There are many remarkable things about octopuses--they're famously intelligent, they have three hearts, their eyeballs work like prisms, they can change color at will, and they can "see" light with their skin. One of the most striking things about these creatures, however, is the fact that each of their eight arms almost seems to have a mind of its own, allowing an octopus to multitask in a manner that humans can only dream about. At the heart of each arm is a structure known as the axial nervous cord (ANC), and a new study published January 15 in Nature Communications examines how the structure of this cord is fundamental to allowing the arms to act as they do. Cassady Olson, first author on the paper, explains to Popular Science that understanding the ANC is crucial to understanding how an octopus's arms work: "You can think of the ANC as equivalent to a spinal cord running down the center of every single arm." Olson explains that "there are many gross similarities [between the ANC and vertebrates' spinal cords]--there is a cell body region, a neuropil region, and long tracts to connect the arms and brains in each."

Robots have progressed over the years from clunky hunks of metal to complex, AI-enabled machines capable of running, speaking, and even painting pictures. But even with all those advances humans still can't help but place robots in bizarre and uncomfortable situations. This year, researchers took advanced robots and had them clean up karate-chopped Coke cans, suck up cigarette butts, wear a fleshy, lab-grown face, and pick up dog poo. Two-legged, humanoid robots, which could one day work on factory floors, were gut-punched and forced to wear festive clothes while performing acrobatics. Here are just a few of the oddest things we did to robots this year.

OpenAI is an unconventional company in many ways, but last November it borrowed a page from the standard tech industry playbook: It held a developer conference where CEO Sam Altman urged software makers to build on top of ChatGPT. The company said it would soon launch a marketplace where developers and non-techies alike could create custom functions for the chatbot and make money by sharing them with the world. The reaction to that news was mixed, with some hailing the birth of a new platform and others turning a laundry app demoed onstage into a meme. But whether meme-worthy or momentous, OpenAI's app store is part of a broader strategy to maintain its edge in the competitive AI landscape. Like Apple and Google's YouTube have done so well, OpenAI now wants to incentivize developers and creators to supply fresh content for its platform, so that it can keep offering new experiences that draw in users.

As facial recognition software becomes easier to acquire, businesses are using it to surveil and analyze customers. Bloomberg's Parmy Olson explains where and how the technology is being deployed. Parmy Olson is a journalist and opinion columnist for Bloomberg. Previously, Olson wrote about technology for The Wall Street Journal and Forbes. In 2012, she published her book: We Are Anonymous: Inside the Hacker World Of LulzSec and the Global Cyber Insurgency.

This is Work in Progress, a newsletter by Derek Thompson about work, technology, and how to solve some of America's biggest problems. Sign up here to get it every week. In 2013, researchers at Oxford published an analysis of the jobs most likely to be threatened by automation and artificial intelligence. At the top of the list were occupations such as telemarketing, hand sewing, and brokerage clerking. These and other at-risk jobs involved doing repetitive and unimaginative work, which seemed to make them easy pickings for AI.

The Defense Innovation Unit is bringing together the best of commercially available artificial intelligence technology and the Defense Department's vast cache of archived medical data to teach computers how to identify cancers and other medical irregularities. The result will be new tools medical professionals can use to more accurately and more quickly identify medical issues in patients. The new DIU project, called "Predictive Health," also involves the Defense Health Agency, three private-sector businesses and the Joint Artificial Intelligence Center. The new capability directly supports the development of the JAIC's warfighter health initiative, which is working with the Defense Health Agency and the military services to field AI solutions that are aimed at transforming military health care. The JAIC is also providing the funding and adding technical expertise for the broader initiative.

But rather than nudging out the need for humans, the artificial intelligence might stand to enhance the creative process. Or so the experts say. The implication of AI on design is a major theme of the 21st century, with experts from many fields discussing the AI's entanglements with fashion, design, media, art and beyond. Entrepreneur Camilla Olson was in town to promote her fashion-tech software solution Savitude, which uses AI to recommend clothing based on a shopper's shape and proportions. Before Savitude, Olson founded two predictive modelling companies and designed an eponymous fashion label, both of which informed her insights into solving fashion's fit issues.

By all appearances, May Mobility was a scrappy success story. The autonomous transportation startup made its debut at Y Combinator's demo day in 2017, with a team that had been working on driverless tech since the third U.S. Defense Advanced Research Projects Agency (DARPA) Grand Challenge in 2017. Within the span of a few years, May had a roster of paying customers in Michigan, Ohio, and Rhode Island as it raised tens of millions in venture capital from investors including Toyota and BMW. But on the inside looking out, it was a different story. May engineers struggled to maintain and upgrade the company's vehicle platform, at one point spending months attempting to install an air conditioning system in the depths of summer.

Application performance management company New Relic has begun to apply machine learning (ML) and artificial intelligence (AI) to automate incident response, management and remediation. If successful, the new features could serve to mitigate a major source of lost IT productivity among organizations with often different operations to manage, including multicloud and on-premises infrastructures. New Relic AI offers a wide sweep of AIOps capabilities to help reduce "noise" and other distractions when managing workflows. The idea is to solve a common pain point of having to devote IT resources to respond to an often overwhelming number of telemetry alerts. Such "noisy" alerts often consist of false positives.

In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.