Pattern Recognition and Machine Learning Solution

Pattern recognition and machine learning solution concepts are fascinating when it comes to both humans and technology. For humans, we are discussing inherent traits and abilities. For machines, we realize these traits can be taught and repeated. Putting the two together can save time, money, labor, and lives.

Pattern Recognition

Pattern recognition is a skill virtually every human brain is able to do from birth. Babies learn feeding cycles, sleep times, and stimulus and response patterns within a few months of being born. As children get older, they learn to identify faces of family members, and speech patterns. They then begin to learn to read words and even choose music they love.

In various industries, adults became trained to perfect their pattern recognition skills and became handwriting experts, fingerprint analysts, and even detectives in criminal situations.

Linguists studied ancient, “dead” languages, and mathematicians became physicists, who worked on clean energy and getting us to the moon.

Pattern recognition is necessary for so many aspects of human life that we often forget we are doing it.

AI Pattern Recognition

As technological advances were made, however, and artificial intelligence showed its usefulness to society, pattern recognition became one of the first elements built into computers. Machine learning pattern recognition allowed for facial recognition software and license plate tracking.

Today, engineers have designed applications that can take over virtually all trivial pattern recognition tasks that once required humans, like fingerprint analysis or language translation, and they have built upon it to great heights.

Now, machines can offer medical diagnoses, analyze economic data, and predict weather conditions, all as examples of pattern recognition.

And if you think about it, these functions are quite simple when an artificial intelligence is allowed to perform them because they simply require data acquisition and classification.

Recognizing repeating patterns is quite easy for a machine that only has to perform that duty without emotion, bias, or distraction, all issues that lead to human error when our fellow men are expected to recognize patterns.

Pattern Recognition Is Not Replacing Humans

Still, some fear pattern recognition algorithms will replace humans, which is not the point, and can never be fully true.

Yes, AI will replace human labor where the labor is at its most mundane, simply recognizing a pattern. Machines have proven much more successful, by a wide margin, than humans ever have been, and we can be grateful they provide life data to help humans analyze it and then make decisions no machine should ever be trusted with.

The much more positive reality than humans being replaced by machines is that those machines performing highly complex pattern recognition functions free humans up to do the work no machine can ever do.

No artificial intelligence can be entrusted with making life or death decisions or engaging in the running of governments, local and national. Nor can humans be replaced in the arts — music, literature, film, and visual media like paintings and photography require the involvement of human emotion, something a machine can only mimic and never actually acquire.

Pattern recognition is critical to a highly functioning society, but it is not the only function of society — far from it.

Pattern Recognition Technologies

Pattern recognition technologies are many, but they all involve three basic approaches:

Statistical

Statistical modeling in artificial intelligence trains the pattern recognizer to draw on quantitative features from large data and then compare, but not relate, those features. This means the software is only asked to identify a category and place select data into that category.

A good example of statistical pattern recognition is when your email server recognizes an email as spam or junk and automatically relocates it to those boxes.

Structural

Structural modeling, also called syntactic pattern recognition, teaches the machine to not only identify patterns but also to relate those patterns to each other. This approach is similar to the type of pattern recognition humans do.

An example of structural pattern recognition is a heart monitoring system, which recognizes healthy heart rhythms and can create an alert when that rhythm goes outside of normal range. Modern glucose monitors are also highly adept at alerting the patient wearing the monitor when glucose levels are out of healthy range.

Neural

Finally, neural modeling designs AI that can perform syntactic pattern recognition and continue to learn as it recognizes those patterns. Essentially, it expands its database of information and grows with that expansion, become better at recognizing and relating data as it receives more “knowledge.” Neural pattern recognition is, obviously, the closest to human pattern recognition we can get.

The various types of pattern recognition we have to work with today include patterns that come from images, from sounds, from human voices, and from speech. The technologies that draw on these elements are used in industries that range from finance to biometrics and from law enforcement to writing and graphic design.

Oculyze

Oculyze entered the scene with pattern recognition technology to help our colleagues in the fermentation industry (brewers, winemakers, bioethanol producers) identify healthy yeast. With a few simple images uploaded to our network, our software can inform the client if the yeast is alive or dead and how many yeast cells there are in a particular sample, saving time & money and optimizing the fermentation process.

From there, our technology has grown by leaps and bounds.

Now, we have pattern recognition software that can identify insects, monitor uterine health in cows, analyze irregularities in the human eye, and count colony forming units on agar. And so much more.

We are now in a position to customize our software to the needs of clients with wide-ranging interests and areas of expertise, all thanks to machine learning and pattern recognition.

And not only have we designed AI pattern recognition to be of use to our clients, saving them time, money, and energy, but we have heard back from our clients that the software is exceeding their expectations.

Which, really, is all we can ask for.


The Oculyze image analysis technology combines methodical pattern recognition with artificial intelligence and deep learning, allowing us to develop more robust systems with less data than with deep learning alone.

With our Image Analysis Platform, you can save hundreds of hours of work that would have gone into manual labor. Image analysis automation provides you with accurate results and secure data management in less time, so you can save your resources for something that brings more value to your business.

Let artificial intelligence do the hard work for you. Start saving time and costs now! Want to know more? Contact us and we’ll be happy to help!

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