Artificial Intelligence, or AI, is a powerful tool that uses computer programs to allow machines to learn and perform specific tasks. AI learns from data and experience, just like people do. It can recognize patterns, understand and respond to language, make predictions and even generate creative products such as images, poems or code. We use AI in many ways already, often without even realizing it. For example, AI is the brainpower behind virtual assistants such as Siri or Alexa, it helps recommend products you might like online, it translates languages, and it even powers features in your smartphone such as facial recognition. It’s important to remember that AI is not perfect – it’s still a new and developing technology. Ethical implications, potential biases, and the impacts AI may have on jobs and society are important to consider. Additionally, it’s important to recognize that, when used responsibly, AI has the potential to revolutionize industries, improve efficiency and enhance our lives in countless ways.
The history of AI traces back to the early 20th century with the foundational work of mathematicians like Alan Turing, who envisioned machines capable of mimicking human intelligence in his publication, “Computing Machinery and Intelligence.” The official birth of AI as a field occurred in 1956 at the Dartmouth Conference, where leading scientists convened to explore the potential of creating intelligent machines. Despite early successes, such as the computer programs Logic Theorist and General Problem Solver, progress was slow.
Over the decades, researchers delved into symbolic AI, which was best suited for structured data and rule-based tasks. The American Association of Artificial Intelligence, which is now known as the Association for the Advancement of Artificial Intelligence (AAAI), was founded in 1979. However, AI faced setbacks during the 1970s and 1980s due to limited computing power and a lack of enormous amounts of available data.
In 1997, IBM developed Deep Blue, a chess-playing expert system which beat the world chess champion, Gary Kasparov, in a highly publicized match, becoming the first program to beat a human chess champion.
The following blog post “Can Machines Think?” from Harvard's Graduate School of Arts and Sciences provides an additional timeline and more information about the evolution of AI over time.
The field experienced resurgence with advancements in neural networks and expert systems in the following decades. The proliferation of the internet in the 1990s and 2000s accelerated AI research and applications, paving the way for its integration into various aspects of society.
The 2010s marked a transformative era for AI with the rise of deep learning, revolutionizing tasks such as image recognition, speech processing and natural language understanding. AI continues to evolve and permeate every facet of modern life, from virtual assistants and autonomous vehicles to healthcare diagnostics and personalized recommendations.
Reference:
Anyoha, R. (2017). The History of Artificial Intelligence. Harvard Graduate School of Arts and Sciences, Science in the News. Retrieved June 1, 2024, from https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
AI is woven into the fabric of our lives today. From the way our phones predict the next word we’ll type to how streaming services suggest movies we might like, AI is everywhere, and it is rapidly changing how we learn, work and live. Understanding AI isn’t just for people who are tech-savvy; it’s a vital part of digital literacy, which includes skills and knowledge everyone needs to navigate the technology we encounter every day. Knowing how AI works helps us move through this digital landscape with confidence.
AI is reshaping education and work. In schools, it is being used to personalize learning experiences for students, making education more effective and engaging. In the workforce, AI is transforming different careers. Whether you dream of being a doctor, an engineer, an artist or even opening your own business, AI will likely be part of your future workplace. Yet, alongside its benefits, AI also poses challenges. For instance, there are concerns about privacy as AI systems gather vast amounts of personal data. In education, while AI can personalize learning experiences, there are worries about over-reliance on technology and its potential to widen educational inequalities. So, whether you’re a student, a teacher or someone in the workforce, grasping the basics of AI is key to thriving in our increasingly digital world.
AI works by learning from large amounts of data, called training sets. Training set data can be images, text, numbers, sounds and sensor readings, among others. The more data in a training set, and the more representative that data set is of all related subsets of that data, the better the AI program can become at performing specific tasks.
AI programs process training set data by breaking it down into smaller pieces, extracting useful information, and organizing it in a way that the computer can understand. Then, AI systems use specific algorithms to analyze the processed data to look for patterns, trends and relationships.
For example, if you show an AI program a large number of various cat and dog images, it can eventually learn to tell them apart on its own based on what it learned about each kind of animal from the training set data. This learning process in which AI systems learn from the data they analyze is called machine learning. They identify patterns and use that knowledge to make predictions or decisions.
