December Monthly News Digest: Data Science & Analytics
This is the Burtch Works’ Monthly News Digest, the place to visit for a quick high-level breakdown of the month’s top stories from the world of data science & analytics.
LinkedIn CEO Ryan Roslansky: “Skills, Not Degrees, Matter Most in Hiring”
Ryan Roslansky, CEO of LinkedIn, estimates there are roughly 10 billion years’ worth of work experience locked up in the heads of the site’s 875 million users. It’s LinkedIn’s job, Roslansky says, to tap into that massive skills base, free the knowledge within, and improve both the global workforce and the lives of individual workers.This article covers a conversation highlighting LinkedIn’s vast trove of worker data, adaptive leadership opportunities in a world of inflation, and the importance of putting skills at the center of hiring decisions and de-emphasizing degrees. The topic of internal mobility is highlighted, signaling a need for organizations to understand the skills and goals of their existing workforce to provide them with opportunities that will support collective long-term growth.Read the full article here.
ChatGPT: Social Media’s Latest Favorite Tool for Getting Answers Online
ChatGPT, which launched this week, is a quirky chatbot developed by artificial intelligence company OpenAI. Exploding in popularity, only five days after its release ChatGPT has already surpassed 1 million users. On its website, OpenAI states that ChatGPT is intended to interact with users “in a conversational way.” The dialogue format makes it possible for ChatGPT to answer follow up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.Chatbots are not a new technology, but ChatGPT has already impressed many technologists with its ability to mimic human language and speaking styles while also providing coherent and topical information. Some people theorized that Google could lose its value as the No. 1 search engine because of the early success of the chatbot.Read the full article here.
Intellectual Property Laws Grow Murkier as Artificial Intelligence (AI) Grows More Advanced
The legal implications of AI-generated creations are slowly inspiring actions. Following the influx of cases tied to AI-generated imagery, the U.S. Copyright Office said in February that such works aren’t eligible for copyright due to the lack of human authorship. The proliferation of AI-generated creations—not just images—will profoundly affect copyright and trademark application volumes. Since applying for a trademark involves searching for any conflicting application, the likelihood of stumbling upon one can increase. Businesses might get stuck in needless intellectual property conflicts—a ‘legal minefield,’ as legal experts say.Experts say it’s highly likely that the ambiguity regarding AI-generated content will remain in the coming years. According to the World Intellectual Property Organization (WIPO), as it stands, the world currently has two legal options to rely on. The first is, as demonstrated by the U.S. Copyright Office’s decision, to deny copyright to all non-human-generated content. The second is to credit the creator for any work generated by any AI programs.Read the full article here.
Building Airbnb Categories with ML and Human-in-the-Loop
This is the first of a three-part series that dives into how Airbnb is using machine learning to improve customer experience. During the initial rollout of a new category-based property classification approach, Airbnb used a human in the loop approach. The project combined high power machine learning models with human review to develop a robust method for classifying properties in a variety of ways.One part of the process is taking properties confirmed by humans to fit a given category and then using cosine similarity to find more like it. This works well with harder-to-define categories like “Amazing Views”. There are also models that rank property images to identify which is best to show for that property. The article provides details on some solid results in terms of precision and recall.Read the full article here.
Don’t Track Your Equivalent of Chicken Efficiency
Analytics blogger Kevin Hillstrom uncovered a hilarious example of how setting the wrong metric can lead to very silly business outcomes. While we can’t be sure that the example of chicken efficiency isn’t a tall tale, it is entirely believable and plausible. When you focus people on one metric only, they can take it to the extreme and produce outcomes worthy of a Dilbert cartoon.The point of Kevin’s post was that for all of the press around Black Friday and Cyber Monday sales, the “sell at any price to get traffic” approach of those events are actually losing a lot of businesses money instead of making them money. Getting too caught up in generating sales without any thought to profitability is a lot like the restaurant worker chasing chicken efficiency without any thought to sales volume.Read the full article here.
Massive Traffic Experiment Pits Machine Learning Against ‘Phantom’ Jams
UC Berkeley researchers analyzed a common problem – the phantom traffic jam that appears to have no cause whatsoever as you pass through it. By using AI-powered automobiles, researchers tried to inject proactive tuning of traffic flow into Nashville’s morning commute. The initial results indicate that just a small percentage of AI-driven vehicles working in concert can materially impact traffic.The key to the researchers’ approach was that they didn’t just place individual cars operating independently into the traffic. Instead, the AI-powered vehicles were also coordinating amongst themselves to work as a team. Smoothing decelerations and reacting less strongly to minor braking from cars in front can make a difference. Will analytics solve our traffic woes?Read the full article here.This post was developed with input from Bill Franks, internationally recognized thought leader, speaker, and author focused on data science & analytics.