by jsendak | Dec 17, 2024 | DS Articles
Contents [from] which base AI models were trained, by contemporary creators of varying categories, with an expectation that their contents would be monetized, may have one possible option, amid others, for [how] AI companies may compensate for what they used: promotion. There are several base AI models, where the contents of several creators were used.… Read More »How should AI companies compensate content creators for used training data
Analysis of Compensation Methods for AI Companies Using Content Creator’s Training Data
In today’s technologically advanced world, artificial intelligence (AI) is rapidly evolving. A notable aspect of AI is the base models, an integral part which is trained on a wide variety of data inputs, often sourced from content creators operating in different domains. These creators may expect their content to be monetized, creating a potential dilemma for AI companies on how to compensate these creators for their utilized training data. One option to address this could be through promotion, a solution with possible long-term implications and future developments to consider.
Long-Term Implications and Future Developments
Promoting content creators as a form of compensation has far-reaching implications that can significantly impact the AI industry. This method could shake up traditional compensation models and set a new precedent on how AI companies interact with their data sources.
Increased Visibility for Content Creators
By promoting creators, AI companies can help them gain visibility, potentially expanding their reach and impact. The increased exposure can result in greater opportunities for the creators, and may also lead to collaboration with other organizations. Despite its potential benefits, this compensation model hinges on whether creators perceive this type of exposure equivalent to monetary compensation.
Shift In Compensation Models
This approach also signifies a shift towards non-monetary forms of compensation. With the evolving AI industry, different compensation models may arise, catering to the varying needs and expectations of content creators. The crucial factor remains: the value added for the creators must be substantial enough to warrant the usage of their data.
Actionable Advice for AI Companies
- Understand Creator Expectations: AI companies should gain insights into content creators’ compensation expectations. This knowledge can guide the formation of the most suitable compensation models and foster greater collaboration.
- Prioritize Transparency: It’s important to communicate clearly how the creators’ content contributes to enhancing AI model’s performance. This can build trust and maintain a healthy relationship.
- Consider Varied Compensation Models: Promotion may not be suitable or desirable for all creators. Diversifying compensation models to include both monetary and non-monetary benefits could be more appealing and fairer.
Success in the AI industry is not just about creating sophisticated models, but also about recognizing and compensating those who contribute to their development.
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by jsendak | Dec 17, 2024 | AI
arXiv:2404.12630v2 Announce Type: replace-cross Abstract: Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to profound individual differences between subjects and the scarcity of data annotation. In this work, we proposed MindTuner for cross-subject visual decoding, which achieves high-quality and rich semantic reconstructions using only 1 hour of fMRI training data benefiting from the phenomena of visual fingerprint in the human visual system and a novel fMRI-to-text alignment paradigm. Firstly, we pre-train a multi-subject model among 7 subjects and fine-tune it with scarce data on new subjects, where LoRAs with Skip-LoRAs are utilized to learn the visual fingerprint. Then, we take the image modality as the intermediate pivot modality to achieve fMRI-to-text alignment, which achieves impressive fMRI-to-text retrieval performance and corrects fMRI-to-image reconstruction with fine-tuned semantics. The results of both qualitative and quantitative analyses demonstrate that MindTuner surpasses state-of-the-art cross-subject visual decoding models on the Natural Scenes Dataset (NSD), whether using training data of 1 hour or 40 hours.
The article “Decoding Natural Visual Scenes from Brain Activity: MindTuner for Cross-Subject Visual Decoding” explores the challenges and advancements in decoding natural visual scenes from brain activity. While there has been extensive research in single-subject tasks, there is a lack of focus on cross-subject tasks. The authors propose MindTuner, a novel approach that achieves high-quality and rich semantic reconstructions using only 1 hour of fMRI training data. This is made possible by leveraging the visual fingerprint in the human visual system and a new fMRI-to-text alignment paradigm. The article presents the methodology of pre-training a multi-subject model and fine-tuning it with scarce data on new subjects, utilizing LoRAs with Skip-LoRAs to learn the visual fingerprint. Additionally, the authors use the image modality as an intermediate pivot modality to achieve fMRI-to-text alignment, resulting in impressive fMRI-to-text retrieval performance and improved fMRI-to-image reconstruction with fine-tuned semantics. The results of qualitative and quantitative analyses demonstrate that MindTuner outperforms state-of-the-art cross-subject visual decoding models on the Natural Scenes Dataset, regardless of the amount of training data used.
