Artificial intelligence is driving a new industrial revolution. Its scope of influence involves personal lives in a small scale, and it affects national security in a large scale. It makes people feel a little worried while looking forward to it. In fact, the current state of development of artificial intelligence is not exactly the same as the assumptions made by futurists or science fiction movies.
At present, more and more industries are developing artificial intelligence technology. New concepts and new algorithms are emerging in an endless stream. However, the main application fields of artificial intelligence are image recognition and language translation. The image recognition field is more advanced than language translation. The former is in some cases. The index exceeds the human level, but the language translation ability is not as good as that of human beings. This is the basic situation of the progress of this technology.
Specifically, artificial intelligence can do the following four aspects of work.
First, data classification. Artificial intelligence can classify data such as images or sounds, the processing of camera-collected data in the security field, and the recognition and diagnosis of medical images. These two fields are developing rapidly and have broad industrial prospects.
Second, anomaly detection. Artificial intelligence can help detect abnormal behaviors such as fraudulent financial transactions or new malicious software, and can be used to monitor large-scale data in real time. The central bank and other market financial institutions are working on similar applications.
Third, forecast. After discovering a certain pattern from massive data, artificial intelligence can predict future behavior based on statistical characteristics. This is widely used in fields such as automatic filling of search engines, video and product recommendations on online shopping platforms, and can also be used for weather forecasts.
Fourth, optimization. Artificial intelligence can optimize complex systems and tasks. For example, the US "Deep Thinking" company used machine learning to optimize the energy efficiency of Google's data center, saving 40% of the energy used for cooling, and increasing the overall energy efficiency by 15%.
In summary, artificial intelligence does have many advantages. Its response time is faster than that of humans. In many fields and scenarios, it has better accuracy, reliability and agility than humans, and it is more patient and alert than humans. At the same time, it can work independently of humans.
Weaknesses that cannot be ignored in the applicationIt cannot be ignored that the current artificial intelligence technology also has weaknesses. The US think tank "New American Security Center" recently released the report "Artificial Intelligence: What Every Decision Maker Needs to Know", suggesting that some of the weaknesses of artificial intelligence may have implications for national security and other fields. Have a huge impact.
First, vulnerability. The current artificial intelligence system is still unable to understand behavior beyond the scene or context. It will not expose this weakness in the scope of fixed rules such as chess or games. Once the scene changes or the change exceeds a certain range, the artificial intelligence You may not be able to "think" immediately.
Second, unpredictability. Users cannot predict what decisions artificial intelligence will make. This is both an advantage and also a risk, because the system may make decisions that are not in line with the original intention of the designer.
Third, weak interpretability. Artificial intelligence may help humans make good decisions, but the reason for the decision is still a "black box." For example, image recognition capabilities can find school buses in pictures, but cannot explain which image features make it make such judgments. In the field of medical diagnosis, the reason for diagnosis is often important.
Fourth, security issues and loopholes. The machine will focus on the result rather than the process. By finding the loopholes in the system, it can achieve the goal in the literal sense, but the method it adopts is not necessarily the original intention of the designer. For example, the website will recommend some extremist videos because irritating content can increase browsing time. For another example, the network security system will determine that people are the main cause of destructive software implantation, so people are simply not allowed to enter the system.
Sixth, system accidents. In an adversarial environment where algorithms cannot be shared, systematic errors can easily occur, leading to incalculable decisions. For example, in the field of national security, two competing algorithms will adopt certain behaviors that lead to devastating consequences in order to gain advantages. Especially in the process of network security and electronic warfare, the confrontational decision-making will be very rapid, and it is almost too late for humans to stop.
Seventh, the human-computer interaction failed. Although letting machines provide suggestions and humans making the final decision is a common way to solve some of the weaknesses of artificial intelligence, this problem cannot be fundamentally solved due to the different cognitive abilities of decision makers on system limitations or system feedback. In 2016, in the crash of Tesla's self-driving car, human operators failed to understand the prompts given by the system and a fatal accident occurred. Similar challenges are faced in many fields such as military, border security, traffic safety, and law enforcement.
Eighth, machine learning vulnerabilities can be exploited by adversaries. In the adversarial process, the adversary will also release toxic data or use certain loopholes in the behavior of artificial intelligence to launch attacks. There is no effective way to resist such attacks.
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