AI in Higher Education
Metasite
Ray Schroeder
UPCEA Sr. Fellow
Professor Emeritus UIS
Metasite
Ray Schroeder
UPCEA Sr. Fellow
Professor Emeritus UIS
Artificial Intelligence - the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. (https://www.lexico.com/en/definition/artificial%20intelligence
Machine Learning - Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it... learn for themselves. [https://expertsystem.com/machine-learning-definition/
Deep Learning - In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, but they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
Algorithm - In computing, an algorithm is a precise list of operations that could be done by a Turing machine. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages. . https://simple.m.wikipedia.org/wiki/Algorithm [Ray - example Python]
Supervised and Unsupervised Learning - In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
Reinforcement Learning - In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
Imitation Learning - Generally, imitation learning is useful when it is easier for an expert to demonstrate the desired behaviour rather than to specify a reward function which would generate the same behaviour or to directly learn the policy. The main component of IL is the environment, which is essentially a Markov Decision Process (MDP). https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
Neural Network in AI - A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy. https://aws.amazon.com/what-is/neural-network/
DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. https://openai.com/dall-e-2/
GPT-3 - GPT-3, or the third generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text. GPT-3's deep learning neural network is a model with over 175 billion machine learning parameters. To put things into scale, the largest trained language model before GPT-3 was Microsoft's Turing NLG model, which had 10 billion parameters. https://www.techtarget.com/searchenterpriseai/definition/GPT-3
Generative AI refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing text, audio and video files, images, and even code to create new possible content. Generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content. https://indiaai.gov.in/article/here-is-how-generative-ai-is-reinventing-the-creative-space
AI has the potential to remake our class-based teaching and learning paradigm. It has always struck me as ill-conceived that we teach 20 or 30 students at a time. I understand the economy of doing so, but not the pedagogical sense. An AI-powered adaptive model of teaching includes frequent probing assessments throughout the learning term, analyzed by AI to determine the depth and breadth of knowledge of the learner in the topical area and adapt the syllabus to achieve mastery of the outcomes at the end of the term. Gaps in learning can be identified in these assessments, and adjustments can be built into the flexible, personalized syllabus for the student. There are a whole host of ways in which AI can improve learning outcomes, lessen the workload on faculty and staff, and ensure that our learners are getting the best, most relevant education possible.
https://www.insidehighered.com/digital-learning/blogs/online-trending-now/ai-transforming-education
The ‘smart’ approach to education is typically the incorporation of Machine Learning (ML) in learning and development. Machine Learning leverages Artificially Intelligent methods to teach systems how to make informed decisions without any human intervention. This is done by feeding data to a machine learning algorithm which is then able to process the data and make inferences for future events. Personalized learning, smart grading, skill gap assessment, and better ROI: The importance of incorporating Machine Learning in Online Learning cannot be overstated.
https://www.kdnuggets.com/2022/12/machine-learning-benefit-online-learning.html
As GPT-4 rumors fly around NeurIPS 2022 this week in New Orleans (including whispers that details about GPT-4 will be revealed there), OpenAI has managed to make plenty of news in the meantime. On Monday, the company announced a new model in the GPT-3 family of AI-powered large language models, text-davinci-003, part of what it calls the “GPT-3.5 series,” that reportedly improves on its predecessors by handling more complex instructions and producing higher-quality, longer-form content. Meanwhile, today OpenAI launched an early demo of ChatGPT, another part of the GPT-3.5 series that is an interactive, conversational model whose dialogue format “makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”
https://venturebeat.com/ai/openai-debuts-chatgpt-and-gpt-3-5-series-as-gpt-4-rumors-fly/
Artificial-intelligence research company OpenAI is in discussions over potentially selling at least $300 million in shares in a tender offer that would give it a roughly $29 billion valuation, the Wall Street Journal reported Thursday. The offering of shares of OpenAI — known for AI programs like the chatbot ChatGPT and the image-generator Dall-E 2 — would make it among the most highly-valued startups in the U.S., the Journal said. The valuation would be more than twice its valuation of $14 billion in 2021, according to the Journal.
