This is the second part of the series. You can read the first part of the article here.
Back in the 80s, we marveled at technologies like what were shown in Back to the Future. We hoped for the day when we could have our own hoverboards, biometric devices and headsets that could receive calls and let you watch TV. Today, technology has given us these and more.
Aside from hoverboards, biometric devices, gaming headsets and other wearables, we now also have consumer drones, video calls, Xbox Kinect and other hands-free gaming systems and tablets like the iPad.
Our technology at present is so advanced that it has become common to suggest or expect even the most unexpected devices to come out in the market. Two of the highly talked about advance technologies are Big Data and Artificial Intelligence (AI). And when we talk about these two, the concept of “Deep Learning” is never far behind.
Big Data sounds like a very big word that it often comes out intimidating – like its some complex idea that’s difficult to grasp; that is if you are not a techy person. While Big Data can be powerful, it is not at all what you perceive it to be. Its concept is actually quite simple: it is a collection of data taken from different traditional and digital sources. It is a new method of storing, managing and manipulating data.
All data collected and stored are from internal and external sectors of a company or an organization. Big Data is, most often than not, intended for analysis and discovery, and is useful in making more accurate predictions and decisions. For example, a company selling a new product wants to know if their regular customers will like it. They can use Big Data to collect information regarding customer preferences and buying attitudes to come up with a decision.
If you want to know what Big Data looks like, just imagine a huge warehouse with stacks and stacks of products. Actually, this was what Big Data was all about in the old days; large warehouses with data equipped with business intelligence solutions that can be used for reporting. What we have today is similar to this, but there’s no physical address or location – and all processes are done in real time.
Big Data has worked wonders for many organizations because it has made them more “intelligent”.
When we talk about someone or something being intelligent, one of the first technology outputs that come to mind is Artificial Intelligence. Although it has been around for years, AI or machine learning has not made an impact as major as it is creating nowadays.
Simply put, Artificial Intelligence is all about making computers behave like humans. AI is a term that was created by John McCarthy back in 1965. Several areas of specialization are connected to AI, including expert systems or computers programmed to come up with decisions in real life situations, natural language or computers programmed to understand human languages, and computers programmed to play games against humans.
Siri on your iPhone is a good example of AI. So is Google Now, Google’s personal assistant.
The Relationship of Big Data and AI
The information that you collect from Big Data is used to understand customers and help you come up with a strategy for satisfying them; by giving them what they need and want. However, this can sometimes fall short and you will have to find a way to understand complex analysis. This is where Artificial Intelligence comes in. AI can perform tasks faster than humans. Because of AI, machines can think and act like humans. Therefore, tasks are performed better and in a more efficient manner. Information can be processed in the fastest time possible.
Coupled with deep learning, Artificial Intelligence can prove to be a major factor in Big Data networks. Deep learning is a technology that trains machines to classify, recognize and categorize data patterns by stimulating its “brain”. It is a neural network that leverages major amounts of data intended to solve difficult or complicated tasks.
Some good examples of deep learning are Google Brain and DeepMind.
Big Data, Artificial Intelligence and Deep Learning are Interconnected
AI can assist deep learning through unsupervised data. These data are fed to the machine – and on its own, it will find a way to do tasks that have to be done. Thus, we do not need to tell them what they should do. This symbiosis can result to a lot of positive changes, particularly in terms of inventions. However, this will entail a lot of work for our machines. They will need to go beyond what they already have, beyond the data. This can be done, but at this point, much has to be improved to Artificial Intelligence.
AI technology is already remarkable as it is. But, if we want to reach the level where we can go far beyond controlling driverless cars or enjoying the conveniences of having a home robot, we need to aim for more. As it is, there is a limit to the data instilled in today’s AI. Home robots can only do so much. Our driverless cars can only function to a limit. Thus, we need to push some more and go beyond what we already have in our hands.
Geometric Intelligence founder and CEO Gary Marcus believes that it is important to bring the ultimate AI to the table. This means combining the best of what people can do with the best of what machines are able to do. However, to achieve this, we need to go back to the basics – and that is psychology. We need to find the time to study ourselves, human beings. This will help us reach a deeper understanding of humans, which we can then use to formulate and develop Marcus’ ideal AI.
Despite the fact that a lot can still be improved and new technologies can still be developed, Artificial Intelligence and Big Data are essential in a highly technological world like ours. We don’t need Marty McFly to tell us this. We only need to see all the developments cropping up one after the other. After hoverboards, home robots, flying drones and wearable technology, we’re bound to see more. We just need to give the ultimate AI a little more time to take off.
Photo courtesy of A Health Blog.