Signal from Noise: How Applied Machine Learning Techniques Ease Information Overload

January 6, 2023

These days, being able to find enough data isn't a problem for business analysts. To the contrary, what they must contend with is a tsunami of data that can overwhelm them every time they conduct a basic online search.

Information overload is a serious problem. No one has enough time to look at, let alone process, all of the info that's available on any given topic. As a result, valuable pieces of data can easily get lost, which means that for many companies certain challenges just aren't getting solved.

Handling Data at Top Speeds

The main difficulty is that there are so many more kinds of data now than there were just a couple decades ago. Social media posts, YouTube videos, blog articles and a vast array of other items must be sifted through routinely. And when you launch an internet search, approximately 80 percent of the data that you'll obtain will be unstructured and thus extremely hard to organize.

Fortunately, though, systems of applied machine learning can scan countless charts, paragraphs, images and videos to locate and highlight the most helpful info pertaining to a particular subject. This "signal-from-noise" approach allows analysts to focus on what they do best: synthesizing relevant facts to arrive at meaningful conclusions.

What's more, applied machine learning applications can present the data that they select in a variety of visual formats. That means that people can review this info in ways that are easier for their eyes to take in and their brains to sort.

Moreover, machine learning programs can "watch" videos in mere seconds as they hunt for certain images, places or faces. Thus, editing reams of footage -- from security cameras, for instance -- or compiling exciting promotional clips becomes much less complicated.

Computers can also look at medical body scans to pick out tumors or other suspicious spots. And by employing natural language processing, machines can read through massive numbers of texts, pull out crucial statistics and generate thorough reports for business leaders.

Perhaps the greatest business value that you can derive from this software is in the way it can help your company predict future events. Through those data-based forecasts, you can make decisions and create strategies that are likely to drive long-term growth and profitability for your brand.

No Biases Here

Apart from speed, there's a vital reason why machines are better than people at distinguishing signal from noise. Whether they realize it or not, human beings often have inherent biases that come into play whenever they're looking at data. Even if it's on a subconscious level, they seek facts that confirm what they've known to be true in the past or what they wish would be true for their company or industry.

That's troublesome since technologies and consumer tastes can shift suddenly. As a result, analysts can misinterpret info, make inaccurate predictions and fail to perceive new trends. On the other hand, free of such favoritism, artificial intelligence systems can discern new patterns that can lead to creative breakthroughs for businesses.

Examples from the Real World

You can observe all kinds of fields to see how vital signal-from-noise processes have become. For starters, stock traders can use programs that explore tweets and other social media postings in search of hot tips. In addition, such software arranges those pieces of data according to how urgent they are. That way, traders can look at the most important items first and use them to make snap decisions.

For their part, marketers can compile detailed informational packets about their clients and prospective clients. This business insight aids them in shaping their campaigns. For example, if a convenience store chain begins selling a new brand of diapers, its advertisers can use the statistics they get from their machine learning software to email customers with babies and toddlers.

To take another case, artificial intelligence is beginning to transform biomedical studies. This type of software can rapidly comb through biological data and find patterns. It can then anticipate how pathogens will react to different substances, how medications will affect certain patients and so on. Likewise, such a program could figure out whether a disease has variants. Those disease variations might have different characteristics and require different diagnoses and methods of treatment.

Machine learning is even starting to make computer users safer. That is, by crawling online marketplaces and sending data to classifiers, this kind of system can discover software that hackers have created and put up for sale. Such malware could be designed to exploit security lapses in programs like Microsoft Word and Windows.

Brave New Data

From predicting factory accidents before they happen to helping doctors make cancer prognoses, the advanced machine learning techniques that Moodwire supplies are revolutionizing medicine, scientific research, engineering, the arts and humanities -- essentially society as we know it. Big data now makes the world go round, and Moodwire, a true thought leader, can give you a system that will let you harness that info to make your dreams come true.


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