Karen Levy, author of the book “Data Driven: Truckers, Technology, and the New Workplace Surveillance,” says AI tech is becoming common in trucking, to the dismay of truckers

Trucking can be dangerous job – long, often tedious hours behind the wheel, the unpredictability of the weather and of course, other drivers.

And yet, trucking is an essential part of supply chain. 72% of the nation’s freight gets from point A to point B in a truck, according to the American Trucking Associations.

Most of those holiday gifts you might be enjoying right now got to you on a truck. So truck drivers are an essential part of our economy.

The companies that hire and manage those drivers have started bringing a lot more technology into big rigs, including artificial intelligence and sometimes automation.

Marketplace’s Kimberly Adams spoke with Karen Levy, author of “Data Driven: Truckers, Technology, and the New Workplace Surveillance” about how these tools are being used in the industry. 

The following is an edited transcript of their conversation.

The company’s image synthesis technology can realize an AI human that is complete in non-verbal parts such as lip-syncing, movements, and facial expressions.

By Sahil Pawar

Seoul-based Deep Brain AI launched a paid service called Re;memory for those who would like to speak again with their deceased loved ones, even if it is just virtually. 

When a loved one dies, a family is often left with only digital memories such as photos and videos. However, these are only one-way modes of communication where one can see or hear their loved ones but cannot interact back with them.

DeepBrain AI takes this trove of information and a pre-interview to put together a private virtual meeting in their studio where one can have unfinished conversations with their loved ones.

Examples of protein complexes modeled by AF2Complex residing between the inner and outer membranes of E. coli.

by Audra Davidson

Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex—short for AlphaFold 2 Complex—is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

“We essentially conduct computational experiments that try to figure out the atomic details of supercomplexes (large interacting groups of proteins) important to biological functions,” explained Jeffrey Skolnick, Regents’ Professor and Mary and Maisie Gibson Chair in the School of Biological Sciences, and one of the corresponding authors of the study. With AF2Complex, which was developed last year by the same research team, it’s “like using a computational microscope powered by deep learning and supercomputing.”

In their latest study, the researchers used this “computational microscope” to examine a complicated protein synthesis and transport pathway, hoping to clarify how proteins in the pathway interact to ultimately transport a newly synthesized protein from the interior to the outer membrane of the bacteria—and identify players that experiments might have missed. Insights into this pathway may identify new targets for antibiotic and therapeutic design while providing a foundation for using AF2Complex to computationally expedite this type of biology research as a whole.