What is artificial intelligence (AI)?
Overview
Artificial intelligence (AI) is the ability of a machine or computer to imitate the capabilities of the human mind. AI taps into multiple technologies to equip machines in planning, acting, comprehending, learning, and sensing with human-like intelligence. AI systems may perceive environments, recognize objects, make decisions, solve problems, learn from experience, and imitate examples. These abilities are combined to accomplish actions that would otherwise require humans to do, such as driving a car or greeting a guest.
Artificial intelligence
Why is AI growing in popularity?
Artificial Intelligence may have entered everyday conversation over the last decade or so but it has been around for decades. The relatively recent rise in its prominence is not by accident.
AI technology, and especially machine learning, relies on the availability of vast volumes of information. The proliferation of the Internet, the expansion of cloud computing, the rise of smartphones, and the growth of the Internet of Things have created an enormous quantity of data that grows every day. This treasure trove of information combined with the huge gains made in computing power has made the rapid and accurate processing of enormous data possible.
Today, AI is completing our chat conversations, suggesting email responses, providing driving directions, recommending the next movie we should stream, vacuuming our floors, and performing complex medical image analyses.
What is the history of AI?
The history of artificial intelligence goes as far back as ancient Greece. However, it’s the rise of electronic computing that made AI a real possibility. Note that what is considered AI has changed as the technology evolves. For example, a few decades ago, machines that could perform optical character recognition (OCR) or simple arithmetic were categorized as AI. Today, OCR and basic calculations are not considered AI but rather an elementary function of a computer system.
- 1950s – Alan Turing, a man famous for breaking the WWII ENIGMA code used by the Nazis, publishes the Computing Machinery and Intelligence paper in the Mind. He attempts to answer the question of whether machines can think. He outlines the Turing Test that determines whether a computer shows the same intelligence as a human. The test holds that an AI system should have the ability to hold a conversation with a human without the human knowing they are speaking to an AI system. The first ever AI conference is held at Dartmouth College. It’s here that the term artificial intelligence was first used.
- 1960s – The US Department of Defense through DARPA takes great interest in AI and embarks on developing computer programs that mimic human reasoning. Frank Rosenblatt builds the Mark 1 Perceptron computer based on a neural network that learns through experience.
- 1970s – DARPA completes various street mapping projects.
- 1980s – A more complex wave of AI emerges. Neural networks with backpropagation algorithms find widespread application in AI systems.
- 1990s – Exponentially growing volumes of data are produced. Powerful computers process large quantities of data quickly. The Deep Blue supercomputer defeats world chess champion Garry Kasparov twice. The Genome Sequencing project and other similarly complex undertakings generate vast information. Computing advances make it possible for this data to be stored, accessed, and analyzed.
- 2000s – The Internet Revolution drives AI to unprecedented heights. Big data joins corporate lexicon. DARPA rolled out intelligent personal assistants long before Alexa, Siri, Cortana, and Google Assistant became household names. This paves the way for the reasoning and automation that’s a part of present day personal computers and smartphones. That includes smart search systems and decision support systems that augment and complement human abilities.
- 2010s – China’s search giant Baidu unveils the Minwa supercomputer that relies on a convolutional neural network to identify, analyze, and categorize images with higher accuracy than the average human. The AlphaGo deep neural network program from DeepMind beats Go world champion Lee Sodol in a five-game match. Go is an ancient Chinese game that’s considerably more complex than chess.
- 2020s – This period has seen rapid advancement in AI capabilities, particularly in language models and generative AI. It's also been a time growing public awareness and discussion of AI's potential impacts on society, work, and daily life. Highlights include OpenAI releasing GPT-3, showcasing impressive natural language abilities, followed by GPT-4 with significant improvements. ChatGPT was also launched, bringing conversational AI to the mainstream, as well as DallE for image creation. DeepMind's AlphaFold makes a breakthrough in protein structure prediction. The European Union proposes the AI Act, aiming to regulate AI development and use. There are continued advancements in multimodal AI systems (combining text, image, and audio) and an increasing focus on AI alignment and safety research.
How does AI work?
Artificial Intelligence asserts that there are principles governing the actions of intelligent systems. It is based on reverse-engineering human capabilities and traits onto a machine. The system uses computational power to exceed what the average human is capable of. The machine must learn to respond to certain actions. It relies on historical data and algorithms to create a propensity model. Machines learn from experience to perform cognitive tasks that are ordinarily done by the human brain. The system automatically learns from features or patterns in the data.
