What is AI?

Artificial intelligence (AI) encompasses the fields of computer and data science focused on building machines with human intelligence to perform tasks like learning, reasoning, problem-solving, perception, and language understanding. Instead of relying on explicit instructions from a programmer, AI systems learn from data that enables them to handle complex problems and simple repetitive tasks, improving how they respond over time.

AI handles many tasks faster with exceptional accuracy and reliability, freeing humans from repetitive and tedious chores. Businesses and organizations save time and money by automating and optimizing routine processes, using the technology to stay connected with customers and gain a competitive edge.

AI is part of everyday life. If you've used a self-service kiosk to check in before a flight, typed keywords into a search bar and received suggested results, or communicated with a digital assistant, you've interacted with AI.

AI Technologies Include:

• Autonomous vehicles
• Biometrics
• Chatbots
• Decision management
• Deep learning platforms
• Digital assistants
• Digital image processing
• Entertainment streaming apps
• Facial, speech, and image recognition
• Fraud detection
• Gaming
• Generative AI tools like ChatGPT
• GPS navigation
• Image generators
• Natural language processing and text analytics
• Pattern recognition
• Personalized marketing
• Robotics, mechatronics, and automation
• Security and surveillance
• Self-driving vehicles
• Speech recognition
• Virtual assistants
• Weather prediction

Fundamental Components of AI

• Data: AI systems learn and make decisions based on data, and they require large quantities of information to train effectively, especially for machine learning (ML) models.
• Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. Machine learning algorithms, for instance, learn and make predictions and decisions without explicit programming. 
• Computing power: AI algorithms often need significant computing resources to process large quantities of data and run complex algorithms.

Four Basic Functions of AI

• Learning. A key aspect of AI is learning, which allows AI systems to digest data and enhance their functions without direct human coding.
• Reasoning and decision-making. Reasoning and decision-making systems employ logical rules, probability models, and algorithms to reach conclusions and make reliable decisions based on inference.
• Problem-solving. Problem-solving in AI involves processing data, manipulating it, and applying it to devise solutions for specific issues.
• Perception. The perception component of AI includes tasks like image recognition, object detection, image segmentation, and video analysis.

Understanding Traditional vs. Generative AI

Traditional AI analyzes and interprets data using programmed rules, algorithms, and historical data to make predictions and decisions. It's often trained on carefully curated data for specific purposes. It is known for its precision, reliability, and efficiency in performing repetitive tasks. Traditional AI is often used in finance, healthcare, and manufacturing.

Generative AI creates new content, such as text, images, music, video, or software code, in response to a user's request or prompt. Generative AI learns to identify patterns and create new variations based on those patterns. Generative AI is known for its creativity and adaptability. It's often used in music, design, and marketing.

Some Artificial Intelligence Terms

• Computer vision. Computer vision techniques assist computers in seeing and understanding digital images and videos in order to teach themselves about the context of visual data. Computer vision applications include object tracking, image classification, and facial recognition.
• Fuzzy logic. Fuzzy logic helps to solve issues or statements that can either be true or false. It is used for reasoning about uncertain concepts.
• Expert systems. An expert system program specializes in a singular task to solve intricate problems using human-like decision-making capabilities.
• Robotics. Robots are programmed machines that can independently carry out complex series of actions, helping humans with tedious and repetitive tasks.
• Machine learning. Machine learning (ML) focuses on using data and algorithms to imitate the way that humans learn. This enables machine learning systems to gradually improve their accuracy without being specifically programmed to do so.
• Neural networks/deep learning. Neural networks are at the heart of deep learning algorithms, which simulate the complex decision-making power of the human brain. They rely on training data to learn and improve their accuracy over time.
• Natural language processing. Natural language processing (NLP) refers to the development of algorithms and techniques that allow computers to understand both text and spoken words like humans can.

A Brief History of AI

The modern groundwork for AI began in the early 1900s, but the biggest strides were made in the middle of the 20th century, when pioneers like Alan Turing began exploring foundational concepts like artificial neural networks, machine learning, and symbolic reasoning.

The term artificial intelligence was coined and came into popular use in the mid-1950s following Turing's publication of "Computer Machinery and Intelligence," a paper that proposed a test of machine intelligence called the Imitation Game. Turing's publication eventually became the Turing Test, which experts used to measure computer intelligence.

AI technology continued to develop from the mid-1950s to the late 1970s, as computers became faster, cheaper, more accessible, and could store more information. During this time, the first AI programming languages were created, machine learning algorithms were improved, and books and films began to explore the idea of robots. But computers were still millions of times too weak to exhibit intelligence. Research funding declined until the 1980s.

The 1980s were a period of rapid growth and interest in AI following breakthroughs in research and additional government funding. Deep learning techniques and the use of expert systems became more popular, both of which allowed computers to learn from their mistakes and make independent decisions.

The early 1990s showed some strides forward in AI research, including the first AI system that could defeat a reigning world champion chess player. The early 2000s saw innovations such as the first robot vacuum and the first commercially available speech recognition software.