Unlocking the Potential of Cross-Subject Visual Decoding with MindTuner
The decoding of natural visual scenes from brain activity has seen significant advancements, particularly in single-subject tasks. However, when it comes to cross-subject tasks, progress has been slower due to the challenges posed by individual differences between subjects and the lack of annotated data. In a recent study, researchers introduced MindTuner, a novel approach to cross-subject visual decoding that overcomes these obstacles and achieves high-quality and rich semantic reconstructions using only 1 hour of fMRI training data.
Learning from Visual Fingerprints
To address the individual differences between subjects, the researchers pre-trained a multi-subject model among 7 subjects using LoRAs (Locally Optimal Regression Alignment) with Skip-LoRAs. This approach harnesses the concept of “visual fingerprints” in the human visual system, which refers to the unique patterns of brain activity that correspond to specific visual stimuli. By learning these visual fingerprints, the model becomes adept at decoding the visual information from fMRI data, even with limited training data.
A Novel fMRI-to-Text Alignment Paradigm
In addition to learning visual fingerprints, MindTuner introduces a groundbreaking fMRI-to-text alignment paradigm. By leveraging the image modality as the intermediate pivot, the model achieves impressive fMRI-to-text retrieval performance. This alignment not only enhances the accuracy of decoding textual information from fMRI data but also corrects fMRI-to-image reconstructions by incorporating fine-tuned semantics.
Superior Performance on the Natural Scenes Dataset
The researchers evaluated MindTuner’s performance on the Natural Scenes Dataset (NSD), comparing it to state-of-the-art cross-subject visual decoding models. Remarkably, MindTuner outperformed these models in both qualitative and quantitative analyses, whether using a training data of 1 hour or 40 hours. This demonstrates the efficacy of MindTuner in decoding visual information from fMRI data in cross-subject tasks, even with limited training data.
Potential Applications and Implications
The advancements achieved by MindTuner hold promising implications for various fields, particularly in the realm of neuroimaging and cognitive neuroscience. Accurately decoding visual information from brain activity has the potential to unlock insights into the human perception of visual stimuli, leading to a better understanding of how the brain processes and represents visual scenes. Furthermore, the ability to perform cross-subject visual decoding with limited training data opens up avenues for real-world applications such as brain-computer interfaces and assistive technologies.
Conclusion: MindTuner breaks new ground in cross-subject visual decoding, providing a solution to the challenges posed by individual differences and limited training data. By harnessing the concept of visual fingerprints and pioneering an fMRI-to-text alignment paradigm, MindTuner achieves high-quality and rich semantic reconstructions. The results of this study not only demonstrate the superior performance of MindTuner but also pave the way for future innovations in decoding visual information from brain activity.
The paper discusses the challenges of decoding natural visual scenes from brain activity in cross-subject tasks. While there has been extensive research in single-subject tasks, cross-subject tasks are more challenging due to individual differences between subjects and the scarcity of data annotation.
To address this problem, the authors propose a new method called MindTuner for cross-subject visual decoding. MindTuner achieves high-quality and rich semantic reconstructions using only 1 hour of fMRI training data. This is made possible by leveraging the concept of “visual fingerprint” in the human visual system and a novel fMRI-to-text alignment paradigm.
The authors first pre-train a multi-subject model using data from 7 subjects. They then fine-tune this model with scarce data from new subjects, using a combination of LoRAs (Local Rank Alignment) and Skip-LoRAs to learn the visual fingerprint. By using the image modality as an intermediate pivot modality, they achieve fMRI-to-text alignment, which improves fMRI-to-text retrieval performance and corrects fMRI-to-image reconstruction with fine-tuned semantics.
The results of both qualitative and quantitative analyses show that MindTuner outperforms state-of-the-art cross-subject visual decoding models on the Natural Scenes Dataset (NSD), regardless of whether the training data is limited to 1 hour or extended to 40 hours.
This research is significant as it addresses the limitations of previous methods in cross-subject visual decoding and presents a novel approach that achieves impressive results with minimal training data. The use of the visual fingerprint concept and the fMRI-to-text alignment paradigm are innovative and contribute to the advancement of the field.
