This work defends the 'Whole Hog Thesis': sophisticated Large Language Models (LLMs) like ChatGPT are full-blown linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. We argue against prevailing methodologies in AI philosophy, rejecting starting points based on low-level computational details ('Just an X' fallacy) or pre-existing theories of mind. Instead, we advocate starting with simple, high-level observations of LLM behavior (e.g., answering questions, making suggestions) -- defending this data against charges of metaphor, loose talk, or pretense. From these observations, we employ 'Holistic Network Assumptions' -- plausible connections between mental capacities (e.g., answering implies knowledge, knowledge implies belief, action implies intention) -- to argue for the full suite of cognitive states. We systematically rebut objections based on LLM failures (hallucinations, planning/reasoning errors), arguing these don't preclude agency, often mirroring human fallibility. We address numerous 'Games of Lacks', arguing that LLMs do not lack purported necessary conditions for cognition (e.g., semantic grounding, embodiment, justification, intrinsic intentionality) or that these conditions are not truly necessary, often relying on anti-discriminatory arguments comparing LLMs to diverse human capacities. Our approach is evidential, not functionalist, and deliberately excludes consciousness. We conclude by speculating on the possibility of LLMs possessing 'alien' contents beyond human conceptual schemes.

Intelligent systems that aim at mastering language as humans do must deal with its semantic underspecification, namely, the possibility for a linguistic signal to convey only part of the information needed for communication to succeed. Consider the usages of the pronoun they, which can leave the gender and number of its referent(s) underspecified. Semantic underspecification is not a bug but a crucial language feature that boosts its storage and processing efficiency. Indeed, human speakers can quickly and effortlessly integrate semantically-underspecified linguistic signals with a wide range of non-linguistic information, e.g., the multimodal context, social or cultural conventions, and shared knowledge. Standard NLP models have, in principle, no or limited access to such extra information, while multimodal systems grounding language into other modalities, such as vision, are naturally equipped to account for this phenomenon. However, we show that they struggle with it, which could negatively affect their performance and lead to harmful consequences when used for applications. In this position paper, we argue that our community should be aware of semantic underspecification if it aims to develop language technology that can successfully interact with human users. We discuss some applications where mastering it is crucial and outline a few directions toward achieving this goal.

Role requiring'No experience data provided' months of experience in Los Angeles A fast-growing eCommerce company based in New York City is looking for a talented Senior Python Developer to join their expanding team. They want passionate people who will come on board to work on exceptional projects and web applications. You will ideally have: • Expertise in Python, ideally 5 years • Great knowledge of Django • Experience with Amazon Web Services (AWS) • Great testing practices • Agile project management • Proven delivery methods and organisational skills • JavaScript with React.JS is advantageous This is an excellent opportunity to work for a fast-growing Tech Organization in NYC. Apply now to find out more! Apply Here Role requiring'No experience data provided' months of experience in Los Angeles The biggest challenge we face is building out a system that analyzes years' worth of end-user behavior and their interactions across many different channels of communication. Behavioral Competencies: Attention to Detail Independent Self-Starter Highly Organized Critical Thinker Problem Solver Excellent Communicator Ability to Prioritize Team Work & Collaboration Multi-Tasker with Strong Sense of Urgency Ability to work in a remote environment Benefits Permanent Flexible Hybrid work schedule (remote) Medical insurance Dental insurance Vision insurance Prescription drug coverage 401K 401K with company match Life Insurance Health Spending Account (HSA) Flex Spending Account (FSA) Paid holidays Phone/Internet Stipend Paid time off Employee Referral Program Community service programs Culture Company Mission and Highlights: mPulse Mobile is reimagining health engagement to inspire healthier lives and deeper relationships between healthcare organizations and their consumers.

We investigate novel parameter estimation and goodness-of-fit (GOF) assessment methods for large-scale confirmatory item factor analysis (IFA) with many respondents, items, and latent factors. For parameter estimation, we extend Urban and Bauer's (2021) deep learning algorithm for exploratory IFA to the confirmatory setting by showing how to handle user-defined constraints on loadings and factor correlations. For GOF assessment, we explore new simulation-based tests and indices. In particular, we consider extensions of the classifier two-sample test (C2ST), a method that tests whether a machine learning classifier can distinguish between observed data and synthetic data sampled from a fitted IFA model. The C2ST provides a flexible framework that integrates overall model fit, piece-wise fit, and person fit. Proposed extensions include a C2ST-based test of approximate fit in which the user specifies what percentage of observed data can be distinguished from synthetic data as well as a C2ST-based relative fit index that is similar in spirit to the relative fit indices used in structural equation modeling. Via simulation studies, we first show that the confirmatory extension of Urban and Bauer's (2021) algorithm produces more accurate parameter estimates as the sample size increases and obtains comparable estimates to a state-of-the-art confirmatory IFA estimation procedure in less time. We next show that the C2ST-based test of approximate fit controls the empirical type I error rate and detects when the number of latent factors is misspecified. Finally, we empirically investigate how the sampling distribution of the C2ST-based relative fit index depends on the sample size.

Artificial intelligence and machine learning are quickly becoming an integral part of healthcare delivery. Both on the clinical care and operational side of healthcare organizations, AI has is powering technology that keeps patients safe and improves efficiency for the revenue cycle, supply chain and more. Here are 100-plus companies in the healthcare space using artificial intelligence. To add a company to this list, contact Laura Dyrda at ldyrda@beckershealthcare.com. AiCure is an AI and advanced data analytics company that uses video, audio and behavioral data to better understand the connection between patients, disease and treatment. It allows physicians to have access to clinical and patient insights. Aiva Health developed Aiva, the first voice-powered care assistant.

We describe a comprehensive framework for narrative understanding based on Episodic Logic (EL). This situational logic was developed and implemented as a semantic representation and commonsense knowledge representation that would serve the full range of interpretive and inferential needs of general NLU. The most distinctive feature of EL is its natural language-like expressiveness. It allows for generalized quantifiers, lambda abstraction, sentence and predicate modifiers, sentence and predicate reification, intensional predicates (corresponding to wanting, believing, making, etc.), unreliable generalizations, and perhaps most importantly, explicit situational variables (denoting episodes, events, states of affairs, etc.) linked to arbitrary formulas that describe them. These allow episodes to be explicitly related in terms of part-whole, temporal and causal relations. Episodic logical form is easily computed from surface syntax and lends itself to effective inference.