Accessing and understanding contemporary and historical events of global impact such as the US elections and the Olympic Games is a major prerequisite for cross-lingual event analytics that investigate event causes, perception and consequences across country borders. In this paper, we present the Open Event Knowledge Graph (OEKG), a multilingual, event-centric, temporal knowledge graph composed of seven different data sets from multiple application domains, including question answering, entity recommendation and named entity recognition. These data sets are all integrated through an easy-to-use and robust pipeline and by linking to the event-centric knowledge graph EventKG. We describe their common schema and demonstrate the use of the OEKG at the example of three use cases: type-specific image retrieval, hybrid question answering over knowledge graphs and news articles, as well as language-specific event recommendation. The OEKG and its query endpoint are publicly available.

We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP? To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for Language Models), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference. GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language Policy Optimization) that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluations.

This is Work in Progress, a newsletter by Derek Thompson about work, technology, and how to solve some of America's biggest problems. Artificial-intelligence news in 2023 has moved so quickly that I'm experiencing a kind of narrative vertigo. Just weeks ago, ChatGPT seemed like a minor miracle. Soon, however, enthusiasm curdled into skepticism--maybe it was just a fancy auto-complete tool that couldn't stop making stuff up. In early February, Microsoft's announcement that it had acquired OpenAI sent the stock soaring by $100 billion.

Linh C. Ho has held executive leadership roles for a number of global tech companies and currently serves as Chief Growth Officer at Zelros. It is no surprise that technology as ubiquitous as artificial intelligence (AI) would eventually require ethical guardrails. Just this past fall, the White House announced a Blueprint for an AI Bill of Rights. In it, the administration proposes a five-part framework for companies using automated systems in their operations: effective and safe systems; data privacy; protections against algorithmic discrimination; notice and explanation; and human alternatives, consideration and fallback. Together, the five principles in the Bill of Rights form an overlapping set of backstops--safeguards intended to help keep the American public free from any harm caused by the unchecked use of AI and other emerging technologies.

This paper used Chat GPT to create Irish Limericks. During the creation process, a pattern was observed that seemed to create positive Limericks for liberal politicians and negative Limericks for conservative politicians. Upon identifying this pattern, the sample size was expanded to 80 and some mathematical calculations were made to determine whether the actual results were different from what probability theory would suggest. It was found that, at least in some cases, the AI was biased to favor liberal politicians and disfavor conservatives.

Every time a new application of AI is announced, I feel a short-lived rush of excitement -- followed soon after by a knot in my stomach. This is because I know the technology, more often than not, hasn't been designed with equity in mind. One system, ChatGPT, has reached 100 million unique users just two months after its launch. The text-based tool engages users in interactive, friendly, AI-generated exchanges with a chatbot that has been developed to speak authoritatively on any subject it's prompted to address. In an interview with Michael Barbaro on the The Daily podcast from the New York Times, tech reporter Kevin Roose described how an app similar to ChatGPT, Bing's AI chatbot, which also is built on OpenAI's GPT-3 language model, responded to his request for a suggestion on a side dish to accompany French onion soup for Valentine's Day dinner with his wife.

Like so many cities before it, Phoenix went all out to host the Super Bowl earlier this month. Expecting about 1 million fans to come to town for the biggest American sporting event of the year, the city rolled out a fleet of self-driving electric vehicles to ferry visitors from the airport. Robots sifted through the trash to pull out anything that could be composted. There were less visible developments, too. In preparation for the game, the local authorities upgraded a network of cameras around the city's downtown--and have kept them running after the spectators have left.

Back in April 2021, the European Commission published its proposal for the Artificial Intelligence Regulation ("AI Regulation), which is currently making its way through the European legislative process. This draft AI Regulation seeks to harmonise rules on artificial intelligence by ensuring AI products are sufficiently safe and robust before they enter the EU market. The AI Regulation is intended to apply to what the EU terms "AI systems". The most recent iteration of this concept is defined (in summary) as all systems developed through machine learning approaches and logic, and knowledge-based approaches. This is a wide definition aimed to accommodate future developments in AI technology but extends to much of modern AI software. The broad scope of this definition is narrowed by the operational impact of the draft legislation, as the AI Regulation takes a'risk-based approach' to governing AI systems.

Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries. The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena--especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives.

Looks like it's not just teachers and professors who are worried about ChatGPT. Last week, investment bank JP Morgan announced it was cracking down on the use of OpenAI's AI-powered chatbots as part of restrictions imposed around third-party software. Citigroup and Goldman Sachs are also restricting the use of ChatGPT by employees. IT services firm Tata Consultancy Services is a little more optimistic, saying generative AI platforms like ChatGPT will create an "AI co-worker" and not replace jobs. The Microsoft-backed software, for sure, is not going anywhere.