Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.

Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a data set of 1.8K authentic, English-language, CVs from the US, covering 16 occupations, allowing us to partially control for the confound occupation-specific gender base rates. We find that (1) women use more verbs evoking impressions of low power; and (2) classifiers capture gender signal even after data balancing and removal of pronouns and named entities, and this holds for both transformer-based and linear classifiers.

If you know anything about Ghost Robotics, it's likely one of two things: 1) They make robot dogs. A majority of the Philadelphia firm's press coverage has revolved around these facts, along with some coverage of its systems being used to patrol the U.S. border. It's shameful how both parties fight tooth nail to defend their ability to pump endless public money into militarization. From tanks in police depts to corrupt military contracts, funding this violence is bipartisan non-controversial, yet healthcare housing isn't. Ghost has thus far not demonstrated any manner of ethical qualms when it comes to its work with military and law enforcement -- but it's the company's product design that could ultimately get it in hot water.

Tuvalu's foreign minister, Simon Kofe, told the COP27 climate summit yesterday that Tuvalu would look to the metaverse to preserve its culture and history. With global temperatures expected to rise as much as 2.8 degrees Celsius by the end of the century, the Pacific island nation is particularly vulnerable to rising sea levels. At last year's COP26 summit, Kofe addressed the conference while standing knee-deep in seawater to highlight the climate change threat. Climate scientists anticipate the entire country will be underwater by the end of the 21st century. Addressing the climate summit, Kofe said: "As our land disappears, we have no choice but to become the world's first digital nation. Our land, our ocean, our culture are the most precious assets of our people. And to keep them safe from harm, no matter what happens in the physical world, we'll move them to the cloud."

Large-scale language models often learn behaviors that are misaligned with user expectations. Generated text may contain offensive or toxic language, contain significant repetition, or be of a different sentiment than desired by the user. We consider the task of unlearning these misalignments by fine-tuning the language model on signals of what not to do. We introduce Quantized Reward Konditioning (Quark), an algorithm for optimizing a reward function that quantifies an (un)wanted property, while not straying too far from the original model. Quark alternates between (i) collecting samples with the current language model, (ii) sorting them into quantiles based on reward, with each quantile identified by a reward token prepended to the language model's input, and (iii) using a standard language modeling loss on samples from each quantile conditioned on its reward token, while remaining nearby the original language model via a KL-divergence penalty. By conditioning on a high-reward token at generation time, the model generates text that exhibits less of the unwanted property. For unlearning toxicity, negative sentiment, and repetition, our experiments show that Quark outperforms both strong baselines and state-of-the-art reinforcement learning methods like PPO (Schulman et al. 2017), while relying only on standard language modeling primitives.

Fair Machine Learning endeavors to prevent unfairness arising in the context of machine learning applications embedded in society. Despite the variety of definitions of fairness and proposed "fair algorithms", there remain unresolved conceptual problems regarding fairness. In this paper, we dissect the role of statistical independence in fairness and randomness notions regularly used in machine learning. Thereby, we are led to a suprising hypothesis: randomness and fairness can be considered equivalent concepts in machine learning. In particular, we obtain a relativized notion of randomness expressed as statistical independence by appealing to Von Mises' century-old foundations for probability. This notion turns out to be "orthogonal" in an abstract sense to the commonly used i.i.d.-randomness. Using standard fairness notions in machine learning, which are defined via statistical independence, we then link the ex ante randomness assumptions about the data to the ex post requirements for fair predictions. This connection proves fruitful: we use it to argue that randomness and fairness are essentially relative and that both concepts should reflect their nature as modeling assumptions in machine learning.

Regardless of where we land on these legal questions, the various actors in the generative AI field are already gearing up for… something. The companies making millions from this tech are entrenching themselves: repeatedly declaring that everything they're doing is legal (while presumably hoping no one actually challenges this claim). Getty Images recently banned AI content because of the potential legal risk to customers ("I don't think it's responsible.

Competition in the robot dog market is getting ugly. As The Robot Report explains, Boston Dynamics is suing Ghost Robotics for allegedly infringing seven patents linked to its Spot quadruped. The Spirit 40 and Vision 60 (shown above) purportedly borrow key technologies from Spot, including systems for self-righting and climbing stairs. Boston Dynamics says it asked Ghost Robotics to review Spot-related patents in July 2020, five months after the launch of the Spirit 40. After that, Boston claims to have sent two cease-and-desist letters asking Ghost to stop marketing its robot canines.

Graham Katz: Our tool, it's designed to make the process of sort of patent landscape analysis accessible to the general public by using the AI technology. And there are two aspects of the AI technology that we've brought to bear. One facilitates the querying and document retrieval, as we call it, that uses natural language processing, to analyze the data set. So analyze the set of patents that we've loaded into the system to see what the key topics are for each patent and the key claims. And the other is what we call the natural language query system, which takes key terms in your query and finds the patents that are relevant to those.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. California prosecutors have asked a court to delay a trial for a Tesla Model S driver who faces manslaughter charges over a 2019 crash that left two people dead, according to court documents. The trial, originally scheduled to take place on Tuesday in the Los Angeles area, marks the first criminal case against a driver who was using partially automated driving technology at the time of the crash. Prosecutors want to push back the trial to late February or later, saying two police officers assigned to the case would be on medical leave and vacation.