The Rise of AI: What Does It Mean for Society When Machines Make Art?
You may have seen it: all your friend posting cool AI generated avatars — images generated by AI.
First discussed all the way back in the 1950s, AI has now become the major topic in the art world.
The popularity of AI-generated images has risen as tech companies make it easy to create them. For example, the app Lensa, which makes AI-generated avatars featuring people’s faces, is currently the number one app in the app store.
However, the app has been shrouded in controversy, with many artists claiming the AI is stealing art.
While that isn’t how the app works, it does beg the question:
Where is the line between artist and machine?
In the 70’s when sampling first appeared in the music industry, many musicians and record labels reacted similarly to how people are reacting now. Labels interpreted sampling as stealing, and people called for musicians to boycott using samples in their work.
We all know how that situation turned out. Many leading thinkers believe the current situation is likely to turn out the same way.
Stable Diffusion and other diffusion models work by basically vomiting pixels at a canvas randomly and asking “Is that a bird?” (or whatever you asked it for).
It then repeats that process over and over until it finds a pattern in the pixelated noise that matches something it knows to be associated with the concept of “bird”. It’s trained on millions of publicly available images.
One common and valid criticism is that there are images in the dataset that shouldn’t be there. Artists are stating that if they had been asked for consent for their art to be in the training data, they wouldn’t have given it.
However, proponents of the tech are saying that the use of artists’ work they willingly published to the internet was included in the terms of service that they didn’t read. They also argue that the tech works so similarly to the way our minds do — by learning and associating concepts — that it’s fair use.
No matter which way you slice it, it’s a controversial topic, and one that isn’t likely to ever go away.
AI has the potential to be incredibly powerful and has already made a huge impact on our lives. From determining what content you see in your social media feed to detecting cancer, AI can be used for good, or…not so good.
A growing number of people are concerned that AI might be too dangerous. AI is often used to spread false information and to steal personal information through cyber attacks. There are algorithms that code, including the Github Copilot and ChatGPT, meaning there are likely already bots designing a better versions of themselves.
What does the rise of AI mean for society?
The concept of machine learning dates back to the 1952, but it has only become more widely used and developed in recent years due to advances in computer technology and the availability of large amounts of data. Some of the key milestones in the development of machine learning include:
- 1943: Warren McCulloch and Walter Pitts create the first neural network.
- 1952: Arthur Samuel introduces the concept of “learning” in a computer program.
- 1959: Marvin Minsky and John McCarthy coin the term “artificial intelligence” and establish the MIT Artificial Intelligence Project.
- 1962: MIT computer scientist Arthur Samuel creates a program that learns to play checkers.
- 1966: Bernard Widrow and Marcian Hoff develop the first neural network-based learning algorithm.
- 1976: The first major AI conference is held at the University of Edinburgh.
- 1980: The first international machine learning conference is held at the University of California, Los Angeles.
- 1986: Geoffrey Hinton, David Rumelhart, and Ronald Williams publish a paper on backpropagation, which becomes the most widely used algorithm for training neural networks.
- 1990s: AI becomes more widely used and advanced due to advances in computer technology and the availability of large amounts of data.
- 2000s: AI is used in a wider range of applications, including natural language processing, image and speech recognition, and predictive analytics.
- 2010s: Deep learning, a type of machine learning that uses neural networks, becomes more widely used and leads to significant advances in AI.
- 2020s: AI continues to evolve and advance, with new applications and developments in the field.
In all that time, the public has thought of artificial intelligence as science fiction. Now Jarvis and GLaDOS are knocking on our doorstep. Let’s hope the machine goes the way of Vision and not Ultron.
The rise of AI brings with it both unprecedented opportunities and unforeseeable challenges. It is important that we understand the implications of AI on society in order to ensure AI is used responsibly and ethically.
AI has the power to revolutionize many aspects of our lives, from diagnosing medical conditions to predicting natural disasters, and will continue to have a greater impact on our daily lives.
With the right safeguards in place, AI can be used for the benefit of humanity. AI developers have a moral and ethical responsibility to ensure AI evolves in ways that protect human rights.
It is up to us as a society to make sure AI is used for good.
References:
- Minsky, M., & McCarthy, J. (1959). Artificial intelligence: A proposal. Technical Memorandum AI-1, Massachusetts Institute of Technology.
- McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133.
- Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408.
- Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. Institute of Radio Engineers Transactions on Information Theory, IT-6(4), 103–114.
- Hinton, G., Rumelhart, D., & Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
- AI Now Institute. AI Policy: Principles and Best Practices” (2020) https://ainowinstitute.org/ai-policy/. Accessed June 24, 2020.
- AI Now Institute. AI & Social Justice: A Call To Action” (2018) https://ainowinstitute.org/ai-social-justice/. Accessed June 24, 2020.
