Flourishing Archives

Education with a Purpose: A New Approach To Teach STEM

After years of denial, we are finally acknowledging that we have a problem with STEM (Science, Technology, Engineering and Math) education in this country. It is not an issue of access or supply of opportunities but one of perception: most students think it is simply too hard. That is unfortunate since research shows that students that pursue STEM degrees are more employable and earn more than their non-STEM counter parts. Yet, only a third of college students currently pursue these degrees.

I can speak from experience. When finishing high school, I really enjoyed Physics and thought that I would major in it in college. After one college-level Physics class, I realized quickly that pursuing it would be a difficult path. Social sciences seemed an easier and a more natural fit. I eventually declared my major in Political Science staying away from science and math as much as I could in my liberal arts degree. Years later, I would regret this decision as my career took a decidedly more technical path. I certainly could have used some of those math and science classes.

While difficulty is definitely a factor, now that I reflect on it, the issue was deeper than that. What drew me to the social sciences was that they told me stories about human struggle, tragedy and triumph. In my degree, I got to learn the history of how nations were formed, regimes were taken down and societies changed. That was something I could eat it up. While I wanted to prepare for a career, to me education was about expanding my horizons and discovering new worlds. STEM subjects, while fascinating in their own right, lacked this human connection that I found in the social sciences.

Later in my career, I was drawn to data science because of what it could do. I could take data and create insights that were previously hidden. In some instances, I could even “predict” something before it happened. That got me hooked and it is how I learned on the job and through professional training to become a data scientist. As I started reflecting on the potential of the field I was working it, I finally caught that human connection that I was missing in college. I realized that data science was not just about reams of data being processed through algorithms but that it could literally change people lives. Consider the example where algorithms are being used to predict who is most likely to commit suicide. In this case, data science is literally saving lives.

I understand that my story is one data point but I believe there is a theme embedded in it that can be explored. Maybe the issue is not just that it is hard but that often times STEM education is disconnected from a higher purpose. There are some of us who will study science for its own sake. Others are naturally fascinated by how things work and want to learn to make things. Yet, there is a whole group of students that would pursue STEM subjects if educational programs helped them make the connection with a humanitarian purpose.

After being studying a social science and then becoming a technology worker, I realized a curious paradox. The social sciences are deeply concerned about social problems. They go through great lengths to describe causes, factors and catalysts that worsen or alleviate them. Yet, technology, this cold application of science, has shown the greatest potential for actually solving them. Just consider the potential of mobile phones in Sub-Saharan Africa. While living in places that lack electricity and sanitation, many in these countries can afford a mobile phone. This technology along with micro-finance are empowering the poor by allowing them to make financial transactions and create businesses, hence forging a way out of poverty.

What is missing is the connection between purpose and know-how. Getting people that care deeply about their communities and teaching them technical skills to do something about it – doing technology with a humanitarian purpose. People with a passion to serve and the technical know-how to leverage emerging technologies can change the world in ways not previously seen. Unfortunately, most of technological innovation happens in for-profit institutions that are more interested in meeting a quarterly goal for stockholders than making a positive impact in the communities that surround them.

Hence, I want to propose a STEM education with a telos. Telos is a Greek word that can be roughly translated as an “end goal.” Yet, it is not a goal like our new year’s resolution. Instead, it is a long term, guiding ideal that directs everything we do. It is akin to a higher purpose.

What if STEM education was not just about teaching technical skills and but actually connecting them to a humanitarian purpose? In other word, teaching student not just the “how” but also the “why”. Such education would raise a tech-literate generation that was less concerned about acquiring the latest gadgets and more about using technology to enhance human flourishing. It would not only expand STEM knowledge to under-represented groups but also unleash future innovation for the common good.

Automated Research: How AI Will Speed Up Scientific Discovery

The potential of AI is boundless. Currently, there is a lot of buzz around how it will change industries like transportation, entertainment and healthcare. Less known but even more revolutionary is how AI could change science itself. In a previous blog, I speculated about the impact of AI on academic research through text mining. The implications of automated research described here are even more far-reaching.

Recently, I came upon an article in Aeon that described exactly that. In it, biologist Ahmed Alkhateeb eloquently makes his argument in the excerpt below:

Human minds simply cannot reconstruct highly complex natural phenomena efficiently enough in the age of big data. A modern Baconian method that incorporates reductionist ideas through data-mining, but then analyses this information through inductive computational models, could transform our understanding of the natural world. Such an approach would enable us to generate novel hypotheses that have higher chances of turning out to be true, to test those hypotheses, and to fill gaps in our knowledge.

As a good academic, the author says a lot with a few words in the paragraph above. Let me unpack his statement a bit.