I mean, there's a few things, right? So the most obvious thing and the thing people come away with if they play with ChatGPT for just a few minutes is, wow, I can cheat on essays with this thing. And then if they spend a couple more minutes, they say, well, I can cheat on creating software code or translating language. But the uses actually go way beyond that, and I've been amazed by what some of my students have been reporting about how they're using the capabilities.
Released on Nov. 30, ChatGPT and GPT-3.5 were publicly unveiled by OpenAI—a leader in generative artificial intelligence. I wondered what this release might mean for the future of continuing higher education. Of course, nothing had yet been written about the potential of this just-released version, so I asked ChatGPT to write a short poem about it. In just three seconds, far faster than I could have typed the words, the poem was complete on my screen. Let’s deconstruct the poem and look closely at what ChatGPT has referenced.
Welcome to this short instructional teachers guide to using ChatGPT. ChatGPT is a powerful tool that can help teachers enhance student learning - remember to keep asking it questions to refine the outcome. Sometimes, when you're close to getting exactly what you want, it's helpful to restart the conversation with your newly clarified prompt. By following this guide, you will learn how to effectively incorporate ChatGPT into your teaching practice and make the most of its capabilities. We will provide specific examples and strategies aligned with CESE NSW's "What Works Best" to help you get started.
Faculty members and administrators are now reckoning in real time with how—not if—ChatGPT will impact teaching and learning. Inside Higher Ed caught up with 11 academics to ask how to harness the potential and avert the risks of this game-changing technology. The following edited, condensed advice suggests that higher ed professionals should think a few years out, invite students into the conversation and—most of all—experiment, not panic.
https://www.insidehighered.com/news/2023/01/12/academic-experts-offer-advice-chatgpt
1: 'Market research done in minutes' by Andy Gray > https://lnkd.in/gT_2ra2n
2: '10 techniques to get massively ahead with AI' by Rob Lennon > https://lnkd.in/g5hqrvv9
3: 'Use ChatGPT for writing LinkedIn hooks' by Filipa Canelas > https://lnkd.in/grHBSuiz
4: 'Use ChatGPT for creating compelling LinkedIn content' by Sam Szuchan > https://lnkd.in/g5A2ga4c
5: 'Create a Content Strategy in 7 steps' by Louis Smith > https://lnkd.in/gE8H3C5A
6: 'Awesome apps on top of ChatGPT' by ChatGPT > https://lnkd.in/gkJh5kgK
7: 'ChatGPT: Everything You Really Need to Know' by Bernard Marr > https://lnkd.in/g8PQV9W4
8: 'Generative AI Landscape' by Arockia Liborious > https://lnkd.in/gitM4ADn
9: 'Ad scripts for inspiration' by Andy Gray > https://lnkd.in/gEC7xyP2
10: 'Microsoft Is In Talks To ACQUIRE A 49% Stake Worth $10 billion In ChatGPT Owner OpenAI' by Eddie Donmez > https://lnkd.in/g4ShpH7G
Well, in the rest of our lifelong learning/work, we don't often ask each other to do quizzes and long essays. We assess each other and build reputation through LONG-TERM ENGAGEMENT. You know if a colleague is good at their work or not, because you see what they do in an authentic context every day for a long time, or perhaps you follow them on social media for a long time. It’s the same in a homeschool, or an apprenticeship, or any really small class. In short, we need to embrace that AI is going to be a huge part of our lives when creating anything. There is no gain in banning it or avoiding it. It's actually easier (and better) to use this moment to restructure our education processes to be useful and appropriate in today's environment (which is full of opportunities).
We are seeing chat bots supporting those in emotional distress and making referrals. AI is increasingly used for assessments of student learning. These are all valuable and enhance our efficiency and effectiveness. They save money and raise satisfaction. Yet, it is not until you meet “AI face-to-face” in the form of GPT-3 (Generative Pre-trained Transformer 3) that you realize just how overwhelming the impact will be....
