Artificial intelligence (AI) is a wide-ranging branch of computer science that aims to build machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every industry.
Artificial intelligence allows machines to match, or even improve upon, the capabilities of the human mind. From the development of self-driving cars to the proliferation of generative AI tools, AI is increasingly becoming part of everyday life.
Artificial intelligence refers to computer systems that are capable of performing tasks traditionally associated with human intelligence — such as making predictions, identifying objects, interpreting speech and generating natural language. AI systems learn how to do so by processing massive amounts of data and looking for patterns to model in their own decision-making. In many cases, humans will supervise an AI’s learning process, reinforcing good decisions and discouraging bad ones, but some AI systems are designed to learn without supervision.
Over time, AI systems improve on their performance of specific tasks, allowing them to adapt to new inputs and make decisions without being explicitly programmed to do so. In essence, artificial intelligence is about teaching machines to think and learn like humans, with the goal of automating work and solving problems more efficiently.
Artificial intelligence aims to provide machines with similar processing and analysis capabilities as humans, making AI a useful counterpart to people in everyday life. AI is able to interpret and sort data at scale, solve complicated problems and automate various tasks simultaneously, which can save time and fill in operational gaps missed by humans.
AI serves as the foundation for computer learning and is used in almost every industry — from healthcare and finance to manufacturing and education — helping to make data-driven decisions and carry out repetitive or computationally intensive tasks.
Many existing technologies use artificial intelligence to enhance capabilities. We see it in smartphones with AI assistants, e-commerce platforms with recommendation systems and vehicles with autonomous driving abilities. AI also helps protect people by piloting fraud detection systems online and robots for dangerous jobs, as well as leading research in healthcare and climate initiatives.
Artificial intelligence systems work by using algorithms and data. First, a massive amount of data is collected and applied to mathematical models, or algorithms, which use the information to recognize patterns and make predictions in a process known as training. Once algorithms have been trained, they are deployed within various applications, where they continuously learn from and adapt to new data. This allows AI systems to perform complex tasks like image recognition, language processing and data analysis with greater accuracy and efficiency over time.
The primary approach to building AI systems is through
machine learning (ML), where computers learn from large datasets by identifying patterns and relationships within the data. A machine learning algorithm uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been programmed for that certain task. It uses historical data as input to predict new output values. Machine learning consists of both
supervised learning (where the expected output for the input is known thanks to labeled data sets) and
unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).
Machine learning is typically done using
neural networks, a series of algorithms that process data by mimicking the structure of the human brain. These networks consist of layers of interconnected nodes, or “neurons,” that process information and pass it between each other. By adjusting the strength of connections between these neurons, the network can learn to recognize complex patterns within data, make predictions based on new inputs and even learn from mistakes. This makes neural networks useful for recognizing images, understanding human speech and translating words between languages.
Deep learning is an important subset of machine learning. It uses a type of artificial neural network known as deep neural networks, which contain a number of hidden layers through which data is processed, allowing a machine to go “deep” in its learning and recognize increasingly complex patterns, making connections and weighting input for the best results. Deep learning is particularly effective at tasks like image and speech recognition and natural language processing, making it a crucial component in the development and advancement of AI systems.
Natural language processing (NLP) involves teaching computers to understand and produce written and spoken language in a similar manner as humans. NLP combines computer science, linguistics, machine learning and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it. NLP mainly tackles
speech recognition and natural language generation, and it’s leveraged for use cases like spam detection and
virtual assistants.
Computer vision is another prevalent application of machine learning techniques, where machines process raw images, videos and visual media, and extract useful insights from them. Deep learning and
convolutional neural networks are used to break down images into pixels and tag them accordingly, which helps computers discern the difference between visual shapes and patterns. Computer vision is used for
image recognition, image classification and object detection, and completes tasks like
facial recognition and detection in self-driving cars and robots.
Artificial intelligence can be classified in several different ways.
AI can be organized into two broad categories:
weak AI and strong AI.
Weak AI (or narrow AI) refers to AI that automates specific tasks. It typically outperforms humans, but it operates within a limited context and is applied to a narrowly defined problem. For now, all AI systems are examples of weak AI, ranging from email inbox spam filters to
recommendation engines to
chatbots.
Strong AI, often referred to as
artificial general intelligence (AGI), is a hypothetical benchmark at which AI could possess human-like intelligence and adaptability, solving problems it’s never been trained to work on. AGI does not actually exist yet, and it is unclear whether it ever will.
AI can then be further categorized into
four main types: reactive machines, limited memory, theory of mind and self-awareness.
