• John Ennis

Eye on AI - February 15th, 2020


Welcome to Aigora's "Eye on AI" series, where we round up exciting news at the intersection of consumer science and artificial intelligence!

This week’s “Eye on AI” leads off with a look at a new technology that could transform “in-person” interviews, then transitions to recent advances in disease identification and drug development AI application, and how those advances may lead to more informed product development.


Virtual Sportscaster a Sign of Things to Come in Human / AI Interactions



We begin with exciting news in virtual AI out of The Next Web, through their article titled, “Reuters built a prototype for automated news videos using Deepfakes tech.” The article describes how Reuters news recently unveiled a new virtual AI sportscaster that could potentially deliver numerous threads of real-time news at once.


“Designed as a proof-of-concept, the system takes real-time scoring data from football matches and generates news reports complete with photographs and a script,” writes The Next Web reporter Tristian Greene. “Synthesia and Reuters then use a neural network similar to Deepfakes and prerecorded footage of a real news anchor to turn the script into a ‘live’ video of the news anchor giving up-to-the-second scoring updates.”

While Reuters was quick to point out that their new sportscaster is only a prototype at the moment and not something they’re currently planning to implement, it’s easy to see how this type of technology could be mass-deployed in airports, bus stations, or news sites with limited newscasters (or budgeting). I’m also personally interested in how this technology might be utilized for real-time product or experience surveys, which best serve companies for candid product feedback.


AI Beginning to Transform the Medical Industry for the Better



Next, we transition to medical AI, with two new articles out of MIT Technology Review with enormous implications. The first, titled “An algorithm that can spot cause and effect could supercharge medical AI”, details how a new AI algorithm inspired by quantum cryptography can look at large medical databases for common links of cause and effect.


“The method doesn’t use machine learning but is instead inspired by quantum cryptography, in which a mathematical formula can be used to prove that nobody is eavesdropping on your conversation,” writes MIT Tech Review reporter Will Heaven. “Dhir and Lee treat data sets as conversations and variables that influence those data sets in a causal way as eavesdroppers. Using the math of quantum cryptography, their algorithm can identify whether or not these effects exist.”

Dhir and Lee’s work on causal links has been peer-reviewed and will appear at the respected Association for Advancement of Artificial Intelligence conference in New York this week, and could potentially supercharge self-diagnosing chatbot health apps. If the raw data is available, Dhir and Lee claim, their algorithm can “identify variables as well as a clinical study could.”


Piggybacking off of that positive – if not somewhat aspirational – AI news, let’s take a look at a second article out of MIT Technology Review, titled “AI could help design better drugs that don’t clash with other medication,” which describes a new system that predicts a proposed drug’s chemical structure, which could help prevent adverse drug interactions, one of the leading causes of patient death.


“According to the FDA, serious adverse drug interactions could kill more than 100,000 hospitalized people in the US every year,” writes MIT Tech reporter Karen Hao. “But traditional ways of avoiding such interactions during drug development require expensive and laborious physical testing and clinical trials to catalogue all the proposed drug’s possible chemical interactions with existing ones.”

The testing system, as described by Hao, takes two drugs and generates a prediction on how or whether they will affect one another by translating the 3D chemical structures of drugs into a character format known as SMILES that could be read by a neural network. After testing their system on two common drug interaction data sets, researchers found that the new system performed better than state-of-the-art results from existing AI systems. A paper on the study, led by researchers at health information technology company IQVIA, will be presented at the Association for the Advancement of Artificial Intelligence later this week.


If these new medical developments really are as good as advertised, they could help transform the healthcare industry for the better. Still – healthcare has never been known for its expediency. Expect any new developments to take some time before maturation.


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