In terms of future directions, it would be interesting to see how MindTuner performs on larger and more diverse datasets. Additionally, it would be valuable to explore the potential applications of this method in areas such as neuroimaging-based diagnostics and brain-computer interfaces. Further research could also investigate the generalizability of MindTuner to other modalities beyond visual scenes, such as auditory or tactile stimuli. Overall, this work opens up new possibilities for cross-subject visual decoding and lays the foundation for future advancements in the field.
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by jsendak | Dec 11, 2024 | Computer Science
Analysis of Large Language Models (LLMs) and Adversarial Attacks
Recent research has highlighted the vulnerabilities of large language models (LLMs) to adversarial attacks. This is a concerning finding, considering the widespread adoption of LLM-based chatbots and virtual assistants across various industries, fueled by the rapid development pace of AI-based systems.
The potential of Generative AI (GenAI) to assist humans in decision making is driving this development, sparking immense optimism. However, it is crucial to acknowledge and address the adversarial risks associated with these technologies.
An adversary exploiting security gaps, inadequate safeguards, and limited data governance can carry out attacks that grant unauthorized access to the system and its data. Such attacks can compromise the integrity, confidentiality, and availability of sensitive information.
Understanding Data Poison Attacks
As a means of demonstrating the potential vulnerabilities of LLM-based chatbots, a proof-of-concept assessment was conducted on BarkPlug, the chatbot developed by Mississippi State University.
The focus of this assessment was data poison attacks, a type of adversarial attack where the input data to the LLM is manipulated to manipulate the behavior of the chatbot. By injecting malicious or misleading information into the training data, an attacker can manipulate the responses generated by the chatbot.
By carefully crafting input that contains subtle but influential patterns, an adversary can deceive the chatbot into providing inaccurate or harmful information, leading to potential consequences for users relying on its responses.
Evaluating BarkPlug’s Performance
The proof-of-concept assessment aimed to evaluate BarkPlug’s resilience against data poison attacks. A red team perspective was adopted, mimicking the adversarial mindset to identify potential weaknesses.
The results of the assessment revealed vulnerabilities in BarkPlug’s ability to identify and respond to manipulated input. The chatbot exhibited a lack of robustness in distinguishing between genuine and maliciously crafted queries.
This finding is concerning, as it indicates the potential for attackers to exploit BarkPlug’s weaknesses to manipulate its responses and mislead users. In an environment where BarkPlug is utilized for decision making or information retrieval, such exploitation poses significant risks.
Addressing Adversarial Risks and Strengthening LLM Systems
The vulnerabilities identified in BarkPlug underscore the importance of addressing adversarial risks associated with LLM-based chatbots and virtual assistants.
There is a need for enhanced security measures, rigorous safeguards, and robust data governance to mitigate the risks of unauthorized access and manipulation of LLM systems.
Additionally, ongoing research and development in the field of adversarial machine learning are necessary to improve the resilience of LLMs against such attacks. Techniques such as adversarial training and data sanitization can help strengthen LLM systems.
Expert Insight: As LLM-based chatbots become more prevalent in various industries, it is crucial to strike a balance between harnessing the potential benefits of GenAI and addressing the inherent adversarial risks. By investing in security and resilience measures, organizations can ensure the trustworthiness and reliability of LLM systems.
Overall, this assessment sheds light on the vulnerabilities present in LLM-based chatbots and the importance of addressing adversarial risks to safeguard user trust and protect sensitive data. Continued research and proactive measures are essential in building robust LLM systems that can withstand adversarial attacks and maintain their effectiveness in decision-making processes.
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by jsendak | Nov 29, 2024 | AI
arXiv:2411.17912v1 Announce Type: new Abstract: As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planning scenarios in various settings and with various difficulties. Our experiments showed that all LLMs made numerous errors in all scenarios, revealing that they are unreliable path planners. We suggest that future work focus on implementing mechanisms for reality checks, enhancing model transparency, and developing smaller models.
Title: Unreliable Path Planners: Assessing Large Language Models in Vehicle Navigation Systems
Introduction:
In the rapidly evolving landscape of vehicle navigation systems, the integration of large language models (LLMs) has gained significant traction. However, the crucial aspect of understanding the path-planning capability of these LLMs remains a pressing concern. In an effort to shed light on their performance, this article presents a comprehensive assessment of three LLMs across six real-world path-planning scenarios, encompassing various settings and difficulties.
