Guided by a vision – often captured as a Portrait of a Graduate – co-constructed with local leaders, community members, students, and families, state policymakers can develop policies that equitably and effectively support students and educators in transforming learning experiences.
The Aurora Institute highlights the importance of collaborative efforts in creating education systems that truly meet the diverse needs of every student.
The Aurora Institute has spent years working with states looking to advance competency-based systems, and has identifieda set of key state policy leversthat policymakers can put into action to build more personalized and competency-based systems. These shifts should be guided by a vision–co-constructed with local leaders, community members, students, and families–for what students need to know and be able to do upon graduating.
There has been a move away from the traditional “Bachelor’s or Bust” mentality towards recognizing the value of diverse career pathways that may not necessarily require a four-year degree.
Local entities such as states, school districts, and private organizations have played a crucial role in implementing and scaling up career pathways programs.
While much has been written on this topic (see resources below), this post, in the context of our OECD study of five Anglophone countries, will attempt to provide a backdrop on what was happening at the federal level in the U.S. over the last several decades to help catalyze this shift in career pathways and offer a snapshot of how this work is evolving in two very different states—Delaware and Texas.
the internet eliminated time and place as barriers to education, and
generative AI eliminates access to expertise as a barrier to education.
Just as instructional designs had to be updated to account for all the changes in affordances of online learning, they will need to be dramatically updated again to account for the new affordances of generative AI.
The Curious Educator’s Guide to AI | Strategies and Exercises for Meaningful Use in Higher Ed — from ecampusontario.pressbooks.pub by Kyle Mackie and Erin Aspenlieder; via Stephen Downes
This guide is designed to help educators and researchers better understand the evolving role of Artificial Intelligence (AI) in higher education. This openly-licensed resource contains strategies and exercises to help foster an understanding of AI’s potential benefits and challenges. We start with a foundational approach, providing you with prompts on aligning AI with your curiosities and goals.
The middle section of this guide encourages you to explore AI tools and offers some insights into potential applications in teaching and research. Along with exposure to the tools, we’ll discuss when and how to effectively build AI into your practice.
The final section of this guide includes strategies for evaluating and reflecting on your use of AI. Throughout, we aim to promote use that is effective, responsible, and aligned with your educational objectives. We hope this resource will be a helpful guide in making informed and strategic decisions about using AI-powered tools to enhance teaching and learning and research.
Annual Provosts’ Survey Shows Need for AI Policies, Worries Over Campus Speech — from insidehighered.com by Ryan Quinn Many institutions are not yet prepared to help their faculty members and students navigate artificial intelligence. That’s just one of multiple findings from Inside Higher Ed’s annual survey of chief academic officers.
Only about one in seven provosts said their colleges or universities had reviewed the curriculum to ensure it will prepare students for AI in their careers. Thuswaldner said that number needs to rise. “AI is here to stay, and we cannot put our heads in the sand,” he said. “Our world will be completely dominated by AI and, at this point, we ain’t seen nothing yet.”
Is GenAI in education more of a Blackberry or iPhone? — from futureofbeinghuman.com by Andrew Maynard There’s been a rush to incorporate generative AI into every aspect of education, from K-12 to university courses. But is the technology mature enough to support the tools that rely on it?
In other words, it’s going to mean investing in concepts, not products.
This, to me, is at the heart of an “iPhone mindset” as opposed to a “Blackberry mindset” when it comes to AI in education — an approach that avoids hard wiring in constantly changing technologies, and that builds experimentation and innovation into the very DNA of learning.
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For all my concerns here though, maybe there is something to being inspired by the Blackberry/iPhone analogy — not as a playbook for developing and using AI in education, but as a mindset that embraces innovation while avoiding becoming locked in to apps that are detrimentally unreliable and that ultimately lead to dead ends.
Randomized-controlled experiments investigating novice and experienced teachers’ ability to identify AI-generated texts.
Generative AI can simulate student essay writing in a way that is undetectable for teachers.
Teachers are overconfident in their source identification.
AI-generated essays tend to be assessed more positively than student-written texts.
Can Using a Grammar Checker Set Off AI-Detection Software? — from edsurge.com by Jeffrey R. Young A college student says she was falsely accused of cheating, and her story has gone viral. Where is the line between acceptable help and cheating with AI?
ChatGPT shaming is a thing – and it shouldn’t be — from futureofbeinghuman.com by Andrew Maynard There’s a growing tension between early and creative adopters of text based generative AI and those who equate its use with cheating. And when this leads to shaming, it’s a problem.
Excerpt (emphasis DSC):
This will sound familiar to anyone who’s incorporating generative AI into their professional workflows. But there are still many people who haven’t used apps like ChatGPT, are largely unaware of what they do, and are suspicious of them. And yet they’ve nevertheless developed strong opinions around how they should and should not be used.