AI is founded on two pillars: engineering and cognitive science. Engineering involves building the tools that rely on human-comparable intelligence. Large volumes of data are combined with a series of instructions (algorithms) and rapid iterative processing. Cognitive science involves emulating how the human brain works and brings to AI multiple fields including machine learning, deep learning, neural networks, cognitive computing, computer vision, natural language processing, and knowledge reasoning.
Are AI systems monolithic?
Artificial Intelligence isn’t one type of system. There are the simple, low-level AI systems focused on performing a specific task such as forecasting the weather, business data analysis, taxi hailing, and digital assistants. This is the type of AI, called "narrow AI," that the average person is most likely to interact with. Its main purpose is driving efficiency.
On the other end of the spectrum are advanced systems that emulate human intelligence at a more general level and can tackle complex tasks. These include thinking creatively, abstractly, and strategically. Strictly speaking, this kind of truly sentient machine, called "artificial general intelligence" or AGI, only exists on the silver screen for now, though the race toward its realization is accelerating.
Where is artificial intelligence used?
Humans have pursued artificial intelligence in recognition of how invaluable it can be for business innovation and digital transformation. AI can cut costs and introduce levels of speed, scalability, and consistency that is otherwise out of reach. You probably interact with some form of AI multiple times each day. The applications of AI are too numerous to exhaustively cover here. Here’s a high level look at some of the most significant ones.
1. Cybersecurity
As cyberattacks grow in scale, sophistication, and frequency, human-dependent cyber defenses are no longer adequate. Traditionally, anti-malware applications were built with specific threats in mind. Virus signatures would be updated as new malware was identified.
But keeping up with the sheer number and diversity of threats eventually becomes a near impossible task. This approach was reactive and depended on the identification of a specific malware for it to be added to the next update.
AI-based anti-spam, firewall, intrusion detection/prevention, and other cybersecurity systems go beyond the archaic rule-based strategy. Real-time threat identification, analysis, mitigation, and prevention is the name of the game. They deploy AI systems that detect malware traits and take remedial action even without the formal identification of the threat.
AI cybersecurity systems rely on the continuous feed of data to recognize patterns and backtrack attacks. By feeding algorithms large volumes of information, these systems learn how to detect anomalies, monitor behavior, respond to threats, adapt to attacks, and issue alerts.
2. Speech recognition and natural language processing
Also referred to as speech-to-text (STT), speech recognition is technology that recognizes speech and converts it into digital text. It’s at the heart of computer dictation apps, as well as voice-enabled GPS and voice-driven menus.
Natural language processing (NLP) relies on a software application to decipher, interpret, and generate human-readable text. NLP is the technology behind Alexa, Siri, chatbots, and other forms of text-based assistants. Some NLP systems use sentiment analysis to make out the attitude, mood, and subjective qualities in a language.
3. Image recognition
Also known as machine vision or computer vision, image recognition is artificial intelligence that allows one to classify and identify people, objects, text, actions, and writing occurring within moving or still images. Usually powered by deep neural networks, image recognition has found application in self-driving cars, medical image/video analysis, fingerprint identification systems, check deposit apps, and more.
4. Real-time recommendations
E-commerce and entertainment websites and apps leverage neural networks to recommend products and media that will appeal to the customer based on their past activity, the activity of similar customers, the season, the weather, the time of day, and more. These real-time recommendations are customized to each user. For e-commerce sites, recommendations not only grow sales but also help optimize inventory, logistics, and store layout.
5. Automated stock trading
The stock market can be extremely volatile in times of crisis. Yet, it’s near impossible for a human to react quick enough to market-influencing events. High-frequency trading (HFT) systems are AI-driven platforms that make thousands or millions of automated trades per day to optimize stock portfolios for large institutions.
6. Ride-sharing services and self-driving cars
Lyft, Uber, and other ride-share apps use AI to connect requesting riders to available drivers. AI technology minimizes detours and wait times, provides realistic ETAs, and calculates surge pricing during spikes in demand.
Self-driving cars are not yet standard in most of the world but there’s already been a concerted push to embed AI-based safety functions to detect dangerous scenarios and prevent accidents.
7. Autopilot technology
Unlike land-based vehicles, the margin for error in aircraft is extremely narrow. Aircraft manufacturers had to push safety systems and become one of the earliest adopters of artificial intelligence.