Note: If the training set data is limited or only represents a small subset, the AI program’s learning will be biased toward the data introduced in the training set. This is called algorithmic bias, and its consequences for individuals and the world around us can be very serious.
For example, if the only dog images shown to the AI program were of chihuahuas, the AI would likely draw the conclusion that all dogs are chihuahuas and therefore would probably not recognize an image of a German shepherd as a dog.
This is why feedback loops are also an important component of AI. As the AI system makes predictions or decisions, it receives feedback – often from humans – on its performance. If the feedback is positive, the system reinforces the pattern it learned. If the feedback indicates that AI’s predictions or decisions were incorrect, it adjusts its algorithm (the set of steps it uses to complete a task) and tries again.
In this way, the AI program is always learning and improving. With each iteration, the AI system refines its algorithm, learns from new data, and becomes more accurate and efficient. Once the AI system has been trained and tested, it can be shared for more widespread use in real-world situations. This could be anything from recommending movies on a streaming platform to diagnosing diseases in healthcare. But it’s important to remember that AI is still developing and can make mistakes – it is up to humans to learn how to use it ethically and responsibly, and to check over AI’s work to make sure its predictions and decisions are accurate and representative as possible.
While AI can be helpful in our everyday lives, there are factors to consider when using this technology. Some of these include:
- Bias: AI systems have the potential to perpetuate and even exacerbate biases present in the data they are trained on. This can lead to discriminatory outcomes, especially in areas such as hiring or lending decisions. One example of this is the way facial recognition software more often misidentified Black people.1
- Data Privacy: AI systems often rely on vast amounts of personal data. There’s a risk of this data being mishandled or exploited, leading to breaches of privacy and potential harm to individuals. There are also concerns about data, including creative works, being appropriated without the consent of the creator and owner of the data.
- Learning: With AI seemingly capable of answering any question and giving written responses and even creating visual and audio works, there are concerns about whether people will come to rely on AI instead of learning things for themselves. Misrepresenting responses created by AI as one’s own raises concerns about academic dishonesty and authenticity. In response, educators may need to redesign assessments to ensure integrity.
- Hallucinations: AI has the potential to provide inaccurate information. While AI systems aim to process and analyze data efficiently, they are not immune to errors. Inaccuracies may arise due to various factors such as incomplete or biased data sets, flawed algorithms, or unforeseen circumstances. This can lead to misleading conclusions or recommendations, especially in critical domains like healthcare or finance, where the consequences of incorrect information can be significant. While AI is continuously improving, it’s essential to implement robust validation processes and continuously monitor AI systems in order to minimize the occurrence of inaccuracies and ensure their reliability.
- Deepfakes: Another serious concern involves AI’s capability to generate realistic but false information, known as “deepfakes.” These can be used to spread misinformation or create convincing but entirely fabricated content including audio and video, posing threats to trust and authenticity in media and communication.
1 Thaddeus L. Johnson, Natasha N. Johnson, Denise McCurdy, Michael S. Olajide,
Facial recognition systems in policing and racial disparities in arrests, Government Information Quarterly, Volume 39, Issue 4, 2022, 101753, ISSN 0740-624X
The resources below are a good place to start. Our AI in Education page has resources for educators, schools and districts about developing AI guidance for educational organizations.
- AI Basics from AI & You provides an introduction to AI, AI literacy and information on the impact of AI. There are also explainer videos, including one on the impact of AI on the 2024 election.
- AI 101 for Teachers from Code.org, ETS, ISTE and Khan Academy is a series of foundational videos that introduce teachers to AI.
- A parent’s guide to AI from internetmatter.org (UK) is an interactive guide to AI concepts, terms and tools with practical advice for parents to support their children with AI literacy and ethical use.
- AI Literacy Lessons (6–12) is a collection quick lessons for students (20 minutes or less) from Common Sense Education that provide an introduction to AI and help address its social and ethical impacts.
The content on this page was partly generated by GPT-3.5, Perplexity and Gemini.
Last updated May 2024. MDE is listening, learning and exploring AI and will provide regular updates as AI technology and our understanding of it evolves.
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