Time

The lesson is expected to take about 25 minutes to complete. There are also reflection exercises, which are very good to do together with your fellow students.

About the lesson ‘What is artificial intelligence and machine learning?’

The lesson on artificial intelligence (AI) and machine learning gives you a basic understanding of AI as a general concept and machine learning as a specific technology that drives AI. In doing the module, you will learn about what we can use AI for – both as ordinary consumers and in an academic context. You will also be introduced to various approaches to designing and developing AI, including the distinction between generative models that produce new data, and discriminative models that make decisions.

Learning outcomes

When you have completed the lesson, we expect that you:

can mention examples of what artificial intelligence (AI) can be used for.
can explain the difference between AI and machine learning (ML).
can explain the difference between generative and discriminative AI.
can identify the most important sources of bias in ML-based models.

Source

The lesson was produced by the University of Copenhagen as part of the University of Copenhagen’s learning resources for digital literacy, 2022.

Course directors:

Anne Kirkegaard, Learning Designer, Centre for Online and Blended Learning
Silas Nyboe Ørting, Postdoc, Department of Computer Science

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What is artificial intelligence?

When trying to define artificial intelligence (AI), we can easily end up defining ‘intelligence’ in general. Looking AI up in Wikipedia, we learn that: “Artificial intelligence (AI) is intelligence demonstrated by machines [...] as opposed to intelligence of humans and other animals”, (Wikipedia/Artificial_Intelligence, 15 May 2023).

If we ask the AI system ChatGPT instead, it replies, for example: “Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans”, (ChatGPT Mar 14 Version, 2023).

Artificial intelligence is thus the actual idea of programming a computer to ‘demonstrate’ or ‘simulate’ human intelligence.

Since we humans can display intelligence in many different ways and for many different purposes, we need to break down the phenomenon and take a closer look at some of the basic methods that researchers and developers employ to program computers to solve specific tasks ‘intelligently’. Let’s start by looking at what types of tasks artificial intelligence can help solve.

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Where do we encounter artificial intelligence in our daily lives?

Artificial intelligence (AI) is integrated in many of the technical solutions that we use on a daily basis, for example when Netflix or Spotify recommends content for us, when social media puts together our news feed, when we use Google Translate, and when the camera in our smartphone optimises the images we take. In all these situations, we are, in a certain sense, passive and unconscious consumers of artificial intelligence.

The launch by the American company OpenAI of the advanced chatbot ChatGPT to the public at the end of 2022 therefore generated a lot of attention. Suddenly, everyone could play with advanced ‘artificial intelligence’ and use it for everything from programming to university assignments.

Ny chatrobot kan klare din eksamen, rette din software og skrive dit essay (DR, 5 December 2022)

ChatGPT is a ‘generative AI’ – in other words an artificial intelligence that can create content on demand. Many of the other AI solutions we encounter in daily life are based on ‘discriminative AI’ – i.e. artificial intelligence that can distinguish between different characteristics in large amounts of data and make decisions on that basis.

Below, we asked ChatGPT to provide examples of how we, as ordinary consumers, encounter AI in everyday life.

Click on the question to read the chatbot’s answer.

How do ordinary people encounter AI in their daily life?

ChatGPT - answer icon.png ChatGPT (2023, May 12):

Ordinary people encounter AI in their daily lives through various applications and services that have integrated AI technologies. Here are some common ways in which people interact with AI:

