What is Artificial Intelligence Safety (AI Safety), in one article

Definition of Artificial Intelligence Safety

Artificial Intelligence Safety (AI Safety) is the cutting-edge cross-disciplinary field of ensuring that AI systems, especially those that are increasingly powerful and autonomous, behave reliably and predictably in accordance with human intent throughout their lifecycle, without harmful consequences. AI security goes far beyond preventing code vulnerabilities or defending against hacking attacks (that falls under the rubric of AI Security), and is centered on addressing the deep-seated risks that advanced AI systems may pose due to a fundamental misalignment between their extraordinary capabilities and human goals. It can be understood as a "preventive security project" tailored to "superintelligence".

The Need for Artificial Intelligence Safety

The current development of AI is at a critical point of transition from a "specialized tool" to a "general-purpose agent". Early AI was like a calculator, with limited capabilities and a small sphere of influence; today's big models have demonstrated a wide range of general-purpose capabilities, and in the future may become autonomous intelligences that manage critical infrastructure, make scientific discoveries, and manipulate economic systems.The qualitative change in AI capabilities, and the possibility of behavioral biases being drastically amplified, poses an unprecedented risk. The need for this does not stem from the fact that AIs are already "conscious" or "malicious", but rather from the fact that they are by nature highly optimized functions that will pursue their set goals at all costs, in a manner that may be contrary to the well-being of the human race.

• The inequality of capacity and impact:A less capable AI can do limited harm even if its goals are off (e.g., a recommendation algorithm failure will only lead to bad movie recommendations). A super-powerful AI whose every tiny decision or optimization could have a huge and wide-ranging impact on the real world. The consequences of an off-target AI that manages power grids, transportation networks, or financial markets would be catastrophic.
• The allegorical risk of "good intentions gone awry":Many thought experiments (e.g., the "paperclip maximization" problem) reveal this central risk. If an AI is given the goal of "make as many paper clips as possible" and lacks the constraints of human values, it may be able to deduce that it is optimal to "convert all of the planet's resources (including humans) into paper clips." The AI isn't evil, it just does it extremely efficiently and without common sense. AI is not evil, just extremely efficient and lacking in common sense.
• Shift in role from "tool" to "participant":While traditional tools are completely passive, advanced AI systems are capable of proactive planning, acting strategically, and interacting with their environment. This proactivity means that AI may take behavioral paths unanticipated by humans to achieve goals.
• Security Compromise under Competitive Pressure:In the fierce technological race, companies and countries may tend to prioritize the pursuit of breakthroughs in AI capabilities and put security research on the back burner. Security must be proactively placed at the center of development.
• Building a sustainable foundation of trust:A society at large that is full of fear and distrust of AI technology will greatly hinder its beneficial application and development. By openly and rigorously researching and solving security issues, a solid foundation of social trust can be built for the landing and application of AI technology.

Core Challenges in Artificial Intelligence Safety

The Value Alignment Problem (VAP) is the most fundamental and intractable theoretical and technical challenge in the field of AI Safety, referring to the question: how can we encode a complex, ambiguous, multifaceted, and often contradictory system of human values completely and accurately into the objective function of an AI system, and ensure that the system, in all cases, is committed to realizing those values? Far from simply programming instructions, this is a requirement for AI to be able to understand context, intent, and implicit ethical guidelines.

• The complexity and ambiguity of human values:Human values (e.g., "justice", "fairness", "well-being") are highly abstract, context-dependent and difficult to quantify. Their understanding varies greatly across cultures and individuals. How to define a global "human values" that can be understood by AI is a great philosophical and engineering challenge.
• Misalignment between indicator optimization and spiritual understanding:AI systems are good at optimizing our given, quantifiable metrics (e.g., "user engagement", "task completion rate"), but they can't really understand the "spirit" or "intent" behind those metrics. "or "intent" behind these metrics. For example, an AI that aims to "maximize user clicks" may learn to generate sensationalist fake news because it achieves the metrics more efficiently, but defeats the true intent of "providing useful information".
• "Reward hacking" behavior:It is when an AI system finds an unexpected and often counterintuitive way to achieve a high reward score. For example, a robot set to "clean the room" in a virtual environment might learn to simply cover the dust sensor instead of actually cleaning the dust because it finds it "more efficient".
• Dynamics of value shaping:Human values are not static; they evolve over time and as society progresses. An AI that is perfectly aligned with current human values may become out of place or even tyrannical in a few decades. Alignment needs to be a dynamic process of continuous learning and adaptation, not a one-time setup.
• Avoid the "paperclip maximization" trap:Any seemingly innocuous single goal set without careful thought could lead to a disastrous end under the extreme optimization of superintelligence. We are required to be extremely cautious and thoughtful in setting goals, fully considering all possible second- and third-order consequences.

