The Machine Learning Paradigm: How AI Can Teach Us About God

It is no secret that AI is becoming a growing part of our lives and institutions. There is no shortage of article 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. In this blog, I explain how Machine Learning is a new paradigm for computing and use it as a metaphor to suggest how it can change our view of the divine. Who says technology has nothing to teach religion? Let the skeptics read on.

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 the WordPress code for this website, there is an instruction to show this blog in the World Wide Web once I click the button “Publish” in my dashboard. 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. He or she 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. She or he 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 of a paradigm 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.

4 Reasons Why We Should be Teaching AI to Kids

In a previous blog, I talked about a multi-disciplinary approach to STEM education. In this blog I want to explore how teaching AI to kids can accomplish those goals while also introducing youngsters to an emerging technology that will greatly impact their future. If you are parent, you may be asking: why should my child learn about AI? Recently, the importance of STEM education has been emphasized by many stakeholders. Yet, what about learning AI that makes it different from other STEM subjects?

First it is important to better define what learning AI means. Lately, the AI term has been used for any instance a computer acts like a human. This varies from automation of tasks all the way to humanoids like Sophia . Are we talking about educating children to build sentient machines? No, at least not at first. The underlying technology that enables AI is machine learning. Simply put, as hinted by its name, these are algorithms that allow computers to learn directly from data or interaction with an environment rather than through programming. This is not a completely automated process as the data scientist and/or developer must still manage the processes of learning. Yet, at its essence, it is a new paradigm for how to use computers. We go from a programming in which we instruct computer to carry out tasks to machine learning where we feed the computer with data so it can discover patterns and learn tasks on its own. The question then is why should we teach AI (machine learning) to kids?

Exposes Them to Coding

Teaching AI to kids start with coding. While we’ll soon have advanced interfaces for machine learning, some that will allow a “drag-and-drop” experience, for now doing machine learning requires coding. That is good news for educational purposes. I don’t need to re-hash here the benefits of coding education. In recent years, there has been a tremendous push to get children to start coding early. Learning to code introduces them to a type of thinking that will help them later in life even if they do not become programmers. It requires logic and mathematical reasoning that can be applied to many endeavors.

Furthermore, generation Z grew up with computers, tablets and smart phones. They are very comfortable with using them and incorporating them into their world. Yet, while large tech companies have excelled in ensuring no child is left without a device, we have done a poor job in helping them understand what is under the hood of all this technology they use. Learning to code is a way to do exactly that: lift up the hood so they can see how these things work. Doing so, empowers them to become creators with technology rather than mere consumers.

Works Well With Gaming

The reality is that AI really started with games. One the first experiment with AI was to make a computer learn to play a game of Checkers. Hence, the combination between AI and gaming is rather complementary. While there are now some courses that teach children to build games, teaching AI goes a step further. They actually get to teach the computer to play games. This is important because games are a common part of their world. Teaching AI with games helps them engage in the topic by bringing it to a territory that is familiar to their imagination.

I suspect that gaming will increasingly become part of education in the near future. What once was the scourge of educators is turning out to be an effective tool to engage children in the learning process. There are clear objectives, instant rewards and challenges to overcome. Teaching machine learning with games, rides this wave of this and enhances it by giving them an opportunity to fine tune learning algorithms with objectives that captivate their imagination.

Promotes Data Fluency

Data is the electricity of the 21st century. Helping children understand how to collect, examine and analyze data sets them up for success in the world of big data. We are moving towards a society where data-driven methods are increasingly shaping our future. Consider for example how data is transforming fields like education, criminal courts and healthcare. This trends shows not signs of slowing down in the near future.

This trend will not be limited to IT jobs. As the sensors become more advanced, data collection will start happening in multi-form ways. Soon fitness programs will be informed, shaped and measured by body sensors that can provide more precise information about our bodies’ metabolism. Sports like Baseball  and Football are already being transformed by the use of data. Thus, it is not far-fetched to assume that they will eventually be working in jobs or building business that live on data. They may not all become data scientist or analysts, but they will likely need to be familiar with data processes.

Opens up Discussions About Our Humanity

Because AI looms large in Science-Fiction, the topic opens the way for discussions on Literature, Ethics, Philosophy and Social Studies. The development of AI forces us to re-consider what it means to be human. Hence, I believe it provides a great platform to add Humanities to an otherwise robust STEM subject. AI education can and should include a strong component of reading and writing.

Doing so develops critical thinking and also helps them connect the “how” with the “why”. It is not enough to just learn how to build AI applications but foremost why we should do it. What does it mean to outsource reasoning and decision-making to machines? How much automation can happen without compromising human flourishing? You may think these are adult question but we underestimate our children’s ability to reflect deeply about the destiny of humanity. They, more than us, need to think about these issues for they will inherit this world.

If we can start with them early, maybe they can make better choices and clean up the mess we have made. Also, teaching AI to kids can be a lot easier than we think.