OpenAI CEO Sam Altman said in a recent interview with ABC News that he's a "little bit scared" of artificial intelligence technology and how it could affect the workforce, elections and the spread of disinformation. OpenAI developed the ChatGPT bot, which creates human-like answers to questions and ignited a new AI craze. "I think people really have fun with [ChatGPT]," Altman said in the interview. But his excitement over the transformative potential of AI technology, which Altman said will eventually reflect "the collective power, and creativity, and will of humanity," was balanced by his concerns about "authoritarian regimes" developing competing AI technology. "We do worry a lot about authoritarian governments developing this," Altman said.

As Machine Learning (ML) gains adoption across industries and new use cases, practitioners increasingly realize the challenges around effectively developing and iterating on ML systems: reproducibility, debugging, scalability, and documentation are elusive goals for real-world pipelines outside tech-first companies. In this paper, we review the nature of ML-oriented workloads and argue that re-purposing existing tools won't solve the current productivity issues, as ML peculiarities warrant specialized development tooling. We then introduce Metaflow, an open-source framework for ML projects explicitly designed to boost the productivity of data practitioners by abstracting away the execution of ML code from the definition of the business logic. We show how our design addresses the main challenges in ML operations (MLOps), and document through examples, interviews and use cases its practical impact on the field.

An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search 'harry potter'. The amount of broad queries where users want to discover new entities, e.g. in music search 'chill lyrical electronica with an atmospheric feeling to it', and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the retrievability of the entities (I) the training data used for dense retrieval models and (II) the distribution of narrow and broad intent queries issued in the system. We propose CtrlQGen, a method that generates queries for a chosen underlying intent-narrow or broad. We can use CtrlQGen to improve factor (I) by generating training data for dense retrieval models comprised of diverse synthetic queries. CtrlQGen can also be used to deal with factor (II) by suggesting queries with broader intents to users. Our results on datasets from the domains of music, podcasts, and books reveal that we can significantly decrease the retrievability bias of a dense retrieval model when using CtrlQGen. First, by using the generated queries as training data for dense models we make 9% of the entities retrievable (go from zero to non-zero retrievability). Second, by suggesting broader queries to users, we can make 12% of the entities retrievable in the best case.

As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. Impressed by the capability of the ChatGPT, many people are wondering about its limits: can GPT-5 (or other future GPT variants) help ChatGPT unify all AIGC tasks for diversified content creation? Toward answering this question, a comprehensive review of existing AIGC tasks is needed. As such, our work comes to fill this gap promptly by offering a first look at AIGC, ranging from its techniques to applications. Modern generative AI relies on various technical foundations, ranging from model architecture and self-supervised pretraining to generative modeling methods (like GAN and diffusion models). After introducing the fundamental techniques, this work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future. Moreover, we summarize their significant applications in some mainstream industries, such as education and creativity content. Finally, we discuss the challenges currently faced and present an outlook on how generative AI might evolve in the near future.

While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in real-world applications. In this work, we propose a novel and easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS). By bridging uniform sampling and CUR sampling, CCS provides extra flexibility that can potentially save sampling costs in applications. In addition, we also provide a sufficient condition for CCS-based matrix completion. Moreover, we propose a highly efficient non-convex algorithm, termed Iterative CUR Completion (ICURC), for the proposed CCS model. Numerical experiments verify the empirical advantages of CCS and ICURC against uniform sampling and its baseline algorithms, on both synthetic and real-world datasets.

ARTIFICIAL intelligence experts have warned that we should slow down the development of AI as it continues to advance at a rapid rate. Several concerns were raised in a recent report by Vox which claimed "pumping the brakes" on AI could be the best thing for humanity. "I'm really scared of a mad-dash frantic world, where people are running around and they're doing helpful things and harmful things, and it's just happening too fast. "If I could have it my way, I'd definitely be moving much, much slower," Ajeya Cotra, an AI-focused analyst at Open Philanthropy, told Vox. Big brands like Google and Microsoft are working on their own AI bots despite growing concerns. Microsoft has even teamed up with ChatGPT creator OpenAI to create its own controversial AI. OpenAI CEO Sam Altman recently admitted that even he is a "little bit scared" of the technology. Altman said in an interview with ABC News that he had concerns about how fast AI could be used to spread disinformation. He also has worries about how it will affect elections and the work place. The CEO said: "I'm particularly worried that these models could be used for large-scale disinformation.

OpenAI CEO Sam Altman admitted his is scared about ChatGPT's abilities, but mainly with how humans will use it Sam Altman recently spoke with ABC NEWS about the company's chatbot and the rollout of the latest iteration of the AI language model, GPT-4. While the chatbot has sparked fears of AI world domination, Altman sees humans as the greatest threat to the technology. 'There will be other people who don't put some of the safety limits that we put on,' he told ABC News. 'Society, I think, has a limited amount of time to figure out how to react to that, how to regulate that, how to handle it.' OpenAI launched GPT-4 last week, touting it as more powerful than its predecessor - so much that it could be'harmful.'

Netflix Games wants to cater to every kind of player. Not only that, says head of external games Leanne Loombe, it wants to be "a publisher that developers from all around the world want to work with." The streaming giant is big on iteration; it keeps a "crawl, walk, run" model as one of its guiding principles. To date it has released 55 games, all mobile titles, ranging from licensed games based on popular shows like Stranger Things and the dating show Too Hot to Handle to established game properties like Tomb Raider and Kentucky Route Zero. The company has also scooped up developers like Oxenfree creator Night School Studios and established an in-house game development division. "Games are one of the biggest forms of entertainment," Loombe said at a recent press briefing.

Mind-blowing technology taps AI to read your dog's nose print and face. Many of us chip our pets these days to keep track of them if anything ever happens. Although, a new AI-driven pet ID app may be aiming to have us forget chips and use our phone's camera to ID our pets' faces. This technology eliminates the need for microchipping and provides a more convenient way to track our pets. The app allows pet owners to register their pets and store their facial features in a database.

Artificial intelligence (AI) tools are certainly impressive at their ability to perform complicated tasks once thought only capable to humans. The revolutionary ChatGPT has been used to pass exams, deliver a sermon, write software and give relationship advice -- to name just a handful of its functions. But, for some people, these technologies have raised a scary question -- could they take my job? A study from Princeton University in New Jersey, US has revealed the 20 occupations most at risk of being made redundant thanks to AI. Taking the top spot is call centre operator, but the following eight are all teachers of different disciplines, including languages, history, law and religion. The authors wrote: 'The effect of AI on work will likely be multi-faceted. In some cases, AI may substitute for work previously done by humans, and in other cases, AI may complement work done by humans.