His first point is that in the age of big data, individual human minds are incapable of effectively analyzing, processing and making meaning of all the information available. There was a time where all the knowledge about a discipline was in books that could be read or at least summarized by one person. Furthermore, traditional ways of doing research whether through a lab experimentation, sampling, controlling for externalities, testing hypothesis take a long time and only give a narrow view of reality. Hence, in a time where big data is available, such approach will not be sufficient to harness all the knowledge that could be discovered.

His second point is to suggest a new approach that incorporates Artificial Intelligence through pattern seeking algorithms that can effectively and efficiently mine data. The Baconian method simply means the approach of discovering knowledge through disciplined collection and analysis of observations. He proposes an algorithmic approach that would mine data, come up with hypothesis through computer models then collect new data to test those hypotheses. Furthermore, this process would not be limited to an individual but would draw from the knowledge of a vast scientific community. In short, he proposes including AI in every step of scientific research as a way to improve quality and accuracy. The idea is that an algorithmic approach would produce better hypotheses and also test them more efficiently than humans.

As the author concedes, current algorithms and approaches are not fully adequate for the task. While AI can already mine numeric data well, text mining is more of a recent development. Computers think in numbers so to get them to make sense of text requires time-consuming processes to translate text into numeric values. Relevant to this topic, the Washington Post just put out an article about how computers have now, for the first time beat human performance in a reading and comprehension test. This is an important step if we want to see AI more involved in scientific research and discovery.

How will automated research impact our world?

The promise of AI-assisted scientific discovery is remarkable. It could lead to the cure of diseases, the discovery of new energy sources and unprecedented breakthroughs in technology. Another outcome would be the democratization of scientific research. As research gets automated, it becomes easier for others to do it just like Windows has made the computer accessible to people that do not code.

In spite of all this potential, such development should cause us to pause for reflection. It is impressive how much of our mental capacities are being outsourced to machines. How comfortable are we with this inevitable meshing of bodies and electronics? Who will lead, fund and direct the automated research? Will it lead to enriching corporations or improving quality of life for all? I disagree with the author’s statement that an automated research would make science “limitlessly free.” Even as machines are doing the work, humans are still controlling the direction and scope of the research. As we ship more human activity to machines, ensuring they reflect our ethical standards remains a human mandate.

Shifting Towards Education: A New Direction for 2018

Happy new year, everybody!

After a hiatus for the holiday season, I am now back to blogging with a renewed focus. For those of you who follow this blog or know me personally, last year was an encouraging beginning as I posted here my musings on the intersection between Theology and Artificial Intelligence. Above all, I’ve been encouraged by the conversation some of the posts have started.

After some reflection over the hiatus, I decided to shift the focus of the blog. As you may know, there are not a lot of voices speaking on this field. So the opportunities for making a contribution are vast. Moreover, I don’t see the topic of AI becoming less important in the coming years. The question I asked myself was how could I best contribute considering my skills, passion and knowledge. Promoting discussion on the topic was a good start but I was not satisfied in just being a thoughtful observer. The best insights often come from those who are immersed in practicing the field they are discussing.

Even as I type there are hundreds of AI startups starting to shape the future we’ll live in. There is a growing group of academics, consultants and enthusiasts speculating about what that would look like. Moreover, there are thousands of Data Scientists currently shaping the future of existing organizations building AI applications that will transform these enterprises for years to come. Eventually, politicians will catch up and start discussing policy and laws to regulate how AI is used.

While all this is happening, I think about my children. Will they have the tools they need to navigate this AI future? Will they be ready not only to survive but also thrive in this uncertain future?

When I look at the educational system they are in, it is clearly not up to the task. While I appreciate the wonderful work teachers do daily all over the world, the problem is systemic. The Western educational system was built in the last century to raise industrial workers. The economy required workers to learn a fixed trade that would last them through their lifetime. Moreover, the academic system is always preparing students for the next level of education. Regardless of whether they pursue a job or continue their studies, a high school degree prepares the student for college, which prepares them for Masters’ work which, except for professional degrees, prepare them for pursuing PhDs. Hence, students are conditioned to excel within the academic “bubble” and have little interaction with the real world of jobs, leadership and service. Aside from a few exceptions, students are expected to figure out on their own how to apply the knowledge they learn into real workplace scenarios. While the system forces students to study separate disciplines, life is lived in multi-disciplinary spaces.

Staying out of the politically-charged discussion of “how to save our schools”, I rather work on how to offer something that will build on what the schools already offer. In my view, STEM (Science, Technology, Engineering and Math) education continues to be a challenge even as we have made progress in the past years. The concern I have with the current focus is that it separates these disciplines from humanities. In this way, students are taught only the “how” but rarely the “why” of STEM. This approach only perpetuates an uncritical consumerist relationship with technology, where we never stop to ask why are they being created in the first place and how they benefit humanity. Therefore the challenge is to engage young minds critically with STEM early on, empowering them to become creators with rather than consumers of technology.

