Search
Close this search box.

Category: Algorithms

Algorithm is the underlying digital technology that powers all burgeoning AI applications. Algorithms are basically a set of rules or instructions. In the case of AI, they most often derived through data itself rather than by programmers. As they take a growing role in society, it is important we understand how they work taking into account impact on human well-being.

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.

AIgorithmic Matchmaking: How AI is Changing Marriage

In a previous post, I mentioned that AI was changing relationships. In this multi-part blog, I want to delve further on this topic to describe how AI is changing marriages. I will focus on algorithmic matchmaking which is present in dating sites like e-Harmony. Because these online dating sites have been around for a while, the idea of using technology for matchmaking has gained some social acceptance. However, from a historic perspective, this is nothing short of revolutionary. For centuries we have resorted to family members, traditions and even our own whims for finding our mate. Now this is being done through Artificial Intelligence. While e-Harmony arranged marriages account for a small portion of marriages (only about 4% at the moment), given our dismal record on divorces, I see adoption of this method to continue to grow in the coming years.

Before criticizing this trend as another example of technological over-reach, it is important to understand how these applications work. To enter the company’s database, the spouse-to-be needs to answer over 150 questions about themselves. This includes personal preferences, demographic (gender, age, place or residence), and behavioral data based on website click-data. This data is then aggregated with other users and historical data. The data is fed into machine learning algorithm that then assign different weights to each factors making it possible to quantify how compatible two users are to each other. So for example, if the historical data shows that male Nascar fans tend to stay together with females that like Harley-Davidson bikes, then users with these traits score higher in compatibility and are more likely to be paired together. The model analyzes hundreds of traits in this manner producing a final compatibility score between pairs of users.

What is different about e-Harmony is that they actually track their relationships success and are able to feed that knowledge into their models. In other words, they are able to match people based on how successful marriages were for similar past versions of these new users. With 16 years of data, the models should have a solid baseline for compatibility. Also, unlike other dating sites, e-Harmony does not allow the user to look for dates. They are limited to the matched produced by the algorithm. That is, they trust that their model is better than the user him/herself in looking for long-lasting relationships. If their numbers are to be believed, divorce rate of e-Harmony couples is around 3.8%. Now, I would sure love to see whether these numbers hold out over a period of decades.

While I am not privy to all the variables and methodology used by the site, their approach seems sound. They are basically quantifying data available from both their user base and psychological research on the topic and making that wisdom available to relationship seekers. It is as if you could evaluate a large number of marriages at once to try to figure out what works and what doesn’t. Certainly, a computer would do that job better than just relying on one person’s experience. While we are limited to our own experience and others around it, models can aggregate results from thousands of people. I see this approach expanding beyond dating site to becoming an enhancement for matchmaking in the offline world.

This does not mean all marriage partners should be chosen through this process. The model’s foundational idea is that marriages live or die because of compatibility. That may not be the deciding factor for all marriages. Also, people change in ways that questionnaires cannot capture. Moreover, even if the approach proves out effective over-time, I don’t see entire societies changing century-old traditions to adopt this new way of choosing mates. Technology changes fast, people not so much.

This is the first part of this blog where we explore how AI is impacting relationships. In this case, we have an example of AI altering relationship between humans. The next level of impact is how AI is reinventing machines interfaces in a way that may displace human relationships. This is AI not enhancing but actually replacing human interaction.

This is when things get complicated.

Who gets to decide our future?

 

Business insider published a provocative article suggesting a transition to come where our devices will progress from being detached to wearable, and eventually to implanted. Elon Musk just launched Neuralink, a company seeking to develop a neural lace that could upload our thoughts. Sweedish startup Epicenter is now implanting microchips in their employees to act as cards so they can open doors and pay for a smoothie. Can this be the beginning of a whole new industry that wants to shape us into cyborgs?

This is not scientific fiction anymore but part of a near future. Technology is basically the outgrowth of humanity’s desire to create tools. Tools are extensions of our bodies so we can perform tasks more proficiently.  Yet, these technologies are taking tools to a different level: they are no longer extensions but would become actual parts of our bodies. This is clearly a new frontier we have scarcely considered.

