Signal's Creator Is Helping Encrypt Meta AI Moxie Marlinspike says the technology powering his encrypted AI chatbot, Confer, will be integrated into Meta AI. The move could help protect the AI conversations of millions of people. Moxie Marlinspike, cofounder of the Signal Foundation, says his new privacy-focused AI platform, Confer, will be integrated into Meta AI. Moxie Marlinspike, the privacy advocate who created the secure communication app Signal and its widely used open source encryption protocol, said this week that his privacy-focused AI platform, Confer, will start incorporating its technology into Meta's AI systems. Every day, billions of chat messages sent through Signal, Meta's WhatsApp, and Apple's Messages are protected by end-to-end encryption .

Signal's Founder Built a Chatbot That Can't Spy on You Welcome back to, TIME's new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? What to Know: Signal's founder is working on encrypted chatbots Moxie Marlinspike, the cryptographic prodigy who wrote the code that underpins Signal and WhatsApp, has a new project--and it could be one of the most important things happening in AI right now. The tool, named Confer, is an end-to-end encrypted AI assistant. It uses smart math to ensure that even though the compute-intensive process of running the AI still happens on a server in the cloud, the only person who can access the unscrambled details of that computation is you, the user.

Natural Language Inference (NLI) is the task of determining whether a sentence pair represents entailment, contradiction, or a neutral relationship. While NLI models perform well on many inference tasks, their ability to handle fine-grained pragmatic inferences, particularly presupposition in conditionals, remains underexplored. In this study, we introduce CONFER, a novel dataset designed to evaluate how NLI models process inference in conditional sentences. We assess the performance of four NLI models, including two pre-trained models, to examine their generalization to conditional reasoning. Additionally, we evaluate Large Language Models (LLMs), including GPT-4o, LLaMA, Gemma, and DeepSeek-R1, in zero-shot and few-shot prompting settings to analyze their ability to infer presuppositions with and without prior context. Our findings indicate that NLI models struggle with presuppositional reasoning in conditionals, and fine-tuning on existing NLI datasets does not necessarily improve their performance.

There may be most likely nobody who hasn't heard of Synthetic Intelligence. AI was as soon as in contrast to the invention of fireside, a discovery which modified human race without end. Similar to fireplace, AI has permeated each a part of our lives and is altering it for the higher. Machine studying is a department of AI; it is all about creating an algorithm, analyzing information, studying from information, course ofing information, figuring out and making use of patterns on information with minimal intervention by humans. Shifting in the direction of the definition of Machine Studying, "Machine Studying is the appliance or department of Synthetic Intelligence (AI) that is the capacity to be taught from information, prepare information, establish patterns, and enhance general person expertise. It focuses on creating the pc program which might simply analyze the info."

The communications satellite Intelsat 901 had lived a useful life, having beamed signals back and forth from Earth since 2001. But by late 2019, it was starting to run out of fuel. Without an intervention, it would have to go live in a "graveyard orbit"--a region away from operational instruments. There, beyond the population of more lively satellites, Intelsat 901 would ellipse impotently around Earth, along with other satellites that were perhaps totally functional but running on empty. But luckily for this Intelsat, an intervention was on the horizon.

January 5, 2018 --The idea that there might be gigantic alien structures orbiting a distant star just bit the dust. After citizen astronomers spotted data in 2015 revealing that KIC 8462852, a star about 1,000 light years away, was dimming and brightening in a strange way, one of many explanations proposed by astronomers involved some sort of "megastructures" orbiting the star – perhaps built by aliens to harvest stellar energy. That imaginative suggestion rocketed the star to fame. But Louisiana State University astronomer Tabetha Boyajian and colleagues collected more data on the star, nicknamed "Tabby's Star" for Dr. Boyajian, and they found that the star's strange flickering was thanks to something much more mundane: ordinary dust. We see it in many different ways, and the data that we took showed a clear signature of this being what we would see from dust," Boyajian says. This may be a disappointing outcome for those hoping for proof of an alien civilization. But Tabby's Star's rise to stardom highlights a deeply entrenched human psychological quirk: When presented with a puzzling phenomenon, our knee-jerk instinct is to ask not what created it, but who. Scientists say that as social animals, we are evolutionarily predisposed to see agency and intentionality in the world around us. And when it comes to astronomical mysteries, aliens seem to fit. "It's the duct tape of science," says Seth Shostak, senior astronomer for the SETI Institute. Because we don't know what aliens might do, they could explain anything. But why do we do that? "It's not just aliens," says Christopher French, a psychologist and founder of the Anomalistic Psychology Research Unit at Goldsmiths, University of London. "We do have a natural tendency to assume that anything odd, or, superficially at least, inexplicable, that there must be some sort of intentionality behind it, some sort of intelligence, there must be a purpose, somebody or something has done that for a particular purpose.