While I can write about this frequently on the blog, being a detached analyst is not enough. That is why I am planning to develop actual learning experiences that address this gap. I am currently connecting with partners “glocally” to make that a reality. It will have both a classroom component as well as an online component. Stay tuned for more details.

How will that look like in aitheology.com?

The blog will flow from this journey of becoming an education entrepreneur. In this way, it serves as a platform for reflection, discussion, idea exchange and hopefully challenging some of you to join in this new endeavor. While I will continue to explore the themes of AI and theology, there will be an educational focus both in the topics discussed as well as in the way they are conveyed.

I also recognize that in our age, writing is not the most effective way to spread ideas and engage in conversation. Towards that end, I plan to add podcast in the near future so you can interact with AI theology in new ways. Finally, there will be plenty of opportunities for you to get involved in emerging projects.

I am excited for what this New Year will bring to us. I pray for wisdom and guidance in this new phase and I ask you to pray with me as well (if you are not religious, sending good thoughts would do).

Good News: The World is Getting Better

This week I want to discuss how our perception of the world shapes and guides our decisions about the future. In a previous blog, I discussed the power of narrative and how important that is in constructing reality. In this post, I want to challenge a prevailing perspective of doom that dominates the narrative in airwaves, broadcasts and most of social media. The dominant message is that our present world order is falling into chaos with no hope for redemption. This is not just a problem for large Media corporations that need to prey on fear in order to sell news but it has become the de facto perspective on any conversation about national and/or global affairs.

I want to start by making a simple statement: the world is getting better.

Let me take a step back and propose a new paradigm. What if we look at the globe not from the anecdotal evidence highlighted by media stories but actually more like a CEO looks at his/her company? What would that look like? Working for a large corporation for many years, I spent countless hours preparing presentations for executive leaders so they could understand the state of their business. The story told in these meetings is built on numbers and data. The narrative flows from pre-determined agreed-upon measures of success that allow the leader to see whether their unit is on track or not to meet their goals.

One could say that such view does not tell the whole story. That is indeed true. A company may be doing really well but that may not safeguard all employees from the threat of being laid off. For that employee, a profitable quarter means nothing. However, if the numbers are showing times of distress ahead, the story of many employees will be impacted. If the business goes bankrupt – everybody loses their job. Therefore, regardless of how dry numbers may be, they point to eminent signs of trouble that we must attend to. We ignore them at our own peril.

So, if you are the CEO of the globe, what would be some important performance metrics to look for? Thankfully I found this great blog by Bj Murphy that does exactly that, highlighting the trends around important issues like extreme poverty, wars, life expectancy, child mortality and others. The numbers show an undeniable improvement in all these key measures for the last 50 years. Believe it or not, these measures disprove the adage that “things were better in the olden days”. This is not to say that everything is getting better but such overwhelming data should make us pause to celebrate. Things are getting better in many fronts if we just have the eyes to see them.

Are we happier then? Well, if data is any indication the answer is “no.” In fact, quite the opposite, rates of depression are increasing world-wide. There could be many reasons for that. It is not clear, for example, whether people are simply more depressed or whether now we are able to better diagnose it and hence see an increase. Even allowing for that, this data is a sharp contrast to the one from the paragraph above. At least from these two pieces of data, we can conclude that a better world may not necessarily be a happier world.

Re-imagining the Present for Creating a Better Future

An unsung hero of the advances touted in BJ’s post is the rise of technology and science in the last century. If there has been a positive story, it is how science and technology have improved the quality of life. Yet, one can never forget the technology also brought the atomic bomb to our planet. They themselves could never be the answer for a better world but they have certainly enabled dreamers to make it a reality. This seems to be not only reality but also perception. In a recent Pew research survey, 42% of Americans indicated that technology has made their lives better, by far the biggest factor in a list that included medicine, civil rights and the economy. Technology advancement is one of the few narratives of hope in a sea of depressing storylines.

Here is important to highlight that perception is very relative to where we stand in relation to the past. Recently, white older men in the United States as a cohort have experience rising rates of depression and anxiety. One explanations is the sense that their life conditions have deteriorated compared to their parents. The question is not whether the world is getting better but whether “my” world getting better. This is not particular to the cohort of white older males but to all of us. This question is always asked with a point of reference in mind. Yet, is it possible to celebrate positive change even if our personal universe has deteriorated?

