Artificial Intelligence (AI) is revolutionizing almost every industry. Deep Learning (DL), an AI methodology, is propelling the high-tech industry to the future with a seemingly endless list of applications ranging from object recognition for systems in autonomous vehicles to potentially saving lives -- helping doctors detect and diagnose cancer with greater accuracy. In this article, we'll outline some interesting applications of deep learning in cybersecurity and how you can use deep learning to improve security measures within your organization. Deep learning is a subtype of Machine Learning (ML) and belongs to the broader category of artificial intelligence. Deep learning uses Artificial Neural Networks (ANNs), which are designed to mimic the functionality and connectivity of neurons in the human brain. Deep learning gets its name because it uses deeper networks compared to other AI methods like ML.

The military's new gadget works by amplifying light that's already out there, either from the moon, stars or sources on the ground. The device senses tiny amounts of photons reflected off seemingly dark objects. Then, the photons pass over an internal surface engineered to convert light into electrons. The electrons are amplified by striking a quarter-sized glass plate that has millions of tiny holes in it. Then, they pass a screen coated with phosphor, a fluorescent substance, to create an image.

After reading a news article -- whether the subject matter is U.S. politics, a movie review, or a productivity tip -- you can turn to someone else and give them a general idea of what it's about, right? Or if you read a novel, you can classify it as maybe sci-fi, literary fiction, or a romance. Humans tend to be pretty good at classifying texts. And these days, computers can do it, too. For a recent machine learning project, I downloaded consumer complaints from the Consumer Financial Protection Bureau and developed models to classify the complaints into one of five product categories.

Nathan Copeland knows a thing or two about brain implants. More than a decade after a car crash left him paralyzed from the chest down, Copeland enrolled in a medical trial that helped him to regain his sense of touch. The breakthrough saw scientists implant chips in his brain that allowed him to control a robotic hand. Now, in his mid-30s, he's become the focal point of another scientific breakthrough. Thanks to a new brain interface experiment, Copeland was able to feel the sensation of touch when his robotic hand came into contact with a surface or object.

Join the virtual forum to hear from government and commercial sector executives as they share insight on how AI technology is shaping the future of …

Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the …

Cybersecurity seeks to leverage AI and ML to improve the speed at which they detect cyber threats and mitigate risks.

It felt good to love again, in that big empty house. Virginia Kellner got the cat last November, around her ninety-second birthday, and now it's always nearby. It keeps her company as she moves, bent over her walker, from the couch to the bathroom and back again. The walker has a pair of orange scissors hanging from the handlebar, for opening mail. Virginia likes the pet's green eyes.

As enterprise deployments of machine learning continue at a strong pace, including in mission-critical environments such as in contact centers, for fraud detection and in regulated sectors like healthcare and finance for example, they are doing so against a backdrop of rising and evermore ferocious cyberattacks. Take, for example, the SolarWinds hack in December 2020, arguably one of the largest on record, or the recent exploits that hit Exchange servers and affected tens of thousands of customers. Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the technology arriving in April 2021. The EU's landmark proposals build on GDPR legislation, carrying heavy penalties for enterprises that fail to consider the risks and ensure that trust goes hand in hand with success in AI. Altogether, a climate is emerging in which the significance of securing machine learning can no longer be ignored.

A report released Wednesday outlines how impactful today's artificial intelligence and neural networks could be if programmed to automate disinformation campaigns. Conducted by Georgetown's Center for Security and Emerging Technology, the report studies how OpenAI's GPT-3--a powerful AI system that generates text based on prompts from humans--could automate the future generation of disinformation campaigns. Researchers looked into GPT-3's capabilities after it authored a September op-ed in The Guardian--the first article written entirely by AI. "If GPT-3 can write seemingly credible news stories, perhaps it can write compelling fake news stories; if it can draft op-eds, perhaps it can draft misleading tweets," the report states. "In light of this breakthrough, we consider a simple but important question: can automation generate content for disinformation campaigns?" Researchers evaluated GPT-3's performance on six tasks common to most disinformation campaigns, including the operation carried out by Russia's Internet Research Agency in 2016.