The surge in generative AI capabilities has affected sectors such as drug discovery and creative text generation, fueling widespread enthusiasm about its potential to revolutionize scientific discovery through efficient exploration of knowledge combinations. But is this belief well-founded? This belief is rooted in the recombinant growth theory, which posits that innovation accelerates when existing ideas are iteratively combined. However, the theory encounters two significant challenges in understanding the nature of breakthroughs. First, breakthroughs such as relativity replacing Newtonian physics drive progress through competition, because they are fundamentally substitutive of older ones. Second, the recombinant strategy often only generates different ideas rather than better ones. Building on these, our study indicates the limitation of combinatorial view of innovation and point to the role of idea competition rather than combination in advancing science, even in the age of AI. Our results suggest that breakthroughs occur when ideas compete, not when they combine, and that combining more ideas tends to result in smaller innovations. This challenges the combinatoric metaphor of innovation that has captivated academia for three decades and complements subsequent studies equating content novelty with transformative innovation. Policymakers and researchers should focus on fostering environments that encourage idea competition and the development of AI systems capable of generating novel, disruptive ideas.
相關內容
生成式人工智能是利用復雜的算法、模型和規則，從大規模數據集中學習，以創造新的原創內容的人工智能技術。這項技術能夠創造文本、圖片、聲音、視頻和代碼等多種類型的內容，全面超越了傳統軟件的數據處理和分析能力。2022年末，OpenAI推出的ChatGPT標志著這一技術在文本生成領域取得了顯著進展，2023年被稱為生成式人工智能的突破之年。這項技術從單一的語言生成逐步向多模態、具身化快速發展。在圖像生成方面，生成系統在解釋提示和生成逼真輸出方面取得了顯著的進步。同時，視頻和音頻的生成技術也在迅速發展，這為虛擬現實和元宇宙的實現提供了新的途徑。生成式人工智能技術在各行業、各領域都具有廣泛的應用前景。
Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of the recorded audio to the recording location. For example, a voice message may be the only investigative cue to narrow down the candidate sites for a crime. Up to now, several works provide tools for closed-set recording environment classification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, closed-set tools are not applicable without retraining on a sufficient amount of training samples for each case and respective candidate set. In addition, a forensic tool has to deal with audio material from uncontrolled sources with variable properties and quality. In this work, we therefore attempt a major step towards practical forensic application scenarios. We propose a representation learning framework called EnvId, short for environment identification. EnvId avoids case-specific retraining. Instead, it is the first tool for robust few-shot classification of unseen environment locations. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, environment characteristics or recording position mismatches. Our code and datasets will be made publicly available upon acceptance.
The success of open source software (OSS) projects relies on voluntary contributions from various community roles.Being a committer signifies gaining trust and higher privileges. Substantial studies have focused on the requirements of becoming a committer, but most of them are based on interviews or several hypotheses, lacking a comprehensive understanding of committers' qualifications.We explore both the policies and practical implementations of committer qualifications in modern top OSS communities. Through a thematic analysis of these policies, we construct a taxonomy of committer qualifications, consisting of 26 codes categorized into nine themes, including Personnel-related to Project, Communication, and Long-term Participation. We also highlight the variations in committer qualifications emphasized in different OSS community governance models. For example, projects following the core maintainer model value project comprehension, while projects following the company-backed model place significant emphasis on user issue resolution. Then, we propose eight sets of metrics and perform survival analysis on two representative OSS projects to understand how these qualifications are implemented in practice. We find that the probability of gaining commit rights decreases as participation time passes.The selection criteria in practice are generally consistent with the community policies. Developers who submit high-quality code, actively engage in code review, and make extensive contributions to related projects are more likely to be granted commit rights. However, there are some qualifications that do not align precisely, and some are not adequately evaluated. This study contributes to the understanding of trust establishment in modern top OSS communities, assists communities in better allocating commit rights, and supports developers in achieving self-actualization through OSS participation.
Goal-conditioned policies are generally understood to be "feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the next action to take. However, under what circumstances such a policy can be learned and how efficient the policy will be are not well understood. In this paper, we present a circuit complexity analysis for relational neural networks (such as graph neural networks and transformers) representing policies for planning problems, by drawing connections with serialized goal regression search (S-GRS). We show that there are three general classes of planning problems, in terms of the growth of circuit width and depth as a function of the number of objects and planning horizon, providing constructive proofs. We also illustrate the utility of this analysis for designing neural networks for policy learning.
We consider Dynamic Treatment Regimes (DTRs) with one sided non-compliance that arise in applications such as digital recommendations and adaptive medical trials. These are settings where decision makers encourage individuals to take treatments over time, but adapt encouragements based on previous encouragements, treatments, states, and outcomes. Importantly, individuals may choose to (not) comply with a treatment recommendation, whenever it is made available to them, based on unobserved confounding factors. We provide non-parametric identification, estimation, and inference for Dynamic Local Average Treatment Effects, which are expected values of multi-period treatment contrasts among appropriately defined complier subpopulations. Under standard assumptions in the Instrumental Variable and DTR literature, we show that one can identify local average effects of contrasts that correspond to offering treatment at any single time step. Under an additional cross-period effect-compliance independence assumption, which is satisfied in Staggered Adoption settings and a generalization of them, which we define as Staggered Compliance settings, we identify local average treatment effects of treating in multiple time periods.
