Ten years ago, I used my first post-college paycheck to meet an astrologer. Guided by a rickety stairway, I entered her Alphabet City apartment-turned-sanctum and was greeted by an eccentric Aquarius donning avant-garde garb (she had moved to New York in the 1980s and appeared to still be living in that era). A bathtub in the living room set the scene for a mystical encounter. However, the enchantment quickly dissipated as the reading commenced and I was handed a 33-page printout, which the astrologer read aloud. I had grown up in a household where astrology was a fervent topic of discussion, and I knew most of my planetary placements by heart.

Wining and dining, wooing clients with creative offers, and cashing big bonuses provide the glamor to sales work. Drafting answers to hundreds of dull questions posed by a prospective customer's request for proposals? Mercifully for workers, after months of speculation about ChatGPT-style AI taking over white-collar work, the corporate chore of responding to RFPs is one of the first that generative AI is disrupting. In April, communications software maker Twilio introduced RFP Genie, a generative AI tool that digests an RFP, scours thousands of internal files for relevant information, and uses OpenAI's GPT-4 to generate a suitable response. The company's sales staff simply copy and paste the text over into a formal document and make a few adjustments.

Welcome to the sixth edition of the AI Index Report! This year, the report introduces more original data than any previous edition, including a new chapter on AI public opinion, a more thorough technical performance chapter, original analysis about large language and multimodal models, detailed trends in global AI legislation records, a study of the environmental impact of AI systems, and more. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI.

Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning. Meta's open release of Llama models and OpenAI's APIs for fine-tuning GPT-3.5 Turbo on custom datasets also encourage this practice. But, what are the safety costs associated with such custom fine-tuning? We note that while existing safety alignment infrastructures can restrict harmful behaviors of LLMs at inference time, they do not cover safety risks when fine-tuning privileges are extended to end-users. Our red teaming studies find that the safety alignment of LLMs can be compromised by fine-tuning with only a few adversarially designed training examples. For instance, we jailbreak GPT-3.5 Turbo's safety guardrails by fine-tuning it on only 10 such examples at a cost of less than $0.20 via OpenAI's APIs, making the model responsive to nearly any harmful instructions. Disconcertingly, our research also reveals that, even without malicious intent, simply fine-tuning with benign and commonly used datasets can also inadvertently degrade the safety alignment of LLMs, though to a lesser extent. These findings suggest that fine-tuning aligned LLMs introduces new safety risks that current safety infrastructures fall short of addressing -- even if a model's initial safety alignment is impeccable, it is not necessarily to be maintained after custom fine-tuning. We outline and critically analyze potential mitigations and advocate for further research efforts toward reinforcing safety protocols for the custom fine-tuning of aligned LLMs.

A prominent theory of affective response to music revolves around the concepts of surprisal and expectation. In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences. To date, however, these models have been limited to compute exact probabilities through hand-crafted features or restricted to linear models which are likely not sufficient to represent the complex conditional distributions present in music. In this work, we propose to use modern deep probabilistic generative models in the form of a Diffusion Model to compute an approximate likelihood of a musical input sequence. Unlike prior work, such a generative model parameterized by deep neural networks is able to learn complex non-linear features directly from a training set itself. In doing so, we expect to find that such models are able to more accurately represent the 'surprisal' of music for human listeners. From the literature, it is known that there is an inverted U-shaped relationship between surprisal and the amount human subjects 'like' a given song. In this work we show that pre-trained diffusion models indeed yield musical surprisal values which exhibit a negative quadratic relationship with measured subject 'liking' ratings, and that the quality of this relationship is competitive with state of the art methods such as IDyOM. We therefore present this model a preliminary step in developing modern deep generative models of music expectation and subjective likability.

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disease, including Alzheimer's disease (AD). However, it is a great challenge to map brain structure-function connections due to various reasons. In this work, a bidirectional graph generative adversarial networks (BGGAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BGGAN can employ features of direct and indirect brain regions to learn the mapping function between structural domain and functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BGGAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using ADNI datasets show that the both the generated structure connections and generated function connections can improve the identification accuracy of AD. More importantly, based the proposed model, it is found that the relationship between brain structure and function is not a complete one-to-one correspondence. Brain structure is the basis of brain function. The strong structural connections are almost accompanied by strong functional connections.

However, the generation of new objects builds mainly on the understanding of the hidden structure of the training dataset followed by a sampling from a multi-dimensional normal variable. In particular each sample is independent from the others and can repeatedly propose same type of objects. To cure this drawback we introduce a kernel-based measure quantization method that can produce new objects from a given target measure by approximating it as a whole and even staying away from elements already drawn from that distribution. This ensures a better diversity of the produced objects. The method is tested on classic machine learning benchmarks.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Social media giant Meta Platforms said on Wednesday that it has started rolling out generative artificial intelligence (AI) tools that can create content like image backgrounds and variations of written text for all advertisers. The company started testing these tools in May, giving access to a select group of advertisers in a "testing playground". The tools will be available in Meta's Ads Manager and their rollout will be completed next year.

SILICON Valley's feverish embrace of large language models (LLMs) shows no sign of letting up. Google is integrating its chatbot Bard into every one of its services, while OpenAI is imbuing its own offering, ChatGPT, with new senses, such as the ability to "see" and "speak", envisaging a new kind of personal assistant. But deep mysteries remain about how these tools function: what is really going on behind their shiny interfaces, which tasks are they truly good at and how might they fail? Should we really be betting the house on technology with so many unknowns?