Click to learn more about author Samuel Bocetta.

The speed at which technology evolves is astounding. Consider the fact that 40 years ago the world wide web was little more than a government side project. Now flash forward to the present and the majority of people across the globe carry a wireless-enabled device in their pocket.

But then other times, you think about how technologically advanced the world is and wonder why things aren’t happening faster? Shouldn’t we be living in George Jetson’s world by now, complete with flying cars and robotic butlers? The truth is that computers improve in small increments that look more impressive when you take a step back and examine the context.

Now that so much of technology has shifted to the cloud, where restraints on storage and memory are rarely an issue, there is an expectation for growth to kick off. In this article, we’ll dive into the topics of artificial intelligence and machine learning to understand the effect the technology may have in the next few years.

The Machine Learning Movement

Despite all of the power they contain, personal computers are still manual machines. In order to execute a task, you need to use inputs like a mouse, keyboard, or touchscreen to tell the system what to do. Coding languages allow you to script these kinds of actions, but they still require a human to define the steps and rules.

Computer scientists have long known there is a more efficient way to handle data processing, but the opportunity did not exist until recently when cloud computing took off. This represented the rise of the trend called Big Data, which essentially means that companies and organizations finally had the resources to store and analyze insanely large collections of information, sometimes to our detriment.

But individual people cannot look through millions of database records on their own. Machine learning solves that by allowing programmers to teach computers how to identify patterns and make connections without human intervention. The most powerful artificial intelligence platforms today all use machine learning algorithms that run on Big Data repositories.

Data Quality Concerns

Let’s consider one of the most basic types of robotic equipment that exists on the market today: the motorized vacuum cleaners that can sweep an entire house and even empty their filter and recharge themselves. These devices have a series of sensors to detect internal and external factors, and that data is then fed into computerized systems to allow the robot to respond in an appropriate manner.

Now imagine that one of the vacuum’s sensors is receiving faulty data which makes the device think that its filter is constantly full. This will result in the vacuum repeatedly trying to empty the filter and never accomplishing its primary task. The scenario is a perfect example of the concept of garbage in/garbage out.

Big Data systems only provide value if the information input is accurate, reliable, and meaningful. Even small portions of faulty information can result in a serious Data Quality issue. This has the potential to slow down or completely derail efforts to advance machine learning systems.

The Big Data vs. Privacy Conflict

As discussed, the quality of information in Big Data systems is pivotal to their success in building better artificial intelligence and machine learning algorithms. For this reason, companies and organizations are always looking to inject their repositories with the most recent data available.

However, in many cases this desire for data conflicts with individual privacy concerns. Given the scale of major data breaches that have hit various industries in recent years, consumers are worried about how their private information is captured, stored, and used.

As a result, a significant number of internet users have turned to various software tools to try and slow the loss of online privacy. These tools allow them to stay anonymous and hidden, thus making the process of collecting useful data of any sort more difficult for those who would try. One of these, a virtual private network (VPN), not only encrypts your data but also adjusts the IP address of your device and therefore changes how your data looks to companies collecting it.

The recent popularity of VPNs has given rise to hundreds of options, though you should read their privacy policies carefully, especially if you’re in the market for any of the free versions. In particular, pay attention to whether or not they log any information from your internet session.

Many do, which renders the whole point of using it moot. Though there are adequate free VPNs to be found, keep in mind the old adage that if the product costs nothing, then you are the product. In this case, expect that your data might be sold to third parties or turned over to government agencies on demand. 

The Hybrid Model

The terms artificial intelligence and machine learning can be somewhat deceiving. They suggest a future where computers are able to obtain, synthesize, and capitalize on data inputs without any humans being involved in the process. In reality, technology has a long way to go before becoming that autonomous.

The most likely outcome is a continuation of the hybrid model, where computer engineers and scientists work alongside Big Data systems to leverage their processing power to achieve desirable outcomes. Machines will need guidance and instructions or else they could easily lose sight of the end goal.

The most likely scenario is a continuation of growth within a sector known as the Internet of Things (IoT). This includes all sorts of consumer and industrial devices that include computer chips and network connectivity to make their systems smarter and more responsive.

The IoT has dramatically changed the public perception of what a robot is and what it can do. Robots do not need to be motorized devices that simulate human actions. Instead, they can use artificial intelligence and machine learning in appliances we use every day, such as washing machines or light bulbs. These smart devices are connected to the internet and rely on Big Data systems to grow their abilities.

The Bottom Line

The era of Big Data is often taken literally, with companies and developers eager to throw more and more information at systems in hopes of increased values or profits. However, quantity of this tsunami of data is not enough to guarantee advancements in the field of computer engineering. Quality is much more important when it comes to building smart devices and robots.

Looking ahead to the future, human intelligence will remain a critical piece of how Big Data systems evolve. People will work together with machines in a hybrid arrangement to leverage computing power alongside Big Data and drive business results in a controlled way.