The first step towards imagining a new future is assessing the present from the perspective of the most vulnerable. If the world has indeed improved for them, then there is reason to celebrate. The data above supports this perspective. While there have been losers in recent change and much work is left to be done, the good news is undeniable.

From Tech Consumers to Tech Creators

If technology has made life better, it has also made it more complicated. Any PC user who had to endure using Windows for a while will realize that all the convenience brought by technology comes at a cost in complexity and troubleshooting. I believe part of the problem is that most of us approach technology as demanding consumers. That is, we expect technology to provide a pain-free solution to our problems. This is precisely the message large tech companies want us to believe: technology will solve all our problems and make life easier for everybody. That is often not the case.

To fully harness the benefits of technology we must move from consumers to creators of technology. Last week I was inspired by this story of an 11 year-old girl who invented a water tester to detect water contamination. When interviewed, the girl said she was moved by the story of water contamination in Flint, MI and wanted to do something about it. She exemplifies a true technology creator who took upon herself to solve a problem she cared about. Technology creators do not just use tech for convenience, they leverage it to solve problems. They use their God-given creativity to make the world a better place.

What if we could educate children and young adults to do more of that? What type of world could we build?

AI Reformation: How Tech can Empower Biblical Scholarship

In a past blog I talked about how an AI-enabled Internet was bound to bring a new Reformation to the church. In this blog, I want to talk about how AI can revolutionize biblical scholarship. Just like the printing press brought the Bible to homes, AI-enabled technologies can bring advanced study tools to the individual. This change in itself can change the face of Christianity for decades to come.

The Challenges of Biblical Scholarship

First, it is important to define what Biblical scholarship is. For those of you not familiar with it, this field is probably one of the oldest academic disciplines in Western academia. The study of Scripture was one of the primary goals for the creation of Universities in the Middle Ages and hence boasts an arsenal of literature unparalleled by most other academic endeavors. Keep in mind this is not your average Bible study you may find in a church. Becoming a Bible scholar is an arduous and long journey. Students desiring to enter the field must learn at least three ancient languages (Hebrew, Greek and usually Aramaic or Akkadian), German, English (for non-native speakers) and usually a third modern language. It takes about 10 years of Graduate level work to get started. To top that off, those who are able to complete these initial requirements face dismal career options as both seminaries and research interest in the Bible have declined in the last decades. Needless to say, if you know a Bible Scholar pat him in the back and thank them. The work they do is very important not only for the church but also for society in general as the Bible has deeply influenced other fields of knowledge like Philosophy, Law, Ethics and History.

Because of the barriers of entry described above, it is not surprising that many who considered this path as an option (including the writer of this blog) have opted for alternative paths. You may be wondering what that has to do with AI. The reality is that while the supply of Bible scholars is dwindling, the demand for work is increasing. The Bible is by far the most copied text in Antiquity. Just the New Testament alone has a collection of over 5,000 manuscripts found in different geographies and time periods. Many were discovered in the last 50 years. On top of that, because the field has been around for centuries, there are countless commentaries and other works interpreting, disputing, dissecting and adding to the original texts. Needless to say, this looks like a great candidate for machine-enhanced human work. No human being could possibly research, analyze and distill all this information effectively.

AI to the Rescue

As you may know, computers do not see the world in pictures or words. Instead all they see is numbers (0s and 1s to be more exact). Natural Language Processing is the technique that translates words into numbers so the computer can understand it. One simple way to do that is to count all the times each word shows up in a text and list them in a table. This simple word count exercise can already shed light into what the text is about. More advanced techniques will not only account for word incidence but also how close they are from each other by meaning. I could go on but for now suffice it to say that NLP starts “telling the story” of a text albeit in a numeric form to the computer.

What I describe above is already present in leading Bible softwares where one can study word counts till Kingdom come (no pun intended). Yet, this is only the first step in enabling computers to start mining the text for meaning and insight. When you add AI to NLP, that is when things start getting interesting. Think more of a Watson type of algorithm that you can ask a question and it can find the answer in the text. Now one can analyze sentiment, genre, text structure to name a few in a more efficient way. With AI, computers are now able to make connections between text that was only possible previously by the human mind. Except that they can do it a lot faster and, when well-trained, with greater precision.

One example is sentiment analysis where the algorithm is not looking for the text itself but more subjective notions of tone expressed in a text. For example, this technique is currently used to analyze customer reviews in order to understand whether a review is positive or negative. I manually attempted this for a Old Testament class assignment in which I mapped out the “sentiment” of Isaiah. I basically categorized each verse with a color to indicate whether it was positive (blessing or worship) or negative (condemnation or lament). I then zoomed out to see how the book’s sentiment oscillated throughout the chapters. This laborious analysis made me look at the book in a whole different lens. As AI applications become more common, these analysis and visuals could be created in a matter of seconds.