To minimize the likelihood and impact of human error, autopilot systems have been flying military and commercial aircraft for decades. They use a combination of GPS technology, sensors, robotics, image recognition, and collision avoidance to navigate planes safely through the sky while keeping pilots and ground crew updated as needed.
8. Software test automation
Artificial intelligence accelerates and simplifies test creation, execution, and maintenance through AI-powered intelligent test automation. AI-based machine learning and advanced optical character recognition (OCR) provide for advanced object recognition, and when combined with AI-based mockup identification, AI-based recording, AI-based text matching, and image-based automation, teams can reduce test creation time and test maintenance efforts, and boost test coverage and resilience of testing assets.
9. Functional testing
Artificial Intelligence allows you to test earlier and faster with OpenText™ Functional Test Automation products. It combines extensive technology support with AI-driven capabilities to deliver the speed and resilience that supports rapid application changes within a continuous delivery pipeline.
10. Enterprise service management
Both IT and business face the challenges of too many manual, error-prone workflows, an ever-increasing volume of requests, employees dissatisfied with the level and quality of service, and more. Artificial intelligence and machine learning technology can take service management to the next level:
- Smart search capabilities enable employees to find answers easily and quickly
- Virtual agents or bots can perform tasks using natural language processing (NLP)
- Intelligent analytics enable workflow optimization and automation
- Metrics from unstructured data, for example user surveys, can be gathered and analyzed more efficiently.
What is true of IT support, is also true for ESM; AI makes operations and outcomes better.
How do I get started with artificial intelligence?
There are plenty of ways you could leverage artificial intelligence for your business to stay competitive, drive growth, and unlock value. Nevertheless, your organization doesn’t have infinite resources, so you must prioritize. Begin by defining what your organization’s values and strategic objectives are. From that point, assess the possible applications of AI against these values and objectives. Choose the AI technology that is bound to deliver the biggest impact for the business.
The world is only going to grow more AI-dependent. It’s no longer about whether to adopt AI, but when. Organizations that tap into AI ahead of their peers could gain a significant competitive advantage. Developing and pursuing a well-defined AI strategy is where it all begins. It may take a bit of experimenting before you know what will work for you.
How does OpenText help enterprises with artificial intelligence?
Customers already trust information management solutions from OpenText to help manage private data sets, from B2B transactions to operational content to application code and intellectual property. Now, without having to move your data, you can use OpenText Aviator AI capabilities to get the most out of your information.
Here are some of the benefits of AI that’s built for business:
Keep data private and secure: Your proprietary data should not have to be in public domains to run LLMs. Experiment with vetted LLMs in a sandbox environment to try new use cases while keeping your private data sets secure.
Employ the right AI model for the right job: One size does not fit all. We help to vet LLMs against use cases and have a model squadron to get you started. It's about the outcomes you want from AI, and how we can help you achieve them.
Make the AI pivot with a trusted partner: Business and technology transformations never end. OpenText™ Professional Services helps you explore the AI use cases and models that apply to your business and safely navigate the complexities of AI.
What are key areas of artificial intelligence addressed by OpenText?
The following are enterprise AI capabilities offered by the OpenText Aviator for Business software:
OpenText™ IT Operations Aviator: Redefine Tier 1 business support functions with self-service capabilities from generative AI for IT operations.
OpenText™ Experience Aviator: Transform communications with private generative AI for customer success.
OpenText™ Business Network Aviator: Revolutionize connectivity across the internet of clouds with AI for supply chains.
OpenText™ DevOps Aviator: Elevate millions of developers with AI for DevOps.
OpenText™ Content Aviator: Supercharge intelligent workspaces with AI content management to modernize work.
OpenText™ Cybersecurity Aviator: Improve your security posture with the power of AI threat detection.
OpenText also offers OpenText Aviator for Technologists, AI engineering platforms and tools to help your organization seamlessly establish information flows and orchestrate data:
OpenText™ Aviator Platform: Enable smarter decisions with enterprise AI platforms to process, organize, and analyze large data sets of all types.
OpenText™ Aviator IoT: Connect people, systems, and things with IoT AI to better manage high-value assets and accelerate your business.
OpenText™ Aviator Lab: Experiment with AI with our professional services experts and explore what you can do with AI in the OpenText Private Cloud.
OpenText™ Aviator Search: Give users access to the answers they need, faster and easier, with multi-repository AI-based search that lets you customize everything from clicks to conversations.
OpenText™ Aviator Thrust: Build it your way with OpenText Cloud AI APIs that create real-time information flows to enable custom applications and workflows.
