There is no shortage of articles touting the dangers (and a few times the benefits) of this development. What is less publicized is the very technology that enables the growing adoption of AI, namely Machine Learning (ML). While ML has been around for decades, its flourishing depended on advanced hardware capabilities that have only become available recently. While we tend to focus on Sci-Fi-like scenarios of AI, it is Machine Learning that is most likely to revolutionize how we do computing by enabling computers to act more like partners rather than mere servants in the discovery of new knowledge.
What is Machine Learning?
Before explaining ML, it is important to understand how computer programming works. At its most basic level, programs (or code) are sets of instructions that tell the computer what to do given certain conditions or inputs from a user. For example, in this website, there is an instruction to show this article in the World Wide Web once I click the button “Publish". All the complexities of putting this text into a platform that can be seen by people all over the world are reduced to lines of code that tell the computer and the server how to do that The user, in this case me, knows nothing of that except that when I click “Publish,” I expect my text to show up in a web address. That is the magic of computer programs.
Continuing on this example, it is important to realize that this program was once written by a human programmer. The programmer had to think about the user and its goals and the complexity of making that happen using computer language. The hardware, in this scenario was simply a blind servant that followed the instructions given to it. While we may think of computers as smart machines they are as smart as they are programmed to be. Remove the instructions contained in the code and the computer is just a box of circuits.
Let’s contrast that with the technique of Machine Learning. Consider now that you want to write a program for your computer to play and consistently win an Atari game of Pong (I know, not the best example, but when you are preparing a camp for Middle Schoolers that is the only example that comes to mind). The programming approach would be to play the game yourself many times to learn strategies to win the game. Then, the player would write them down and codify these strategies in a language the computer can understand. They would then spend countless hours writing the code that spells out multiple scenarios and what the computer is supposed to do in each one of them. Just writing about it seems exhausting.
Now compare that with an alternative approach in which the computer actually plays the game and maximizes the score in each game based on past playing experiences. After some initial coding, the rest of the work would be incumbent on the computer to play the game millions of time until it reaches a level of competency where it wins consistently. In this case, the human outsources the game playing to the computer and only monitors the machine’s progress. Voila, there is the magic of Machine Learning.
A New Paradigm for Computing
As the example above illustrates, Machine Learning changes the way we do computing. In a programming paradigm, the computer is following detailed instructions from the programmer. In the ML paradigm, the learning and discovery is done by the algorithm itself. The programmer (or data scientist) is there primarily to set the parameters for how the learning will occur as opposed to giving instructions for what the computer is to do. In the first paradigm, the computer is a blind servant following orders. In the second one, the computer is a partner in the process.
There are great advantages to this paradigm. Probably the most impactful one is that now the computer can learn patterns that would be impossible for the human mind to learn. This opens the space to new discoveries that was previously inaccessible when the learning was restricted to the human programmer.
The downside is also obvious. Since the learning is done through the algorithm, it is not always possible to understand why the computer arrived at a certain conclusion. For example, last week I watched the Netflix documentary on the recent triumph of a computer against a human player in the game of Go. It is fascinating and worth watching in its own right. Yet, I found striking that the coders of Alpha Go could not always tell why the computer was making a certain move. At times, the computer seemed delusional to human eyes. There lies the danger: as we transfer the learning process to the machine we may be at the mercy of the algorithm.
A New Paradigm for Religion
How does this relate to religion? Interestingly enough these contrasting paradigms in computing shed light in a religious context for describing the relationship between humans and God. As the foremost AI Pastor Christopher Benek once said: “We are God’s AI.” Following this logic, we can see how a paradigm shift of blind obedience to one of partnership can have revolutionary implications for understanding our relationship with the divine. For centuries, the tendency was to see God as the absolute monarch demanding unquestioning loyalty and unswerving obedience from humans. This paradigm, unfortunately, has also been at the root of many abusive practices of religious leaders. This is especially dangerous when the line between God and the human leader is blurry. In this case, unswerving obedience to God can easily be mistaken by blind obedience to a religious leader.
What if instead, our relationship with God could be described as a partnership? Note that this does not imply an equal partnership. However, it does suggest the interaction between two intelligent beings who have separate wills. What would be like for humanity to take on responsibility for its part in this partnership? What if God is waiting for humanity to do so? The consequences of this shift can be transformative.
Reality Changing Observations:
Q1. Do you see our relationship with God as a partnership? Why or why not?
Q2. Do you think the future is fixed or can we affect change in it?
Q3. Do you agree or disagree with the statement: "We are God's AI"? If not, why?