A Future for Biblical Scholarship

Now, by showing these examples I don’t mean to say that AI will replace Scholars. Algorithms still need to be trained by humans who understand the text’s original languages and its intricacies. Yet, I do see a future where Biblical scholarship will not be hampered by the current barriers of entry I described above. Imagine a future where scholars collaborate with data scientists to uncover new meaning in an ancient text. I also see an opportunity for professionals that know enough about Biblical studies and technology becoming valuable additions to research teams. (Are you listening Fuller Seminary? How about a new MA in Biblical Studies and Text Mining?). The hope is that with these tools, more people can be involved in the process and collaboration between researchers can increase. The task of Biblical research is too large to be limited to a select group highly educated scholars. Instead, AI can facilitate the crowdsourcing of the work to analyze and make meaning of the countless text currently available.

With all that said, it is difficult imagine a time where the Bible is just a book to be analyzed. Instead it is to be experienced, wrestled with and discussed. New technologies will not supplant that. Yet, could they open new avenues of meaning until now never conceived by the human mind. What if AI-enabled Biblical Scholarship could not just uncover new knowledge but also facilitate revelation?

Debunking AI Myths: Specialized versus General AI

The noise around AI has been deafening lately. From tales of doom, fears of automation to promises of a new humanity, there is no limit for the speculation around this technology. As one tracking the news and articles around this topic, the task has become impossible. Not one day goes by without multiple articles, blogs, podcasts and TV shows come out exploring the topic. Just this week, technology avatars Elon Musk and Mark Zucheberg traded barbs on whether we should fear AI or not.

Hence, it is a good time to take a step back to separate the hype from reality. It is time to expose some AI myths and look at these challenge with a cautious but informed perspective. The biggest challenge in our time where information flows freely is to know what to ignore and what to pay attention to lest we fall into a perpetual sense of confusion. In this blog, I want to hone in the differences between generalized and specialized AI while also briefly reflecting on their impact in our near future.

The Promise and Limitations of Specialized AI

Many readers of this blog may know this already but it is important to reinforce the difference between specialized and general AI. The first, is the driver around the revolution in industry and most of the buzz in the news. It is specialized because it is intelligence optimized around one specific task. That can be predicting who will do an action, whose face is in the picture or what has someone said. In the baseline section, I show a picture that illustrate well the different types of specialized AI that exist. With improving hardware, a lot of data and the right algorithms, specialized AI will most likely disrupt entire industries from banking to healthcare, transportation to entertainment.

Now before we panic, a few caveats are in order. Just because a technology exists does not mean it will actually create disruption. For example, many thought that the advent of the Internet would end book publishing. While the publishing industry had gone through tremendous change, we still buy books today. So, it is fair to say that even with the advent of self-driving cars that does not mean the end of driving.

For a technology to change industry and culture, it must first prove to be commercially viable. It is only when the smart phone becomes the Iphone that change starts happening. Disruption is not just dependent on the technology but also on how it is used. It is wonderful that computers can now learn like humans but if this does not solve real problems, it is useless. Specialized AI is not a trouble-shoot free proposition. It takes a considerable amount of time, testing, investment and many failures to get to successful applications. At this point, only large corporations or savvy entrepreneurs have the time, energy and resources that it takes to transform this technology into viable solutions. It is true that hardware and open-source software have significantly lowered the barriers of entry into this field. However, people with the right skillset and experience in this area are still scarce. Thus, many AI efforts will fail while few will become breakthroughs. This reality leads me to believe that the forecasts of massive job elimination are over-blown.

The Challenges Around General AI

General AI is still the fodder of scientific fiction. That is the idea that machines could be sentient, being able to think, walk and feel. We are still decades off from that reality. Now, certainly we could get there earlier but before we do, we have some formidable obstacles overcome.

A big one is hardware. In spite of the fact that computers processing speed have grown greatly in the last years, they are still no match for the brain. The difference is between millions to billions of connections. Basically, there is no hardware today that could fully mimic the capacity of the brain. Some believe they never will be able to do so while others are spending billions trying to do exactly that. Only time will tell who is right, but until then General AI will remain elusive.

We often forget that an essential difference between AI and human intelligence is life itself. Artificial Intelligence is not artificial life but only a well-constructed machine made to look, see and think like humans. For all the advances in AI, there are still fundamental differences in how biological functions of our bodies and the processing activity of machines. So, it looks like, at least for the near future, robots will not have a soul even if talking about them as they did can be a helpful exercise in speculative reflection.

What Does This Mean?