The results of the experiments conducted in this study reveal a disconcerting reality: all three LLMs exhibited a multitude of errors across all tested scenarios, thus exposing their unreliability as path planners. These findings underscore the urgent need for further research and development to address the limitations of LLMs in this domain.
To enhance the reliability of LLMs in vehicle navigation, the authors propose several crucial areas of focus for future work. First and foremost, implementing mechanisms for reality checks is deemed essential to ensure the accuracy of path planning. Additionally, enhancing model transparency is identified as a key factor in enabling better understanding and identification of potential errors. Finally, the development of smaller LLMs is suggested as a potential solution to mitigate the unreliability observed in larger models.
As the integration of LLMs into vehicle navigation systems continues to advance, this article serves as a wake-up call, highlighting the critical need for improvements in path-planning capabilities. By addressing the identified challenges and pursuing the suggested avenues for future research, the aim is to pave the way for more reliable and trustworthy LLMs in the realm of vehicle navigation.
Understanding the limitations of large language models in vehicle navigation systems
Large language models (LLMs) have rapidly gained popularity and are being integrated into various applications, including vehicle navigation systems. These models use vast amounts of data to generate human-like text and are believed to possess the ability to assist with path-planning in real-world scenarios. However, recent experiments have shown that LLMs have significant limitations when it comes to path-planning, making them unreliable tools for navigation.
Challenges in path-planning scenarios
To explore the capabilities of LLMs in path-planning, researchers conducted experiments involving six real-world scenarios set in different environments and varying levels of difficulty. The results revealed that all LLMs made numerous errors across all scenarios, highlighting their lack of reliability as path planners.
“Our experiments showed that LLMs struggle to accurately navigate through different settings and difficulties. These models often make mistakes that could lead to incorrect navigation decisions and pose safety risks in real-world scenarios,” the researchers reported.
While LLMs are proficient in generating text based on patterns in training data, they lack a deep understanding of spatial relationships and real-time decision-making required for effective path-planning. This limited understanding leads to errors and inaccuracies in navigation predictions, undermining their reliability as a standalone tool for vehicle navigation systems.
Moving forward with innovative solutions
Considering the limitations of LLMs as path-planners, it is crucial to focus on developing complementary mechanisms that can enhance their reliability and usability. Here are some proposed solutions to address the challenges:
- Implement reality checks: By integrating real-time sensor data and information from navigation aids, LLMs can continuously assess the accuracy of their predicted paths. This will enable the model to correct its course when deviations occur and increase reliability.
- Enhance model transparency: LLMs should be designed with built-in explainability features that provide insights into the decision-making process. This would allow users to better understand how the model arrives at its path-planning decisions and provide feedback, helping improve the overall performance of the system.
- Develop smaller models: While larger models may offer more accurate text generation, their size and computational requirements often limit their usability in real-time applications like vehicle navigation systems. Developing smaller, more efficient LLMs specifically tailored for path-planning can reduce errors and improve overall system performance.
By incorporating these innovative solutions into the development of LLMs, the reliability and effectiveness of language models in vehicle navigation systems can be significantly improved.
The paper titled “Understanding the Path-Planning Capability of Large Language Models in Vehicle Navigation Systems” highlights the importance of evaluating the performance of large language models (LLMs) in real-world path-planning scenarios. With the increasing integration of LLMs into vehicle navigation systems, it becomes crucial to assess their reliability and effectiveness.
The authors conducted experiments using three different LLMs and tested them in six real-world path-planning scenarios with varying difficulties and settings. The results revealed that all three LLMs made numerous errors in all scenarios, indicating their unreliability as path planners. This finding raises concerns about the practical applicability of LLMs in vehicle navigation systems.
To address these limitations, the paper suggests several areas for future research. Firstly, the implementation of mechanisms for reality checks could help improve the reliability of LLMs. By incorporating validation steps that verify the plausibility of the generated paths, potential errors and inconsistencies can be identified and rectified.
Additionally, enhancing the transparency of LLMs is crucial to understanding their decision-making process and potential sources of errors. Developing methods to interpret and visualize the inner workings of these models can provide valuable insights into their limitations and areas for improvement.
