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Deep-learning-based multisensor fusion creates opportunities and challenges. [ABSTRACT FROM AUTHOR]

Turning toward nature, I contend that biological artificial general intelligence design is based on five pillars. I propose a crucible approach that would "melt" these elements by using (the equivalent of) computational "heat." [ABSTRACT FROM AUTHOR]

As artificial intelligence models rapidly grow in size, their potential is stimulated. [ABSTRACT FROM AUTHOR]

On the color spectrum, energy-hungry artificial intelligence (AI) is red, and energy-efficient AI is green. How will the colors of AI be represented in the future? [ABSTRACT FROM AUTHOR]

Adversarial machine learning (AML) is a recent research field that investigates potential security issues related to the use of machine learning (ML) algorithms in modern artificial intelligence (AI)-based systems, along with defensive techniques to protect ML algorithms against such threats. The main threats against ML encompass a set of techniques that aim to mislead ML models through adversarial input perturbations. Unlike ML-enabled crimes, in which ML is used for malicious and offensive purposes, and ML-enabled security mechanisms, in which ML is used for securing existing systems, AML techniques exploit and specifically address the security vulnerabilities of ML algorithms. [ABSTRACT FROM AUTHOR]

Recent artificial intelligence (AI) advances and the consequent gains in convenience signify that AI adoption is imperative. The major questions are how AI may be used appropriately and what the contexts for such use are. [ABSTRACT FROM AUTHOR]

In the application of cloud storage, a user no longer possesses his files in his local depository. Thus, he is concerned about the security of the stored files. Data confidentiality and data robustness are the main security issues. For data confidentiality, the user can first encrypt files and then store the encrypted files in a cloud storage. For data robustness, there are two concerns: service failure, and service corruption. We are concerned about data robustness in cloud storage services. Lin and Tzeng proposed a secure erasure code-based storage system with multiple key servers recently. Their system supports a repair mechanism, where a new storage server can compute a new ciphertext from the ciphertexts obtained from the remaining storage servers. Their system considers data confidentiality in the cloud, and data robustness against storage server failure. In this paper, we propose an integrity check scheme for their system to enhance data robustness against storage server corruption, which returns tampered ciphertexts. With our integrity check scheme, their storage system can deal with not only the problem of storage server failure, but also the problem of storage server corruption. The challenging part of our work is to have homomorphic integrity tags. New integrity tags can be computed from old integrity tags by storage servers without involvement of the user's secret key or backup servers. We prove the security of our integrity check scheme formally, and establish the parameters for achieving an overwhelming probability of a successful data retrieval. [ABSTRACT FROM AUTHOR]

A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the Internet. Storing data in a third party's cloud system causes serious concern over data confidentiality. General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness. [ABSTRACT FROM PUBLISHER]

Many practical applications benefit from machine learning and artificial intelligence technologies, but their security needs to be studied in more depth. We discuss the risk of poisoning attacks against the training stage of machine learning and challenges of defending against them. [ABSTRACT FROM AUTHOR]

Distributed networked storage systems provide the storage service on the Internet. We address the privacy issue of the distributed networked storage system. It is desired that data stored in the system remain private even if all storage servers in the system are compromised. The major challenge of designing these distributed networked storage systems is to provide a better privacy guarantee while maintaining the distributed structure. To achieve this goal, we introduce secure decentralized erasure code, which combines a threshold public key encryption scheme and a variant of the decentralized erasure code. Our secure distributed networked storage system constructed by the secure decentralized erasure code is decentralized, robust, private, and with low storage cost. [ABSTRACT FROM PUBLISHER]

Wide-scale adoption of intelligent algorithms requires artificial intelligence (AI) engineers to provide assurances that an algorithm will perform as intended. In this article, we discuss the formalization of important aspects of AI assurance, including its key components. [ABSTRACT FROM AUTHOR]

If society demands that a bank's use of artificial intelligence systems is "fair," what is the bank to actually do? This article outlines a pragmatic and defensible answer. [ABSTRACT FROM AUTHOR]