From DSC: Yes…that sounds like how many faculty members viewed online learning, even though they had never taught online before.
The rapid rise of artificial intelligence appears to be taking a toll on the shares of online education companies Chegg and Coursera.
Both stocks sank by more than 10% on Tuesday after issuing disappointing guidance in part because of students using AI tools such as ChatGPT from OpenAI.
“Combining AI Literacy with Core Humanities Learning Goals: Practical, Critical, and Playful Approaches”
It was amazing to get to do an in-person keynote at @csunorthridge‘s AI Pedagogy Showcase.
Sharing slides: https://t.co/LDIGAZ3ORO 1/2
— Anna Mills, annamillsoer.bsky.social, she/her (@AnnaRMills) May 3, 2024
Synthetic Video & AI Professors— from drphilippahardman.substack.com by Dr. Philippa Hardman Are we witnessing the emergence of a new, post-AI model of async online learning?
TLDR: by effectively tailoring the learning experience to the learner’s comprehension levels and preferred learning modes, AI can enhance the overall learning experience, leading to increased “stickiness” and higher rates of performance in assessments.
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TLDR: AI enables us to scale responsive, personalised “always on” feedback and support in a way that might help to solve one of the most wicked problems of online async learning – isolation and, as a result, disengagement.
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In the last year we have also seen the rise of an unprecedented number of “always on” AI tutors, built to provide coaching and feedback how and when learners need it.
Perhaps the most well-known example is Khan Academy’s Khanmigo and its GPT sidekick Tutor Me. We’re also seeing similar tools emerge in K12 and Higher Ed where AI is being used to extend the support and feedback provided for students beyond the physical classroom.
Given the potential ramifications of artificial intelligence (AI) diffusion on matters of diversity, equity, inclusion, and accessibility, now is the time for higher education institutions to adopt culturally aware, analytical decision-making processes, policies, and practices around AI tools selection and use.
Last week a behemoth of a paper was released by AI researchers in academia and industry on the ethics of advanced AI assistants.
It’s one of the most comprehensive and thoughtful papers on developing transformative AI capabilities in socially responsible ways that I’ve read in a while. And it’s essential reading for anyone developing and deploying AI-based systems that act as assistants or agents — including many of the AI apps and platforms that are currently being explored in business, government, and education.
The paper — The Ethics of Advanced AI Assistants— is written by 57 co-authors representing researchers at Google Deep Mind, Google Research, Jigsaw, and a number of prominent universities that include Edinburgh University, the University of Oxford, and Delft University of Technology. Coming in at 274 pages this is a massive piece of work. And as the authors persuasively argue, it’s a critically important one at this point in AI development.
Key questions for the ethical and societal analysis of advanced AI assistants include:
What is an advanced AI assistant? How does an AI assistant differ from other kinds of AI technology?
What capabilities would an advanced AI assistant have? How capable could these assistants be?
What is a good AI assistant? Are there certain values that we want advanced AI assistants to evidence across all contexts?
Are there limits on what AI assistants should be allowed to do? If so, how are these limits determined?
What should an AI assistant be aligned with? With user instructions, preferences, interests, values, well-being or something else?
What issues need to be addressed for AI assistants to be safe? What does safety mean for this class of technologies?
What new forms of persuasion might advanced AI assistants be capable of? How can we ensure that users remain appropriately in control of the technology?
How can people – especially vulnerable users – be protected from AI manipulation and unwanted disclosure of personal information?
Is anthropomorphism for AI assistants morally problematic? If so, might it still be permissible under certain conditions?
[Per Chris Dede] We often think of the product of teaching as the outcome (e.g. an essay, a drawing, etc.). The essence of education, in my view, lies not in the products or outcomes of learning but in the journey itself. The artifact is just a symbol that you’ve taken the journey.
The process of learning — the exploration, challenges, and personal growth that occur along the way — is where the real value lies. For instance, the act of writing an essay is valuable not merely for the final product but for the intellectual journey it represents. It forces you to improve and organize your thinking on a subject.
This distinction becomes important with the rise of generative AI, because it uniquely allows us to produce these artifacts without taking the journey.
As I’ve argued previously, I am worried that all this hype around LLMs renders them a “type of digital duct-tape to hold together an obsolete industrial-era educational system”.
Speaking of AI in our learning ecosystems, also see:
Usage Guidelines for AI Generative Tools at College Unbound These guidelines were created and reviewed by College Unbound students in Spring 2023 with the support of Lance Eaton, Director of Faculty Development & Innovation. The students include S. Fast, K. Linder-Bey, Veronica Machado, Erica Maddox, Suleima L., Lora Roy.
A recent study has found that scientific citations generated by ChatGPT often do not correspond to real academic work. The study, published in the Canadian Psychological Association’s Mind Pad, found that “false citation rates” across various psychology subfields ranged from 6% to 60%. Surprisingly, these fabricated citations feature elements such as legitimate researchers’ names and properly formatted digital object identifiers (DOIs), which could easily mislead both students and researchers.
