Red Hat Research Quarterly
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Since the inception of artificial intelligence research, computer scientists have aimed to devise machines that think and learn like human beings. What else could AI do?
Parallelism promises to make programs faster, yet it also opens many new pitfalls and makes testing programs much harder.
To design effectively for our users, we need to learn more about them. If we don’t, we may make a product that our users can’t be efficient in, or worse, a product that our users have no need for in the first place.
The recent advances in AI and telecommunications are enabling a new set of complex cyber-physical systems, including those for safety-critical applications.
Bayesian statistical methods can make predictive data analysis more accurate. In this article, we evaluate possible solutions to the challenge of refining and increasing the value of high-volume data streams.
Bayesian statistical methods can make predictive data analysis more accurate. In this article, we evaluate possible solutions to the challenge of refining and increasing the value of high-volume data streams.
If safety-critical systems fail, they can cause significant damage, including loss of life. In this article we consider methods to verify their behavior in production.
Why teaching more teachers is essential to computer science education.
How did a group of three library students become part of an international force for promoting programming education? A Red Hatter who was there has the story.
Passwords made are to be memorable, so they are not usually secure enough for encryption software. That’s where derivation functions come in, transforming a password into a more suitable cryptographic key.
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