Virtual Assistants: Virtual assistants like Siri, Google Assistant, Alexa, and Cortana are AI-powered technologies that reside on smartphones, smart speakers, and other devices. They can perform tasks based on voice commands, answer questions, set reminders, play music, provide weather updates, and control smart home devices.
Smart Home Devices: AI is integrated into smart home devices such as thermostats, security systems, cameras, and lighting systems. These devices can learn from user preferences and adjust settings accordingly, providing convenience and energy efficiency.
Streaming Services: Platforms like Netflix and Spotify use AI algorithms to recommend movies, TV shows, and music based on users' viewing and listening history. They analyze user preferences and behavior to provide personalized content suggestions.
Social Media: AI algorithms are employed in social media platforms to curate users' news feeds and recommend content. They analyze user behavior, interests, and engagement patterns to deliver personalized and relevant content.
Online Shopping: E-commerce platforms use AI to personalize product recommendations based on users' browsing and purchase history. AI algorithms can also be used for price optimization, fraud detection, and chatbots for customer support.
Voice Recognition and Transcription: AI-powered voice recognition technologies are used in applications like voice-to-text transcription, voice search, and voice commands in smartphones, voice-controlled appliances, and navigation systems.
Navigation and Ride-Sharing Apps: Navigation apps like Google Maps and Waze utilize AI to analyze traffic patterns, optimize routes, and provide real-time updates on road conditions. Ride-sharing apps like Uber and Lyft employ AI algorithms to match drivers with passengers and estimate fare prices.
Online Customer Support: Many companies use AI-powered chatbots on their websites to provide automated customer support. These chatbots can answer frequently asked questions, provide product recommendations, and assist with basic troubleshooting.
Email Spam Filters: Email providers utilize AI algorithms to filter spam and detect phishing attempts. These algorithms analyze email content, sender reputation, and user feedback to identify and block unwanted messages.
Smartphone Cameras: AI is often used in smartphone cameras to enhance photo quality, apply filters and effects, and enable features like facial recognition, object detection, and augmented reality.

These are just a few examples of how ordinary people encounter AI in their daily lives. As AI continues to advance, its integration into various technologies and services will become even more pervasive.

You can read more about how ChatGPT works in the lesson The technology behind ChatGPT and large language models.

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What can we use AI for in an academic context?

One of the strengths of AI is the technology’s ability to analyse large amounts of data and find patterns, correlations and anomalies. AI can therefore be used in many different academic contexts. ChatGPT suggests the following ways of using AI for scientific purposes.

How is AI used for scientific purposes?

ChatGPT - answer icon.png ChatGPT (2023, May 12):

AI is increasingly used in scientific research and exploration to accelerate discoveries, analyze complex data, and simulate intricate systems. Here are several ways AI is utilized for scientific purposes:

Data Analysis and Pattern Recognition: AI algorithms can analyze large and complex datasets in scientific fields like genomics, astronomy, and particle physics. AI helps identify patterns, correlations, and anomalies in the data that might not be immediately apparent to human researchers. This can lead to new scientific insights and discoveries.
Drug Discovery and Development: AI is employed in drug discovery to expedite the process of identifying potential drug candidates. AI algorithms can analyze vast amounts of molecular data, predict drug-target interactions, simulate molecular structures, and optimize drug design. This can help in the development of new treatments and therapies.
Genomics and Bioinformatics: AI plays a vital role in genomics research, where it assists in genome sequencing, variant analysis, gene expression analysis, and predicting protein structures. AI algorithms aid in understanding genetic diseases, identifying biomarkers, and facilitating personalized medicine.
Climate Modeling and Environmental Analysis: AI is used in climate modeling to simulate complex climate systems, predict weather patterns, and assess climate change impacts. AI algorithms analyze environmental data from satellites, weather stations, and sensors to generate accurate predictions and models.
Robotics and Automation: AI-powered robots are used in scientific research and exploration, such as space exploration, deep-sea exploration, and laboratory automation. These robots can perform tasks in challenging and hazardous environments, gather data, and assist researchers in their work.
Scientific Image Analysis: AI algorithms are employed to analyze and interpret scientific images, including microscopy images, satellite images, and astronomical images. AI can aid in identifying objects, patterns, and anomalies in these images, enhancing scientific understanding.
Simulation and Modeling: AI is utilized to simulate complex systems and phenomena that are challenging to study experimentally. AI-driven simulations enable researchers to understand and predict the behavior of complex systems, such as protein folding, galaxy formation, and climate dynamics.
Scientific Literature and Knowledge Extraction: AI algorithms can process and analyze vast amounts of scientific literature, extracting relevant information, summarizing research articles, and identifying connections between different scientific studies. This helps researchers stay updated with the latest advancements and facilitates knowledge discovery.
Robot-Assisted Experiments: AI-powered robots can assist scientists in performing experiments, carrying out repetitive tasks, and collecting data. These robots can optimize experimental parameters, conduct high-throughput screenings, and enable faster and more efficient experimentation.
Data Visualization and Exploration: AI techniques, such as machine learning and data visualization, aid in exploring complex scientific data, identifying trends, and extracting meaningful insights. AI algorithms help researchers visualize and interpret data in a more intuitive and comprehensive manner.

These are just a few examples of how AI is used for scientific purposes. The integration of AI technologies continues to expand, enhancing scientific research and enabling breakthroughs across various disciplines.

