"I'm not a fan of AI," Nebojša Vujinović Vujo says. The admission surprises me: He has built a bustling business by snapping up abandoned news outlets and other websites and stuffing them full of algorithmically generated articles. Although he accepts that his model rankles writers and readers alike, he says he's simply embracing an unstoppable new tool--large language models--in the same way people rationally swapped horse-drawn buggies for gas-powered vehicles. They're making my planet bad," he says. I connected with Vujo after digging into the strange afterlife of indie women's blog The Hairpin, which shut down in 2018. In place of the voicey, funny blog posts it was known for, the site began churning out AI-generated, search-engine-optimized pablum about dream interpretations and painfully generic relationship advice like "effective communication is vital." When I emailed an address listed on the zombie site's About Us page, Vujo responded, claiming that it was just one of more than 2,000 sites he operates, in an AI-content-fueled fiefdom built by acquiring once-popular domains fallen on hard times. He's the CEO of the digital marketing firm Shantel, which monetizes its AI-populated sites through programmatic ads, sponsored content, and selling the placement of "backlinks" to website owners trying to boost their credibility with search engines. He often targets distressed media sites because they have built-in audiences and a history of ranking highly in search results. The foundation of that business is a long-established practice known as domain squatting--buying up web domains that once belonged to established brands and profiting off their reputations with Google and other search engines. Lily Ray, senior director of SEO at the marketing agency Ampsive, calls it "the underbelly of the SEO industry." But Vujo is part of a wave of entrepreneurs giving this old trade a new twist by using generative AI. It's dusk where I live in Chicago when I talk via Zoom with Nebojša Vujinović Vujo. It's midnight in Belgrade, Serbia, where he lives with his girlfriend and their toddler, but he's wide awake and chatty. Vujo attributes his erratic sleep schedule to years of late nights working as a DJ and still makes music--he likes to mix pop with Balkan folk and is working on a new song called "Fat Lady." But right now he's eager to talk, human-to-human, about his AI-fueled hustle. He gets why writers are unhappy that their work has been erased and replaced by clickbait. But he defends his choices, pointing out that his life has been tougher than that of the average American blogger. Although ethnically Serbian, Vujo was born in what is now known as Bosnia and Herzegovina, and his family fled during the breakup of Yugoslavia. "I had two wars I escaped.

Almost every day, a publication announces layoffs or shuts down. Sports Illustrated just let go almost all of its staff after weathering an embarrassing scandal about AI-generated articles. It's unclear what the desiccated magazine's future holds, but the sad fate of another formerly great outlet offers a preview of what may await fallen media properties. In 2018, the indie women's website The Hairpin stopped publishing, along with its sister site The Awl. This year, The Hairpin has been Frankensteined back into existence and stuffed with slapdash AI-generated articles designed to attract search engine traffic.

To advance quality assurance in the welding process, this study presents a robust deep learning model that enables the prediction of two critical welds Key Performance Characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a comprehensive range of laser welding Key Input Characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two deep learning networks are employed with multiple hidden dense layers and linear activation functions to showcase the capabilities of deep neural networks in capturing the intricate nonlinear connections inherent within welding KPCs and KICs. Applying deep learning networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving Mean Absolute Error (MAE) values as low as 0.1079 for predicting welding depth and 0.0641 for average pore volume. Additionally, the validity verification demonstrates the reliability of the proposed method. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying merely on monitoring for defect classification.

Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications. Good visualization tools are needed to help domain experts make sense of the complex outputs of folding pathway simulations of such reactions. Here we present ViDa, a new approach for visualizing DNA reaction folding trajectories over the energy landscape of secondary structures. We integrate a deep graph embedding model with common dimensionality reduction approaches, to map high-dimensional data onto 2D Euclidean space. We assess ViDa on two well-studied and contrasting DNA hybridization reactions. Our preliminary results suggest that ViDa's visualization successfully separates trajectories with different folding mechanisms, thereby providing useful insight to users, and is a big improvement over the current state-of-the-art in DNA kinetics visualization.

Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DNA reaction trajectories that uses a 2D embedding of the secondary structure state space underlying the CTMC model. To this end, we integrate a scattering transform of the secondary structure adjacency, a variational autoencoder, and a nonlinear dimensionality reduction method. We augment the training loss with domain-specific supervised terms that capture both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA hybridization reactions. Our results demonstrate that the domain-specific features lead to significant quality improvements over the state-of-the-art in DNA state space visualization, successfully separating different folding pathways and thus providing useful insights into dominant reaction mechanisms.

The collaboration between data scientists from the Wyss Institute's Predictive BioAnalytics Initiative and synthetic biologists in Wyss Core Faculty member Jim Collins' lab at MIT was created to apply the computational power of machine learning, neural networks, and other algorithmic architectures to complex problems in biology that have so far defied resolution. As a proving ground for their approach, the two teams focused on a specific class of engineered RNA molecules: toehold switches, which are folded into a hairpin-like shape in their "off" state. When a complementary RNA strand binds to a "trigger" sequence trailing from one end of the hairpin, the toehold switch unfolds into its "on" state and exposes sequences that were previously hidden within the hairpin, allowing ribosomes to bind to and translate a downstream gene into protein molecules. This precise control over the expression of genes in response to the presence of a given molecule makes toehold switches very powerful components for sensing substances in the environment, detecting disease, and other purposes.

DNA has often been compared to an instruction book that contains the information needed for a living organism to function, its genes made up of distinct sequences of the nucleotides A, G, C, and T echoing the way that words are composed of different arrangements of the letters of the alphabet. DNA, however, has several advantages over books as an information-carrying medium, one of which is especially profound: based on its nucleotide sequence alone, single-stranded DNA can self-assemble, or bind to complementary nucleotides to form a complete double-stranded helix, without human intervention. That would be like printing the instructions for making a book onto loose pieces of paper, putting them into a box with glue and cardboard, and watching them spontaneously come together to create a book with all the pages in the right order. But just as paper can also be used to make origami animals, cups, and even the walls of houses, DNA is not limited to its traditional purpose as a passive repository of genetic blueprints from which proteins are made – it can be formed into different shapes that serve different functions, simply by controlling the order of As, Gs, Cs, and Ts along its length. A group of scientists at the Wyss Institute for Biologically Inspired Engineering at Harvard University is investigating this exciting property of DNA molecules, asking, "What types of systems and structures can we build with them?" They've decided to build robots.