Artificial intelligence definition
Artificial intelligence (AI) is a vast branch of computer science concerned with a development in software that allows computer systems to perform tasks that imitate human cognitive intelligence, such as visual perception, speech recognition, decision-making, and language translation.
How does artificial intelligence work?
AI systems work by processing large amounts of data and using algorithms to identify patterns, make predictions, and make decisions. Typically, humans oversee the functionality of AI and are able to encourage useful behavior and discourage behavior that is not useful.
While AI is intended to augment human activity, reduce human error, and ultimately make our lives easier, it is costly to develop and its ability to mimic human behavior illuminates its ability to take over some human jobs. Therefore, there should be rules, regulations, and an understanding of the best practices for using this immensely powerful tool.
A type of AI is machine learning. Through machine learning, a system will learn by identifying patterns and relationships within large, labeled datasets. The AI will then be able to make predications and develop outcomes without the need of labeled data.
AI can reason by using mathematical algorithms based on techniques that including linear algebra or probability rules. Sometimes symbols are used to represent knowledge, this is known as symbolic reasoning and can be used for medical diagnosis. For example, when a patient has particular symptoms an AI can determine what disease they have.
This means that an AI can improve itself, making corrections to its performance along the way. It is also possible to give feedback to these systems to help them improve as it learns. One example is self-driving vehicles. Sometimes objects or conditions block sensors on the car that allow the AI to understand its surroundings. Through self-correction, the AI can determine what the obtrusive object is and still carry out its function.
Techniques like deep learning allow for an AI system to be creative. This involves the AI generating new data based on data it has been trained on. This can be used to generate new images, creating art, and more.
Types of artificial intelligence
Machine Learning (ML)
Machine learning is a subset of artificial intelligence (AI) that involves developing algorithms and statistical models enabling computer systems to learn and program themselves from experiences without being explicitly programmed. In other words, machine learning involves creating computer systems that can learn and improve on their own by analyzing data and identifying patterns, rather than being programmed to perform a specific task.
This is a subset of machine learning that uses neural networks to analyze large amounts of data. Deep learning algorithms are made up of three or more layers of neural networks, making them complex systems. They can learn to recognize patterns in images, videos, and text, and can be used for applications such as self-driving cars and medical diagnosis.
Natural Language Processing (NLP)
This is a field of AI that focuses on understanding and processing human language. NLP algorithms can be used to analyze and respond to customer queries, translate between languages, and generate human-like text.
This is a field of AI that focuses on enabling machines to see and interpret visual data, such as images and videos. Computer vision algorithms can be used for applications such as facial recognition, object detection, and autonomous vehicles.
AI can also be used to develop intelligent robots that can perform tasks such as assembly line work, inventory management, and even customer service.
Artificial intelligence in business
AI is revolutionizing the way cyber security is done because you can now analyze large amounts of data, detect, and respond to cyber threats at machine speed.
AI tools like chatbots that pop up on websites when you visit a company’s home page, are an example of how AI is used by businesses to support consumers. These chat bots can help consumers by answering questions and providing recommendations.
Sales and Marketing
AI can be used to analyze customer data and identify trends, helping businesses create more targeted marketing campaigns and improve sales forecasting.
Supply chain management
AI can help businesses optimize their supply chains by predicting demand, identifying bottlenecks, and improving logistics planning.
AI can analyze financial data to identify patterns and trends, helping businesses make better investment decisions and manage risk.
AI can be used to screen job applications, conduct initial interviews, and even provide training and development opportunities to employees.
Advantages and disadvantages of AI
AI is becoming more prevalent and easier to access for the everyday individual. Of late, it has been a buzzword, but the implications of AI technology throughout social, enterprise, and educational landscapes is becoming increasingly evident.
AI has the power to augment human activity. For regular people this could have benefits across day-to-day activity. Some examples include self-driving vehicles, smart phones with voice command, improved cancer screening, and many more use cases that help make tasks more accessible and simpler.
- Avoids human error
- Works around the clock
- Can manage large amounts of data
- Practical and rational
- Performing repetitive work
- Quick decision making
However, something so powerful has potential drawbacks. There are ethics to using AI technology and a lack of knowledge on how AI works can sometimes lead to a misunderstanding of its service or purpose. For example, AI learns based off data it was given, therefore it can be limited in its output. Knowing its potential drawbacks is vital to using AI ethically. For more on the ethical use of AI click here --> Ethics of using AI
Artificial intelligence in cyber security
In cyber security, AI is a double-edged sword. Its use by cyber-attackers is still in its infancy, but Darktrace expects that the mass availability of generative AI tools like ChatGPT will significantly enhance attackers’ capabilities by providing better tools to generate and automate human-like attacks. There are three areas where Darktrace sees potential for AI to significantly enhance the capabilities of attackers: increasing the sophistication of low-level threat actors, increasing the speed of attacks through automation and eroding trust among users.
AI is widely available and can be used by threat actors seeking to infiltrate security systems of both small and large organizations. Ultimately, human centered security teams fighting against an AI powered attack lack speed, scale, and adaptability that an AI system can provide. Therefore, many organizations have turned to AI based security systems to help fight back against AI launched attacks.
Using AI can help an organization prevent cyber attacks and improve their overall detection and response systems by automating several menial tasks typically done manually, understanding end user behavior, enhance incident response, and more.
AI cyber security in action
Humans find it hard to function logically during a crisis, particularly in cyber security where the variables are fast-moving. AI cyber security can help human security teams overcome cognitive biases and manage cyber incidents more effectively.
At Darktrace, we saw that AI could address an existential threat – defending people, businesses and nations from a world of constantly evolving threats. This threat is only poised to grow as AI is increasingly used by attackers. That’s why we became one of the first to apply AI to cyber security and built a completely AI native technology platform aimed at freeing the world of cyber disruption.
Darktrace Cyber AI Loop
Darktrace's Cyber AI Loop is made up of four AI-powered product families - PREVENT, DETECT, RESPOND, AND HEAL – that operate in any digital environment. They can operate on external data, internally in cloud infrastructure or applications, email systems, endpoints, the corporate network, or industrial systems. This comprehensive feedback system allowing each capability to inform the other and ultimately hardening the entire security system, working throughout the attack lifecycle before an attack even happens all the way through to the aftermath of a cyber attack.
The Cyber AI Loop uses machine learning algorithms to continuously learn and update its knowledge of how an organization operates. It can spot zero days, insider threats, and novel threats that have gotten through other defenses. It applies algorithmic models to identify novel threats, as well as spot already-known threats.
That functionality feeds into a micro-decision-making AI engine, which allows organizations to continue normal business operations during an in-progress attack, responding to fast-moving attacks like ransomware at machine speed - and it can operate in autonomous or human confirmation mode.