As you can see from ChatGPT’s responses, there is also considerable potential for using AI for teaching purposes. However, you should be very aware that AI language models can provide inaccurate answers. Read more in the lesson Algorithms determine your search results.

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Reflection exercise

Where and how does it make sense to use AI within your subject area?

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Which methods are used to develop AI?

AI is a rapidly developing field where many different avenues are being pursued. Therefore, there are also many different ways of explaining the basic approaches and methods – and you can come across many terms that are related, overlapping or superior/inferior in relation to each other. At the same time, researchers and developers are becoming increasingly good at combining methods that were previously developed individually, resulting in the development of programs with ever more advanced ‘intelligence’.

When we ask ChatGPT about the most important methods within the field of AI, we get yet another 10-point list!:

What are the most important methods used within AI?

ChatGPT - answer icon.png ChatGPT (2023, May 12):

Within AI, several important methods and techniques are commonly used. Here are some of the key methods used within AI:

Machine Learning (ML): Machine learning is a core method in AI that enables systems to learn and improve from data without explicit programming. ML algorithms can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training models on labeled data to make predictions or classify new instances. Unsupervised learning focuses on finding patterns and structures in unlabeled data. Reinforcement learning involves learning optimal actions through interaction with an environment and receiving rewards or penalties.
Deep Learning: Deep learning is a subset of machine learning that focuses on artificial neural networks with multiple layers. Deep learning models, known as deep neural networks, can automatically learn hierarchical representations from raw data. They excel at tasks such as image and speech recognition, natural language processing, and complex pattern recognition.
Natural Language Processing (NLP): NLP involves the ability of machines to understand, interpret, and generate human language. Techniques within NLP include text classification, sentiment analysis, named entity recognition, machine translation, question answering, and language generation. NLP often utilizes methods like word embeddings, recurrent neural networks (RNNs), and transformer models.
Computer Vision: Computer vision focuses on teaching machines to understand and interpret visual information. It involves techniques such as image classification, object detection, image segmentation, facial recognition, and image generation. Deep learning approaches, especially convolutional neural networks (CNNs), have revolutionized computer vision tasks.
Reinforcement Learning (RL): Reinforcement learning is a learning paradigm where an agent learns optimal actions through interaction with an environment. The agent receives feedback in the form of rewards or penalties based on its actions. RL algorithms, such as Q-learning and policy gradients, aim to maximize long-term rewards by learning an optimal policy.
Probabilistic Graphical Models: Probabilistic graphical models provide a framework for representing and reasoning about uncertainty in AI. They combine probability theory with graph theory and enable modeling complex relationships and dependencies between variables. Examples include Bayesian networks and Markov random fields.
Evolutionary Algorithms: Evolutionary algorithms are inspired by biological evolution and natural selection. They involve generating a population of solutions and applying operators like mutation, crossover, and selection to evolve and improve the solutions over generations. Evolutionary algorithms are often used in optimization problems and search spaces with a large number of possible solutions.
Knowledge Representation and Reasoning: Knowledge representation and reasoning (KRR) focuses on representing and utilizing knowledge in AI systems. It involves capturing knowledge in a structured form, such as ontologies, semantic networks, or rule-based systems, and using logical or probabilistic reasoning methods to draw inferences and make decisions.
Transfer Learning: Transfer learning is a technique that allows models to leverage knowledge learned from one domain or task to improve performance in a different but related domain or task. Pretrained models, such as those trained on large-scale datasets like ImageNet, are often used as a starting point for transfer learning.
Ensemble Learning: Ensemble learning combines multiple models, typically using different algorithms or trained on different subsets of data, to make predictions. By aggregating the predictions of diverse models, ensemble methods can often improve overall performance and generalization.

These methods represent a subset of the techniques used within AI, and there are numerous other specialized algorithms and approaches depending on the specific problem domain and application.

Machine learning (ML) is an absolutely central approach to AI. You can learn more about ML and the underlying methods ‘supervised learning’, ‘unsupervised learning’ and ‘reinforcement learning’ in the lesson How can we use Machine Learning?

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Ordinary terms for different types of AI

The list above also lacks some fairly common terms for different types of AI, which you may encounter:

Pattern-based vs rule-based AI

Watch a short video in which Professor Thomas Bolander from DTU Compute talks about the two approaches:

(Video only available in Danish).

