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) …
We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.
Moscow, Russia, 2020-Nov-27 -- /Travel PR News/ -- Sergei Konyakhin, Director of the Production Modeling Department of JSC Sheremetyevo International Airport, gave a presentation at the Artificial Intelligence Systems 2020 on November 24 conference showing how Sheremetyevo International Airport uses artificial intelligence (AI) systems to effectively manage the airport. The conference was part of the online forum TAdviser Summit 2020: Results of the Year and Plans for 2021. The discussion among of top managers of large companies and leading experts in the IT industry centered on issues related to the implementation of artificial intelligence technologies in the activities of Russian enterprises. Sheremetyevo Airport has developed and implemented systems for automatic long-term and short-term planning of personnel and resources. As a result, the planning system was calibrated based on real processes and its previous weaknesses were eliminated; recommendation systems were implemented allowing dispatchers to manage resources taking into account future events; and the company was able to significantly optimize expenses.
As deep learning has grown in popularity over the last two decades, more and more companies and developers have created frameworks to make deep learning more accessible. Now there are so many deep learning frameworks available that the average deep learning practitioner probably isn't even aware of all of them. With so many options available, which framework should you pick? In this article, I will give you a tour of some of the most common Python deep learning frameworks and compare them in a way that allows you to decide which framework is the right one to use in your projects. I have purposely bundled these two frameworks together because the latest versions of TensorFlow are tightly integrated with Keras.
Deep learning models (aka neural nets) now power everything from self-driving cars to video recommendations on a YouTube feed, having grown very popular over the last couple of years. Despite their popularity, the technology is known to have some drawbacks, such as the deep learning "reproducibility crisis"-- as it is very common for researchers at one to be unable to recreate a set of results published by another, even on the same data set. Additionally, the steep costs of deep learning would give any company pause, as the FAANG companies have spent over $30,000 to train just a single (very) deep net. Even the largest tech companies on the planet struggle with the scale, depth, and complexity of venturing into neural nets, while the same problems are even more pronounced for smaller data science organizations as neural nets can be both time-and cost-prohibitive. Also, there is no guarantee that neural nets will be able to outperform benchmark models like logistic regression or gradient-boosted ones, as neural nets are finicky and typically require added data and engineering complexities.
A graphic illustration showing how the technology combines the core software needed to drive AI with image-capturing hardware, in a single electronic device. Prototype tech shrinks AI to deliver brain-like functionality in one powerful device. Researchers have developed artificial intelligence technology that brings together imaging, processing, machine learning, and memory in one electronic chip, powered by light. The prototype shrinks artificial intelligence technology by imitating the way that the human brain processes visual information. The nanoscale advance combines the core software needed to drive artificial intelligence with image-capturing hardware in a single electronic device.
In machine learning (ML), if the situation when the model does not generalize well from the training data to unseen data is called overfitting. As you might know, it is one of the trickiest obstacles in applied machine learning. The first step in tackling this problem is to actually know that your model is overfitting. That is where proper cross-validation comes in. After identifying the problem you can prevent it from happening by applying regularization or training with more data. Still, sometimes you might not have additional data to add to your initial dataset. Acquiring and labeling additional data points may also be the wrong path. Of course, in many cases, it will deliver better results, but in terms of work, it is time-consuming and expensive a lot of the time.
The Great Barrier Reef is the world's largest coral reef ecosystem and one of the seven natural wonders of the world. However, it is under great threat. A recent study by the ARC Centre of Excellence Coral Reef Studies revealed that the Great Barrier Reef has lost half its coral in the past three decades to mass bleaching events caused by rising water temperatures. Reef corals have an annual reproduction event every November and December that has the potential to see healthy coral spread their larvae, with the help of the ocean's current, to parts of the reef that have been affected by bleaching. The challenges faced by researchers are figuring out how to identify and map these healthy reefs, how to evaluate the way reefs can be protected, and how to monitor the dangers faced by corals.
In machine learning terminology, the sum of squared error is called the "cost". This equation is therefore roughly "sum of squared errors" as it computes the sum of predicted value minus actual value squared. The 1/2mis to "average" the squared error over the number of data points so that the number of data points doesn't affect the function. See this explanation for why we divide by 2. In gradient descent, the goal is to minimize the cost function. We do this by trying different values of slope and intercept.
If you're worried about facial recognition firms or stalkers mining your online photos, a new tool called Anonymizer could help you escape their clutches. The app was created by Generated Media, a startup that provides AI-generated pictures to customers ranging from video game developers creating new characters to journalists protecting the identities of sources. The company says it built Anonymizer as "a useful way to showcase the utility of synthetic media." The system was trained on tens of thousands of photos taken in the Generated Media studio. The pictures are fed to generative adversarial networks (GANs), which create new images by pitting two neural networks against each other: a generator that creates new samples and a discriminator that examines whether they look real. The process creates a feedback loop that eventually produces lifelike profile photos.
Buoy Health, a Boston, MA-based AI-powered healthcare navigation platform, today announced the completion of a $37.5 million Series C funding round. Cigna Ventures and Humana led the funding round and were joined by Optum Ventures, WR Hambrecht Co, and Trustbridge Partners. To date, Buoy has raised $66.5 million. Today, hospitals and insurance companies are increasingly investing in digital health innovations like Buoy to solve problems related to accessing the healthcare system and helping patients to get to the right care setting on the first attempt. Founded in 2014 by a team of doctors and computer scientists working at the Harvard Innovation Laboratory, Buoy Health uses AI technology to provide personalized clinical support the moment an individual has a health concern.