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In 2009, James Dashner published the dystopian science fiction book "The Maze Runner." The plot centers around a group of boys who become lost in the Glade, a mystical maze, without any knowledge of their past or the outside world. They must navigate the constantly shifting maze, discover an escape route, and evade the deadly Grievers.

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Attn to Literacy / Writings, -S. Prosser

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Attn to Literacy / Writings, -S. Prosser

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Stories presented in Literacy & Literature have a huge impact on opinions and proper use of technology.  I am in a unique position as an educator to protect and support adolescents mental and physical well being in their exposure to technology in my classroom.

I support both traditional media, inputs & outputs, to be utilized in the classroom.  Simple sketches & diagrams, scale models & dioramas, still have a place in the classroom today.  Then exploration of new technologies such as CAD (Computer Aided Design), 3D printing, drone technology, and more can help students witness and plan the future outlook of item's to come.

Dystopian novels such as some of those listed under the "Books" pad have interesting and relevant stories to what we are experiencing today. Items such as global warming, government spying, and violence stoked and supported with technology are some areas expressed in these works of literature that are content area's in my technology electives.

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References:

Devlin, H. (2020, February 16). AI systems claiming to “read” emotions pose discrimination risks. The Guardian. https://www.theguardian.com/technology/2020/feb/16/ai-systems-claiming-to-read-emotions-pose-discrimination-risks

Gloria, K. (2022, January 13). Improving the human condition in a Digital World. Improving the Human Condition in a Digital World. https://www.aspeninstitute.org/blog-posts/improving-the-human-condition-in-a-digital-world/

What is Artificial Intelligence (AI) ?. IBM. (n.d.). https://www.ibm.com/topics/artificial-intelligence

What will you create?. Gencraft. (n.d.). https://gencraft.com/generate

YouTube. (2023, April 19). Artificial Intelligence Revolution; unlikely adventures of David Grann | 60 minutes full episodes. YouTube. https://www.youtube.com/watch?v=TUCnsS72Q9s

Company Logo's provided by Google Images

Book and Movie covers provided by Google Images

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This padlet is designed as an exploratory tool for students (or teachers) to reference and research throughout a year of learning about technology, drawing, design, and more offered in my high school classes.

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Each column expresses different ideas and modes of literature, all relating to technology education and themes learned in CURI 6045 - Literacy & Literature.

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This program is used extensively in my DDP (Drawing, Design, Production. Students use it to create 3D computer models and this lead to beautiful drawings and 3D prints.

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"Our culture’s dependency on technology to work, eat, learn, govern, be entertained, and stay in touch underscores that digitally mediated experiences already profoundly shape our understanding of what it means to be human.

The scenarios are vast. Whether it’s via comment sections, video chat, AI agents, or newsfeed content, how the mediums through which we interact shape human-to-human interactions remains an open research question. What we communicate and how we do so is a performative expression of how we see ourselves and others around us. It is becoming clearer that how we talk about, engage with, and design technology must better align with our needs as humans, not just as end users. Yet, as a collective (from industry to academia to the public), we lack the necessary language to fully describe our modern relationship with technology. As a result, we often fall back on binary differentiation between online and offline experiences" (Gloria & Digital, 2022).

Reference:

Gloria, K. (2022, January 13). Improving the human condition in a Digital World. Improving the Human Condition in a Digital World. https://www.aspeninstitute.org/blog-posts/improving-the-human-condition-in-a-digital-world/

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This is one of the earlier films that premiered in 1927.  This is a silent movie that's main storyline revolves a highly stylized futuristic city where a beautiful and cultured utopia exists above a bleak underworld populated by mistreated workers.

The robot is similar in style and design to the more recognized C3PO from the Star Wars movies.

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Suzanne Collins wrote the dystopian novel "The Hunger Games," which unfolds in the future within the nation of Panem. The story revolves around the annual Hunger Games, a televised event organized by the ruling Capitol, where children from each district are compelled to engage in a fight to the death for the entertainment of the Capitol's citizens. In this grim setting, Katniss Everdeen, a young girl, selflessly volunteers to participate in the Games.

