How does artificial intelligence (AI) manipulate data? In this article, discover how AI manipulates incoming information and what you can do to reduce risks.
The implementation of artificial intelligence (AI) systems is on the rise in contemporary life. As more of us interact with AI applications on a regular basis, it’s important to understand how these digital tools work.
The manner in which AI manipulates data is essential for understanding how it works, what it can do, and the potential risks that exist as a result. While there are many advantages to AI data manipulation, responsible and ethical usage is critical.
In this article, we’ll cover the foundational principles of AI data manipulation to help users and businesses understand its impact on everyday life.
How does AI work with data?
As human beings, we receive a regular stream of incoming information. While we don’t think of this information as data, our brains process it as such. Humans are capable of retaining data, understanding it mentally and emotionally, and learning from it. This process influences our future decisions, actions, and knowledge.
In simple terms, artificial intelligence seeks to mimic this basic physiological process. But instead of the human brain performing the work, AI relies on computer systems, algorithms, and code to accomplish similar results.
This foundational process is the stage on which AI data manipulation is set. In the sections that follow, we’ll unpack the options AI systems have when using and manipulating data.
In humans, neural networks are an interconnected webs of neurons and transmitters that help the brain recognize and process incoming information. Most of these electrical processes take place without conscious thought.
In artificial intelligence, neural networks are interwoven algorithms that are designed to mimic this process. The units or nodes of an artificial network are called “artificial neurons.”
Neural networks originated in the 1950s-1970s and are often considered one of the earliest implementations of artificial intelligence.
As AI became more advanced, machine learning entered the stage. As the next phase in artificial intelligence development, machine learning introduced AI systems that learned and improved gradually over time. In many ways, machine learning serves as the basis for AI data manipulation.
Machine learning utilizes algorithms, predictive analysis, statistics, and data mining to continually improve its data consumption. Each of these tools helps the AI application become more adept, which produces better results with every new experience.
Deep learning is the AI technology driving present day innovation. It’s a subset of machine learning that builds upon the longstanding history of neural networks to accomplish new tasks at fast speeds and with high accuracy.
Modern day data manipulation wouldn’t be possible without the advances in deep learning. Even with mass amounts of data, deep learning makes it possible for AI applications to perform better and scale to fit current growth and demand.
Neural networks, machine learning, and deep learning together make it possible for AI data manipulation to work efficiently and produce results for human users.
What is data manipulation?
Although there are several connotations of the term “data manipulation,” the most basic definition refers to the ways in which artificial intelligence machines read incoming data and translate it into usable information for people to leverage.
In this sense, data manipulation is the process of cleansing data to make it more actionable. Whether the manipulation process takes place automatically or manually, it’s necessary due to the sheer amount of data sent and received on a recurring basis.
There is even a specific programming language called DML (data manipulation language) that can modify data sets with specific actions; steps included in DML are insertion, deletion, updating, and modification.
Benefits of artificial intelligence and data manipulation
When applied in this sense, data manipulation has numerous benefits to the end user and to any organization or business that relies on trustworthy data. Advantages include:
Better data organization – Data manipulation can help users and teams avoid messy or duplicate data. When information is haphazard and disorganized, it is difficult for AI systems to read and use it. Moreover, human use is clumsy and inefficient.
Increased data value – Most AI applications exist for a specific purpose. Typically, users who rely on AI want the greatest return on investment. Data manipulation ensures that the most valuable data is available and ready to use at any given time, for any given project.
Reduced manual cleanup – When working with sets of data, it’s important to consistently check for errors and potential issues. When performed manually, this audit process is time-consuming and tedious. With AI data manipulation, users don’t have to waste precious time or resources on cleanup.
Practical examples of artificial intelligence data manipulation
Artificial intelligence is making waves in the business marketplace for its far-reaching use and adaptability. A well-built AI application should be able to collect incoming data and to manipulate it so that it can fulfill its purpose.
The examples below showcase how AI data manipulation can positively affect the life and livelihood of consumers and businesses.
Recognizing and flagging counterfeits
As e-commerce and online sales have steadily increased, so too have the attempts to sell counterfeit goods. This problem is especially true for large online retailers that allow independent sellers to market their own products to consumers.
To win the fight against counterfeit sales (and to boost consumer trust), more online retailers are using smart AI data tools to recognize genuine products from fake ones. AI software uses text and image recognition to spot small variations and call for the removal of certain products.
Collectively, this flagging process relies on AI capabilities to intake text and images, distinguish characteristics of real products, and take action based on that data.
Personalized recommendation engines
If you’ve watched a video streaming service or listened to music through an app, you’ve probably received personalized recommendations that match up to your current activity. These predictions are based on AI tools that receive, interpret, and suggest new data based on past choices.
Recommendation engines often employ filtering techniques and stored product details to manipulate incoming data into good suggestions. The result is an intuitive experience that users can enjoy and benefit from.
Risks of data manipulation
Although the common uses of AI data manipulation are fairly innocent, there are notable risks and disadvantages. Industry leaders like Deloitte and Gartner warn that modern IT leaders must become more knowledgeable about fending off AI misuse.
Unfortunately, AI data manipulation can also be wrongly used to fool algorithms and interfere with data that would otherwise be safe and reliable. Many times, this comes from extreme examples of user interference, such as when a group of users intentionally stokes an AI application with bad or inappropriate data. If the AI application learns to use the faulty data, it never performs as intended.
Can AI data manipulation be used to cause harm?
While the nefarious ways to manipulate data shouldn’t deter well-meaning users from leveraging artificial intelligence, it’s wise to stay on guard against improper use.
Although AI data manipulation can be a force for good in the world of business, there are several specific ways that threat actors use it to create chaos or get what they want.
A deepfake appears in any video or image that replaces the real person with someone else’s likeness. Usually, there are enough similarities to create an extremely convincing replica.
While deepfakes can be used for fun and trickery, they can also be extremely harmful to modern governments and democracies. When deepfakes are used to reproduce media of political leaders or other important officials, the result (often generated with AI) can sway public opinion and cause significant unrest and alarm.
Here’s a few examples of deepfakes used in real life.
Personal data harvesting
Data harvesting is when any company or organization gathers the personal information of individual users without their consent. The most popular and well-known example of this is the Cambridge Analytica scandal, which manipulated public data in order to alter personal perceptions of political candidates and ideas.
In this example, AI manipulation is powerful and psychological. Personal information can be used against individuals in unprecedented ways.
An obvious harmful example of AI data manipulation is when cyber criminals hack into personal or business accounts using exposed information. Hacking often exposes existing vulnerabilities and then takes advantage of those to gain unauthorized access to accounts.
Unfortunately, AI data manipulation can be used to hack into massive public infrastructure systems or self-driving vehicles. Instances of this type of hacking have been demonstrated in recent years by professional data security experts. It is worthwhile for governments and transportation regulators to prepare for this specific hacking threat in order to protect citizens and the public at large.
Conclusion and takeaways
With the right motivations and intentions, AI data manipulation can be used to make artificial intelligence systems even stronger and more user-friendly. The benefits include savings in time, resources, and effort. With appropriate manipulation strategies, users can take advantage of more organized and actionable data in order to meet any end goal.
As with most technology, there are ways that AI data manipulation can be exploited. IT and business leaders have a responsibility to be mindful of these risks and to make informed decisions on the use of AI data manipulation.
If you want to learn more about how AI can improve your business or marketing strategies, reach out to us today to get connected to the perfect AI company for your specific needs.