Collectionless Artificial Intelligence

• URL: http://arxiv.org/abs/2309.06938v2
• Date: Fri, 15 Sep 2023 09:21:02 GMT
• Title: Collectionless Artificial Intelligence
• Authors: Marco Gori and Stefano Melacci
• Abstract summary: This paper sustains the position that the time has come for thinking of new learning protocols. Machines conquer cognitive skills in a truly human-like context centered on environmental interactions.
• Score: 24.17437378498419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By and large, the professional handling of huge data collections is regarded as a fundamental ingredient of the progress of machine learning and of its spectacular results in related disciplines, with a growing agreement on risks connected to the centralization of such data collections. This paper sustains the position that the time has come for thinking of new learning protocols where machines conquer cognitive skills in a truly human-like context centered on environmental interactions. This comes with specific restrictions on the learning protocol according to the collectionless principle, which states that, at each time instant, data acquired from the environment is processed with the purpose of contributing to update the current internal representation of the environment, and that the agent is not given the privilege of recording the temporal stream. Basically, there is neither permission to store the temporal information coming from the sensors, thus promoting the development of self-organized memorization skills at a more abstract level, instead of relying on bare storage to simulate learning dynamics that are typical of offline learning algorithms. This purposely extreme position is intended to stimulate the development of machines that learn to dynamically organize the information by following human-based schemes. The proposition of this challenge suggests developing new foundations on computational processes of learning and reasoning that might open the doors to a truly orthogonal competitive track on AI technologies that avoid data accumulation by design, thus offering a framework which is better suited concerning privacy issues, control and customizability. Finally, pushing towards massively distributed computation, the collectionless approach to AI will likely reduce the concentration of power in companies and governments, thus better facing geopolitical issues.

Related papers

• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices [0.0]
  Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities.
  arXiv  Detail & Related papers  (2024-03-14T07:40:32Z)
• CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI [5.331052581441263]
  CollaFuse is a novel framework inspired by split learning. It enables shared server training and inference, alleviating client computational burdens. It has the potential to impact various application areas, such as the design of edge computing solutions, healthcare research, or autonomous driving.
  arXiv  Detail & Related papers  (2024-02-29T12:36:10Z)
• Private Knowledge Sharing in Distributed Learning: A Survey [50.51431815732716]
  The rise of Artificial Intelligence has revolutionized numerous industries and transformed the way society operates. It is crucial to utilize information in learning processes that are either distributed or owned by different entities. Modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes.
  arXiv  Detail & Related papers  (2024-02-08T07:18:23Z)
• Nature-Inspired Local Propagation [68.63385571967267]
  Natural learning processes rely on mechanisms where data representation and learning are intertwined in such a way as to respect locality. We show that the algorithmic interpretation of the derived "laws of learning", which takes the structure of Hamiltonian equations, reduces to Backpropagation when the speed of propagation goes to infinity. This opens the doors to machine learning based on full on-line information that are based the replacement of Backpropagation with the proposed local algorithm.
  arXiv  Detail & Related papers  (2024-02-04T21:43:37Z)
• Your Room is not Private: Gradient Inversion Attack on Reinforcement Learning [47.96266341738642]
  Privacy emerges as a pivotal concern within the realm of embodied AI, as the robot accesses substantial personal information. This paper proposes an attack on the value-based algorithm and the gradient-based algorithm, utilizing gradient inversion to reconstruct states, actions, and supervision signals.
  arXiv  Detail & Related papers  (2023-06-15T16:53:26Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application [9.977688793193012]
  Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention. Recent regulatory restrictions on data privacy preclude uploading sensitive local data to data centers. We propose an efficient industrial federated learning framework for AIoT in terms of a face recognition application.
  arXiv  Detail & Related papers  (2022-06-21T14:03:20Z)
• A review of Federated Learning in Intrusion Detection Systems for IoT [0.15469452301122172]
  Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. Deep learning technologies opened the door to build more complex and effective threat detection models. Current approaches rely on powerful centralized servers that receive data from all their parties. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection.
  arXiv  Detail & Related papers  (2022-04-26T17:00:07Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.