Compared to minutia-based fingerprint representations, fixed-length representations are attractive due to simple and efficient matching. However, fixed-length fingerprint representations are limited in accuracy when matching fingerprints with different visible areas, which can occur due to different finger poses or acquisition methods. To address this issue, we propose a localized deep representation of fingerprint, named LDRF. By focusing on the discriminative characteristics within local regions, LDRF provides a more robust and accurate fixed-length representation for fingerprints with variable visible areas. LDRF can be adapted to retain information within any valid area, making it highly flexible. The matching scores produced by LDRF also exhibit intuitive statistical characteristics, which led us to propose a matching score normalization technique to mitigate the uncertainty in the cases of very small overlapping area. With this new technique, we can maintain a high level of accuracy and reliability in our fingerprint matching, even as the size of the database grows rapidly. Our experimental results on 21 datasets containing over 140K fingerprints of various finger poses and impression types show that LDRF outperforms other fixed-length representations and is robust to sensing technologies and impression types. Besides, the proposed matching score normalization effectively reduces the false match rate (FMR) in large-scale identification experiments comprising over 5.11 million fingerprints. Specifically, this technique results in a reduction of two orders of magnitude compared to matching without matching score normalization and five orders of magnitude compared to prior works.
A long continuous integration (CI) build forces developers to wait for CI feedback before starting subsequent development activities, leading to time wasted. In addition to a variety of build scheduling and test selection heuristics studied in the past, new artifact-based build technologies like Bazel have built-in support for advanced performance optimizations such as parallel build and incremental build (caching of build results). However, little is known about the extent to which new build technologies like Bazel deliver on their promised benefits, especially for long-build duration projects. In this study, we collected 383 Bazel projects from GitHub, then studied their parallel and incremental build usage of Bazel in 4 popular CI services, and compared the results with Maven projects. We conducted 3,500 experiments on 383 Bazel projects and analyzed the build logs of a subset of 70 buildable projects to evaluate the performance impact of Bazel's parallel builds. Additionally, we performed 102,232 experiments on the 70 buildable projects' last 100 commits to evaluate Bazel's incremental build performance. Our results show that 31.23% of Bazel projects adopt a CI service but do not use Bazel in the CI service, while for those who do use Bazel in CI, 27.76% of them use other tools to facilitate Bazel's execution. Compared to sequential builds, the median speedups for long-build duration projects are 2.00x, 3.84x, 7.36x, and 12.80x, at parallelism degrees 2, 4, 8, and 16, respectively, even though, compared to a clean build, applying incremental build achieves a median speedup of 4.22x (with a build system tool-independent CI cache) and 4.71x (with a build system tool-specific cache) for long-build duration projects. Our results provide guidance for developers to improve the usage of Bazel in their projects.
Recent studies introduced effective compression techniques for Large Language Models (LLMs) via post-training quantization or low-bit weight representation. Although quantized weights offer storage efficiency and allow for faster inference, existing works have indicated that quantization might compromise performance and exacerbate biases in LLMs. This study investigates the confidence and calibration of quantized models, considering factors such as language model type and scale as contributors to quantization loss. Firstly, we reveal that quantization with GPTQ to 4-bit results in a decrease in confidence regarding true labels, with varying impacts observed among different language models. Secondly, we observe fluctuations in the impact on confidence across different scales. Finally, we propose an explanation for quantization loss based on confidence levels, indicating that quantization disproportionately affects samples where the full model exhibited low confidence levels in the first place.
Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no consensus on the definition of "attribution", leading to many competing methods with little systematic evaluation, complicated in particular by the lack of ground truth attribution. To address this, we propose a dataset modification procedure to induce such ground truth. Using this procedure, we evaluate three common methods: saliency maps, rationales, and attentions. We identify several deficiencies and add new perspectives to the growing body of evidence questioning the correctness and reliability of these methods applied on datasets in the wild. We further discuss possible avenues for remedy and recommend new attribution methods to be tested against ground truth before deployment. The code is available at \url{//github.com/YilunZhou/feature-attribution-evaluation}.
Non-convex optimization is ubiquitous in modern machine learning. Researchers devise non-convex objective functions and optimize them using off-the-shelf optimizers such as stochastic gradient descent and its variants, which leverage the local geometry and update iteratively. Even though solving non-convex functions is NP-hard in the worst case, the optimization quality in practice is often not an issue -- optimizers are largely believed to find approximate global minima. Researchers hypothesize a unified explanation for this intriguing phenomenon: most of the local minima of the practically-used objectives are approximately global minima. We rigorously formalize it for concrete instances of machine learning problems.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
小貼士
登錄享
相關主題
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191