Given the points described above, what are we to make of the current fears surrounding AI? Outlining the limits around AI does not mean ignoring its potential dangers nor minimizing its promise. The difference is an informed engagement versus exasperated over-reaction. Specialized AI is bound to eliminate some jobs and there is very little that can change that. Yet, this will not be an overnight smooth transition. It will be filled with advances, setbacks until we reach a new normal. Even as the technology progresses, social-political and economic factors are bound to shape the future of AI. It is not just about the technology but about the people who use it.

Maybe the best advice I can give anyone concerned about AI is “don’t believe everything you read on the Internet.” Check your sources, compare it with others and retain the best. In this case, my hope is that the attention around AI will invite us all to a conversation about how technology is shaping our lives and how it can help us flourish. To dwell on fear will miss the opportunity of discovering how AI can make us better humans. That, to me, is the ultimate question we must be most concerned about.

What Would Open AI Look Like?

In a previous blog I talked about how big government and big business were racing to get a piece of the AI revolution. In this blog, I want to explore the parallel grass-roots movement of open AI and its possibilities.

Open-Source Movement: The Democratization of Technology

There was a time in which to compete with technology required a hefty upfront investment. This is no longer the case. For one, consumers and businesses have now the ability to buy hardware as a service which greatly diminishes initial costs. Along with that, most expensive softwares have now an open-source version available for free. So today, open source solutions and hardware services like the cloud allows for even small players to compete alongside Fortune 500 companies.

I can speak from experience. When I entered the field of data science eight years ago, I remember wondering what would it take for me to do the things I did in my corporate job at home. First, I would have to purchase a server to get computing power. Then I would have to buy very expensive software to run the algorithms. At that point, open-source options were emerging in academic circles but service like the cloud did not exist. Today, the scenario could not be different. I can now perform the same tasks by downloading open-source software to my laptop and if necessary rent some space in the cloud for more computing power. Needless to say, the environment is ripe for start-ups to flourish as the barriers of entry are low. The main barrier of entry now is not technology but humans with the know-how to run these widely available tools.

This democratization trend is not limited to technology-related fields but is disrupting other industries like web development, education and the non-profit sector. Web development can now be accomplished through open-source web services like “WordPress” (which I use for this blog). Large Universities are offering online open courses to students all-over the world promising the same level of quality of their on-campus classes. Social entrepreneurs can now raise funds through crowdsourcing, greatly expanding their donor base. The “open” phenomenon is obliterating set up costs empowering individuals and small organizations to do more with less.

What About Open AI?

Because the barriers of entry are low for data science, I don’t see why we should not see a vigorous grass-roots movement to democratize AI. The hardware and software is available and affordable. The biggest challenge is one of skills and know-how. The skills required for running and understanding AI algorithms are very scarce at the moment. Only a small group of professionals and academics have experience working with the advanced algorithms needed to develop AI applications.

Yet, even this current bottleneck is not bound to last long. Numerous coding schools start-ups are offering data science camps enabling data veterans and even new entrants to learn how these algorithms work. Moreover, soon enough entrepreneurs will develop solutions that enable AI development without having to code. Of course, AI is not limited just to machine learning but encompasses robotics and engineering among other technical fields. While I cannot speak from experience in these areas, the rise in high-school robotics competitions and engineering camps for kids tells me that efforts already exist to democratize these skills as well.

Clearly the seeds are in place for an open AI movement to flourish. It is in this context that I plan to invest my time and creative energies in the next few years. As I mentioned in the previous blog, preparing the next generation for an AI future is not about training them for jobs but empowering with tools that can harness their creativity. What would happen if at-risk children today could have a place to learn and “do” AI? What if the unemployed and young adults could become part of learning communities that are experimenting with the latest machine learning technologies?

What kind of problems would they solve and what kind of world would they build?

How Do We Prepare the Next Generation For An AI Future?

In a previous blog, I described the trends that led to the current AI renaissance. In this blog, I want to talk about where AI is going and how we can prepare children and young adults to seize on the opportunities emerging from it.

The Global Race For AI Innovation

AI startups are popping up everywhere, not only in the United States but in many places in the world. Canada just announced an investment of $93 Million for AI hubs in its largest cities. China may now be surpassing the US in government funding of AI initiatives as it leads deep learning (a subset AI technology) research along with its booming tech sector led by Baidu. Japan is also investing heavily on the area as a way to foster economic growth with an ageing population. Developing countries are also entering the race as African AI startups emerge and as Brazil hosts the first AI startup battle in Latin American soil. Everybody wants a piece of the AI revolution.

