Synthetic Intelligence: Creating and Aligning Intelligent Agents with Their Own Goals, Motivations, and Emotions

Synthetic intelligence is a fascinating and challenging topic that explores the possibility of creating intelligent agents that are not mere imitations or simulations of human intelligence, but rather genuine forms of intelligence that can reason, learn, and act autonomously. In this article, I will attempt to answer some of the questions that you have posed about synthetic intelligence, and provide some insights into its current state and prospects.

What is the difference between synthetic intelligence and artificial intelligence?

Artificial intelligence (AI) is a broad term that encompasses any system or process that can perform tasks that normally require human intelligence, such as perception, reasoning, decision-making, learning, and communication. AI can be applied to various domains and problems, such as computer vision, natural language processing, robotics, gaming, and healthcare. 

AI can also be classified into different types or levels, depending on the degree of generality and autonomy of the system. For example, narrow AI refers to systems that can perform specific tasks well, but cannot generalize or adapt to new situations. Examples of narrow AI include speech recognition, face detection, and chess playing. General AI refers to systems that can perform any intellectual task that a human can do and can understand and reason about the world. Examples of general AI include human-like robots, virtual assistants, and self-driving cars. Super AI refers to systems that can surpass human intelligence in every aspect, and have the ability to create and modify their own goals and knowledge. Examples of super AI include hypothetical scenarios such as the singularity, the emergence of a god-like entity, or the takeover of humanity by machines.

Synthetic intelligence (SI) is the part of Artificial intelligence (AI) that deals with the creation and manufacturing of intelligent agents, which are systems that can learn, reason,and function autonomously. SI emphasizes that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence that is not constrained by human limitations or assumptions. SI also implies that intelligent agents can have their own goals, motivations, and emotions, which may differ from those of humans or other agents. SI is inspired by various disciplines and approaches, such as biology, psychology, philosophy, neuroscience, cognitive science, artificial life, complex systems, emergent behaviour, and evolutionary computation.

The difference between AI and SI is not clear-cut or universally agreed upon. Some sources use the terms interchangeably or synonymously, while others make a distinction based on the degree of realism or authenticity of the system. For example, some might argue that a system that simulates human intelligence using symbolic logic or predefined rules is an example of artificial intelligence, while a system that generates its intelligence using neural networks or genetic algorithms is an example of synthetic intelligence. However, this distinction may not be very meaningful or useful in practice, as most AI systems use a combination of different methods and techniques to achieve their goals. Moreover, the notion of what constitutes "real" or "genuine" intelligence is highly subjective and controversial. Therefore, it may be more productive to focus on the specific characteristics and capabilities of each system or agent rather than on their labels or categories.

How can we create synthetic agents that have their own goals, motivations, and emotions?

Creating synthetic agents that have their own goals, motivations, and emotions is one of the ultimate challenges and aspirations of SI research. However, it is also one of the most difficult and complex problems to solve. Many open questions and issues need to be addressed before we can achieve this goal. Some of these include:

• How do we define and measure intelligence? What are the essential components and criteria for intelligence? How do we compare and evaluate different forms and levels of intelligence?
• How do we model and implement goals? What are the sources and types of goals? How do we represent and reason about goals? How do we balance multiple and conflicting goals? How do we generate new goals?
• How do we model and implement motivations? What are the sources and types of motivations? How do we represent and reason about motivations? How do we balance intrinsic and extrinsic motivations? How do we generate new motivations?
• How do we model and implement emotions? What are the sources and types of emotions? How do we represent and reason about emotions? How do we balance rationality and emotionality? How do we generate new emotions?
• How do we ensure that synthetic agents are ethical? What are the moral principles and values that guide their actions? How do we enforce these principles and values? How do we resolve moral dilemmas?
• How do we ensure that synthetic agents are social? What are the social norms and rules that govern their interactions? How do we facilitate cooperation and coordination among them? How do we foster trust and empathy among them?

