Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that corporate AI research increasingly concentrates on pre-deployment areas -- model alignment and testing & evaluation -- while attention to deployment-stage issues such as model bias has waned. Significant research gaps exist in high-risk deployment domains, including healthcare, finance, misinformation, persuasive and addictive features, hallucinations, and copyright. Without improved observability into deployed AI, growing corporate concentration could deepen knowledge deficits. We recommend expanding external researcher access to deployment data and systematic observability of in-market AI behaviors.

The extensive industrialization of artificial intelligence (AI) since the mid-2010s has increasingly motivated artists to address its economic and sociopolitical consequences. In this chapter, I discuss interrelated art practices that thematize creative agency, crowdsourced labor, and delegated artmaking to reveal the social rootage of AI technologies and underline the productive human roles in their development. I focus on works whose poetic features indicate broader issues of contemporary AI-influenced science, technology, economy, and society. By exploring the conceptual, methodological, and ethical aspects of their effectiveness in disrupting the political regime of corporate AI, I identify several problems that affect their tactical impact and outline potential avenues for tackling the challenges and advancing the field.

CORPORATE BOARDS are besotted with artificial intelligence. Worldwide spending on AI is expected to rise from $38bn this year to $98bn by 2023, estimates IDC, a research firm. So far, though, only one in five companies aware of the technology's potential has incorporated machine learning into its core business. One reason for the slow uptake is the dearth of quality data to teach algorithms to perform useful tasks. The most common form of AI, called "supervised learning", requires feeding software stacks of pre-tagged examples of, say, cat pictures until it can tell a feline image apart by itself.

CORPORATE BOARDS are besotted with artificial intelligence. Worldwide spending on AI is expected to rise from $38bn this year to $98bn by 2023, estimates IDC, a research firm. So far, though, only one in five companies aware of the technology's potential has incorporated machine learning into its core business. One reason for the slow uptake is the dearth of quality data to teach algorithms to perform useful tasks. The most common form of AI, called "supervised learning", requires feeding software stacks of pre-tagged examples of, say, cat pictures until it can tell a feline image apart by itself.

As 2018 continues on its downward slope toward a new year, artificial intelligence technologies are becoming more and more useful in every aspect of our lives. Just look around: They're in our smartphones, our social networks, our home smart speakers, our personal vehicles--you name it, there's AI at work somewhere. In fact, experts predict they will only become more prevalent in both our personal and professional experiences, and that's not much of a stretch to believe. Now, as with any newly widespread technology, we're confronted with a global need to make best of use of all this new IT; specifically, how to educate our communities on how to apply guard rails that will ensure the use of AI as both ethical and beneficial to all. There are lots of questions to be answered here; we can only get to a few right now.

The applied AI community in Cologne is launching this Mearch! Cologne AI are quarterly, 3 hour events for Artificial Intelligence practitioners focusing on lessons learned applying AI. Discover: 15 min applied AI talks of industry peers sharing insights and actionable advice based on hands-on experiences applying AI. Check out our applied AI talks below! Share: AI Clinic session for practitioners in the audience to share their specific challenge applying AI and gather initial feedback from industry peers and fellow practitioners.

Your Shopping Cart is empty. How it will impact business, industry, and society. Creating Simple Rules for Complex Decisions Decision making Digital Article This tool can outperform human experts. The First Wave of Corporate AI Is Doomed to Fail Experimentation Digital Article Future iterations will fare better. To Get Consumers to Trust AI, Show Them Its Benefits Technology Digital Article Communicate early and often.

Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isn't the first time companies have made this kind of mistake.