When Sookie Orth sat down to write her college essay last fall, something quickly came to mind. Orth, then a senior at Sequoyah School in Pasadena, began her draft with a declaration: "I learned how to fold a pizza box at the age of nine." She told the story of her years-long connection with Pizza of Venice in Altadena, where she often dined with her family as a little kid. One day, the manager invited her to assemble a box. Impressed with Orth's speed, the woman told her she could work at the pizzeria when she was older.

The rapid advancement of large language models (LLMs) has revolutionized natural language processing (NLP). While these models excel at understanding and generating human-like text, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference for deep neural networks. It leverages network modularity to create sub-models with varying computational loads, sorting them based on computation/accuracy characteristics in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any pretraining and by only replacing standard Supervised Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT) at the same costs. Our approach boosts model efficiency, eliminating the need for multiple models for various scenarios during inference. We show that using this approach, we are able to unlock the potential of intermediate layers of transformers in generating the target output. Our sub-models remain integral components of the original model, minimizing storage requirements and transition costs between different computational/latency budgets. By applying this approach on LLaMa 2 13B for tuning on the Stanford Alpaca dataset and comparing it to normal tuning and early exit via PandaLM benchmark, we show that Sorted Fine-Tuning can deliver models twice as fast as the original model while maintaining or exceeding performance.

Serial tech entrepreneur Ilir Sela started Slice in 2010 with the belief that local pizzerias deserve all of the advantages of major franchises without compromising their independence. Starting with his family's pizzerias, we now empower over 18,000 restaurants (that's nearly triple Domino's U.S. network!) with the technology, services, and collective power that owners need to better serve their digitally minded customers and build lasting businesses. We're growing and adding more talent to help fulfill this valuable mission. That's where you come in. The Challenge to Solve Build up a picture of Slice target users based on their needs, wants, motivations and pain-points.

At Topos, we are fascinated by exactly this type of variation and believe it provides a powerful view into the culture of a location. While data sources like the United States Census are useful for understanding broad demographic trends over decades, they give little insight into what defines the moment-to-moment culture of a city, a neighborhood, a street corner. Inspired by thinkers like Walter Benjamin, who, in his unfinished Arcades Project examined subjects as varied as fashion, construction materials, poetry, lighting, and mirrors in order to understand Paris in the 19th century, we are fascinated by the way seemingly simple, ubiquitous subjects like the coffee we drink or the concerts we go to define a place. However, unlike Benjamin, we are interested in constructing this understanding in a way that can dynamically scale across the globe, allowing us to understand how different locations relate to one another, and how locations evolve in real time. To achieve this, we use data from dozens of different sources and techniques from a wide range of technologies and disciplines including computer vision, natural language processing, statistics, machine learning, network science, topology, architecture and urbanism.

Looking at its storefront, you wouldn't expect Zume pizza to be the kind of business gunning to revolutionize the food-delivery business. Tucked into a quiet commercial park in Mountain View, California, next to a defunct flower shop -- which now serves as the company's engineering bay -- Zume looks more like the countless IT startups that dot Silicon Valley than a pizzeria. One look in the building's kitchen facility belies its benign facade: Instead of chefs tossing dough and slopping sauce, the company has installed a human-robot hybrid workforce that can crank out as many as 400 pizzas an hour and can reportedly have them to your door in a fraction of the time (and price) as the competition. "One of the things that we have always focused on is how to create a system that works for both parties," Zume Pizza co-founder Julia Collins, told Engadget. "How do we create a system that's stable and predictable, which are great conditions for machines, but flexible and collaborative, which are great conditions for human beings?"

Not long after the pizzeria Zume opened for business last year, its kitchen staff noticed a problem with some of its pizzas: they had holes in them. It wasn't the fault of the workers, who rolled out intact dough bases. It wasn't even the recipe -- a Zume pizza base can handle its fair share of toppings. Josh Goldberg, 38, is the chief technology officer of the Mountain View, Calif., pizza joint. Although most pizzerias don't have an engineering staff, let alone a CTO, Zume prides itself on its use of automation to make operations more efficient. It estimates its kitchen can make 10 times more pizzas than a pizzeria with a comparable staff.

Trendytoutdoor pizza ovens have been labelled an environmental menace by scientists. The wood burning stoves used to cook pizzas churn out dangerous emissions which may be polluting some built up urban areas where the crusty favourites are particularly popular. It is not just pizzas that could be taking a bite out of attempts to clean up the environment, the study says. Similar wood burners are also used by many steakhouse restaurants too. The wood burning stoves used to cook pizzas churn out dangerous emissions which may be polluting some built up urban areas where the crusty favourites are particularly popular.