If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Researchers from the University of Missouri and the University of North Carolina at Charlotte with image processing and cybersecurity expertise have been awarded nearly $1.2 million from the National Science Foundation to find out. They're designing an AI program they believe will need only a small number of deepfake examples to start to build its knowledge base. As it learns, the program will be able to spot new deepfake techniques, making more accurate detections and preventing mistakes in identifying content. Relying on a small number of examples overcomes the current challenges of algorithms that typically need a vast number of labeled samples to learn from. By leveraging accumulated knowledge, the deepfake detector will also learn to prevent camouflaged or obscured visual content from being classified as genuine content.
Historically, cybersecurity has been a field dominated by resource-intensive efforts. Monitoring, threat hunting, incident response, and other duties are often manual and time-intensive, which can delay remediation activities, increase exposure, and heighten vulnerability to cyber adversaries. Over the past few years, artificial intelligence solutions have rapidly matured to the point where they can bring substantial benefits to cyber defensive operations across a broad range of organizations and missions. By automating key elements of labor-heavy core functions, AI can transform cyber workflows into streamlined, autonomous, continuous processes that speed remediation and maximize protection.
For example, this spring, the 36th America's Cup is scheduled to showcase the space-age, super-fast AC75 mono-hull yacht. In trials, digital twin technology was used by one team to emulate the performance of sailors in a new AC75 boat; a feat that radically accelerated prototype development compared with previous testing methods. Now deployed in areas such as city planning, healthcare and automotive design, digital twinning also holds immense potential for the manufacturing sector – helping in its ongoing quest for enhanced safety, improved productivity and greater efficiency. Given its newfound status, the term'digital twins' has become somewhat of a catch-all for various associated strains of technical innovation. Augmented reality, enhanced user interfaces and 3D-modelling – to name a few.
For a more in-depth explanation of Forward Propagation and Backpropagation in neural networks, please refer to my other article What is Deep Learning and How does it work? For a given input vector x the neural network predicts an output, which is generally called a prediction vector y. We must compute a dot-product between the input vector x and the weight matrix W1 that connects the first layers with the second. After that, we apply a non-linear activation function to the result of the dot-product. Depending on the task we want the network to do, this prediction vector represents different things.
While taking the first step into the field of machine learning, it is so easy to get overwhelmed by all kinds of complex algorithms and ugly symbols. Therefore, hopefully, this article can lower the entry barrier by providing a beginner-friendly guide. Allow you to get a sense of achievement by building your own ML model using BigQuery and SQL. That's right, we can use SQL to implement machine learning. In a nutshell, BigQuery project contains datasets and a dataset contains tables and models.
Have you brought a dead relative back to life recently? I've revived a half-dozen in the past few days, with varying degrees of success. Grandma was a bit off. Something in her smile wasn't convincing, and when she moved her head, her mouth and eyes didn't quite move at the same speed. But her husband turned out well, and when he looked at me and made a slight smile, it was almost as if to say, "Thanks, kid. Remember when we played hide the thimble, and I'd say'hot' or'cold,' and you'd get a peppermint lozenge when you found it?"
When a new client approaches us in our early days, we are often required to make a business proposal to help them adopt AI and transform their business operations. We used to approach it from the technical mindset: which AI problem is applicable for this business? It would be best if you put the business first. You need first to identify a problem that is worth solving. Whether it can be solved or not can be dealt with later, but at this point, your focus needs to solely on the business, and it's priorities.
The search for planets orbiting other stars has reached industrial scale. Astronomers have discovered over 4,000 of them, more than half using data from the Kepler space telescope, an orbiting observatory designed for this purpose. Launched in 2009, Kepler observed a fixed field of view for many months, looking for the tiny periodical changes in stars' brightness caused by planets moving in front of them. But in 2012 the mission ran into trouble when one of the spacecraft's four reaction wheels failed. These wheels stabilize the craft, allowing it to point accurately in a specific direction.