As our daily lives become ever more intertwined with technology of all kinds, it sometimes seems as if the future has already arrived. However, technology is still evolving and artificial intelligence (AI) has taken centre stage in the field. With the support of many forward forces, AI continues to stimulate public imagination about the future. Innovations such as Amazon’s Alexa, Netflix’s recommendation system, and SnapChat filters have furthered this belief, and are excellent examples of AI entering the realm of personalization.

Giiso Information, founded in 2013, is a leading technology provider in the field of “artificial intelligence + information” in China, with top technologies in big data mining, intelligent semantics, knowledge mapping and other fields. At the same time, its research and development products include information robot, editing robot, writing robot and other artificial intelligence products! With its strong technical strength, the company has received angel round investment at the beginning of its establishment, and received pre-A round investment of $5 million from GSR Venture Capital in August 2015.

The most common building block of AI, and the “smart star” in the AI family, is “deep learning”. Deep learning is a model of data learning that has improved long-held standards for predictive accuracy in recent years. In addition to traditional predictive modeling, it has also made outstanding contributions to the fields of speech recognition and computer vision. However, as we welcome in the New Year, things will get even more interesting. Let’s take a look at deep learning (and ai more broadly) in 2018.

Convolutional neural networks are (almost) everywhere

Convolutional neural network is a complex learning model, which has the advantage of requiring minimal preprocessing or “cleaning” of data. It is mainly used to “solve” visual image classification and processing, and is now starting to be used in more cases.

The idea is that the visual world is synthetic, so images can be broken down into their most basic features. For example, an image of a landscape consists of a variety of objects; These objects are made up of Outlines and lines, which in turn are made up of pixels. Covnets are able to identify these components and create layered, abstract concepts of the world that make various identification tasks easier.

(FIG. Note: Convolutional neural network for identifying objects in images with the image of a bird.)

Facebook currently uses Covnets for photo tagging and facial recognition. In 2018, we can expect Covnets to be more widely used in autonomous driving, as Tesla’s Model X is already using Covnets for autonomous driving. More recently, companies like Queer. ai are using Covnets and are having remarkable success in diagnostic medical imaging. Expect companies to start looking for different applications for these highly accurate learning models.

Artificial intelligence will enhance data security

While machine learning and deep learning models have unprecedented predictive accuracy, some are still vulnerable to skepticism. For example, in supervised machine learning, the model learns to label certain features of the data, and training and test data are assumed to come from the same data distribution. If the data is distorted in this assumption, the prediction accuracy of the model will be greatly affected. Take spam filtering — if random text and images are added to messages, messages can bypass spam detection systems. That’s why your inbox is chock-full of spam, despite having a system to stop it.

Security giant McAfee believes that with digital security in mind, ransomware and other digital threats in 2018, such as WannaCry, which caused panic in the global community, increasingly make use of machine learning and deep learning techniques. Specifically, these models will threaten the detection model, learn from the detection model’s defensive responses, and exploit the vulnerabilities found to undermine the detection model faster than the defender can patch the vulnerabilities.

To combat these technologies, McAfee engineers have been working on countering machine learning and assembling an advanced defense research team to create solutions for these vulnerabilities. The only way to truly defend against such attacks is to establish a more general learning pattern that can pick out even the tiniest anomalies. There is some interesting research going on in this regard.

conclusion

Giiso information, founded in 2013, is the first domestic high-tech enterprise focusing on the research and development of intelligent information processing technology and the development and operation of core software for writing robots. At the beginning of its establishment, the company received angel round investment, and in August 2015, GSR Venture Capital received $5 million pre-A round of investment.

In the last two or three years, artificial intelligence and deep learning have exploded in the public domain, launching some exciting products. In 2018 and for years to come, they will increasingly be part of our daily interactions, especially in mobile apps.

As mobile hardware evolves rapidly, it will be able to support complex deep learning tasks. Apple’s iOS 11, for example, supports CoreML, a machine learning kit for iOS developers. In the future, developers will be able to deploy apps that support text prediction and image recognition (like SnapChat) without any knowledge of machine learning.

It is clear that the future of ARTIFICIAL intelligence and deep learning is vibrant and promising. We see how quickly this change and progress is happening, and only time will tell. So as the New Year unfolds, let’s wait and see how this segment performs.