Now that the big game has come and gone for another year, I have to admit that as I watched the Patriots and the Falcons duke it out, I started realizing how much machine learning (ML) and artificial intelligence can be applied to our sports culture. If you read my 2017 predictions blog, you'll know that I see major implications for ML in the coming years. So let's take a look at how we use technology in sports and fan engagement, and how that can translate to the enterprise. Scouting in sports is a human pursuit. It takes a highly skilled, observant and excellent judge to watch an athlete perform at an amateur level, and understand if he or she has what it takes to go pro.

Constructing a good conference schedule for a large multi-track conference needs to take into account the preferences and constraints of organizers, authors, and attendees. Creating a schedule which has fewer conflicts for authors and attendees, and thematically coherent sessions is a challenging task. Cobi introduced an alternative approach to conference scheduling by engaging the community to play an active role in the planning process. The current Cobi pipeline consists of committee-sourcing and author-sourcing to plan a conference schedule. We further explore the design space of community-sourcing by introducing attendee-sourcing -- a process that collects input from conference attendees and encodes them as preferences and constraints for creating sessions and schedule. For CHI 2014, a large multi-track conference in human-computer interaction with more than 3,000 attendees and 1,000 authors, we collected attendees’ preferences by making available all the accepted papers at the conference on a paper recommendation tool we built called Confer, for a period of 45 days before announcing the conference program (sessions and schedule). We compare the preferences marked on Confer with the preferences collected from Cobi’s author-sourcing approach. We show that attendee-sourcing can provide insights beyond what can be discovered by author-sourcing. For CHI 2014, the results show value in the method and attendees’ participation. It produces data that provides more alternatives in scheduling and complements data collected from other methods for creating coherent sessions and reducing conflicts.

Journal of Arti ial In telligen e Resear h 21 (2004) 287-317 Submitted 06/03; published 03/04 IDL-Expressions: A F ormalism for Represen ting and P arsing Finite Languages in Natural Language Pro essing Mark-Jan Nederhof markjan let.r ug.nl F a ulty of A rts, University of Gr oningen P.O. Dept. of Information Engine ering, University of Padua via Gr adenigo, 6/A I-35131 Padova, Italy Abstra t W e prop ose a formalism for represen tation of nite languages, referred to as the lass of IDL-expr essions, whi h om bines on epts that w ere only onsidered in isolation in existing formalisms. The suggested appli ations are in natural language pro essing, more sp e i ally in surfa e natural language generation and in ma hine translation, where a sen ten e is obtained b y rst generating a large set of andidate sen ten es, represen ted in a ompa t w a y, and then ltering su h a set through a parser. W e study sev eral formal prop erties of IDL-expressions and ompare this new formalism with more ...

Independence -- the study of what is relevant to a given problem of reasoning -- has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, while the syntactic form of independence is computationally easy to check, there are cases in which things that intuitively are not relevant are not recognized as such. We also consider the problem of forgetting, i.e., distilling from a knowledge base only the part that is relevant to the set of queries constructed from a subset of the alphabet. While such process is computationally hard, it allows for a simplification of subsequent reasoning, and can thus be viewed as a form of compilation: once the relevant part of a knowledge base has been extracted, all reasoning tasks to be performed can be simplified.