=
Selected videos with GPT-3 driven avatars:
What It's Like To be a Computer: An Interview with GPT-3 https://www.youtube.com/watch?v=PqbB07n_uQ4
Why Ai Deserves Basic Rights (GPT-3) https://www.youtube.com/watch?v=A-3AcifnZ84
An Interview with GPT-3 + Poetry https://www.youtube.com/watch?v=Eor4tsSOcZs
Pixegami asks GPT-3 to write poetry https://www.youtube.com/watch?v=r3zKcL0iGeA
Leta, GPT-3 + WebGPT AI - Episode 38 (WebGPT questions, truth, fact-checking)
https://www.youtube.com/watch?v=tcicL5IbeAo
=
Engaging GPT-3 yourself:
Sample writing through API https://www.copy.ai/
Meta's offer to sample of GPT-3 https://blenderbot.ai/
"Sophie" (avatar linked API) GPT-3 is hosting an NFT project https://iamsophie.io/
=
More Writing Apps to Try:
Verse by Verse - Google Semantic Experiences https://sites.research.google/versebyverse/
Jasper will write 10,000 words for free https://www.jasper.ai/free-trial
Simplified's FREE long-form writing: https://simplified.com/ai-writer/long-form-ai-writer
Grammarly's AI Writing Assistant https://www.grammarly.com/
Grammarly AI Writing Assistant - REVIEW - Tom Kotze, Contentellect
Grammarly is an AI-powered writing assistant, not a complete AI writing tool. It won’t be able to replace a writer or generate ideas for you. That being said, Grammarly is one of the most advanced AI writing tools for accuracy and quality. It can vastly improve any content, and it’s an absolute must for any writer in today’s world. https://www.contentellect.com/grammarly-review/
=
English faculty read this:
The New Poem-Making Machinery by Simon Rich: https://www.newyorker.com/culture/culture-desk/the-new-poem-making-machinery
=
Art faculty read this:
An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy. - Kevin Roose, NY Times https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html
=
Math faculty read this:
How AI Support Could Add up to Better Math Teaching - Chris Carroll, Maryland Today https://today.umd.edu/how-ai-support-could-add-up-to-better-math-teaching
=
Computer Science faculty read this:
5 AI Tools That Can Generate Code To Help Programmers - Janakiram MSV, Forbes https://www.forbes.com/sites/janakirammsv/2022/03/14/5-ai-tools-that-can-generate-code-to-help-programmers/?sh=24d0449f5ee0
=
Personalization of Learning in Higher Ed through AI
How AI and Data Could Personalize Higher Education - Lasse Rouhiainen, Harvard Business Review: https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education
Investors have got the hots for "generative AI" that can make text and images. But so far, the hype runs ahead of the business results.... A few of what are now styled as generative AI companies have collectively raised hundreds of millions of dollars, spurring a hunt for a new generation of AI unicorns. They’re working on applications including generating music, game development, writing assistants, customer service bots, coding aids, video editing tech, and assistants that manage online communities. Guo has invested in a company that plans to generate legal contracts from a text description—a potentially lucrative application if it can work reliably.
https://www.wired.com/story/ais-new-creative-streak-sparks-a-silicon-valley-gold-rush/
Large language models (LLMs) have made a significant impact on AI research. These powerful, general models can take on a wide variety of new language tasks from a user’s instructions. However, academia, nonprofits and smaller companies' research labs find it difficult to create, study, or even use LLMs as only a few industrial labs with the necessary resources and exclusive rights can fully access them. Today, we release BLOOM, the first multilingual LLM trained in complete transparency, to change this status quo — the result of the largest collaboration of AI researchers ever involved in a single research project. With its 176 billion parameters, BLOOM is able to generate text in 46 natural languages and 13 programming languages.
LaMDA’s conversational skills have been years in the making. Like many recent language models, including BERT and GPT-3, it’s built on Transformer, a neural network architecture that Google Research invented and open-sourced in 2017. That architecture produces a model that can be trained to read many words (a sentence or paragraph, for example), pay attention to how those words relate to one another and then predict what words it thinks will come next. But unlike most other language models, LaMDA was trained on dialogue. During its training, it picked up on several of the nuances that distinguish open-ended conversation from other forms of language. One of those nuances is sensibleness. Basically: Does the response to a given conversational context make sense?
