Machine Learning in Action Lily Adelstein

Google launched two new products today — Pixel 6 and Pixel 6 pro. Center stage at this launch was artificial intelligence and machine learning used to make smartphones more personal. Google is setting the bar high for the use of ML in apps and mobile features.

Among the many new announcements at Amazon’s recent fall event was an exciting update that provides a window into the future of machine learning technology. By now consumers are well aware of voice assistants, and Amazon’s Alexa leads the market. In fact, roughly one-in-four US adults owns a smart speaker, with 70-75% of that market share estimated to be captured by Amazon. This makes smart speakers in general, and Alexa in particular, one of the most recognizable and regular interactions that the average consumer has with machine learning technology. Machine learning is core to Alexa’s ability to understand natural language requests that are made by the user, and to predict how to most accurately respond to those requests.

Robinhood is an American based FinTech company founded in 2013 with the mission to democratize access to public markets. With over 31 million users and 5000 stocks offered, it is a data rich platform. In fact, Robinhood publicly states, “Insights from data power most decisions at Robinhood.”

In this episode we talk a lot about machine learning for financial risk detection and how to help new users become more informed investors.

Some of our most valuable assets live online. From bank information to the entire infrastructure of online businesses. This makes data breaches an incredibly serious violation. As high profile data breaches make headlines, including Twitch most recently and even the U.S. government back in December, more companies are using artificial intelligence (AI) and machine learning (ML) to address cybersecurity concerns. One example is Darktrace, a company that uses AI to identify cyber threats as early as possible.

DJ Mag is in the midst of publishing a series on the future of music and the influence AI is having on the industry. It’s a fascinating look inside one of the many sectors of our economy that is being reimagined with the help of machine learning, and- because of the innately creative quality to this particular industry- it especially highlights questions about the relationship between humans and machines.

Technology promises a wide range of efficiencies; one of the most promising and yet elusive is the removal of human bias from decision making. A recent paper published by New York University (NYU) researchers presents a glimmer of hope for a less biased future. The study found that during Covid-19, “the majority of Black borrowers who received aid from the $800 billion relief program got their loan from a financial technology company, not a bank.” The study explains that fin-tech companies and large banks that employed algorithms and automation demonstrated higher rates of loan approvals for Black borrowers compared to small banks not using the technology.