As business titans imagine this cyborg scenario, the question I want to ask is who gets to decide our future? Just look at how our lives now are shaped by the gadgets that surround us. Are ready to accept them as part of our bodies? In a vacuum of vision, the future belongs to the few who dare imagine it now. Maybe it is time we step into these conversations and start imagining alternative futures.

Are you ready to imagine?

 

 

Augmentation versus Automation: The True Struggle for AI Success

In the discussion about AI, a lot has been said about the fear of automation. Yet, not enough is said about augmentation. Automation replaces human work while augmentation enhances human work. Just think about mowing your grass without a motorized lawn mower and you get the picture of what augmentation looks like. Without it, you would have to cut all the grass, sweep it into piles and then throw it in the trash. The motorized mower does steps 1 and 2 at once while also diminishing your physical exertion in the process. AI technologies do the same but for work that requires thinking.

AI and Augmentation

Visionaries at Amazon and Google, imagine a future in which digital assistants like Alexa will cut through all the tech fragmentation present in our current devices. How? Think about how many apps exist on your phone. Wouldn’t be easier to have all those apps managed by a digital assistant? In organizing and simplifying our digital life, AI could eliminate the current inefficiencies of keeping up with so many apps giving us time to do other things. This would not only help our personal lives but also greatly simplify our work lives.

Think about how many different software you had to learn just to do your job. What if this software could be simplified through an AI interface? Think about a device that you don’t have to type to get what you need, instead you can simply speak to it in normal conversation.

There lies the promise of AI: its ability to augment our abilities to get things done. It can not only remove repetitive and inefficient tasks but also helps us improve on what we already do well. I certainly would love to have a digital assistant help me write this blog faster. It turned out to be a total failure so I am still waiting for better AI writers. The question becomes, will these Silicon Valley titans achieve their dreamed augmentation.

Photo by Science in HD on Unsplash

The False Promises of Automation

Contrary to augmentation, automation seeks to replace humans with machines that do job faster for cost-saving reasons. Think about the demise of manufacturing in this country, mainly driven by automation in factories. Consider the impact of truck drivers with the introduction of driver-less trucks. While companies could save millions by dispensing drivers, the human cost in lost income and social isolation would also be significant.

Automation does not lead to less work. At the beginning of the last century, some believed that because of the progress of technology, soon we would be working 4 hours a day or less. The thinking was that as machines automated manual work, humans would be free to sit by the pool seeping a margarita while the work gets completed. Needless to say, this scenario did not pan out.

Instead, we witnessed was the emergence of whole new work functions that now were needed to maintain the new technological ecosystem. Did we achieve new levels of productivity? Yes, but it certainly was not a linear process. As we could do more with less, organizations also started expecting workers to do more with their tools.

Above all, there has been an exponential increase in complexity. If automation enthusiasts envisioned a simpler future where work became easier they were woefully mistaken. The implementation of computerized machines added a whole sleuth of new requirements that weren’t there before. Surely that created the need for new occupations to emerge. Yet, as we look back at the 3 previous industrial revolutions, did they foster human flourishing?

A Theology for Machines?

As we approach the 4th industrial revolution, this augmentation vs automation framework allows us to reflect theologically on the role of machines. A theological view of technology, one that puts humanity before profit, will focus on steering tools towards augmentation as opposed to automation. It starts with how we view work. Is it a means to an end or an inherent part of our humanity? A utilitarian view of work will easily lead to the immoral way of automation. On the contrary, seeing work as an expression of our God-given humanity, can therefore see machine as allies rather than competitors for work.

Here we can also reflect on tools (technology) as an extension of the Imago Dei on us. God’s image imprint in us compel us to be creators through tools. On the flip side, the Bible often cautions us about the limitations of humanity. The Judeo-Christian tradition teaches us that there is only one Creator God who is greater than humanity. Any human attempt to usurp God’s place will be fraught with disaster.

Regardless of faith tradition, a theological view of technology will often ask the question: is this tool augmenting a human ability or replacing it? If it is replacing, what is the human loss? If it is augmenting, what are its limits? These questions alone should provide us some much-needed guidance as we step into the uncharted waters of Artificial Intelligence. May we ask them sooner rather than later.