https://blog.google/technology/ai/lamda/
=
Google AI Test Kitchen
AI Test Kitchen is an app where people can experience and give feedback on some of Google's latest AI technologies including LaMDA. https://aitestkitchen.withgoogle.com
DALL·E 2 began as a research project and is now available in beta to those who join our waitlist. In January 2021, OpenAI introduced DALL·E. One year later, our newest system, DALL·E 2, generates more realistic and accurate images with 4x greater resolution. DALL·E 2 has learned the relationship between images and the text used to describe them. It uses a process called “diffusion,” which starts with a pattern of random dots and gradually alters that pattern towards an image when it recognizes specific aspects of that image. https://openai.com/dall-e-2/
The concept sounds almost too simple (and futuristic) to be true: Type your idea into a search bar and voila! But for the Before you get started with DALL•E 2, there are three housekeeping rules that you should know: Since you technically create the idea for your artwork, you, by default, are the accredited artist for the AI-generated product. If you choose to download the image, though, there will be a colorful DALL•E 2, watermark on the bottom right corner. There are limits to what you can create with the platform. For example, DALL•E 2's content policy prohibits content that is harmful, deceitful, or political. DALL•E 2 is currently free to use, but there is a catch. You're allotted 50 free credits during your first month's use and 15 free credits after that
Dall-E is at the forefront of artificial intelligence art creation, which anyone can use. AI art generators have been in the news a lot this year, be it for their amazing advances or questionable uses. OpenAI’s Dall-E 2 is one of the major names in this space. It's now open to the public and developers, and soon it’ll be built into Microsoft software and the Bing search engine. But how, exactly, do you work with Dall-E? Is it really as simple as typing in a description—called a prompt—and getting back a picture? To be honest, yes. But there’s a lot more to be aware of if you want to get anywhere close to the perfect result.
https://www.pcmag.com/how-to/how-to-use-dall-e-ai-art-generator
Artificial intelligence can now make better art than most humans. Soon, these engines of wow will transform how we design just about everything. Picture Lee Unkrich, one of Pixar’s most distinguished animators. At mid-career, he says “It feels like a miracle.... When the results appeared, my breath was taken away and tears welled in my eyes. It’s that magical.”
https://www.wired.com/story/picture-limitless-creativity-ai-image-generators/
Forbes’ full conversation with ChatGPT, OpenAI’s newest natural language model, is pasted below. Each of the college admissions essays took less than 10 minutes to complete. Read our story about ChatGPT’s capacity to write college applications here.
Well, in the rest of our lifelong learning/work, we don't often ask each other to do quizzes and long essays. We assess each other and build reputation through LONG-TERM ENGAGEMENT. You know if a colleague is good at their work or not, because you see what they do in an authentic context every day for a long time, or perhaps you follow them on social media for a long time. It’s the same in a homeschool, or an apprenticeship, or any really small class. In short, we need to embrace that AI is going to be a huge part of our lives when creating anything. There is no gain in banning it or avoiding it. It's actually easier (and better) to use this moment to restructure our education processes to be useful and appropriate in today's environment (which is full of opportunities).
Microsoft's $1 billion investment in OpenAI that helped produce ChatGPT could soon be used to improve its search engine, Bing, according to a story from The Information. Bing could be getting ChatGPT-enhanced search capabilities as early as March in a move that capitalizes on Microsoft's $1 billion investment in Open AI in 2019, which included an exclusive multi-year cloud-computing partnership to build new Azure AI supercomputing technologies. Two sources with knowledge of Microsoft's plans told The Information that Microsoft is gearing up to launch a version of Bing that uses ChatGPT AI to answer some search queries, offering a different way of producing results than the list of links offered by Google Search and Bing. It could help change the status quo in the search market -- currently dominated by Google.
Microsoft is in talks to invest an additional $10 billion into ChatGPT owner OpenAI as it looks to integrate the GPT3-based chatbot into its search engine Bing, Semafor reported. In 2019, Microsoft had invested $1 billion in OpenAI to expedite efforts to further artificial general intelligence for “widely distributed economic benefits.” The recent interest to invest $10 billion could be the direct result of ChatGPT’s rising popularity due to its ability to respond to queries on the internet using conversational language, a vastly different approach from what Google and Bing offer currently in the way of links.