"The Hunger Games" delves into themes of oppressive control, media manipulation, and the utilization of advanced technology as a means of dominance, all of which hold relevance to technology education. The Capitol orchestrates the Hunger Games, employing state-of-the-art technology to stage and broadcast the event, creating a spectacle for audiences while exerting control over the districts.

This book was first published in 2008.

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Lois Lowry wrote the dystopian novel "The Giver." The story is set in a supposedly ideal civilization where strict control reigns and the past is entirely forgotten. Jonas, the protagonist, is chosen to become the Receiver of Memory, a role that involves receiving and safeguarding the community's collective memories, both joyful and sorrowful.

Warnings of trading independence for safety round out some ways I found this book connects with technology education courses, especially with new technology everyday that could help enact these measures.

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"1984" is George Orwell's dystopian novel, first published in 1949. The story depicts a society where surveillance, propaganda, and information manipulation are taking over, with the government exerting complete control over every aspect of people's lives.

The story serves as a stark warning about the dangers of unchecked surveillance and the potential misuse of technology to infringe upon personal freedoms and privacy.

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This global research partnership is a great exploratory tool for educators.  They work on providing the resources required to help lift up nations of poverty and expose them to 21st century technology.  The partnership works with the following countries...
~Bangladesh
~Ghana
~Kenya
~Malawi
~Pakistan
~Sierra Leone
~Tanzania

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Google is a great resource for a multitude of reasons. I use their news aggregate service, "Google News" to share articles and stories with students. They are well organized and have dedicated sections to a variety of content areas, such as "Science", "Health", and "Technology". They also feature "Google Doodles" which often feature rich history on the subject matter and act as a great bell-ringer activity in class.

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Microsoft is one of the larger technology companies in the U.S. They offer coding fundamentals and the core software required for design and document set-ups.

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The world's largest technology company, Apple offers a variety of products for all types of consumers.  Most recently, their Apple Vision Pro headset launches, offering a unique opportunity for consumers to experience the immersive wearable technology.

https://www.apple.com/apple-vision-pro/

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The story, set in a dystopia in 2045, follows protagonist Wade Watts on his search for an easter egg in a worldwide virtual reality game, the discovery of which would lead him to inherit the game creator's fortune.  Above is the movie poster from 2018, produced by Steven Spielberg.  Originally, it was a novel from 2011.  This movie highlights the potential of AR & VR systems and has special effects from Industrial Light & Magic.

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In The Martian, the main character played by Matt Damon, has to reverse engineer materials he only has with him on the spaceship and what is on the planet Mars after being stranded there.  This is a real life example of how students could use reverse engineering after I have them employ the technique on cheap little wind up toys in class.  This film premiered in 2015.

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I, Robot I use in my Computer Science class to discuss where we are with artificial intelligence and predictions from the film that became true or those we are still far from.  This film premiered in 2004.

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https://www.aspeninstitute.org/blog-posts/improving-the-human-condition-in-a-digital-world/

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Improving the Human Condition in a Digital World

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Our dependency on technology to work, eat, learn, govern proves that digitally mediated experiences shape our understanding of what it means to be human.

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https://www.theguardian.com/technology/2020/feb/16/ai-systems-claiming-to-read-emotions-pose-discrimination-risks

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Artificial Intelligence (AI) systems that companies claim can "read" facial expressions is based on outdated science and risks being unreliable and discriminatory, one of the world's leading experts on the psychology of emotion has warned.

Lisa Feldman Barrett, professor of psychology at Northeastern University, said that such technologies appear to disregard a growing body of evidence undermining the notion that the basic facial expressions are universal across cultures. As a result, such technologies - some of which are already being deployed in real-world settings - run the risk of being unreliable or discriminatory, she said.