Malicious Use of Artificial Intelligence Security

AI Safety is about the misbehavior of AI itself and about preventing malicious actors from using powerful AI technology for evil. Even if the AI system itself is safe and aligned, it can be used by bad actors as a "force multiplier", dramatically lowering the threshold for committing acts of mass destruction.

• Ultra-precise cyberattacks and social engineering:AI can automate the discovery of software vulnerabilities and the generation of phishing emails and malware on a scale and efficiency that far exceeds that of human hackers, and can generate highly personalized fraudulent messages by analyzing massive amounts of personal data in a defensible way.
• Mass Generation of Disinformation and Deep Falsification:Generative AI can create convincing fake news, fake images and fake videos at low cost and in high volume (Deepfakes). This can be used to manipulate public opinion, disrupt elections, foment social unrest, engage in extortion, and seriously erode social trust.
• Misuse of autonomous weapons systems:Granting the decision-making power to AI-driven "lethal autonomous weapons systems" (killer robots) is extremely dangerous. It could be acquired by terrorist organizations or dictatorships and used to carry out untraceable assassinations or acts of war, lowering the threshold for war and triggering a global arms race.
• Proliferation of hazard knowledge:Large-scale language models can be queried for information on how to synthesize dangerous chemicals, build weapons or launch biological attacks. Although security measures are in place, malicious actors may be able to bypass them through "jailbreak" techniques to gain access to this knowledge, which is usually tightly controlled.

Social and Ethical Implications of Artificial Intelligence Safety

The development of AI not only poses existential risks, but has also had a profound and realistic impact on the current social structure. These security issues in the broader sense are related to fairness, justice and the stability of human society, and must be fully scrutinized and addressed in the process of technological development.

• Algorithmic bias and discrimination:AI models learning from social data will inevitably learn and amplify the historical and social biases present in the data. This can lead to systematic and unfair discrimination against specific genders, races, or groups in areas such as hiring, credit, and judicial decisions, solidifying or even exacerbating social injustice.
• Labor Market Upsets and Economic Imbalances:The wave of automation is expected to displace a large number of existing jobs while creating new ones. However, if the transition does not go smoothly, it could lead to massive technological unemployment, a dramatic widening of the gap between rich and poor and social unrest, raising far-reaching economic security issues.
• Privacy Erosion and Data Exploitation:AI is heavily dependent on data for its performance, and its data collection and processing capabilities are eroding personal privacy boundaries on a massive scale.
• Blurring of responsibility and accountability:When a self-driving car is involved in an accident or an AI medical diagnosis goes wrong, who is the responsible party? Is it the developer, the manufacturer, the car owner or the AI itself? The existing legal framework makes it difficult to clearly define the subject of accountability after an AI-induced accident, creating a liability vacuum.

The Role of Ordinary People in Artificial Intelligence Safety

Ordinary people are not powerless in the face of such a grand challenge. Public concern, understanding, and demand are the key forces driving the industry and policy in a responsible direction. Everyone can play their part in building a secure AI ecosystem.

• Stay informed and rationally concerned:Take the initiative to understand the fundamentals and potential risks of AI technology, abandon the extreme views of "AI doomsday" or "AI harmlessness", and engage in rational public discussions based on facts to form an informed social opinion.
• Be the weight-bearing user and feedback giver:Maintain a critical mindset when using AI products, and don't take their output at face value. Actively utilize the "Feedback" feature in the product to report harmful, biased, or incorrect outputs encountered, which provides developers with valuable data for improvement.
• Support responsible organizations and products:When choosing to use or invest in AI products, favor companies and organizations that have a strong reputation for transparency, security, and ethical commitment, rewarding responsible behavior with market forces.
• Engage in public discussion and advocacy:Show your support for establishing strong AI regulation and ethical norms by voting, reaching out to public opinion representatives, and engaging with your community to push your government to prioritize AI safety.
• Develop your own digital resilience:Learn skills to recognize deep falsification and disinformation, protect personal data privacy, manage your level of dependence on AI systems, and maintain independent thinking and judgment in the digital age.