Although machine learning and deep learning are the elements of artificial intelligence, "deep learning" has been given the title of "smart star" in the family, improving the long-term standard of prediction accuracy. How interesting things will happen in this year when artificial intelligence and deep learning will develop.
As our daily lives are increasingly intertwined with a variety of technologies, sometimes it seems that the future has arrived. However, technology is still evolving and artificial intelligence (AI) has taken center stage in this field. With the support of many forward forces, artificial intelligence continues to inspire public imagination of the future. Innovations in Amazon's Alexa, Netflix's recommendation system, and SnapChat filters further fuel this belief, and these are excellent examples of artificial intelligence entering the field of personalization.
The most common artificial intelligence components, as well as the "smart stars" in the artificial intelligence family, are "deep learning." Deep learning is a model of data learning that has improved the long-standing criteria for predictive accuracy in recent years. In addition to traditional predictive modeling, it also has outstanding contributions in the fields of speech recognition and computer vision. However, as we welcome the New Year, things will become more interesting. Let's take a look at the 2018 deep learning (and broader artificial intelligence) situation.
Convolutional neural networks (almost) everywhereConvolutional neural networks are a complex learning model that has the advantage of requiring minimal pre-processing or "cleaning" of the data. Mainly used to "solve" visual image classification and processing, and now it is applied to more cases.
The idea is that the visual world is synthetic, so images can be broken down into the most basic features. For example, an image of a landscape consists of a variety of objects; these objects consist of outlines and lines, which in turn are made up of pixels. Covnets recognizes these components and creates a layered abstract world concept that makes identification tasks easier.
Currently, Facebook's photo tags and face recognition features use Covnets. In 2018, we can expect that Covnets will be more widely used in the field of autonomous driving. Tesla's ModelX is already using Covnets to implement autopilot related functions. More recently, companies like Quere.ai are using Covnets and have achieved significant success in the diagnosis of medical imaging. The company is expected to start looking for different applications for these highly accurate learning models.
Artificial intelligence will enhance data securityWhile machine learning and deep learning models have unprecedented predictive accuracy, some are still easily questioned. For example, in supervised machine learning, the model learns to mark certain features of the data, and the training and test data are assumed to come from the same data distribution. If the data is distorted in this hypothesis, the prediction accuracy of the model will be greatly affected. Take spam filtering as an example - if random text and images are added to the message, the message may bypass the spam detection system. That's why your inbox is stuffed with spam, even though there is a system that can block it.
Security giant McAfee believes that ransomware and other digital threats (such as "WannaCry" that panic the global community) are increasingly taking advantage of machine learning and deep learning technologies in 2018. Specifically, these models will threaten the detection model, learn from the defense response of the detection model, and exploit the discovered vulnerabilities to destroy the detection model faster than the defender fixes the vulnerability.
To defend against these technologies, McAfee engineers have been researching machine learning and building an advanced defense research team to create solutions for these vulnerabilities. The only way to truly defend against this type of attack is to create a more general learning model and even find the tiniest anomalies. In this regard, some interesting research is underway.
in conclusionIn the past two or three years, artificial intelligence and deep learning have exploded in the public domain, with some exciting products. In 2018 and in the next few years, they will increasingly appear in our daily interactions, especially in mobile applications.
As mobile hardware rapidly evolves, it will be able to support complex deep learning tasks. For example, Apple's iOS11 supports CoreML, a machine learning toolkit for iOS developers. In the future, developers will be able to deploy applications that support text prediction and image recognition (such as SnapChat) without any knowledge of machine learning. It is clear that the future of artificial intelligence and deep learning is full of energy and prospects. We see how fast this change and progress, and only time gives us the answer. Therefore, with the launch of the new year, let us wait and see what the performance of this segment is.
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