It’s a question that many of us encounter in childhood: “Why did you do that?” As artificial intelligence (AI) begins making more consequential decisions that affect our lives, we also want these machines to be capable of answering that simple yet profound question. After all, why else would we trust AI’s decisions?
This desire for satisfactory explanations has spurred scientists at the National Institute of Standards and Technology (NIST) to propose a set of principles by which we can judge how explainable AI’s decisions are. Their draft publication, Four Principles of Explainable Artificial Intelligence (Draft NISTIR 8312), is intended to stimulate a conversation about what we should expect of our decision-making devices. Read More
Daily Archives: August 20, 2020
Renewed calls for increasing AI research and development funds
If you think the United States should lead the world in artificial intelligence then the country should have a national strategy for AI. That’s the thinking behind a series of white papers coming from the Bipartisan Policy Center, and to members of Congress. The first white paper is out and it deals with AI in the workplace. Texas Republican Rep. Will Hurd had more details on Federal Drive with Tom Temin. Read More
Role Of AI In Cyber Security Attacks
Cyber security is a constant concern for businesses of all sizes. There are countless threats to organizational data by a growing host of bad actors, Role Of AI
And the risks of a cyberattack on your business are only growing. A recent study found that 76 percent of U.S. businesses had experienced a cyberattack last year alone. Given the large number of remote workers logging into company files from unsecured networks with no IT supervision, it’s not a question of “if” but “when” your company will become infiltrated. For most businesses, well-known hacks like ransomware or phishing are top of mind. However, attackers are now utilizing new tools and carrying out more detailed campaigns to breach defenses. This calls for more sophisticated defense mechanisms that make use of Artificial Intelligence (AI) and Machine Learning (ML) to protect your technology assets. Read More
Democratization of AI
When company leaders talk about democratizing artificial intelligence (AI), it’s not difficult to imagine what they have in mind. The more people with access to the raw materials of knowledge, tools, and data required to build an AI system, the more innovations that are bound to emerge. Efficiency improves and engagement increases. Faced with a shortage of technical talent? Microsoft, Amazon, and Google have all released premade, drag-and-drop or no-code AI tools that allow people to integrate AI into applications without needing to know how to build machine learning models.
But as companies move toward democratization, a cautionary tale is emerging. Even the most sophisticated AI systems, designed by highly qualified engineers, can fall victim to bias, explainability issues, and other flaws. Read More
Not All Unlabeled Data are Equal:Learning to Weight Data in Semi-supervised Learning
Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples,i.e., all unlabeled examples are equally weighted. But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights – as done in prior work – is no longer possible. Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model’s dependency on one training example. To make the approach efficient, we propose a fast and effective approximation of the influence function. We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. Read More
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