What is artificial intelligence (AI)?
by Christopher Trick, on Feb 21, 2022 11:32:00 AM
From the military to healthcare, artificial intelligence (AI) programs computers to undertake essential tasks through recognizing patterns in large amounts of data.
In this blog, you'll learn how AI equips high-performance compute systems with cognitive powers that match and exceed human abilities to analyze data in real-time and optimize performance.
What is artificial intelligence (AI)?
First coined in 1956, the term "artificial intelligence" (AI) refers to the ability of machines to learn from experience, adjust to new inputs, and perform human-like tasks.
Using AI, computers are trained to accomplish specific tasks by processing large amounts of data and recognizing patterns within that data.
Artificial intelligence research in the 1950s first focused on problem solving and symbolic methods. In the 1960s, the Department of Defense took an interest in AI and began training computers to mimic basic human reasoning.
These early activities paved the way for the automation and formal reasoning capabilities we see in computers today, including decision support and intelligent research.
AI has become increasingly popular due to increased data volumes, advanced algorithms, and computing power and storage improvements.
Why is AI important?
There are six primary ways that artificial intelligence enhances the capabilities of modern computers:
- It automates repetitive learning and discovery through data: AI performs frequent, high-volume, computerized tasks reliably and without fatigue. However, there is still some human assistance involved as people must set up the system and ask questions.
- It adds intelligence to existing products: Conversation platforms, automation, bots, and smart machines can be combined with large amounts of data to improve any technology. For example, Siri was added to many Apple products as an interactive feature to perform tasks based on voice commands. AI can also be applied to security intelligence like smart cams (Ring) to investment analysis.
- It adapts through progressive learning algorithms: AI seeks out the structure and regularities in data so that algorithms can acquire skills. For example, an algorithm can teach itself to play chess or what product to recommend online. Each model adapts when given new data.
- It analyzes more data at a deeper level: AI has neural networks with many hidden layers, making tasks like building a multi-layer fraud-detection system much easier. With enhanced computing power and massive amounts of data, deep learning models can be trained to learn directly from the data (more on that later).
- It achieves incredible accuracy through deep neural networks: The more you interact with products that have AI, the more accurate they become, as they can pick up on patterns. For example, in the medical field, AI techniques can be used to pinpoint cancer and medical images with improved accuracy.
- AI gets the most out of data: Since AI algorithms are self-learning, data is an asset, and all you need to do is apply AI to data to find any information you need. With our world becoming increasingly digitized and the massive amounts of data accompanying this transition, AI provides a competitive advantage. The organization with the best data will ultimately prevail.
But now that we've seen the benefits of AI, you may be asking, "How does this all work?"
Well, let's take a look.
How does AI work?
In a nutshell, AI works by combining large amounts of data with fast processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data.
There are three major subfields of AI: machine learning, neural networks, and deep learning.
- Machine learning (ML): An analytical model building using methods from neural networks, statistics, operations research, and physics to find hidden insights in data without being programmed where to look or what to conclude. (Read more about machine learning here.)
- Neural networks: A type of machine learning made up of interconnected units (like neurons) that process information in response to external inputs and relay information between each unit. Multiple passes at the data are required to find connections and make sense of the undefined data. (Learn more about neural networks here.)
- Deep learning (DL): Another type of machine learning that uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition. (Read more about deep learning here.)
But AI can't operate alone. Let's take a look at some of the technologies that support AI.
What supports AI?
There are six leading technologies that support AI:
- Computer vision: This relies on pattern recognition and deep learning to recognize what is happening in a picture or video. When machines can process, analyze, and understand images, they capture them in real-time and fully process their surroundings.
- Natural language processing (NLP): The is the ability of computers to analyze, understand, and generate human language. The stage after NLP is natural language interactions, which allows people to communicate with computers using everyday language to perform tasks.
- Graphics processing units (GPUs): These are essential to AI because they provide the compute power necessary for processing. In order to train neural networks, big data needs to be quickly processed and analyzed.
- The Internet of Things (IoT): This is the network of physical objects (things) embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems via the Internet. IoT also generates massive amounts of unanalyzed data from connected devices. Automating models with AI allows more of the data to be used.
- Advanced algorithms: Algorithms are being combined and developed in ways that allow for faster data analysis at multiple levels. This type of processing is crucial to identifying and predicting rare events, understanding complex systems, and optimizing different scenarios.
- Application programming interfaces (APIs): These are portable packages of code that make it possible to add AI functionality to existing products and software packages. For example, they can add image recognition capabilities to security systems and Q&A capabilities that describe data, create captions and headlines, or point out interesting patterns or data insights.
What are the downsides of AI?
- High costs: The average cost of a complete AI solution is said to be anywhere from $20,000 to $1,000,000. There is an immense amount of time and money required to successfully implement AI. Additionally, AI needs to run on the latest hardware and software to meet requirements, adding to overall costs.
- Lack of creativity: Though AI can learn and adapt over time through past data and experiences, it cannot be original in its approach. For example, the bot Quill that writes the Forbes Earnings Report is only able to compile a report based on facts and data it has already been fed.
- Increased unemployment: With increasing amounts of AI, jobs involving repetitive tasks normally performed by humans are being replaced by bots. For example, chatbots are eliminating the need for human agents. A study from McKinsey showed that AI will eliminate at least 30 percent of human labor by 2030.
Artificial intelligence is critical in enhancing situational awareness and shortening response times through increased throughout and rapid data analysis to effectively track and engage enemy threats.
Systems with enhanced data processing and increased I/O capabilities can help strengthen C4ISR capabilities and sharpen signal intelligence, whether in a command room or in the field.
From helicopters to submarines to unmanned vehicles, AI equips the machines of warfare to achieve optimal performance across all domains of the modern battlespace.