Do you remember back in the day when warehouses would hire people to track stock manually? As each hour passed, tens of employees would walk around the aisles, taking note of the number of items that had been picked off the shelves and running the numbers so as to figure out how much inventory they had left. Now, many warehouses have moved on to automated systems that use computer vision and weight sensors to track this inventory in real time, thus enabling employees to focus on other business aspects, such as production, sales, and marketing. That is just one example of pattern recognition in the world today. But how exactly are machines able to achieve this level of accuracy, and what does that mean for our future? Let’s get into it.
As much as pattern recognition has become part and parcel of our daily lives, the truth is that there exists a lot of confusion about what it really is. After all, is it artificial intelligence? Is it just another intelligent system? These are the questions that we, at Pattern Recognition Insights, seek to answer through our detailed guides.
Pattern recognition is an international research field that combines computer science, engineering, mathematics, and applied research. Its development over the decades comes down to the input of researchers, universities, and international scientific communities who have tirelessly found gaps in the field and worked at filling them.
But what does all this mean? Well, that is where we get into the practical side. In the past, human beings had to write down the rules that computers had to follow. As such, the sequence was that human beings would feed data to the machines and set out the rules, and the computers would then provide output. As you can imagine, this was a lot of work on the part of the people doing this work, not to mention the time invested in the processes.
Pattern recognition evolved as a way to address this problem. Rather than feeding the machines with rules, humans found a way to feed the machines with both output and data such that the machines could spot patterns in the information and use them to make their own rules. For example, when provided with images, they would learn to recognize differences in shapes, objects, faces, and so on. And when provided with audio, they learned how to identify spoken words and other noises by mapping the sound waves. Thanks to this, machines were able to write their own rules instead of relying on people to tell them what to do when presented with data, which we now refer to as pattern recognition. It is important to note that pattern recognition is considered a subfield of machine learning and data science.
The more that we have embraced technology as a society, the more data that we have been able to generate. And as things stand, we generate so much data that it would be impossible for us to manually review it and extract meaningful insights from it within a short period of time. After all, for each data set, we would need people to enter the data manually, check it for anomalies, run analyses on it, and present the output. With billions of data sets, you can imagine just how much time this would take from other important aspects of our lives.
Pattern recognition serves as a way to lessen this load for us, as it can run the checks and analyses in the background, leaving people to focus on generating data and making use of the findings from the data.
In today’s world, machines are now able to look at millions of examples to figure out which features are most important in data sets. This process is also known as deep learning. But this was not always the case.
In fact, in the beginning, computers were very rigid, such that if you wanted a machine to give you a specific output, you had to write very strict code to make that happen. Say, for example, that you wanted the machine to read out letters, you had to write out the exact measurements for each character, and if any letter was illegible for reasons such as slanting or blurring, the computer could not read it. Even using a different font resulted in failure.
This rule-based era marked the first decades. But as machines got faster, people realized that they could use statistics in their favour by programming the machines based on probability. Programs were now able to calculate probable outcomes based on data patterns, thus reducing the need for humans to write detailed instructions. Even so, humans still had to spell out which features the machines should look for in data sets.
Thankfully, as the data sets have grown bigger and computers evolved even more, we are now able to delegate the heavy lifting to machines, which can now use multi-layered artificial networks to figure things out for themselves.
As much as machines have come a long way, they are still subject to various challenges that impact their accuracy. For starters, data is not always as per the book, and computers are likely to make mistakes or get confused when reviewing messy data. Examples include pictures taken in poor lighting, background noise in audio recordings, or illegible handwriting. Given that such instances are unlikely to match the data the machines used to track patterns, they may fail to derive meaningful or correct insights from such data.
Secondly, machines are likely to develop tunnel vision if they rely too much on their training data. By memorizing specific examples rather than figuring out general rules, machines may find themselves unable to process data that does not play by the same book.
And finally, while computers are highly effective in spotting trends, they lack human intuition to determine whether their insights are practical. As such, they may pick out patterns whose results would not bode well in the real world. Thanks to these reasons, most organizations find that they still have to rely on humans to conduct final checks to ensure that the results are not only accurate but also objective.
It is safe to say that pattern recognition still has a way to go before we can delegate all the work to machines or at least most of it. But while that may be so, many organizations have found effective ways to implement these systems in their operations with great results.
Take the example of medical imaging and diagnostics. Systems are now able to scan thousands of files in seconds and use this information to flag any signs of abnormalities that humans can sometimes miss, and this is enabling hospitals to provide more accurate diagnostics.
Another good example would be in financial fraud detection. Through the use of algorithms, many financial organizations are able to monitor millions of transactions and flag any unusual activities, such as unusual spending habits or logins from unverified locations. In this way, they are able to alert their clients of possible fraud attempts and even block most of these activities before their clients incur losses.
So, while pattern recognition may still be under development and is still facing considerable challenges, we can also agree that it is making a world of difference in our lives as it is, and with more improvement, it could simplify most aspects of our lives.