Narrow/weak AI vs general/strong AI

Artificial narrow intelligence (ANI) (also known as weak AI) is AI systems that have been developed and trained to solve specific and defined tasks such as playing chess, analysing images or generating text responses such as ChatGPT.

Artificial general intelligence (AGI) (also known as strong AI) is the idea of developing AI systems that can solve all kinds of tasks and learn new skills. No AGI systems are available yet, but many researchers and developers are working hard to succeed with the mission.

In this short video, Thomas Bolander talks about the difference between 'Narrow AI' and 'General AI' – and how intelligent ChatGPT really is. (Video available in Danish with English subtitles)

Note: The example with the box and the ball, used by Thomas Bolander in the video above, is handled much better by ChatGPT now, compared to when the video was recorded. The companies behind generative AI systems like ChatGPT are continuously adjusting the systems when they become aware that the AI makes errors, or produces discriminative or otherwise unwanted output.

Deterministic vs non-deterministic AI

AI can be deterministic, i.e. the same input yields the same output every time.

AI can also be non-deterministic, i.e. there is random variation in output for the same input.

People find it difficult to be deterministic. Our decisions are almost always varied, and this variation is rarely without bias. AI can, in principle, be free of bias, but when we develop systems to imitate people, they can easily reproduce the bias displayed by humans.

Generative vs discriminating AI

Generative models can be used to generate new data, for example when asking ChatGPT to answer questions about AI, or when using Midjourney to create images based on our text input.

Image created by Midjourney version 5.1, 22 May 2023. Text input: Photo of students in university auditorium with trees growing, scifi

Generative models are often non-deterministic, i.e. there is random variation in the generated output, even though the input is the same. Variation helps to make generative models interesting. ChatGPT is unlikely to have created the same hype if it gave exactly the same answer to the same question every time.

Discriminative models can be used to distinguish between data or make decisions, for example diagnose pneumonia in X-ray images. Discriminative models are often deterministic, i.e. the same input gives the same output every time. Deterministic models may seem more reliable because there is no variation in output. However, there is no guarantee that the output is correct.

The same model can be generative or discriminative depending on how it is used, and some models are both generative and discriminative. But conceptually, it can help to think of them as two different classes of models.

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How does bias occur in AI systems?

AI systems are often criticised for reproducing bias in the data they are trained on, or the bias of the developers who designed the algorithms. At the same time, AI systems are often so large that it is difficult to know where and how bias arises. ChatGPT provides the following explanation:

Why are some AI systems biased?

ChatGPT - answer icon.png ChatGPT (2023, May 12):
AI solutions can be biased due to various reasons, including:

Biased training data: AI systems learn from data, and if the training data itself contains biases or reflects societal prejudices, the AI system can unintentionally learn and perpetuate those biases. For example, if historical data used to train a hiring AI system has biased outcomes based on gender or ethnicity, the system may inadvertently learn and replicate those biases in its decision-making.
Algorithmic design: Biases can also be introduced through the design and implementation of algorithms. The algorithms may have inherent biases or unintended consequences due to the choices made by developers, such as the selection of certain features or the weighting of different factors.
Lack of diversity in development teams: Bias can arise from the perspectives, experiences, and backgrounds of the development teams. If the teams lack diversity, they may not be fully aware of or sensitive to potential biases in the AI system, leading to biased outcomes.
Inadequate evaluation and testing: Insufficient testing and evaluation of AI systems can fail to uncover biases. If biases are not properly identified and addressed during the development and testing stages, they can persist in the deployed AI system.

Addressing bias in AI solutions requires a combination of careful data selection and preprocessing, algorithmic fairness considerations, diversity in development teams, rigorous testing and evaluation, and ongoing monitoring and feedback loops. It is crucial to have robust ethical frameworks and guidelines in place to mitigate biases and promote fairness and accountability in AI development and deployment.

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Reflection exercise about bias

Use ChatGPT’s examples of sources of bias as your starting point. For each source, consider whether it is possible to completely eliminate the source of the bias.
When talking about bias, we generally do so based on categories that cover large groups of people, for example gender, age, nationality. One of the visions for AI is personalised medicine, i.e. treatment tailored specifically to each patient. Consider what bias means in this context.

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If you want to know more

Here are several resources where you can find out more about AI and machine learning:

Data literacy – What is it and why does it matter?
Elements of AI that introduce you to AI

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