The late 1990s and the early 2000s also saw significant advances in artificial intelligence. Computerized automation began to emerge, and machine learning was applied to many problems in academia and industry due to the availability of powerful computer hardware, immense collections of data, and the application of advanced mathematical methods.

Deep learning started to take off in the early 2010s. An abundance of data, advancements in learning algorithms, and increases in computational power led to achievements in speech recognition, natural language processing, visual recognition, and reinforcement learning.

AI continues to evolve at a rapid pace as an integral part of daily life. The integration of AI into everyday applications impacts industries such as finance, healthcare, transportation, and entertainment. The convergence of AI with other technologies, such as the Internet of Things (IoT), blockchain, and quantum computing, continues.

As AI systems become more powerful and pervasive, concerns about bias, transparency, and accountability have grown. There is a growing focus on ethical and responsible AI practices. Researchers and policymakers are working to establish guidelines and frameworks to ensure AI is developed and deployed responsibly.

What Programming Languages are used in AI?

• C++
• Haskell
• Java
• JavaScript
• Julia
• Lisp
• Prolog
• Python
• R
• Scala

Benefits of AI

• Contributes to medical advances.
• Enables smart decision-making by analyzing trends, providing forecasts, and quantifying uncertainties.
• Enhances customer experiences.
• Allows faster, more efficient research and data analysis.
• Improves business efficiency.
• Manages and automates repetitive tasks.
• Reduces human error and improves accuracy and precision.
• Solves complex business problems.
• Streamlines decision-making.

How Can AI Benefit Humanity?

Artificial intelligence has enormous potential to serve society. Its problem-solving capabilities can help people and communities around the world by tackling some of today's toughest challenges. Applications include:

• Developing new drugs, detecting disease, and improving medical applications.
• Fighting climate change, poverty, and hunger.
• National defense.
• Optimizing renewable energy generation.
• Improving access to education, healthcare, and clean water.
• Improving accessibility for disabled individuals.
• Improving transportation safety and efficiency.

Is AI Dangerous?

AI is a powerful and promising technology that can bring many benefits and opportunities to humanity. But even with its many benefits, the use of AI comes with various concerns.

• Security and safety: AI can pose security and safety threats, such as hacking, cyberattacks, and accidents. AI can also malfunction and cause accidents, such as crashes, explosions, and injuries due to errors, bugs, or glitches.
• Ethical and moral dilemmas: AI can raise ethical and moral dilemmas, such as privacy, bias, accountability, and autonomy. AI can collect and use our personal data, such as our location, preferences, and behavior, and potentially violate our privacy and security. AI can also reflect and amplify human biases, such as racism, sexism, ageism, and other forms of discrimination.
• Humanity and empathy loss: AI can cause humanity and empathy loss, enabling dehumanization, isolation, and alienation. AI can reduce our human interactions and connections and make us more dependent and addicted to technology. AI can also diminish our human values and emotions such as compassion, creativity, and curiosity.
• Data privacy: Due to plentiful and affordable data storage, data persists longer than the people who produced it, making it vulnerable to misuse. Data can also be used for purposes other than originally intended.

Will AI Replace Jobs?

While AI may replace some tedious jobs, it's important to note AI will also create many new jobs. The World Economic Forum's Future of Jobs Report notes that while 85 million jobs may be displaced by automation by 2025, 97 million new roles are projected to emerge in the same time frame. AI's development itself generates employment opportunities, as human activity is required to train and refine AI algorithms, leading to jobs that haven't existed until now. Roles such as machine learning engineers, data scientists, and AI ethics specialists have emerged to design, oversee, and ensure responsible AI deployment.

Who Uses AI?

Tech companies are at the forefront of AI, but industries of all kinds use AI.

• Astronomy
• Automotive
• Agriculture
• E-commerce
• Education
• Finance
• Gaming
• Government
• Healthcare
• Human resources
• Lifestyle
• Marketing
• Navigation
• Robotics
• Security
• Social media
• Travel and transport

The Future of Artificial Intelligence

AI applications affect many aspects of our lives. And AI is predicted to grow even more pervasive as it revolutionizes sectors including health care, education, finance, security, transportation, and advertising. As AI makes difficult tasks less complex and replaces tedious or dangerous tasks, the expectation is that the human workforce will shift our focus to endeavors that require creativity and empathy.

In healthcare, AI is improving medical diagnostics, enabling personalized treatments, and assisting in complex surgical procedures. The transportation sector is experiencing the emergence of autonomous vehicles and intelligent traffic management systems, promising safer and more efficient mobility. In finance and economics, AI is reshaping algorithmic trading, fraud detection, and economic forecasting, altering the dynamics of global markets. And AI is transforming education by offering personalized learning experiences and intelligent tutoring systems.

