Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client's expectations, and preferences, and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is often established using guides like reference songs and demo mixes exchanged between the artist and the engineer and sometimes verbalised using semantic terms. This paper presents the findings of a two-phased exploratory study aimed at understanding how professional mixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semi-structured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnaire was designed and administered to a larger group of 22 mixing engineers during the second phase. The results of this study shed light on the importance of collaboration, empathy, and intention in the mixing process, and can inform the development of smart multi-track mixing systems that better support these practices. By highlighting the significance of these findings, this paper contributes to the growing body of research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.

In need of a quiet getaway after completing my fourth novel, last week I booked a hotel on the seafront in Paignton, Devon and planned to spend three days wandering about and reading in cafes. As soon as I arrived, however, I saw that there were several arcades on the main street and on the pier. Obviously, I had to visit them all. As a child living in Cheshire in the 1980s, I spent many happy summer days in the arcades along the Golden Mile in Blackpool. These vast cathedrals of leisure, their exterior walls covered in flashing multicoloured light bulbs, were crammed with the video games of the era.

Among creative professionals, Generative Artificial Intelligence (GenAI) has sparked excitement over its capabilities and fear over unanticipated consequences. How does GenAI impact User Experience Design (UXD) practice, and are fears warranted? We interviewed 20 UX Designers, with diverse experience and across companies (startups to large enterprises). We probed them to characterize their practices, and sample their attitudes, concerns, and expectations. We found that experienced designers are confident in their originality, creativity, and empathic skills, and find GenAI's role as assistive. They emphasized the unique human factors of "enjoyment" and "agency", where humans remain the arbiters of "AI alignment". However, skill degradation, job replacement, and creativity exhaustion can adversely impact junior designers. We discuss implications for human-GenAI collaboration, specifically copyright and ownership, human creativity and agency, and AI literacy and access. Through the lens of responsible and participatory AI, we contribute a deeper understanding of GenAI fears and opportunities for UXD.

We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as Constraint Satisfaction Problems and use this framework to investigate how the model interacts internally with factual constraints. Specifically, we discover a strong positive relation between the model's attention to constraint tokens and the factual accuracy of its responses. In our curated suite of 11 datasets with over 40,000 prompts, we study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing self-attention patterns, that can predict constraint satisfaction and factual errors, and allows early error identification. The approach and findings demonstrate how using the mechanistic understanding of factuality in LLMs can enhance reliability.

The AI Insight Forums were announced a few months ago by Senate Majority Leader Chuck Schumer as part of his "SAFE Innovation" initiative, which is really a set of principles for AI legislation in the United States. The invite list was heavily skewed toward Big Tech execs, including CEOs of AI companies, though a few civil society and AI ethics researchers were included too. Coverage of the meeting thus far has put a particular emphasis on the reportedly unanimous agreement about the need for AI regulation, and on issues raised by Elon Musk and others about the "civilizational risks" created by AI. (This tracker from Tech Policy Press is pretty handy if you want to know more.) But to really dig below the surface, I caught up with one of the other attendees, Inioluwa Deborah Raji, who gave me an inside look at how the first meeting went, the pernicious myths she needed to debunk, and where disagreements could be felt in the room. Raji is a researcher at the University of California, Berkeley, and a fellow at Mozilla.

GOP presidential candidate Vivek Ramaswamy discusses whether President Biden will be the 2024 Democrat nominee on "Hannity." FIRST ON FOX: Vivek Ramaswamy's Republican presidential campaign is explaining the former CEO's "awakening" on the threat China poses to the United States, following scrutiny for his former company's partnership with a Chinese Communist Party-backed company just a few years ago. Ramaswamy has repeatedly expressed his support for banning American companies from expanding into China. Just Thursday, he unveiled his plan to "decouple" from China in a speech in his home state of Ohio. "Unless you stop turning our companies into lobbying pawns, unless you actually play by the same set of rules abiding by the same standards we agreed to, then we're cutting the cord," he said.