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MacDonald found that a total of 32.3% of the 300 citations generated by ChatGPT were hallucinated. Despite being fabricated, these hallucinated citations were constructed with elements that appeared legitimate — such as real authors who are recognized in their respective fields, properly formatted DOIs, and references to legitimate peer-reviewed journals.
“The conversation about AI bias is broadening,” said psychologist Tara Behrend, PhD, a professor at Michigan State University’s School of Human Resources and Labor Relations who studies human-technology interaction and spoke at CES about AI and privacy. “Agencies and various academic stakeholders are really taking the role of psychology seriously.”
The NY State Bar Association (NYSBA) Task Force on Artificial Intelligence has issued a nearly 80 page report (Report) and recommendations on the legal, social and ethical impact of artificial intelligence (AI) and generative AI on the legal profession. This detailed Report also reviews AI-based software, generative AI technology and other machine learning tools that may enhance the profession, but which also pose risks for individual attorneys’ understanding of new, unfamiliar technology, as well as courts’ concerns about the integrity of the judicial process. It also makes recommendations for NYSBA adoption, including proposed guidelines for responsible AI use. This Report is perhaps the most comprehensive report to date by a state bar association. It is likely this Report will stimulate much discussion.
For those of you who want the “Cliff Notes” version of this report, here is a table that summarizes by topic the various rules mentioned and a concise summary of the associated guidance.
Abigail is a lecturer on two of the Law micro-credentials at University of Galway – Lawyering Technology & Innovation and Law & Analytics. Micro-credentials are short, flexible courses designed to fit around your busy life! They are designed in collaboration with industry to meet specific skills needs and are accredited by leading Irish universities.
The potential for AI’s impact is broad, as it has the ability to impact every aspect of human life, from home to work. It will impact our relationships to everything and everyone in our world. The implications for generative AI on the legal system, from how we deliver legal services to how we deliver justice, will be just as far reaching.
[N]ow we face the latest technological frontier: artificial intelligence (AI).… Law professors report with both awe and angst that AI apparently can earn Bs on law school assignments and even pass the bar exam. Legal research may soon be unimaginable without it. AI obviously has great potential to dramatically increase access to key information for lawyers and non-lawyers alike. But just as obviously it risks invading privacy interests and dehumanizing the law.
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When you can no longer sell the time it takes to achieve a client’s outcome, then you must sell the outcome itself and the client’s experience of getting there. That completely changes the dynamics of what law firms are all about.
Georgia State University College of Law faculty understand this need and designed the Legal Analytics & Innovation Initiative (LAII) to equip students with the competitive skills desired by law firms and other companies that align with the emerging technological environment.
“As faculty, we realized we need to be forward-thinking about incorporating technology into our curriculum. Students must understand new areas of law that arise from or are significantly altered by technological advances, like cybersecurity, privacy and AI. They also must understand how these advances change the practice of law,” said Kris Niedringhaus, associate dean for Law Library, Information Services, Legal Technology & Innovation.
As the legal profession continues to navigate the waters of digital transformation, the importance of use cases stands as a beacon guiding the journey. They are the litmus test for the practical value of technology, ensuring that innovations not only dazzle with potential but also deliver tangible benefits. In the quest to integrate AI and legal technology into legal practice, use cases are not just important but indispensable.
The future of legal tech is not about technology for technology’s sake. It’s about thoughtful, purpose-driven innovation that enhances the practice of law, improves client outcomes, and upholds the principles of justice. Use cases are the roadmap for this future, charting a course for technology that is meaningful, impactful, and aligned with the noble pursuit of law.
These librarians, entrepreneurs, lawyers and technologists built the world where artificial intelligence threatens to upend life and law as we know it – and are now at the forefront of the battles raging within.
… To create this first-of-its-kind guide, we cast a wide net with dozens of leaders in this area, took submissions, consulted with some of the most esteemed gurus in legal tech. We also researched the cases most likely to have the biggest impact on AI, unearthing the dozen or so top trial lawyers tapped to lead the battles. Many of them bring copyright or IP backgrounds and more than a few are Bay Area based. Those denoted with an asterisk are members of our Hall of Fame. .
descrybe.ai, a year-old legal research startup focused on using artificial intelligence to provide free and easy access to court opinions, has completed its goal of creating AI-generated summaries of all available state supreme and appellate court opinions from throughout the United States.
descrybe.ai describes its mission as democratizing access to legal information and leveling the playing field in legal research, particularly for smaller-firm lawyers, journalists, and members of the public.
