Artificial intelligence (AI) technology is advancing rapidly. Demand for AI technology is rising as companies transition to automation. AI offers unprecedented advances in verticals across industries, including advertising, healthcare, logistics, transportation, and more.
Due to the rapid adoption of artificial intelligence technology, the demand for training data sets is growing exponentially. In an effort to make AI predictions more general and accurate, many companies are entering the market by publishing various data sets to train machine learning (ML) algorithms that run across various models. These factors have greatly contributed to the growth of the AI market.
What are the different types of AI?
At a very high level, AI can be divided into two broad categories:
Narrow artificial intelligence
The narrow definition of AI is everything we see in computers today — intelligent systems that have been taught or learned how to perform specific tasks without having to explicitly program how to do it.
This type of machine intelligence exists in speech and language recognition systems for virtual assistants on smartphones, visual recognition systems for self-driving cars, or recommendation engines that suggest products a user might like based on their preferences. Unlike humans, these systems can only learn or be taught how to perform defined tasks, which is why they are called narrow ai.
General artificial intelligence
General AI is very different, it is the type of adaptive intelligence found in humans, a flexible form of intelligence that learns how to perform radically different tasks, from haircuts to constructing tables or reasoning experiences based on the variety of topics it has accumulated.
This is the kind of AI more commonly seen in movies, like Skynet in Terminator, but it doesn’t exist today — ai experts are bitterly divided over how soon it will become a reality.
What can general-purpose AI do?
A 2012/13 survey of four groups of experts by the artificial intelligence researcher Vincent C Muller and the philosopher Nick Bostrom reported that The probability of developing general artificial intelligence (AGI) between 2040 and 2050 is 50%, rising to 90% by 2075. The team went further, predicting that so-called “superintelligence” — which Bostrom defines as “any intelligence that substantially exceeds human cognitive ability in almost all areas of interest” — is expected to occur about 30 years after AGI is achieved.
However, ai experts have been more cautious in their recent assessments. Pioneers in modern AI research, such as Geoffrey Hinton, Demis Hassabis and Yann LeCun, say society is far from developing AGI. Given the skepticism of the leaders in the field of modern AI and the very different nature of modern narrow AI systems from AGI, fears that general-purpose AI will disrupt society in the near future may be unfounded.
That said, some AI experts believe that this prediction is overly optimistic, given our limited understanding of the human brain, and that AGI is still centuries away.
Current AI technologies are still narrowly defined as ARTIFICIAL intelligence, such as face recognition, object recognition, object movement and so on. TSINGSEE black rhino video based on many years of experience in the field of video technology, in the field of artificial intelligence technology + video, continuously research and development, also will be AI detection, intelligent recognition technology integration to various video application scenarios, such as: security monitoring, face detection in video, traffic statistics, risk behavior (rising, falling, shoving, etc.) to detect recognition, etc.
Typical examples are EasyCVR video fusion cloud service, which has AI face recognition, license plate recognition, voice intercom, PTT control, sound and light alarm, surveillance video analysis and data summary capabilities.
What is video recognition?
It is the ability of computers to acquire, process, and analyze data from visual sources (i.e., video). In other words, it allows the computer to “see” thousands of video streams and “make sense” of the information it receives frame by frame.
Video tracking is one of the main differences between image recognition and video recognition. Specifically, it can associate target objects in successive video frames to locate moving objects over time. Video recognition, like computer vision, relies on deep learning.
Where can video recognition be used?
For example, you could equip surveillance cameras with ai-trained video recognition systems to detect anomalies. The video stream serves as the input. When the smart camera detects an anomaly, the system generates an identification result (such as an automatic alarm).
The benefits of using artificial intelligence
**1, Higher quality: ** Unlike humans, algorithms don’t get tired and don’t lose focus. AI models always provide predictable output. The quality of the output depends on the degree of training of the algorithm.
**2, higher efficiency: ** staff do not need to view all the camera videos, but only view the camera videos that may have abnormal situations.
**3, continuity: **AI model does not get sick, does not take a vacation, can work 24×7.
**4, scalability: ** One AI model can be easily copied to other virtual machines to improve processing speed.
5. Faster decisions: ** Because people are able to get more done in less time, they can speed up the decision-making process for camera shots that require intervention. This is a great advantage in an emergency. For example, EasyCVR for security video surveillance and other scenes, support RTSP/RTMP/HTTP-FLV/WS-FLV/HLS and other video streaming formats, support cloud video recording, retrieval, playback, storage and other security video surveillance capabilities, Real-time and automatic detection and recognition of abnormal conditions in the monitoring area (strangers wandering around, climbing and breaking into, fighting, fireworks, etc.). Once abnormal conditions are found, timely capture and save, and upload the alarm information to the platform for manual intervention.