down into two posts: the first covers AI specific newsletters and the second will cover finance/business newsletters.

Mandatory G.O.A.T:

General AI:

  • MIT’s Technology Review, The Algorithm (2x week): MIT does an excellent job here identifying and elaborating on the most important issues in AI. If I were to rely on just one newsletter, it would be The Algorithm
  • Tracer. The State of Fakes. Synthesized (Mondays): I believe the author of the newsletter is Giorgio Patrini (please correct me if I’m wrong), the CEO of Deeptrace. The focus of Tracer is deepfakes, GANs, and generative examples (all things CNNs). Deepfakes are frequently the topic of lunch breaks here at Kairos, so this newsletter always gives me an edge in the debates. In the April 15th newsletter, they summarized the findings of Johns Hopkins’ essay on adversarial images, “Humans can decipher adversarial images.” TLDR (courtesy of Tracer): “humans consistently agreed with an algorithm’s incorrect choice of label when identifying intentionally deceptive images” (algorithms, they’re just like us!).
  • Data Elixir (Tuesdays): This is the only newsletter I read that focuses strictly on data science. I’m not a data scientist, but at the core of our business is making sense of data, so staying ahead of the trends is crucial for understanding what our R&D operation will look like in a year from now. The newsletter includes resources/articles with high-level insights into operations and data (most recently, “Fusing data and design to supercharge innovation — in products and processes” by McKinsey, or “Serverless for data scientists” by Mike Williams), and also technical “How-to[‘s]” such as “Harnessing Organizational Knowledge for Machine Learning” by Google AI. I will take all the how-to’s I can when it comes to labeling data (the bane of my existence as a COO).
  • AI Weekly (Wednesdays): This is always a great collection of AI articles, business articles, and technical essays. Like Data Elixir, there are useful articles for AI firm operations (such as “A Guide to Learning with Limited Labeled Data” by Cloudera (again, the labeled data), or “Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data” a product announcement by TensorFlow). It also keeps me on my “A Game” for fun-facts outside of work, such as how MIT is using AI to invent new foods.
  • Import AI (Mondays): I recently subscribed to this by recommendation from an exec at another facial recognition company. Written by Jack Clark, the newsletter covers all types of AI news — from research findings to new product releases. So far what I like most is their ability to identify and excellently summarize arXiv research essays (a dangerous rabbit hole for me to go down mid-day in the work week); for example, this week, Clark summarized a study where a correlation between the image of a house on Google earth had a correlation with the residents’ risk of getting in a car accident. While such an analysis may not have such obvious implications for all professionals that work in AI, Clark puts the outcomes into perspective, broadening their applicability.
  • Aeon Magazine (M-F): I open this “digital magazine” every day at some point just to scroll and see if any essays stand out or are relevant to my job. It’s a collection of “thought-provoking essays,” ranging from psychology to art to AI. Even if the articles aren’t relevant, I find a lot of the reading cathartic, thought-provoking and a nice escape. On Monday, April 8th when I wrote this blog, there was a thought-provoking essay titled “How much can we afford to forget, if we train machines to remember?” by Gene Tracy, a professor at William & Mary. I was a philosophy major, and other than The Exponential View, the newsletters I read rarely include a philosophical perspective. Subscribe.
  • Abeyon (1x month): Abeyon is a Miami and DC based AI firm selling consulting services and two different NLP based products — chirrp, an enterprise customer engagement chatbot, and clarifi, a data analysis that identifies patterns, attributes in massive volumes of documents. I’m also a close friend of the founder, Mallesh Murugesan and he’s an incredible entrepreneur. He simplifies language processing and analysis in such a way that anyone can understand it and find it interesting, yet he also manages not to compromise on the details or articulation of the tool’s value. Recently, he provided a great overview of NLS. See the archives here.

Niche Biometric Newsletters:

  • Biometric Update. At least once per day, a link to a Biometric Update article is posted by a team member in Slack. I believe that most forms of biometric authentication (such as speech recognition, finger print recognition) will be replaced by facial recognition based authentication. In Biometric Update, I see all of the trends (new products being released, acquisitions, market reports, and I can keep a close eye on our competitors. I also like to follow the intellectual property filings in the industry, and they include regular updates.
  • FindBiometrics. FindBiometrics is one of the leading publications for our niche industry. It covers everything from regulations, such as the Japanese government setting forth outlines for facial recognition and gambling addictions (AKA “problem gambling” in the UK), to facial recognition industry partnerships and product announcements, such as merging facial recognition tools with access control. I’m also a lawyer, and although that part of me died, I need to stay abreast of regulations because they affect our product and sales pipeline, and also our quarterly budgets for audits and compliance work.

That’s not all. I could go on and on, and while about 20 newsletters hit my inbox on any given day (which when it’s hurricane season in Miami, I actually somehow manage to find time to get through a year’s worth of headlines). Here are some notable mentions for general AI stuff: Deep Learning Weekly, OG newsletter KDnuggets, and The Data Science Roundup. For biometric stuff: Planet Biometrics,

What are you reading? Shoot me an email at mary@kairos.com.

Source: Artificial Intelligence on Medium

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