Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees' pain points and anti-patterns, with implications for tool design.

The field of software development is greatly influenced by technology. Software development is constantly evolving and uncertain. Software development trends are essential for businesses that want to be different. This article will focus on software trends for 2022 and beyond. You need to be able to anticipate and adapt to future trends and have in-depth knowledge in order to stay ahead of your competition.

We introduce the task of entity-centric query refinement. Given an input query whose answer is a (potentially large) collection of entities, the task output is a small set of query refinements meant to assist the user in efficient domain exploration and entity discovery. We propose a method to create a training dataset for this task. For a given input query, we use an existing knowledge base taxonomy as a source of candidate query refinements, and choose a final set of refinements from among these candidates using a search procedure designed to partition the set of entities answering the input query. We demonstrate that our approach identifies refinement sets which human annotators judge to be interesting, comprehensive, and non-redundant. In addition, we find that a text generation model trained on our newly-constructed dataset is able to offer refinements for novel queries not covered by an existing taxonomy. Our code and data are available at https://github.

Chances are you've already seen the headline (or some variation thereof): "AI won an art contest, and artists are furious" Here's what happened: A Colorado man entered an art competition at the Colorado State Fair Fine Arts Competition in the category of "digital arts/digitally-manipulated photography". The problem, however, was that he produced the image using Midjourney, an online AI program that produces images based on user text input. He entered the piece using the name "Jason M. Allen via Midjourney", thus disclosing the use of the AI and meeting all competition rules. The judges were not initially aware that AI was used, yet later admitted that they still would have awarded Allen the prize even if they had. As the headline above attests, many artists the world over were not pleased with this outcome.

Slate has relationships with various online retailers. If you buy something through our links, Slate may earn an affiliate commission. We update links when possible, but note that deals can expire and all prices are subject to change. All prices were up to date at the time of publication. Chelsea Conaboy's new book, Mother Brain: How Neuroscience Is Rewriting the Story of Parenthood, is an ambitious look at new science investigating how caregiving changes everyone who does it. Conaboy draws on research from neuroscience and psychology to make sense of the challenging transitions of early parenthood.

We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both tour length and rejection rate for each vehicle, the maximum of which is then fed back to the manager agent to learn better assignments. Experimental results demonstrate that the proposed framework outperforms strong baselines in terms of higher solution quality and shorter computation time. More importantly, the trained agents also achieve competitive performance for solving unseen larger instances.

The role of the artist in creative endeavour has been a much-discussed topic due to recent artwork produced by AI programs such as Midjourney and DALL-E, including a controversial prize winner in Colorado this week. These programs are using artificial intelligence or diffusion machine learning to produce beautiful images. But is it art, and will it put human artists out of a job? Jason Allen's work, Théâtre D'opéra Spatial, created using AI, won a fine art prize in Colorado.Credit:Jason Allen The question "what is art?" has been seminal in the art world for decades. When Australia acquired Blue Poles, critics accused Jackson Pollock of throwing paint at the canvas while drunk.

Last month, a piece of art called Théâtre D'opéra Spatial (that's French for "Space Opera Theater") was entered into the Colorado State Fair's fine art competition by a man named Jason Allen. The piece is a gorgeous "painting" that depicts a giant baroque hall containing three women in flowing red and white robes. The image won first place in the digitally manipulated photography category, and the artist judges at the state fair said the work was the best of the best. At the fair, Allen said the piece was made with Midjourney, an artificial intelligence tool that can create art, but no one really understood what that meant. Once they realized an A.I.-generated piece of art had beaten human artist-created images, a debate opened up.

Professor Chad Jenkins has been elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Jenkins specializes in mobile manipulation robots and human-robot interaction. His research explores how to enable robots to learn from human demonstration in complex environments. His work has been supported through a number of prestigious awards, including a PECASE award, an NSF CAREER Award, an ONR Young Investigator Award, and a Sloan Research Fellowship. Jenkins is also devoted to ensuring that the fields of robotics and AI are accessible to everyone.

Well, every year, for the first week of this module, we engage in what I like to call our Data Science Bootcamp. This is an intensive preparatory week in which I present the students with a sample data science project, from start to finish, to give them a sense of what is expected from their group efforts. Every year I try to create a new project on a contemporary topic. In the past, I have covered topics such as marathon running (linked to Elide Kipchoge's efforts to break the 2-hour barrier), Hollywood movies (is it true that movies are rarely as good as the books on which they are based?), and the COVID pandemic, among others. During the bootcamp week, I describe how to take a topic from a vague project idea to a concrete set of suitable research questions, how to assemble an appropriate dataset, how to clean and analyze the data, and how to use the results of the analysis to carefully answer the research questions in a clear and compelling way.