AI tools like ChatGPT can generate essays. And, as my little thought experiment demonstrated, many people cannot distinguish the words that I put together from the words assembled by ChatGPT. (I assure you, this is Josh typing–or is it?) But did you know that similar technology can also answer multiple choice questions? My frequent co-authors, Mike Bommarito and Dan Katz utilized a different software tool from OpenAI, known as GPT-3.5, to answer the multiple choice questions on the Multistate Bar Examination (MBE). If there are four choices, the "baseline guessing rate" would be 25%. With no specific training, GPT scored an overall accuracy rate of 50.3%. That's better than what many law school graduates can achieve. And in particular, GPT reached the average passing rate for two topics: Evidence and Torts.
https://reason.com/volokh/2023/01/02/can-gpt-pass-the-multistate-bar-exam/
The expectation is mounting up around OpenAI’s ChatGPT-4, which is scheduled for 2023, although there is no official confirmation on either the launch or beta testing of it. GPT-4 stands for Generative Pre-trained Transformer 4. It’s basically an artificial intelligence system that can create human-like text. While the current ChatGPT-3 has 175 billion parameters, ChatGPT-4 might have 1 trillion, or even more, according to some reports. Similarly, it will be capable of text answering, content generation, language translation, and text summarization, just like the current ChatGPT-3.
https://iblnews.org/chatgpt-4-the-fined-tuned-version-of-chatgpt-3-might-prompt-a-major-shift/
Since its beta launch in November, AI chatbot ChatGPT has been used for a wide range of tasks, including writing poetry, technical papers, novels, and essays, planning parties, and learning about new topics. Now we can add malware development and the pursuit of other types of cybercrime to the list. Researchers at security firm Check Point Research reported Friday that within a few weeks of ChatGPT going live, participants in cybercrime forums—some with little or no coding experience—were using it to write software and emails that could be used for espionage, ransomware, malicious spam, and other malicious tasks.
The metaverse skyrocketed into our collective awareness during the height of the pandemic, when people longed for better ways to connect with each other than video calls. Gaming’s hot growth during the pandemic also pushed it forward. But the metaverse became so trendy that it now faces a backlash, and folks aren’t talking about it as much. Yet technologies that will power the metaverse are speeding ahead. One of those technologies is generative AI, which uses deep learning neural networks to produce creative concept art and other ideas based on simple text prompts.
https://venturebeat.com/games/how-generative-ai-could-create-assets-for-the-metaverse-jensen-huang/
I’ve never felt AI writing was advancing very quickly. And then I met ChatGPT. The Facebook teaching page for my university has taken off on the topic, so I took a deep dive into what it can do. I’ve seen it create (in a flash) movie scripts and comic strips, sonnets and grant proposals, graduate course syllabi and lessons. It can execute math problems, showing all its work with written explanations. Nearly any writing prompt one might assign to be completed outside of class (with a few notable exceptions) can be written pretty well, quickly, at no cost, and undetectable by our current plagiarism software by anyone who takes a little time to learn the nuances of ChatGPT. I am spending the day after Christmas writing this because I don’t want anyone to lament, “Why didn’t anyone warn me about this sooner?”
Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful. Now some computational neuroscientists have begun to explore neural networks that have been trained with little or no human-labeled data. These “self-supervised learning” algorithms have proved enormously successful at modeling human language and, more recently, image recognition. In recent work, computational models of the mammalian visual and auditory systems built using self-supervised learning models have shown a closer correspondence to brain function
https://www.quantamagazine.org/self-taught-ai-shows-similarities-to-how-the-brain-works-20220811/
When it comes to artificial intelligence (AI) adoption, there is a growing gap between the haves and the have-nots. According to IBM, the global AI adoption rate went up by 4 percentage points in 2022, reaching nearly 35%. However, the study also found that the gap in AI adoption between larger and smaller companies also grew significantly in the past year. AI projects generally take months to develop and mature, bringing a long gestation period and significant expenses. That’s where AI-as-a-service (AIaaS) comes in: It was born out of a desire to democratize AI for all while addressing the growing demand for AI, cognitive computing and large-scale adoption of cloud-based solutions.
https://venturebeat.com/ai/how-ai-as-a-service-can-help-democratize-ai/
As enrollment in higher education continues trending down, colleges and universities need to get creative in order to strengthen their margins and maintain profitability. At the same time, they need to figure out how to improve the student experience to buck enrollment trends. One way to accomplish these goals is by making smart investments in technology. For example, by investing in artificial intelligence (AI) tools—and AI-powered chatbots in particular—institutions of higher learning can provide their students with better support while empowering their staff to focus on higher-level initiatives and tasks.
