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Artificial Intelligence

To create a superior AI assistant, begin by replicating the unpredictable actions of human beings.

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This small microchip can protect user information while aiding in the effective operation of a mobile phone.

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a hardware solution that enhances the security of machine-learning applications on smartphones. Current health-monitoring apps require large amounts of data to be transferred back and forth between the phone and a central server, which can create security vulnerabilities and inefficiency. To counter this, the…

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This small microchip can protect user information while facilitating effective computing on a mobile phone.

Researchers from MIT and MIT-IBM Watson AI Lab have created a machine-learning accelerator that is resistant to the most common types of cyber attacks. The chip can hold users' sensitive data such as health records and financial information, enabling large AI models to run efficiently on devices while maintaining privacy. The accelerator maintains strong security,…

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Home automation robots learn through an authentic simulation-to-reality cycle.

Roboticists and researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) are working to develop a system that can train robots to perform tasks in specific environments effectively. The ongoing research aims to help robots deal with disturbances, distractions, and changes in their operational environments. For this, they have proposed a method to create…

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This AI Article Discusses an Overview of Modern Techniques Implemented for Denial in LLMs: Establishing Assessment Standards and Indicators for Evaluating Withholdings in LLMs.

A recent research paper by the University of Washington and Allen Institute for AI researchers has examined the use of abstention in large language models (LLMs), emphasizing its potential to minimize false results and enhance the safety of AI. The study investigates the current methods of abstention incorporated during the different development stages of LLMs…

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Stanford researchers introduce RelBench: A Public Benchmark for Deep Learning within Relational Databases.

Relational databases are fundamental to many digital systems, playing a critical role in data management across a variety of sectors, including e-commerce, healthcare, and social media. Through their table-based structure, they efficiently organize and retrieve data that's crucial to operations in these fields, and yet, the full potential of the valuable relational information within these…

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Stumpy: An Efficient and Extensible Python Tool for Contemporary Time Series Analysis

Time series data, used across sectors including finance, healthcare, and sensor networks, is of fundamental importance for tasks including anomaly detection, pattern discovery, and time series classification, informing crucial decision-making and risk management processes. Extracting useful trends and anomalies from this extensive data can be complex and often requires an immense amount of computational resources.…

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