"I don't know how companies can continue to justify what they're doing when it's really clear what the evidence is," she said. "There are some companies that just continue to claim things that can't possibly be true."

Her warning comes as such systems are being rolled out for a growing number of applications. In October, Unilever claimed that it had saved 100,000 hours of human recruitment time last year by deploying such software to analyse video interviews.

The AI system, developed by the company HireVue, scans candidates' facial expressions, body language and word choice and cross-references them with traits that considered to be correlated with job success.

Amazon claims its own facial recognition system, Rekognition, can detect seven basic emotions - happiness, sadness, anger, surprise, disgust, calmness and confusion. The EU is reported to be trialling software which purportedly can detect deception through an analysis of micro-expressions in an attempt to bolster border security.

"Based on the published scientific evidence, our judgment is that [these technologies] shouldn't be rolled out and used to make consequential decisions about people's lives," said Feldman Barrett.

Speaking ahead of a talk at the American Association for the Advancement of Science's annual meeting in Seattle, Feldman Barrett said the idea of universal facial expressions for happiness, sadness, fear, anger, surprise and disgust had gained traction in the 1960s after an American psychologist, Paul Ekman, conducted research in Papua New Guinea showing that members of an isolated tribe gave similar answers to Americans when asked to match photographs of people displaying facial expressions with different scenarios, such as "Bobby's dog has died".

However, a growing body of evidence has shown that beyond these basic stereotypes there is a huge range in how people express emotion, both across and within cultures.

In western cultures, for instance, people have been found to scowl only about 30% of the time when they're angry, she said, meaning they move their faces in other ways about 70% of the time.

"There is low reliability," Feldman Barrett said. "And people often scowl when they're not angry. That's what we'd call low specificity. People scowl when they're concentrating really hard, when you tell a bad joke, when they have gas."

The expression that is supposed to be universal for fear is the supposed stereotype for a threat or anger face in Malaysia, she said. There are also wide variations within cultures in terms of how people express emotions, while context such as body language and who a person is talking to is critical.

"AI is largely being trained on the assumption that everyone expresses emotion in the same way," she said. "There's very powerful technology being used to answer very simplistic questions."

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AI systems claiming to 'read' emotions pose discrimination risks

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Artificial Intelligence (AI) systems that companies claim can "read" facial expressions is based on outdated science and risks being unreliable and discriminatory, one of the world's leading experts on the psychology of emotion has warned.

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Human expression and the human condition are deeply intertwined with technology education, literacy, and literature in several ways...

Technology education refers to the teaching and learning of technological skills and knowledge. In today's digital age, technology has become an essential part of human expression. Through technology education, individuals gain the ability to express themselves creatively and effectively in various digital mediums. This includes skills such as coding, graphic design, video editing, and multimedia production. By acquiring technological literacy, individuals can leverage digital tools to express their ideas, emotions, and experiences in new and innovative ways.

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AI in Soccer - Creating robots to work in human environments

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AI in Soccer - Creating robots to work in human environments

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Google's AI lab in London is using self-learning machines to create robots that can work in human environments.

60 Minutes is a great resource to show students modern, up-to-date stories that are grounded and relatable to a wide audience.

In the soccer playing example, these robots could be programmed in the future to work in dangerous conditions or assist people with mobility issues in safely walking from their car to the grocery store.  Possibilities to weigh the benefits and risks of this technology are endless as class discussions or modeling assignments.

https://www.youtube.com/watch?v=TUCnsS72Q9s

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This photograph was generated with Padlet.com AI to picture service...

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This photograph was generated with Padlet.com AI to picture service...

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While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (PDF, 127 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

https://www.ibm.com/topics/artificial-intelligence

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What is artificial intelligence (AI)?

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind

Learn about watsonx.ai Sign up for AI updates

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper (link resides outside ibm.com), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, " Computing Machinery and Intelligence"(link resides outside ibm.com), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach (link resides outside ibm.com), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Systems that think like humans

Systems that act like humans

Ideal approach:

Systems that think rationally

Systems that act rationally

Alan Turing's definition would have fallen under the category of "systems that act like humans."