This is a rush transcript of'Fox News Sunday' on September 24, 2023. This copy may not be in its final form and may be updated. The chaos at the border grows by the day, as the pressure to take greater action builds yet again on the White House. We need people from the top. HEMMER (voice-over): A border city mayor and Democrat declaring a state of emergency as thousands upon thousands of migrants flow into the country. JOE BIDEN, PRESIDENT OF THE UNITED STATES: Republicans in Congress and my predecessor spent four years gutting the immigration system -- under my predecessor. They continue to undermine our border security today. HEMMER: We'll get reaction from border state Democrat, Texas Congressman Henry Cuellar. President Biden says he'll join the picket line in Michigan on Tuesday, just a day before Donald Trump will be there, too. Meanwhile, another presidential hopeful pushes back. TIM SCOTT (R-SC), PRESIDENTIAL CANDIDATE: We need a president who says we are not going to subsidize unions, period. HEMMER: We'll discuss with a man whose eyes are on the White House, South Carolina Senator Tim Scott. We'll ask Republican National Committee chairwoman Ronna McDaniel what voters can expect to see on stage Wednesday night. JAMES LANKFORD (R-OK): It's a symbol of respect for the country when you dress respectfully when you're doing this responsibility. JOHN FETTERMAN (D-PA): I think there are more important things we should be talking about rather if -- if I dressed like a slob. The number of illegals crossing our border hit another new record. We want to show you our FOX News drone camera from Eagle Pass, Texas. We've been watching remarkable images today of a human flood that shows no sign of receding. And today, a new survey shows how displeased Americans are with the president's border policies. In a moment, we'll speak with border state Democrat, Texas Congressman Henry Cuellar, on that. But, first, to Griff Jenkins who has been in Eagle Pass for what seems like several years now. Well, there's a humanitarian crisis playing out along our southern border in places like here in Eagle Pass, Texas, where migrants have traveled thousands of miles in hopes of reaching the U.S. in numbers far greater than what border officials are able to handle. Actions include sending active duty troops to the border, increasing deportations and granting temporary protective status to nearly half a million Venezuelans, making it easier for them to find work in cities like New York, where officials are struggling to find room for them. Meanwhile, Texas Governor Greg Abbott trying to deter the migrants from entering his state, with miles of dense razor wire, Humvees manning the riverbank and guardsmen in rafts attempting to turn them back.

Large Language Models (LLMs), acting as a powerful reasoner and generator, exhibit extraordinary performance across various natural language tasks, such as question answering (QA). Among these tasks, Multi-Hop Question Answering (MHQA) stands as a widely discussed category, necessitating seamless integration between LLMs and the retrieval of external knowledge. Existing methods employ LLM to generate reasoning paths and plans, and utilize IR to iteratively retrieve related knowledge, but these approaches have inherent flaws. On one hand, Information Retriever (IR) is hindered by the low quality of generated queries by LLM. On the other hand, LLM is easily misguided by the irrelevant knowledge by IR. These inaccuracies, accumulated by the iterative interaction between IR and LLM, lead to a disaster in effectiveness at the end. To overcome above barriers, in this paper, we propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved framework (Furthest Reasoning) and an attached module (Plan Assessor). 1) Furthest reasoning operates by masking previous reasoning path and generated queries for LLM, encouraging LLM generating chain of thought from scratch in each iteration. This approach enables LLM to break the shackle built by previous misleading thoughts and queries (if any). 2) The Plan Assessor is a trained evaluator that selects an appropriate plan from a group of candidate plans proposed by LLM. Our methods are evaluated on three highly recognized public multi-hop question answering datasets and outperform state-of-the-art on most metrics (achieving a 10%-12% in answer accuracy).

N-ary facts composed of a primary triple (head entity, relation, tail entity) and an arbitrary number of auxiliary attribute-value pairs, are prevalent in real-world knowledge graphs (KGs). Link prediction on n-ary facts is to predict a missing element in an n-ary fact. This helps populate and enrich KGs and further promotes numerous downstream applications. Previous studies usually require a substantial amount of high-quality data to understand the elements in n-ary facts. However, these studies overlook few-shot relations, which have limited labeled instances, yet are common in real-world scenarios. Thus, this paper introduces a new task, few-shot link prediction on n-ary facts. It aims to predict a missing entity in an n-ary fact with limited labeled instances. We further propose a model for Few-shot Link prEdict on N-ary facts, thus called FLEN, which consists of three modules: the relation learning, support-specific adjusting, and query inference modules. FLEN captures relation meta information from limited instances to predict a missing entity in a query instance. To validate the effectiveness of FLEN, we construct three datasets based on existing benchmark data. Our experimental results show that FLEN significantly outperforms existing related models in both few-shot link prediction on n-ary facts and binary facts.

Making the contents generated by Large Language Model (LLM) such as ChatGPT, accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each of which needs knowledge to solve. Introducing Information Retrieval (IR) to provide LLM with external knowledge is good potential to solve this problem. However, where and how to introduce IR into LLM is a big challenge. Previous work has the disadvantage that the wrong knowledge retrieved by IR misleads the LLM or breaks the reasoning chain of LLM. In this paper, we propose a novel framework called Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. First, LLM generates the global reasoning chain called Chain-of-Query (CoQ) where each node consists of an IR-oriented query and the answer to the query. Second, IR verifies the answer of each node of CoQ, it corrects the answer that is not consistent with the retrieved information when IR gives high confidence, which improves the credibility. Third, LLM can mark its missing knowledge in CoQ and IR can provide this knowledge to LLM. These three operations improve the accuracy of LLM for complex knowledge-intensive tasks in terms of reasoning ability and knowledge. Finally, SearChain generates the reasoning process and marks references to supporting documents for each reasoning step, which improves traceability. SearChain transforms the topology of reasoning from chain to tree, which can modify the reasoning direction. Experiment shows that SearChain outperforms baselines on complex knowledge-intensive tasks including multi-hop question-answering, slot filling, fact checking, and long-form question-answering.