https://www.fierceeducation.com/technology/4-ways-universities-can-use-ai-streamline-operations
The White House has released the Blueprint for an AI Bill of Rights—which is likely the signature document reflecting the Biden administration’s approach to algorithmic regulation. Paired with a series of agency actions, the Biden administration is working to address many high-priority algorithmic harms—such as those in financial services, health care provisioning, hiring, and more. There is clear and demonstrated progress in implementing a sectorally specific approach to artificial intelligence (AI) regulation. The progress being made, however, is uneven. Important issues in educational access and worker surveillance, as well as most uses of AI in law enforcement, have received insufficient attention.
https://www.lawfareblog.com/ai-bill-rights-makes-uneven-progress-algorithmic-protections
New computing capacities—in areas such as image recognition, robotic manipulation, text processing, natural-language processing, and pattern recognition, and, more generally, the ability to learn and improve rapidly in relatively autonomous ways—represent a break from the hand-coded, rules-based programs of the past. In this view, newer robots and AI represent a clear departure from previous waves of computing, one that accelerates the pace of technological change and job displacement.
With the development of technology, it has become evident that comprehension of both the cognitive and affective channels of human connection is crucial. Emotion recognition is a branch of affective computing that seeks to do this. Understanding someone’s feelings requires being able to recognise, sort through, and interpret verbal and nonverbal cues.
Roughly a third of the content published by Bloomberg News uses some form of automated technology. The system used by the company, Cyborg, is able to assist reporters in churning out thousands of articles on company earnings reports each quarter. The program can dissect a financial report the moment it appears and spit out an immediate news story that includes the most pertinent facts and figures. And unlike business reporters, who find working on that kind of thing a snooze, it does so without complaint.
https://www.nytimes.com/2019/02/05/business/media/artificial-intelligence-journalism-robots.html
Artificial-intelligence software programs that generate text are becoming sophisticated enough that their output often can’t be distinguished from what people write. And a growing number of companies are seeking to make use of this technology to automate the creation of information we might rely on, according to those who build the tools, academics who study the software, and investors backing companies that are expanding the types of content that can be auto-generated.
What does privacy mean in a world where it's possible to detect lying from a brain scan? How should we understand ownership when a group creates a product by directly linking their minds together over an interface? Could an AI have rights? These fascinating questions lie at the intersection of emerging neurotechnologies and law. Luckily, so does our guest this week, Dr. Allan McCay. Allan is Deputy Director of The Sydney Institute of Criminology and an Academic Fellow at the University of Sydney's Law School. He coordinates the Legal Research units at the Sydney Law School, and lectures in Criminal Law.
https://futuratipodcast.com/neurotech-and-the-future-of-law/
AI content generators work by generating text through natural language processing (NLP) and natural language generation (NLG) methods. This form of content generation is beneficial in supplying enterprise data, customizing material to user behavior and delivering personalized product descriptions. Algorithms organize and create NLG-based content. Such text generation models are generally trained through unsupervised pre-training, where a language transformer model learns and captures myriads of valuable information from massive datasets.
https://venturebeat.com/ai/deep-dive-how-ai-content-generators-work/
Technology has repeatedly transformed the news media industry—telegraph, radio, television, and then the internet. Yet despite these evolutions, technology remained the medium and human journalists the messengers. The introduction of AI has changed that model. Today, AI machines designed to perform the communicator role are generating news content independent of humans. That means AI is the medium and the messenger, giving human journalists a new synthetic partner programmed to aid in news gathering. The new study finds many Americans are unaware of the role AI plays in their world, including in the production of news.
https://www.futurity.org/ai-journalism-2778042-2/
Companies are increasingly turning to artificial intelligence (AI) to automate and optimize business functions. But according to recent research, the IT professionals who will be asked to implement the technology have decidedly mixed feelings about it, ranging from optimism to outright dread (and sometimes both at the same time). On the positive side, many IT pros see AI as a beneficial technology that can help advance their careers. Fully 74% of survey respondents agreed with the statement, "AI will automate tasks and enable more time to focus on strategic IT initiatives." The prompt, "AI will create major data privacy issues" drew agreement from 55% of respondents.
