Los Alamos National Laboratory computer scientists have developed a new artificial intelligence (AI) system that may be able to identify malicious codes that hijack supercomputers to mine for cryptocurrency such as Bitcoin and Monero.
“Based on recent computer break-ins in Europe and elsewhere, this type of software watchdog will soon be crucial to prevent cryptocurrency miners from hacking into high-performance computing facilities and stealing precious computing resources,” said Gopinath Chennupati, a researcher at Los Alamos National Laboratory and co-author of a new paper in the journal IEEE Access. “Our artificial intelligence model is designed to detect the abusive use of supercomputers specifically for the purpose of cryptocurrency mining.” Read More
Monthly Archives: August 2020
Facebook and NYU use artificial intelligence to make MRI scans four times faster
AI learns to create MRI scans from a quarter of the data.
If you’ve ever had an MRI scan before, you’ll know how unsettling the experience can be. You’re placed in a claustrophobia-inducing tube and asked to stay completely still for up to an hour while unseen hardware whirs, creaks, and thumps around you like a medical poltergeist. New research, though, suggests AI can help with this predicament by making MRI scans four times faster, getting patients in and out of the tube quicker.
The work is a collaborative project called fastMRI between Facebook’s AI research team (FAIR) and radiologists at NYU Langone Health. Read More
Can artificial intelligence prompt a creative revolution?
It’s an unlikely partnership, but AI can stimulate a ‘new brand’ of human creativity.
- Thanks to our ability to contextualize, think metaphorically and define new patterns, human creativity remains distinguished from machine’s
- Creative transformation using AI is not a one-off change, but a long-form process
- Through freeing up our time, giving us prompts and future-proofing our efforts, AI may help us realize new, exciting, creative directions
Denis Shiryaev used AI to remaster the oldest recorded video, “Roundhay Garden Scene”, England,1888
What is s driving the innovation in NLP and GPT-3?
What’s driving the rapid strides in NLP and will this trend continue?
Here is a simple way to explain the rise and rise of NLP.
Today, GPT-3 is displaying some amazing results. Some call it more like AGI (Artificial General Intelligence). Created by OpenAI with a large investment from Microsoft, GPT stands for Generative Pretrained Transformer.
The three words offer a clue to the success and future trajectory of NLP. Read More
NIST Asks AI to Explain Itself
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
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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