AI News, ARPA artificial intelligence

Stephen Lukasik, 88, Who Pushed Tech in National Defense, Is Dead

His incentive at the time, he wrote in a reminiscence, was to assist the National Security Agency, which employed “vast numbers of transcribers and translators to make sense of a multitude of communication channels they monitored.” In one instance he had ARPA researchers work on using artificial intelligence to transcribe manual Morse code.

His contribution to the Arpanet, he wrote in 2014, “was helping to wrench this R&D project away from its creators, who would have liked to research it forever,” in part to promote military innovation.

Stephen began reading scientific literature at age 10 and at 14 knew what direction his career would take after reading newspaper accounts of the atomic bomb attacks on Japan.

Explainable artificial intelligence

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts.

AI systems optimize behavior to satisfy a mathematically-specified goal system chosen by the system designers, such as the command 'maximize accuracy of assessing how positive film reviews are in the test dataset'.

AI systems sometimes learn undesirable tricks that do an optimal job of satisfying explicit pre-programmed goals on the training data, but that do not reflect the complicated implicit desires of the human system designers.

For example, a 2017 system tasked with image recognition learned to 'cheat' by looking for a copyright tag that happened to be associated with horse pictures, rather than learning how to tell if a horse was actually pictured.[1]

In another 2017 system, a supervised learning AI tasked with grasping items in a virtual world learned to cheat by placing its manipulator between the object and the viewer in a way such that it falsely appeared to be grasping the object.[6][7]

The term 'glass box' has also been used to a system that monitors the inputs and outputs of a system, with the purpose of verifying the system's adherence to ethical and socio-legal values and, therefore, producing value-based explanations [11].

Researchers in clinical expert systems creating neural network-powered decision support for clinicians have sought to develop dynamic explanations that allow these technologies to be more trusted and trustworthy in practice.[2]

In the 2010s public concerns about racial and other bias in the use of AI for criminal sentencing decisions and findings of creditworthiness may have led to increased demand for transparent artificial intelligence.[1]

As regulators, official bodies and general users come to depend on AI-based dynamic systems, clearer accountability will be required for decision making processes to ensure trust and transparency.

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