Reactive machines perceive the world in front of them and react. They can carry out specific commands and requests, but they cannot store memory or rely on past experiences to inform their decision making in real time. This makes reactive machines useful for completing a limited number of specialized duties. Examples include Netflix’s recommendation engine and IBM’s
Deep Blue (used to play chess).
Limited memory AI has the ability to store previous data and predictions when gathering information and making decisions. Essentially, it looks into the past for clues to predict what may come next. Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data, or an AI environment is built so models can be automatically trained and renewed. Examples include
ChatGPT and
self-driving cars.
Theory of mind is a type of AI that does not actually exist yet, but it describes the idea of an AI system that can perceive and understand
human emotions, and then use that information to predict future actions and make decisions on its own.
Self-aware AI refers to artificial intelligence that has
self-awareness, or a sense of self. This type of AI does not currently exist. In theory, though, self-aware AI possesses human-like consciousness and understands its own existence in the world, as well as the emotional state of others.
AI is beneficial for automating repetitive tasks, solving complex problems, reducing human error and much more.
Repetitive tasks such as data entry and
factory work, as well as customer service conversations, can all be automated using AI technology. This lets humans focus on other priorities.
AI’s ability to process large amounts of data at once allows it to quickly find patterns and solve complex problems that may be too difficult for humans, such as predicting financial outlooks or optimizing energy solutions.
AI can be applied through user personalization, chatbots and automated self-service technologies, making the customer experience more seamless and increasing customer retention for businesses.
AI works to advance
healthcare by accelerating medical diagnoses, drug discovery and development and
medical robot implementation throughout hospitals and care centers.
The ability to quickly identify relationships in data makes AI effective for catching mistakes or anomalies among mounds of digital information, overall reducing human error and ensuring accuracy.
While artificial intelligence has its benefits, the technology also comes with
risks and potential dangers to consider.
AI’s abilities to automate processes, generate rapid content and work for long periods of time can mean
job displacement for human workers.
AI models may be trained on data that reflects biased human decisions, leading to outputs that are
biased or discriminatory against certain demographics.
AI systems may inadvertently “
hallucinate” or produce inaccurate outputs when trained on insufficient or biased data, leading to the generation of false information.
The data collected and stored by AI systems may be done so without user consent or knowledge, and may even be accessed by unauthorized individuals in the case of a data breach.
AI systems may be developed in a manner that
isn’t transparent, inclusive or sustainable, resulting in a
lack of explanation for potentially harmful AI decisions as well as a negative impact on users and businesses.
Large-scale AI systems can require a
substantial amount of energy to operate and process data, which increases carbon emissions and water consumption.
Artificial intelligence has applications across multiple industries, ultimately helping to streamline processes and boost business efficiency.
AI is
used in healthcare to improve the accuracy of medical diagnoses, facilitate drug research and development, manage sensitive healthcare data and automate online patient experiences. It is also a driving factor behind medical robots, which work to provide assisted therapy or guide surgeons during surgical procedures.
AI in retail amplifies the customer experience by powering user personalization, product recommendations, shopping assistants and facial recognition for payments. For retailers and suppliers, AI helps automate retail marketing, identify counterfeit products on marketplaces, manage product inventories and pull online data to identify product trends.
In the
customer service industry, AI enables faster and more personalized support. AI-powered chatbots and virtual assistants can handle routine customer inquiries, provide product recommendations and troubleshoot common issues in real-time. And through NLP, AI systems can understand and respond to customer inquiries in a more human-like way, improving overall satisfaction and reducing response times.
AI in manufacturing can reduce assembly errors and production times while increasing worker safety. Factory floors may be monitored by AI systems to help identify incidents, track quality control and predict potential equipment failure. AI also drives factory and warehouse robots, which can automate manufacturing workflows and handle dangerous tasks.
The
finance industry utilizes AI to detect fraud in banking activities, assess financial credit standings, predict financial risk for businesses plus manage stock and bond trading based on market patterns. AI is also implemented across
fintech and banking apps, working to personalize banking and provide 24/7 customer service support.
In the
marketing industry, AI plays a crucial role in enhancing customer engagement and driving more targeted advertising campaigns. Advanced data analytics allows marketers to gain deeper insights into customer behavior, preferences and trends, while
AI content generators help them create more personalized content and recommendations at scale. AI can also be used to automate repetitive tasks such as email marketing and social media management.
Video game developers apply AI to make gaming experiences
more immersive. Non-playable characters (NPCs) in video games use AI to respond accordingly to player interactions and the surrounding environment, creating game scenarios that can be more realistic, enjoyable and unique to each player.