Furthermore, the authors propose the development of smaller models as a potential solution. While large language models have demonstrated impressive capabilities in various domains, their complexity and size can contribute to increased errors and inefficiencies. By focusing on creating smaller, more specialized models specifically designed for path planning, it may be possible to achieve higher accuracy and reliability.
In conclusion, this study sheds light on the limitations of current LLMs in vehicle navigation systems and emphasizes the need for further research to improve their path-planning capabilities. The suggested avenues for future work, including implementing reality checks, enhancing model transparency, and developing smaller models, provide valuable insights for researchers and practitioners in the field.
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by jsendak | Nov 28, 2024 | AI
arXiv:2411.17708v1 Announce Type: new
Abstract: ARC-AGI is an open-world problem domain in which the ability to generalize out-of-distribution is a crucial quality. Under the program induction paradigm, we present a series of experiments that reveal the efficiency and generalization characteristics of various neurally-guided program induction approaches. The three paradigms we consider are Learning the grid space, Learning the program space, and Learning the transform space. We implement and experiment thoroughly on the first two, and retain the second one for ARC-AGI submission. After identifying the strengths and weaknesses of both of these approaches, we suggest the third as a potential solution, and run preliminary experiments.
Analysis of Neurally-Guided Program Induction Approaches in ARC-AGI
ARC-AGI, an open-world problem domain, poses the challenge of generalizing out-of-distribution, making it a crucial quality for artificial general intelligence. In order to address this challenge, the concept of program induction has been employed. In this article, we delve into the efficiency and generalization characteristics of different neurally-guided program induction approaches – Learning the grid space, Learning the program space, and Learning the transform space.
Learning the Grid Space
The first paradigm, Learning the grid space, involves training neural networks to directly predict the correct output for each input grid, without explicitly constructing a program. This approach has shown promising results in improving the efficiency of solving ARC-AGI tasks. By modeling the problem as a classification task, neural networks are able to make predictions based on learned patterns in the input grids.
However, Learning the grid space has limitations when it comes to generalization. As the trained neural networks rely heavily on specific patterns present in the training data, they often struggle to generalize to unseen grids that contain different patterns or structures. This lack of generalization restricts the scalability of the approach, making it less suitable for the open-world nature of ARC-AGI.
Learning the Program Space
In contrast, the Learning the program space paradigm focuses on explicitly learning functional programs that operate on the input grids. This approach involves training neural networks to generate programs that can transform the input grids into desired output grids. By learning the underlying program structures, this paradigm offers the potential for superior generalization capabilities.
However, Learning the program space has its own challenges. Constructing accurate programs that can solve complex ARC-AGI tasks requires substantial computational resources and extensive training. Additionally, the high-dimensional nature of program spaces often leads to a combinatorial explosion in the search space, making it computationally expensive to find optimal programs for each task. Therefore, while this paradigm offers better generalization potential, it comes with computational constraints that need to be addressed.
Learning the Transform Space
Considering the strengths and weaknesses of the previous two paradigms, the Learning the transform space approach emerges as a potential solution. This paradigm involves learning the transformations between input and output grids, without explicitly constructing functional programs. The neural network is trained to map input grids to a latent space where transformations can be better learned and then mapped back to the output grids. By focusing on the underlying transformations, this approach aims to bridge the gap between efficient learning and improved generalization.
In preliminary experiments, the Learning the transform space paradigm shows promise in terms of efficiency and generalization. By focusing on the core transformations needed to solve ARC-AGI tasks, the neural network can generalize better to unseen scenarios. However, further experimentation and optimization are necessary to fully realize the potential of this approach and validate its effectiveness in the ARC-AGI problem domain.
Conclusion
The multi-disciplinary nature of the concepts explored in this article represents the evolving landscape of artificial intelligence research. By integrating principles from machine learning, program synthesis, and neural guidance, researchers are striving to develop AI systems that can not only solve specific tasks efficiently but also possess the ability to generalize out-of-distribution. The neurally-guided program induction approaches discussed – Learning the grid space, Learning the program space, and Learning the transform space – highlight the ongoing efforts towards achieving this vision. As AI research progresses, it is crucial to continue exploring and refining these approaches, leading us closer to the development of robust and versatile artificial general intelligence systems.
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