However, AI brings with it ethical and societal implications. Protecting privacy and ensuring data security are crucial, as AI draws on vast amounts of personal information. Issues of bias and fairness also emerge as AI decision-making algorithms can inadvertently introduce discriminatory practices. The impact of AI on human autonomy raises important questions about the boundaries between human capacity and technological influence.

The challenges and risks associated with AI can't be overlooked, but neither can its tremendous potential. Collaboration between AI and human intelligence can lead to remarkable improvements in human skills and present solutions to complicated issues. AI augmentation, in which humans and machines collaborate, has promise in a variety of fields ranging from healthcare to scientific study. Other AI advancements can promote transparency and trust, as well as improve ethical decision-making.

What Are the Best Degrees for a Job In AI?

Specific degrees in AI are still rare, but AI is an interdisciplinary field that is present in many courses of study, including computer science, mathematics, and statistics. A computer science or data science degree is a common choice for people seeking careers in artificial intelligence. AI-related degrees include:

• Business Analytics
• Computer Science
• Data Science
• Engineering
• Health/Medical Informatics
• Mathematics
• Robotics
• Statistics

Jobs In Artificial Intelligence

Every day more job opportunities in AI are available, from data scientists to information managers to software developers. Some AI-related computer career options and salary estimates are below.

• Artificial intelligence engineers use AI and machine learning techniques to develop applications and systems that help organizations become more efficient. AI engineers can help cut costs, increase productivity and profits, and make business recommendations.
• ArtIficial intelligence research scientists develop techniques and infrastructure to harness AI's power in industries, from health care to financial services.
• Business intelligence developers organize, analyze, and report data, and create visualization models.
• Computer vision engineers create programs that can create and interpret visual information like a human brain would, such as scanning a QR code to view a restaurant menu.
• Data engineers build systems that collect, manage, and convert raw data into usable information for data scientists, business analysts, and other data professionals to interpret.
• Data scientists develop predictive models, forecast patterns and outcomes, and use machine learning techniques to improve products.
• Deep learning engineers help improve AI so it can better mimic the way people acquire knowledge.
• Machine learning engineers develop, build, design, test, maintain, and improve AI systems.
• Natural language processing engineers may create tools that allow AI to recognize speech patterns, and improve existing tools to enhance the user experience.
• Robotics engineers develop robotic applications for industries, including automotive, manufacturing, defense, and medicine.
• Software engineers and developers create software for computers and applications using programming languages, platforms, and architectures to develop applications from computer games to network control systems.

See additional computing salary information and engineering salary information.

What Skills Do You Need In Artificial Intelligence?

• AI ethics
• Critical thinking
• Creativity
• Data modeling and analytics
• Libraries and frameworks
• Machine learning (ML) and deep learning
• Mathematics and statistics
• Natural language processing (NLP)
• Problem-solving
• Programming languages

Pursuing an Artificial Intelligence Degree

There are several paths you can take to start your career in AI. Most entry-level positions require at least a bachelor's degree. The following tips can help prepare you to pursue a degree in an AI-related computing field.

Tips for High School Students

If you're currently in high school and are considering a career in an AI-related computing field, here are some things to consider:

• Focus on your computer and math classes, as these will form the basis for your computing education. Your enjoyment (or not!) of these courses can help you decide if a career in applied computing is right for you.
• Research several different computing degrees to get a sense for the ones you may want to pursue.
• Talk with your teachers, school advisor, parents, friends, and classmates about your choices to get some new perspectives.

Choosing a College for Artificial Intelligence

Choosing the school where you want to study a computing field can be easier if you follow these tips:

• Research colleges and universities that offer computing degree programs. You don't have to major in a program called "artificial intelligence" to start a successful career working with AI. Look for degrees that fall under the computing umbrella.
• Make sure the schools you're considering offer accredited degrees. Many employers will only consider graduates from accredited institutions.
• Visit the schools you're interested in and ask questions about their degree program(s), scholarships, and financial aid opportunities, and application requirements and deadlines. Ask to meet faculty.
• Consider the school's reputation, location, size, total cost of attendance, and student housing options.
• Find out about campus life, academic support, career services, class sizes, and internship opportunities.
• Apply to the schools you think are the right fit beginning in your junior year of high school. Don't miss the application deadline and mark other important deadlines on your calendar!

Tips for College Students

If you're currently in college, consider these tips to help ensure your success in your chosen computing field:

• Work with your academic advisor to choose the appropriate program and courses that will give you the knowledge and skills you'll need for a career in an artificial intelligence field.
• Gain experience through activities like student organizations, cooperative education opportunities (co-ops) and internships, professional conferences, and research projects.
• Form relationships with faculty members in your program. These connections will help you to find relevant computing experiences and jobs, both during college and after you graduate.
• Network with professionals. Whether you're at a conference or an on-campus career fair, seize every opportunity to form connections with professionals in the field—you never know when these contacts could come in handy!
• Keep your resume up to date and brush up on your interviewing skills. Your institution may even have a dedicated career center that can help you.