AI assists militaries on and off the battlefield, whether it’s to help process military intelligence data faster, detect cyberwarfare attacks or automate military weaponry, defense systems and vehicles. Drones and robots in particular may be
imbued with AI, making them applicable for autonomous combat or search and rescue operations.
Specific examples of AI include:
Generative AI tools, sometimes referred to as AI chatbots — including
ChatGPT,
Gemini,
Claude and
Grok — use artificial intelligence to produce written content in a range of formats, from essays to code and answers to simple questions.
Personal AI assistants, like Alexa and Siri, use natural language processing to receive instructions from users to perform a variety of “
smart tasks.” They can carry out commands like setting reminders, searching for online information or turning off your kitchen lights.
Self-driving cars are a recognizable example of deep learning, since they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals and much more.
Many
wearable sensors and devices used in the healthcare industry apply deep learning to assess the health condition of patients, including their blood sugar levels, blood pressure and heart rate. They can also derive patterns from a patient’s prior medical data and use that to anticipate any future health conditions.
Filters used on social media platforms like TikTok and Snapchat rely on algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing.
Generative AI describes artificial intelligence systems that can create new content — such as text, images, video or audio — based on a given user prompt. To work, a generative AI model is fed massive data sets and trained to identify patterns within them, then subsequently generates outputs that resemble this training data.
Generative AI has gained massive popularity in the past few years, especially with chatbots and image generators arriving on the scene. These kinds of tools are often used to create written copy, code, digital art and object designs, and they are leveraged in industries like entertainment, marketing, consumer goods and manufacturing.
Generative AI comes with challenges though. For instance, it can be used to create fake content and
deepfakes, which could spread disinformation and erode social trust. And some AI-generated material could potentially infringe on people’s
copyright and intellectual property rights.
As AI grows more complex and powerful, lawmakers around the world are seeking to regulate its use and development.
The first major step to regulate AI occurred in 2024 in the European Union with the passing of its sweeping
Artificial Intelligence Act, which aims to ensure that AI systems deployed there are “safe, transparent, traceable, non-discriminatory and environmentally friendly.” Countries like China and Brazil have also taken steps to govern artificial intelligence.
Meanwhile, AI regulation in the United States is still a work in progress. The Biden-Harris administration introduced a non-enforceable
AI Bill of Rights in 2022, and then
The Executive Order on Safe, Secure and Trustworthy AI in 2023, which aims to regulate the AI industry while maintaining the country’s status as a leader in the industry. Congress has made several attempts to establish more robust legislation, but it has largely failed, leaving no laws in place that specifically limit the use of AI or regulate its risks. For now, all AI legislation in the United States exists only on the state level.
The
future of artificial intelligence holds immense promise, with the potential to revolutionize industries, enhance human capabilities and solve complex challenges. It can be used to develop new drugs, optimize global supply chains and create exciting new art — transforming the way we live and work.
Looking ahead, one of the next big steps for artificial intelligence is to progress beyond weak or narrow AI and achieve artificial general intelligence (AGI). With AGI, machines will be able to think, learn and act the same way as humans do, blurring the line between organic and machine intelligence. This could pave the way for increased automation and problem-solving capabilities in medicine, transportation and more — as well as
sentient AI down the line.
On the other hand, the increasing sophistication of AI also raises concerns about heightened job loss, widespread disinformation and loss of privacy. And questions persist about the potential for AI to outpace human understanding and intelligence — a phenomenon known as
technological singularity that could lead to unforeseeable risks and possible moral dilemmas.
For now, society is largely looking toward federal and business-level AI regulations to help guide the technology’s future.
Artificial intelligence as a concept began to take off in the 1950s when computer scientist Alan Turing released the paper “
Computing Machinery and Intelligence,” which questioned if machines could think and how one would test a machine’s intelligence. This paper set the stage for AI research and development, and was the first proposal of the
Turing test, a method used to assess machine intelligence. The term “artificial intelligence” was
coined in 1956 by computer scientist John McCartchy in an academic conference at Dartmouth College.
Following McCarthy’s conference and throughout the 1970s, interest in AI research grew from academic institutions and U.S. government funding. Innovations in computing allowed several AI foundations to be established during this time, including machine learning, neural networks and natural language processing. Despite its advances, AI technologies eventually became more difficult to scale than expected and declined in interest and funding, resulting in the first
AI winter until the 1980s.
In the mid-1980s, AI interest reawakened as computers became more powerful, deep learning
became popularized and AI-powered “expert systems” were introduced. However, due to the complication of new systems and an inability of existing technologies to keep up, the second AI winter occurred and lasted until the mid-1990s.
