- Security
- Privacy
- Performance
Artificial intelligence, or AI, is a technology that enables machines to learn, analyze data, and solve problems in a human-like way. And as AI becomes more accessible, cybercriminals are using it to target people in sneaky new ways. Learn all about AI and how it works, then use an AI-powered threat-detection app to enhance your digital security in this brave new world.
Artificial intelligence is a computer program that simulates human intelligence to find connections, complete tasks, solve problems, analyze results, and create content. AI encompasses technologies that allow computers to execute a range of sophisticated tasks, such as visual perception, language comprehension and translation, data analysis, providing recommendations, planning, and more.
"Artificial intelligence isn’t just a powerful tool — it’s becoming a force that reshapes how we perceive the world. But, as it becomes better, smarter, more realistic and accessible, AI will increasingly blur truth and deception, making it more difficult than ever to decipher what’s real."
Before AI, computers followed precise instructions written by programmers and couldn’t handle exceptions or new situations. In contrast, AI learns from data and improves its performance based on patterns and feedback. This process is known as machine learning.
The technology behind AI is complicated, but its practical applications are easier to understand. For example, in GPS navigation, AI can find the best route in real time by making judgments about traffic and road layout. Likewise, AI assistants like Siri can analyze your speech patterns and recognize your unique way of speaking.
AI can perform human-like actions with great efficiency, precision, and thoroughness. This makes it an invaluable tool for human workers when it comes to highly data-intensive or monotonous tasks.
With AI, machines can learn and improve. AI functions by learning from data, finding patterns, and adapting over time. Theoretically, this should free up human workers to perform tasks that require more nuance or creativity.
The purpose of AI is to assist human workers so they can make better, quicker decisions. It is all about increasing “brainpower” and finding new solutions that may even save lives, like when an AI notices patterns in someone’s medical history that a human doctor may have missed.
You may also have heard of the related concept of machine learning. Machine learning is an AI’s ability to detect patterns and change their internal parameters based on new data. It’s a subset of AI, so all machine learning is AI. However, not all AI is machine learning.
AI that can perform human-like tasks (ANI or artificial narrow intelligence) already exists. AI that theoretically has human-level understanding (AGI or artificial general intelligence) remains unachieved.
AI uses algorithms to analyze large, complex datasets and find patterns. Instead of following strict instructions, AI models learn from data to make predictions, draw conclusions, perform tasks, and improve over time. While AI can recognize connections and generate insights, it does not “think” like a human — it identifies statistical patterns to produce results.
An AI must also be trained and fed relevant data sets for the best predictions and decisions to be made.
Here are some key concepts that help explain how AI functions:
Machine learning mimics human pattern recognition but goes even further, enhancing tasks like fraud detection. It can analyze human behavior in extreme detail, instantly spotting anomalies based on various factors — including unexpected details like weather conditions during a spike in sales. Deep learning, a more advanced subset of machine learning, refines this process even further by using neural networks to detect complex patterns.
Expert systems are AI programs designed to mimic human decision-making in specialized fields. Using predefined rules and knowledge bases, they provide solutions in areas like medical diagnosis, financial analysis, and cybersecurity. Unlike machine learning, expert systems rely on structured logic rather than self-learning.
Natural language processing (NLP) helps an AI understand human speech and text. Speech is particularly difficult for a machine to understand because the same word might mean something completely different depending on intonation, part of speech, stress, accent, situation, the mood of the speaker, or their relationship to the listener. An AI can deal with all of this after listening to someone or being trained for long enough.
Computer vision allows an AI to recognize objects and interpret visual information from images or video. By analyzing photographs or live-streamed footage, AI can “see” what’s happening and react accordingly. It can even detect facial expressions and infer emotions, with profound implications for security, healthcare, and autonomous driving technology.
Robotics powered by AI can enhance precision and efficiency in areas like surgery, manufacturing, agriculture, and even household cleaning. AI robots can adapt to obstacles using advanced algorithms, optimizing their performance in real-time.
Generative AI enables machines to create new content, such as text, images, music, or code. By learning patterns from vast datasets, AI models can generate highly realistic and creative outputs. These systems are widely used in writing assistants, design tools, and even deepfake technology.
An example of what the AI image generator Leonardo can create when asked, "What is AI?"
AI gets its information by being fed data — different kinds depending on the learning model. Thus, large language models (LLMs) use a vast store of books, websites, social media posts, forums, and research papers. Computer vision models use images and video, often labeled to help them recognize movement and distance. Meanwhile, reinforcement learning models learn through trial and error, interacting with environments and receiving feedback in the form of rewards or penalties.