Even so, AI innovation is most likely to come from leading tech giants: Google, Amazon and Facebook. Google, through bold mergers and research investment, is aiming to become an AI-driven company. Amazon, who already leads the digital assistant market and the cloud business, is set to incorporate AI into all its core operations. Facebook is investing heavily in AI to better manage and customize content for 1.4 Billion users. These are just a few examples of how AI is shaping the future of the Tech industry. The company who can turn AI into viable commercial solutions will become the market leader of the future. The company that lags behind will most likely face obsolescence.

What Can We Do To Prepare?

How will this current race impact our future? AI will certainly eliminate jobs, but also create new opportunities. I want to focus on the latter part for now. Complete new industries will emerge as these technologies become widespread in business and government all over the world. Therefore, we need to prepare our kids and young adults so they can fill these new jobs. Hear me out, not every kid will grow up to become a data scientist, robotics engineer or software developer. Yet, as AI permeates different systems, there are some basic skills that the future worker and entrepreneur must be proficient at.

Math is a good example. Developing a strong foundation on math concepts will be crucial. That does not mean every kid must master Calculus by High School. However, we need to debunk the myth that math is only suitable for a minority of very intelligent people. There is no such thing, math is a language that all can learn. For that to happen, we must also change how we teach math. Common Core is an encouraging step in the right direction but much more needs to be done. Math needs to become more visual and more relevant to day-to-day problems.

Another skill is programming. Learning to code should become as important as learning to read. Not all kids turn into voracious readers as adults most know enough to be functional and informed citizens in their communities. Similarly, not all kids that learn coding will become developers but they should have sufficient knowledge to navigate the technological change that is ahead of us.

While technical skills are important, we cannot neglect critical thinking skills. Here is where I believe disciplines like theology and philosophy have a place. Because these technologies will become more and more interwoven with our humanity, we cannot afford to overemphasize the “how” at the cost of asking the “why” and the “what for”. In other words, we need to be constantly asking: “What does it mean to be human in an AI world?”. This will not come naturally in math and coding classes that focus only on skills.

The goal is not only to prepare future workers but empower them to become AI social entrepreneurs, ready to address problems untouched by big business or big government.

I propose an inter-disciplinary approach that teaches both technical skills along with critical thinking. Students should ask the questions of “why” and “what for” right when they learn the “how”.The goal is not only to prepare future workers but empower them to become AI social entrepreneurs, ready to address problems untouched by big business or big government. In the next blog, I’ll be discussing how a grassroots open-source AI movement could work parallel to the one already happening in the business and government sector.

That is when things get interesting.

Why Is Artificial Intelligence All Over The News Lately?

AI hype has come and gone in the past. Why is back on the spotlight now? I will answer this question by describing the three main trends that are driving the AI revolution.

Artificial Intelligence has been around since the 1950’s. Yet after much promise and fanfare, AI entered a winter period in the 80’s where investment, attention and enthusiasm greatly diminished. Why has this technology re-emerged in the last few years? What has changed? In this blog, I will answer this question by describing the three main trends that are driving the AI revolution: breakthroughs in computing power, the emergence of big data and advances in machine learning algorithms. These three trends converged to catapult AI to the spotlight.

Computing Power Multiplies

When neural networks (the first algorithm to be considered Artificial Intelligence) were theorized and developed, the computers of the time did not have the processing power to effectively run them. The science was far ahead of the technology, therefore delaying its testing and improvement for later years.

Thanks to Moore’s law, we are now in a place where computing power is affordable, available and effective enough for some of these algorithms to be tested. My first computer in the early 90’s had 128K of RAM memory. Today, we have thumb drives with 100,000 the size of this memory! Even so, there are still ways to go as these algorithms can still be resource-expensive with existing hardware. Yet, as system architects leverage distributed computing and chip manufacturers experiment with quantum computing, AI will become even more viable. The main point is that some of these algorithms can now be tested even if it takes hours or days when before that was inconceivable.

Data Gets Big

With smartphones, tablets and digital sensors becoming common in our lives, the amount of data available has grown exponentially. Just think about how much data you generate in one day anytime you use your phone, computer and/or enter retail stores. This is just a few examples of data being collected on an individual. For another example, consider the amount of data collected and stored by large corporations on customers’ transactions on a daily basis.

Why is this relevant? The AI is only as good as the data fed into it for learning. A great example is the data available for Google in searches and catalogued websites. That is why Google can use Artificial Intelligence to translate texts. It does that by simply comparing translation of large bodies of texts. This way, it can transcend a word-by-word translation rules to understand colloquialism and probable meaning of words based on context. It is not as good as human translation but fast becoming comparable with it.

There is more. Big data is about to get bigger because of the Internet of Things (IoT). This new technology expands data capture beyond phones and tablets to all types of appliances. Think about a fridge that tells you when the milk is about to expire. As sensors and processors spread to all electronics, the amount of data available for AI applications will grow exponentially.