By the mid-2000s, innovations in processing power,
big data and
advanced deep learning techniques resolved AI’s previous roadblocks, allowing further AI breakthroughs. Modern AI technologies like virtual assistants, driverless cars and generative AI began entering the mainstream in the 2010s, making AI what it is today.
(1943) Warren McCullough and Walter Pitts publish the paper “
A Logical Calculus of Ideas Immanent in Nervous Activity,” which proposes the first mathematical model for building a neural network.
(1949) In his book
The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.
(1950) Alan Turing publishes the paper “Computing Machinery and Intelligence,” proposing what is now known as the Turing Test, a method for determining if a machine is intelligent.
(1950) Harvard undergraduates Marvin Minsky and Dean Edmonds build
SNARC, the first neural network computer.
(1956) The phrase “artificial intelligence” is coined at the Dartmouth Summer Research Project on Artificial Intelligence. Led by John McCarthy, the conference is widely considered to be the birthplace of AI.
(1958) John McCarthy develops the AI programming language Lisp and publishes “
Programs with Common Sense,” a paper proposing the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans.
(1959) Arthur Samuel coins the term “machine learning” while at IBM.
(1964) Daniel Bobrow develops STUDENT, an early natural language processing program designed to solve algebra word problems, as a doctoral candidate at MIT.
(1966) MIT professor Joseph Weizenbaum creates Eliza, one of the first chatbots to successfully mimic the conversational patterns of users, creating the illusion that it understood more than it did. This introduced the
Eliza effect, a common phenomenon where people falsely attribute humanlike thought processes and emotions to AI systems.
(1969) The first successful expert systems, DENDRAL and MYCIN, are created at the AI Lab at Stanford University.
(1972) The logic programming language PROLOG is created.
(1973) The Lighthill Report, detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for AI projects.
(1974-1980) Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s Lighthill Report, AI funding dries up and research stalls. This period is known as the “
First AI Winter.”
(1980) Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first AI winter.
(1985) Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.
(1987-1993) As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “
Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.
(1997) IBM’s Deep Blue beats world chess champion Gary Kasparov.
(2006) Fei-Fei Li starts working on the ImageNet visual database, introduced in 2009. This became the catalyst for the AI boom, and the basis on which image recognition grew.
(2008) Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.
(2011) IBM’s Watson handily defeats the competition on Jeopardy!.
(2011) Apple releases Siri, an AI-powered virtual assistant through its iOS operating system.
(2012) Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in the breakthrough era for neural networks and deep learning funding.
(2014) Amazon’s Alexa, a virtual home
smart device, is released.
(2016) Google DeepMind’s
AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.
(2018) Google releases natural language processing engine
BERT, reducing barriers in translation and understanding by ML applications.
(2020) Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.
(2020) OpenAI releases natural language processing model
GPT-3, which is able to produce text modeled after the way people speak and write.
(2021) OpenAI builds on GPT-3 to develop
DALL-E, which is able to create images from text prompts.
(2022) The
National Institute of Standards and Technology releases the first draft of its
AI Risk Management Framework, voluntary U.S. guidance “to better manage risks to individuals, organizations, and society associated with artificial intelligence.”
(2022) OpenAI launches ChatGPT, a chatbot powered by a large language model that gains more than 100 million users in just a few months.
(2022) The White House introduces an AI Bill of Rights outlining principles for the responsible development and use of AI.
(2023) Microsoft launches an AI-powered version of Bing, its search engine, built on the same technology that powers ChatGPT.
(2023) Google announces Bard, a competing conversational AI. This would later become Gemini.
(2023) OpenAI Launches
GPT-4, its most sophisticated language model yet.
(2023) The Biden-Harris administration issues
The Executive Order on Safe, Secure and Trustworthy AI, calling for safety testing, labeling of AI-generated content and increased efforts to create international standards for the development and use of AI. The order also stresses the importance of ensuring that artificial intelligence is not used to circumvent privacy protections, exacerbate discrimination or violate civil rights or the rights of consumers.
(2023) The chatbot Grok
is released by Elon Musk’s AI company xAI.
(2024) The European Union passes the Artificial Intelligence Act, which aims to ensure that AI systems deployed within the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly.
(2024) Claude 3 Opus, a large language model developed by AI company Anthropic,
outperforms GPT-4 — the first LLM to do so.
They may not be household names, but these 42 artificial intelligence companies are working on some very smart technology.
This article was autogenerated from a news feed from CDO TIMES selected high quality news and research sources. There was no editorial review conducted beyond that by CDO TIMES staff.
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