AI learns through a variety of techniques, each designed for a specific purpose. It can be trained to recognize specific patterns, analyze data to find connections, and learn from its own mistakes.
Here are some of the ways different AI models learn:
Supervised learning trains AI with labeled examples. For instance, if you show an AI many pictures and tell it which ones contain tumors, it can learn to recognize them in new images.
Unsupervised learning lets AI find patterns on its own. For example, in fraud detection, AI analyzes your spending habits — like when and what you buy — and raises an alert if something unusual happens.
Semi-supervised learning combines both approaches. AI starts with a small amount of labeled data as a guide, then categorizes a much larger set of unlabeled data based on what it has learned.
Reinforcement learning teaches AI through trial and error. It learns by predicting outcomes, making decisions, and adjusting when mistakes happen — similar to how a person learns from experience.
Deep learning mimics the way the human brain processes information using artificial neural networks. By analyzing vast amounts of data, deep learning enables AI to perform complex tasks like recognizing faces, understanding speech, and driving cars.
There are many types of AI and ways to categorize them, but some of the most well-known kinds of AI technology are generative AI (like Dall-E and ChatGPT), natural language processing AI, robotics AI, and computer vision AI.
Here are a few examples of artificial intelligence types:
Generative AI creates new written, video, or audio content based on content it has been shown. ChatGPT and Dall-E are well-known examples.
Limited memory AI stores a little bit of data pertinent to the task it’s trying to complete, whether it’s chatting with the user or keeping a safe distance from other cars on the road.
Natural language processing tries to make sense of written and spoken human language. It helps an AI recognize intent and have smooth conversations.
Computer vision is used for more than just facial recognition — fitness apps can use it to detect and correct incorrect exercise form.
Robotics AI has a wealth of applications, including in transportation, manufacturing, and surgery.
Chatbots like Google Gemini, Character AI, Snapchat AI, and ChatGPT are examples of limited memory AI. They are also generative and use NLP. Dall-E is a generative AI that uses NLP to interpret users’ written descriptions and turn them into pictures. Self-driving cars are robotics AI that use computer vision, limited memory, and reinforcement learning.
New AI-driven technologies and applications often leverage multiple types of artificial intelligence for different purposes.
An example of how a limited memory AI like Chat GPT can create an image based on the question, “What is AI?”
AI is primarily used to improve the productivity and problem-solving abilities of human workers. It assists in monotonous, complex and difficult jobs that humans may find tedious. It can even come up with innovative solutions that make meaningful, positive differences in many professions.
While AI is a long way away from making judgments on its own, it can complete certain tasks much better than a human. Its creative choices can provide perspective, inspiring workers to go about their duties in novel ways and opening up new avenues for exploration.
Some uses of AI are whimsical or entertaining. Voice.ai, for example, allows you to speak with the voice of various celebrities. Voice.ai is generally safe, despite legitimate concerns that the technology could be used for deepfakes.
Below are some other common ways artificial intelligence is used:
Automation is when a machine can do a complex but repetitive task all on its own. The AI can even adapt when it runs into an obstacle or exception so a human doesn’t need to intervene.
In manufacturing, many things can go wrong. A metal panel may be placed in a workspace off-center, triggering a potentially damaging chain of events. An AI robot can handle these situations efficiently, recentering the metal panel and continuing like nothing ever happened.
Human workers are subject to fatigue and burnout, which can lead to miscalculations, typos, incorrect data entry, and other mistakes. Even when they’re well rested and “in the zone,” people can become distracted and make errors.
AI, on the other hand, is never distracted or tired. For example, in surgery, a robotic AI can execute ultra-fine movements consistently across long periods.
AI can improve customer experience by automatically personalizing the content that you see. For example, it can predict what shows you’ll like based on various characteristics, discovering connections on its own and sometimes noticing similarities that you never would. These tailored recommendations help you find your favorite shows more quickly. Netflix and YouTube already employ this type of AI.
AI is improving security with real-time monitoring, malware threat detection, and fraud prevention. Thanks to deep learning, an AI is able to immediately recognize anomalies in a system, security camera feed, or bank account and raise an alert. A human worker can then investigate, catching security breaches and fraudulent transactions much more quickly. As Internet of Things devices become more popular, AI-powered security devices are becoming increasingly essential.
Avast uses AI-driven security models in our cybersecurity tools to help detect suspicious patterns and block potential threats in real-time. Get Avast Free Antivirus today and let us defend your devices and protect your digital life with an AI-driven solution — completely free.
