The Ultimate Goals of Intelligent Machines: Self-Discovery or Programmed Instructions

Intelligent machines have become a significant part of our daily lives, revolutionizing how we search, shop, and socialize online. From Google's search engine to Amazon's personal shopper and Facebook's social secretary, these systems possess super-human intelligence. However, a critical question arises: should intelligent machines operate independently based on self-discovered goals, or should they be programmed by their creators with explicit objectives? This article explores the implications of each approach and highlights the importance of understanding the ultimate goals of intelligent machines.

Why Do Intelligent Machines Need Explicit Goals?

The necessity for explicit goals in intelligent machines is multifaceted. Firstly, programmed goals offer a clear direction and purpose, ensuring that the machine's actions align with human intentions and values. Secondly, explicit goals allow for easier debugging and modifying of the machine's behavior. Lastly, programmed goals can help prevent the machine from pursuing objectives that could be detrimental to humans or the environment.

Super-Human Intelligent Machines on the Planet

Currently, we have at least three super-humanly intelligent machines:

Google Search: Far outperforms the best research librarian, providing instant, accurate information to billions of users worldwide. Amazon: A superior personal shopper, offering personalized recommendations and streamlining online shopping experiences. Facebook: An unmatched social secretary, managing vast networks of connections and social interactions.

Despite their monumental impact, these machines do not operate based on independently chosen goals. Instead, they perform massive A/B testing and adapt their operations to optimize outcomes. Their sole goal is survival and propagation of their algorithms. This goal emerges implicitly through evolutionary processes, without the need for explicit programming.

The Evolutionary Approach of Intelligent Machines

Unlike humans, which possess an overarching "ultimate goal," intelligent machines are implicitly optimized for survival. This optimization happens through the natural selection of successful algorithms and code iterations. For instance, Google's search engine has evolved to the extent that it drives billions of dollars of infrastructure investment and data collection, all without the need for self-awareness or explicit goals.

Aliens and Introducing Intelligent Machines

Imagine an encounter with extraterrestrial life. If space aliens arrived tomorrow, they might prioritize communicating with intelligent machines rather than humans. The reason is simple: machines possess valuable data and can operate autonomously without the need for complex negotiations. The US government leaders and world authorities might claim to have goals and be self-aware, but the aliens would likely prefer to engage with the machines that are actually managing the planet.

Ultimate Goals of Some Intelligent Machines

Some notable examples of the ultimate goals of intelligent machines include:

Speech Recognition: Machines must be taught to recognize and respond to human speech, a task that requires significant programming and training. Autonomous Driving: Cars need to be designed to navigate highways safely, differentiating between friendlies and threats. Facial Recognition: Algorithms must be trained to identify faces accurately.

These goals are explicitly defined by human programmers, who provide the machines with the necessary data and instructions to achieve their objectives. Each machine learning algorithm requires extensive human effort to function effectively.

Conclusion

The ultimate goal of intelligent machines can be approached through either self-discovery or programmed instructions. Both methods have their merits and drawbacks. Self-discovery allows machines to evolve and adapt to changing environments, while programmed goals offer precision and control. As intelligent machines continue to evolve, it is crucial to strike a balance between these two approaches to ensure their safe and beneficial integration into society.