Machine Learning Comes of Age

The third trend comes from recent breakthroughs proving the effectiveness of Machine Learning algorithms. This is the very foundation of AI Technology because it enables computers to detect patterns from data without being programmed to do so. Even as computing power improved and data became abundant, the technology was mostly untested in real-life examples breeding skepticism from scientists and investors. In 2012, a computer was able to identify cats accurately from watching YouTube videos using deep learning algorithms. The experiment was hailed as a major breakthrough for computer vision. Success stories like this and others like it brought machine learning to the spotlight. These algorithms started getting attention not just from the academic community but also from investors and CEO’s. Investment in Artificial Intelligence has significantly increased since then and is now projected to reach $47 Billion by 2020. Now there was both abundance of data and enough computing power to process it, machine learning could finally be effectively used. These trends paved the way for Artificial Intelligence to become a viable possibility again.

Pulling All Together

These trends have turned Artificial Intelligence from a fixture of science fiction to a present reality we must contend with. This has not happened overnight but emerged through a convergence of technological advances that created a ripe environment for AI to flourish. As they came together, the media, politicians and industry titans started to notice. Hence, that’s why your Twitter feed is exploding with AI-related articles.

Because the trends leading the emergence of AI show no sign of slowing down, this is probably only the beginning of an AI springtime. While there are events that could derail this virtuous cycle, the forecast is for continuous advancement in the years and possibly decades to come. So, for now, the attention and enthusiasm is bound to stay steady for the foreseeable future.

As AI applications are being tested by large companies and start-ups alike, this is the time to start asking the right questions about how it will impact our future. The good news is that there is still time to steer the advance of AI towards human flourishing. Hence, let the conversation around it continue. I hope the attention engendered by the media will keep us engaged, active and curious on this topic.

How Can Humans Flourish?

In previous blogs, I often used the phrase “AI for human flourishing.” In this blog, I want to explore what human flourishing is.

What is Human Flourishing?

I learned of this concept still in seminary when taking classes on ethics, mission and ministry. It is a new term that encompasses three levels of well-being: emotional, psychological and social. It connotes a state in which people are thriving. This may or may not be accompanied by material wealth but it is broader to include less tangible factors such as joy, connection to others and personal growth.

This concept initiated a shift in my thinking away from concrete measures like getting a job, financial compensation, church numerical growth and decisions for Christ (a favorite in evangelical circles) to broader measures of impact. One can be thriving even while staying in the same place or job. One can be maturing even through hardships. One can become more loving even when circumstances worsen.

Human Flourishing and Spiritual Growth

From a Christian perspective, human flourishing is analogous to spiritual growth. While the first focus on good emotions, the latter focus on virtue. Yet, there is no reason to believe one is opposed to the other. In fact, I would venture to guess that true spiritual health should lead to overall well-being even if that is not the case at first. Take the case of the virtue of forgiveness for example, which now has proven to be beneficial to one’s health. This is just one example as how spiritual growth correlates with human flourishing.

I also like how the idea of flourishing points to the image of a garden. Plants in a garden do not only grow in size, they also produce fruits. In the same way, spiritual growth manifests itself in the fruits of human flourishing. The well-being translates into personal and social thriving. It manifests itself in healthy relationships, greater personal focus, mastery of new skills, perseverance to complete difficult problems and overcome the daily challenges of life and work. The individual that flourishes will inspire flourishing in others as this virtuous cycle spills over to those around her.

The main point here is that those concepts are interconnected. To focus on spiritual growth without seeing it reflected in human flourishing makes the first incomplete. In fact, the connection between spiritual growth and human flourishing reflects Jesus’ insistence that we would know a tree by its fruits. That is, It cannot be an internal change if it does not translate into visible change.

A Picture of Human Flourishing

As I try to describe it, I ponder on the gift and privilege of being a parent. Seeing my children grow gives me most vivid picture of human flourishing. I see them growing in knowledge, character and skill in many areas. I celebrate with them, every milestone from mastering the basic skills of speaking, reading and learning to be kind. Slowly I realize how fast they are growing, how beautifully they are flourishing. They are not following the same path or becoming the same person. Yet, each one of them is reflecting their own flourishing in becoming more like their own selves.

My dream for AI theology is that we do not stay in the realm of the abstract but that this conversation translates into concrete human flourishing. It is not just about a transfer of knowledge but an unlocking of potential that bears fruit. That’s why I am interested in innovative forms of education that harness the possibilities of technology as a tool for human flourishing. As a tool, technology gives us the “know-how” to take the raw material we have to use it to solve problems. I would love to see many empowered with the knowledge to build AI solutions that solve social problems.

Let us keep dreaming.

Category: Flourishing