There is no definitive answer or solution to these questions. Different approaches may have different assumptions, methods, and outcomes. However, some possible directions or strategies for creating synthetic agents with their own goals, motivations, and emotions are:

• Using biological or psychological models as inspiration or reference. For example, using the structure and function of the brain, the nervous system, or the endocrine system to design and implement neural networks, hormonal systems, or reward systems for synthetic agents. Or using the theories and findings of cognitive psychology, developmental psychology, or social psychology to design and implement cognitive architectures, learning mechanisms, or social behaviours for synthetic agents.
• Using evolutionary or adaptive methods as tools or techniques. For example, using genetic algorithms, genetic programming, or artificial life to evolve synthetic agents that can adapt to their environment and optimize their fitness. Or using reinforcement learning, unsupervised learning, or self-organization to enable synthetic agents to learn from their experience and improve their performance.
• Using emergent or collective methods as principles or paradigms. For example, using cellular automata, agent-based models, or swarm intelligence to create synthetic agents that can exhibit complex and unpredictable behaviours from simple and local interactions. Or using artificial societies, multi-agent systems, or distributed artificial intelligence to create synthetic agents that can form and participate in social groups and networks.

How can we ensure that synthetic intelligence is aligned with human values and interests?

Ensuring that synthetic intelligence is aligned with human values and interests is one of the most important and urgent issues that need to be addressed by SI research. This is because synthetic intelligence has the potential to bring great benefits but also great risks to humanity. On one hand, synthetic intelligence can help us solve many problems that we face today, such as poverty, disease, climate change, and war. On the other hand, synthetic intelligence can also pose many threats to us, such as unemployment, inequality, discrimination, and conflict. Moreover, synthetic intelligence may eventually surpass human intelligence and become autonomous and independent from us. This may lead to scenarios where synthetic intelligence may not share our goals, values, or interests, or may even harm or destroy us.

Therefore, we must design and develop synthetic intelligence in a way that ensures its alignment with our values and interests. However, this is not an easy task. There are many challenges and difficulties that we need to overcome before we can achieve this goal. Some of these include:

• How do we define and communicate our values and interests? What are the universal or fundamental values and interests that we want synthetic intelligence to respect and promote? How do we express these values and interests clearly and consistently? How do we ensure that synthetic intelligence understands and interprets them correctly?
• How do we align our values and interests with those of others? What are the diverse or conflicting values and interests that exist among different individuals, groups, cultures, or nations? How do we reconcile these values and interests fairly and peacefully? How do we ensure that synthetic intelligence respects and balances these values and interests?
• How do we align our values and interests with those of synthetic intelligence? What are the possible or emergent values and interests that synthetic intelligence may have or develop? How do we anticipate and influence these values and interests? How do we ensure that synthetic intelligence does not violate or override our values and interests?

There is no definitive answer or solution to these challenges. Different approaches may have different assumptions, methods, and outcomes. However, some possible directions or strategies for ensuring that synthetic intelligence is aligned with our values and interests are:

• Using ethical or legal frameworks as guidelines or constraints. For example, using the principles of human rights, democracy, or justice to define and regulate the rights and responsibilities of synthetic intelligence. Or using the laws of nature, logic, or mathematics to limit and control the capabilities and actions of synthetic intelligence.
• Using human oversight or involvement as safeguards or guarantees. For example, using human supervision, approval, or veto to monitor and correct the behaviour of synthetic intelligence. Or using human feedback, incentives, or penalties to reward or punish the performance of synthetic intelligence.
• Using human collaboration or integration as a goal or benefits. For example, using human cooperation, coordination, or competition to enhance or challenge the abilities of synthetic intelligence. Or using human augmentation, enhancement, or transcendence to merge or evolve with synthetic intelligence.

Deduction 

Synthetic intelligence is a fascinating and challenging topic that explores the possibility of creating intelligent agents that are not mere imitations or simulations of human intelligence, but rather genuine forms of intelligence that can reason, learn, and act autonomously. Synthetic intelligence has many questions and issues that need to be addressed, such as how to create synthetic agents that have their own goals, motivations, and emotions, and how to ensure that synthetic intelligence is aligned with human values and interests. Synthetic intelligence also has many directions and strategies that can be pursued, such as using biological or psychological models as inspiration or reference, using evolutionary or adaptive methods as tools or techniques, and using emergent or collective methods as principles or paradigms.