https://www.pcmag.com/news/survey-it-pros-remain-conflicted-over-ais-potential-peril
Erik Brynjolfsson is the director of Stanford’s Digital Economy Lab and has long written about AI’s effect on labor. He gets why AI creators have been so enchanted with mimicking human abilities. It caters to a desire to play god, creating life forms in our own image. “Every culture has a myth about this,” Brynjolfsson says. But mythology may not be the best framework for software development. He thinks economic growth lies in building AI that augments humans: It should do things people can’t.
https://www.wired.com/story/ai-shouldnt-compete-with-workers-it-should-supercharge-them-turing-trap/
A class-action lawsuit filed in a federal court in California this month takes aim at GitHub Copilot, a powerful tool that automatically writes working code when a programmer starts typing. The coder behind the suit argues that GitHub is infringing copyright because it does not provide attribution when Copilot reproduces open-source code covered by a license requiring it. The lawsuit is at an early stage, and its prospects are unclear because the underlying technology is novel and has not faced much legal scrutiny. But legal experts say it may have a bearing on the broader trend of generative AI tools. AI programs that generate paintings, photographs, and illustrations from a prompt, as well as text for marketing copy, are all built with algorithms trained on previous work produced by humans.
https://www.wired.com/story/this-copyright-lawsuit-could-shape-the-future-of-generative-ai/
From assessment to ethics and job security, a new Jisc report highlights AI’s challenges and successes and provides insight into upcoming developments.... We need to begin preparing our systems and data now to support integration and allow development of more of these kinds of systems in the future. Jisc is uniquely placed to work together with the higher education sector to enable institutions to plan how they will use AI efficiently, effectively and ethically. To find out more, take a look at AI in Tertiary Education: A Summary of the Current State of Play, Jisc’s latest report on AI in tertiary education.
https://www.timeshighereducation.com/campus/whats-next-ai-higher-education
It’s not every day you have to persuade a panel of scientists that you’re human. But this was the position I found myself in at the Loebner Prize competition, an annual Turing test, in which artificial intelligence programs attempt to pass themselves off as people. The British mathematician Alan Turing probed one of computing’s biggest theoretical questions: Could machines possess a mind? If so, how would we know? In 1950, he proposed an experiment: If judges in typed conversations with a person and a computer program couldn’t tell them apart, we’d come to consider the machine as “thinking.” He predicted that programs would be capable of fooling judges 30 percent of the time by the year 2000. They came closest at the 2008 Loebner Prize competition when the top chatbot (as a human-mimicking program is called) fooled 3 of 12 judges, or 25 percent.
https://www.smithsonianmag.com/science-nature/man-or-computer-can-you-tell-the-difference-132577533/
Synthetic creativity is a commodity now. Ancient philosophers will turn in their graves, but it turns out that to make creativity—to generate something new—all you need is the right code. We can insert it into tiny devices that are presently inert, or we can apply creativity to large statistical models, or embed creativity in drug discovery routines. What else can we use synthetic creativity for? We may feel a little bit like medieval peasants who are being asked, “What would you do if you had the power of 250 horses at your fingertips?” We dunno. It’s an extraordinary gift. What we do know is we now have easy engines of creativity, which we can aim into stale corners that have never seen novelty, innovation, or the wow of creative change. Against the background of everything that breaks down, this superpower can help us extend the wow indefinitely. Used properly, we can make a small dent in the universe. ~ Kevin Kelly, Wired
https://www.wired.com/story/picture-limitless-creativity-ai-image-generators/
~~~~~~~~~~~~~~
Ray's Daily Curated Reading Lists and Social Media. Blogs with daily updates on the field of online / continuing learning in higher education
UPCEA Professional, Continuing and Online Education Update http://continuingedupdate.blogspot.com/
Economics and Change in Higher Education by UPCEA http://recessionreality.blogspot.com/
OER Update by UPCEA https://oerupdate.blogspot.com/
Ray's Curation by UPCEA https://rayscuration.blogspot.com/
Alternative Credentials in Higher Ed by UPCEA https://altcred.blogspot.com
Twitter https://twitter.com/rayschroeder
rschr1@uis.edu ~ rayschroeder@gmail.com - ray@upcea.edu
Senior Fellow, University Professional and Continuing Education Assn.
Professor Emeritus, University of Illinois Springfield