Data is a critical component of artificial intelligence (AI). Many experts would probably even argue that data is in fact the most critical component of our most advanced form of technology to date. A fair analogy could even look something like, gravity is to Earth as data is to AI. Does that mean any kind of data works when it comes to AI software? Depends on who you ask. Much of the industry would likely argue that in order for artificial intelligence products and solutions to actually work, the data being gathered to train that AI must be good and/or clean data. Beyond Limits likes to take that notion and turn it on its head a bit.
We aren’t about to sit here and claim that, when it comes to building AI software solutions for your business, good/clean data isn’t ideal. Beyond Limits loves good, clean data. However, we live in the real world, serving clients from important industries like Energy, Utilities, Healthcare, Financial Services, and more. Such critical sectors involve complex real-world challenges that consistently engage with imperfect scenarios. Flexibility is key.
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Why AI is Wild About Data
“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.” (SAS Institute Inc.)
Simply put, the recommendation you receive from a machine learning (ML), deep learning, hybrid, or another approach to AI software products and solutions is the output – and that determination is made based on your input (data). Input is commonly delivered in the form of datasets. When it comes to substantive datasets, extensive number sequences such as quantitative data come to mind. However, qualitative data via videos, pictures, etc., is also equally important.
In terms of AI systems, whatever the dataset type, they are generally categorized as either structured (clean/good/etc.) or unstructured (missing/misleading/etc.). These identifiers matter because not all AI software solutions are equipped to handle one category or the other. In fact, the majority of AI and ML tools currently on the market cannot handle your data when it is unstructured. This reality makes it vital to be discerning when choosing an AI solution for your company.
Now, let’s talk about what “clean data” looks like, according to long-held industry paradigms. Clean data is often characterized utilizing the following factors that include:
Your data is obtainable whenever and wherever necessary. An example might be real-time clickstream data that can exhibit exactly where in the buying route consumers are confronted with issues and encouraged to continue along and complete their purchase.
Every system across your data network encompasses unchanged information. Essentially, all your datasets should sit within the parameters to which they have each been proportioned. An example might be modeling material prices, wherein parameters such as cloth, leather, suede, etc., must all encompass pricing data that sits within one of those respective classifications.
Your data instances are unique. Much like stability, every part of your data must be distinctive to precisely the parameters it attends. An example might be simply trying to prevent a scenario wherein identical prices of a particular material sit within two different parameters.
Your data obeys the organization of its classification (particularly when it comes to time-series datasets) and isn’t comprised of data that impedes the AI’s learnings and reasoning. Typically, allowing the AI to learn from recent data while uninhibited is ideal; the amount of included historical data will be determined by the purpose of the solution up for discussion.
Your data correctly signifies the events or scenarios in question. Accurate, trusted data sources should encompass the input fed into your AI solution so that your output won’t be jumbled, ensuring the answer/recommendation produced by the AI is correct.
Your data is as thorough as possible. An example might involve a compilation of phone numbers with the presumption that data will also contain corresponding information regarding area codes.
The Beyond Limits Clean Data Workaround
Clean data is always ideal but habitually elusive. Beyond Limits Cognitive AI products and solutions were designed for exactly this problem. Our Cognitive AI has the ability to both utilize and coordinate your unstructured data, analytics, and other critical inputs. This cutting-edge modernization will go right on behaving as it’s supposed to, even in the presence of imperfect data that isn’t entirely accessible, stable, distinctive, authentic, correct, or comprehensive. This is accomplished through an ideal marriage, merging the finest of conventional numeric (ML) AI techniques with innovative symbolic AI approaches, providing the system with what can only be described as human-like reasoning and intelligence.
“There are a lot of different methods for dealing with missing data that range from very simplistic statistical and interpolation approaches to advanced machine learning neural network techniques. With machine learning, you can utilize neural networks or autoencoders to essentially learn behavior from a group and infill those missing data sets as much as possible. You can also make use of the other analog data to which you do have access in order to try and predict what might make up the missing parts.” (Beyond Limits Manager AI Solutions, Michael Krause)
Cognitive AI encodes your most vital experts’ knowledgebases, expertise, experiences, and best practices – then circulates that data throughout all factions of your whole organization’s operations. This proficiency is key to releasing the value from superficially unmeasurable or unideal data inputs. These human-emulative knowledgebases grant the system an unparalleled ability to analyze suggested remedial action against widely accepted best practices, sanctioning the system to learn and grow ever more adept over time as it gathers new datasets with unique inputs and underlying meanings. Therein lies the value in AI software unlike ever before, this boost in unhindered access to such respected expert data, instantly accepted by your industry professionals, results in fortified trust in the technology that’s designed to help them.
WANT TO GET MORE INTO THE DATA WEEDS? CHECK OUT OUR BLOG SERIES, WHERE WE TALK IN-DEPTH WITH OUR EXPERTS ON EVERYTHING TO DO WITH DATA & AI – HERE AT DATA DEN.
The Future of Your Most Valuable Asset- Data in the Capable Hands of Cognitive AI
“We’ve moved beyond deep learning techniques to embed human knowledge and experiences into the AI algorithms, allowing for more complex decision-making to solve never-seen-before problems — those problems without historical data or references. Machine learning techniques equipped with encoded human knowledge allow for AI that lets users edit their knowledge base even after it’s been deployed. As it learns by interacting with more problems, data, and domain experts, the systems will become significantly more flexible and intelligent.” (Beyond Limits CEO, AJ Abdallat, CMSWire)
Data lives all around us, permeating the day-to-day through your technology, businesses, and pretty much everywhere else, playing a part in almost everything. Straight out the gate, we are here to tell you that while your company’s data is absolutely a vital component to building the right enterprise-scale AI to solve your most important problems – our AI ensures that adoption and implementation of this seemingly intimidating technology are not dependent on the “perfection” of your data. Why should they be when that simply is not the world in which we live? Enter Cognitive AI.
TO LEARN EVERYTHING ABOUT HOW TO BEST LEVERAGE YOUR DATA WITH BEYOND LIMITS COGNITIVE AI, GET THE WHOLE SCOOP – HERE.
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