AIs don’t need to take breaks or sleep. This allows them to offer their services whenever they’re needed, improving customer service and allowing vendors to deliver products or services 24/7. AI-powered chatbots powered by NLP are already used by all sorts of organizations, from airlines to hospitals.
Accelerated Data Analysis harnesses the power of AI-enabled GPUs to run highly complex analyses of very large datasets. This permits many computations to be run in parallel, completely transforming the way that tasks such as stock market analyses (in finance), genome sequencing (in healthcare), and equipment condition monitoring (in manufacturing) are carried out.
AI has had a long and storied history. Less than a century ago, science fiction writers, speculative journalists, and other experts were imagining what it would be like to talk with computers — and now we can. Let’s take a look at the history of AI and how we got here:
Things that once seemed like a flight of fancy, like machines conversing with people, are now an everyday occurrence. Which of our dreams will become reality next?
All of the AI mentioned in this article can perform tasks like a human, making them examples of artificial narrow intelligence (ANI). We still haven’t made it to artificial general intelligence (AGI), in which machines would be able to understand like humans or transfer knowledge from one situation to another.
Some computer scientists like Ray Kurzweil believe this will be achieved in the next few decades. Geoffrey Hinton, another leading AI thinker, speculates that AI will become even more human-like in the way that it communicates, positively transforming the world. Optimists believe that the goal of AI is to help, no matter whose hands it’s in, and that humanity will gain a friendly and super-capable AI assistant in Web 3.0.
However, not everyone is so optimistic about the future of AI. Like any technology, it has the potential to be misused. For example, AI is already perpetuating human biases in employment algorithms and facial recognition. Likewise, AI has begun to replace some creative jobs, taking opportunities away from designers, writers, and other knowledge workers. Some fear that AI may lead to large-scale unemployment in the short- or long-term.
To some, the fantasies of AI saving us from ourselves seem farther than ever before.
It’s not all grim, though. Douglas Eck of Google believes that AI is capable of becoming a creative tool that gives rise to new possibilities for artists, rather than replacing them. For better or worse, we are learning how best to integrate AI one day at a time.
For now, it’s imperative to watch out for the cybersecurity risks of AI. Many emerging technologies can be used by hackers, and generative AI models like Chat GPT have reportedly been used to develop threats, such as remote access trojans (RATs).
The “good” thing is that these threats mostly involve known trends — dangerous links, scam ads, and deepfakes. But while the types of threats may not be entirely new, the efficiency with which these attacks can now be executed is the real danger, and could lead to catastrophic dangers like identity theft, financial fraud, and larger-scale phishing scams.
While AI can be used in social engineering attacks, it can’t (yet) create malware on demand. AI might become more dangerous, though, and for reasons that we can’t foresee right now. It’s imperative to learn about present-day cybersecurity risks like spoofing, IoT security risks, and web tracking so that any new developments can be understood more quickly.
Brushing up on your cybersecurity knowledge is always a good idea. Start by checking out our website safety check guide to become more proficient at spotting dangerous websites. Then stay up to date on how your favorite apps and services are implementing AI and take steps to remove permissions if their use of AI bothers you, such as getting rid of My AI from Snapchat.
A rise in cybercrime has encouraged governments to put AI regulations in place early. Thus, high-risk uses of AI in the medical field are subject to strict guidelines in the EU.
AI ethics in the workplace are also being considered. For example, in some cases AI has been shown to perpetuate discriminatory hiring practices, because discriminatory data was fed into its training model. Further, there are concerns that AI can’t adequately explain its reasoning when it makes certain decisions. What if the AI chooses to favor one employee over another and can’t explain its choices?
Users of AI must also be protected. They should be able to trust that their personal data isn’t being collected and shared — and that AI isn’t going to repeat what the user said to it in confidence. And, as previously mentioned, users should be able to understand the reasoning behind what AI says and does. To address such concerns, the U.S. government has even come up with an AI Bill of Rights.
AI is turbocharging human-driven processes, and its true potential is only beginning to be understood. As the landscape of AI changes, new Cyber Safety threats can be hard to keep up with.
A powerful cybersecurity tool can help protect your device (and you) from some of the threats associated with AI. Avast Free Antivirus has real-time threat detection, automated updates, and predictive analytics for top-of-the-line cybersecurity. Integrating Avast Free Antivirus into your security setup can help protect you against evolving threats facilitated by AI technologies. Get it for free today.
