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.