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Over the years, artificial intelligence has gone through many cycles of hype, but even to
skeptics, the release of OpenAI's ChatGPT seems to mark a turning point. The last time generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing. And it's not just language: Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types.

The applications for this technology are growing every day, and we're just starting to
explore the possibilities. But as the hype around the use of AI in business takes off,
conversations around ethics become critically important. To read more on where IBM stands within the conversation around AI ethics, read more here.

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Weak AI-also called Narrow AI or Artificial Narrow Intelligence (ANI)-is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. 'Narrow' might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)-also known as superintelligence-would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, it's worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. "Deep" in deep learning refers to a neural network comprised of more than three layers-which would be inclusive of the inputs and the output-can be considered a deep learning algorithm. This is generally represented using the diagram below.

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn't necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

Generative AI refers to deep-learning models that can take raw data - say, all of Wikipedia or the collected works of Rembrandt - and "learn" to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified
representation of their training data and draw from it to create a new work that's similar,
but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types. Among the first class of models to achieve this cross-over feat were variational autoencoders, or VAEs, introduced in 2013. VAEs were the first deep-learning models to be widely used for generating realistic images and speech.

"VAEs opened the floodgates to deep generative modeling by making models easier to
scale," said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab.
"Much of what we think of today as generative AI started here."

Early examples of models, like GPT-3, BERT, or DALL-E 2, have shown what's possible. The future is models that are trained on a broad set of unlabeled data that can be used for different tasks, with minimal fine-tuning. Systems that execute specific tasks in a single domain are giving way to broad AI that learns more generally and works across domains and problems. Foundation models, trained on large, unlabeled datasets and fine-tuned for an array of applications, are driving this shift.

When it comes to generative AI, it is predicted that foundation models will dramatically
accelerate AI adoption in enterprise. Reducing labeling requirements will make it much
easier for businesses to dive in, and the highly accurate, efficient AI-driven automation they enable will mean that far more companies will be able to deploy AI in a wider range of mission-critical situations. For IBM, the hope is that the power of foundation models can eventually be brought to every enterprise in a frictionless hybrid-cloud environment.

There are numerous, real-world applications of AI systems today. Below are some of the most common use cases:

Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search-e.g. Siri-or provide more accessibility around texting.

Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.

Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.

Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.

Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

1950: Alan Turing publishes Computing Machinery and Intelligence. In the paper, Turing-famous for breaking the Nazi's ENIGMA code during WWII-proposes to answer the question 'can machines think?' and introduces the Turing Test to determine if a computer can demonstrate the same intelligence (or the results of the same intelligence) as a human. The value of the Turing test has been debated ever since.

1956: John McCarthy coins the term 'artificial intelligence' at the first-ever AI conference at Dartmouth College. (McCarthy would go on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon create the Logic Theorist, the first-ever running AI software program.

1967: Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that 'learned' though trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research projects.

1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.

1997: IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).

2011: IBM watson beats champions Ken Jennings and Brad Rutter at Jeopardy!

2015: Baidu's Minwa supercomputer uses a special kind of deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.

2016: DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves!). Later, Google purchased DeepMind for a reported USD 400 million.

2023: A rise in large language models, or LLMs, such as ChatGPT, create an
enormous change in performance of AI and its potential to drive enterprise value.
With these new generative AI practices, deep-learning models can be pre-trained on
vast amounts of raw, unlabeled data.

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What is Artificial Intelligence (AI) ? | IBM

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Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

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Here is another text to photo application using AI tools.  This is a great tool that could be manipulated in a variety of ways for classroom / educational use

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Here is another text to photo application using AI tools.  This is a great tool that could be manipulated in a variety of ways for classroom / educational use

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Depiction of person gaining literacy skills through technology and engineering education.

Provided by:
https://gencraft.com/generate