As the FlexOS research study “Generative AI at Work” concluded based on a survey amongst knowledge workers, ChatGPT reigns supreme. … 2. AI Tool Usage is Way Higher Than People Expect – Beating Netflix, Pinterest, Twitch. As measured by data analysis platform Similarweb based on global web traffic tracking, the AI tools in this list generate over 3 billion monthly visits.
With 1.67 billion visits, ChatGPT represents over half of this traffic and is already bigger than Netflix, Microsoft, Pinterest, Twitch, and The New York Times.
Something unusual is happening in America. Demand for electricity, which has stayed largely flat for two decades, has begun to surge.
Over the past year, electric utilities have nearly doubled their forecasts of how much additional power they’ll need by 2028 as they confront an unexpected explosion in the number of data centers, an abrupt resurgence in manufacturing driven by new federal laws, and millions of electric vehicles being plugged in.
The tumult could seem like a distraction from the startup’s seemingly unending march toward AI advancement. But the tension, and the latest debate with Musk, illuminates a central question for OpenAI, along with the tech world at large as it’s increasingly consumed by artificial intelligence: Just how open should an AI company be?
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The meaning of the word “open” in “OpenAI” seems to be a particular sticking point for both sides — something that you might think sounds, on the surface, pretty clear. But actual definitions are both complex and controversial.
In partnership with the National Cancer Institute, or NCI, researchers from the Department of Energy’s Oak Ridge National Laboratory and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.
The early vibrations of AI have already been shaking the newsroom. One downside of the new technology surfaced at CNET and Sports Illustrated, where editors let AI run amok with disastrous results. Elsewhere in news media, AI is already writing headlines, managing paywalls to increase subscriptions, performing transcriptions, turning stories in audio feeds, discovering emerging stories, fact checking, copy editing and more.
Felix M. Simon, a doctoral candidate at Oxford, recently published a white paper about AI’s journalistic future that eclipses many early studies. Swinging a bat from a crouch that is neither doomer nor Utopian, Simon heralds both the downsides and promise of AI’s introduction into the newsroom and the publisher’s suite.
Unlike earlier technological revolutions, AI is poised to change the business at every level. It will become — if it already isn’t — the beginning of most story assignments and will become, for some, the new assignment editor. Used effectively, it promises to make news more accurate and timely. Used frivolously, it will spawn an ocean of spam. Wherever the production and distribution of news can be automated or made “smarter,” AI will surely step up. But the future has not yet been written, Simon counsels. AI in the newsroom will be only as bad or good as its developers and users make it.
We proposed EMO, an expressive audio-driven portrait-video generation framework. Input a single reference image and the vocal audio, e.g. talking and singing, our method can generate vocal avatar videos with expressive facial expressions, and various head poses, meanwhile, we can generate videos with any duration depending on the length of input video.
New experimental work from Adobe Research is set to change how people create and edit custom audio and music. An early-stage generative AI music generation and editing tool, Project Music GenAI Control allows creators to generate music from text prompts, and then have fine-grained control to edit that audio for their precise needs.
“With Project Music GenAI Control, generative AI becomes your co-creator. It helps people craft music for their projects, whether they’re broadcasters, or podcasters, or anyone else who needs audio that’s just the right mood, tone, and length,” says Nicholas Bryan, Senior Research Scientist at Adobe Research and one of the creators of the technologies.
There’s a lot going on in the world of generative AI, but maybe the biggest is the increasing number of copyright lawsuits being filed against AI companies like OpenAI and Stability AI. So for this episode, we brought on Verge features editor Sarah Jeong, who’s a former lawyer just like me, and we’re going to talk about those cases and the main defense the AI companies are relying on in those copyright cases: an idea called fair use.
The FCC’s war on robocalls has gained a new weapon in its arsenal with the declaration of AI-generated voices as “artificial” and therefore definitely against the law when used in automated calling scams. It may not stop the flood of fake Joe Bidens that will almost certainly trouble our phones this election season, but it won’t hurt, either.
The new rule, contemplated for months and telegraphed last week, isn’t actually a new rule — the FCC can’t just invent them with no due process. Robocalls are just a new term for something largely already prohibited under the Telephone Consumer Protection Act: artificial and pre-recorded messages being sent out willy-nilly to every number in the phone book (something that still existed when they drafted the law).
EIEIO…Chips Ahoy!— from dashmedia.co by Michael Moe, Brent Peus, and Owen Ritz
Here Come the AI Worms — from wired.com by Matt Burgess Security researchers created an AI worm in a test environment that can automatically spread between generative AI agents—potentially stealing data and sending spam emails along the way.
Now, in a demonstration of the risks of connected, autonomous AI ecosystems, a group of researchers have created one of what they claim are the first generative AI worms—which can spread from one system to another, potentially stealing data or deploying malware in the process. “It basically means that now you have the ability to conduct or to perform a new kind of cyberattack that hasn’t been seen before,” says Ben Nassi, a Cornell Tech researcher behind the research.
Last month, I discussed a GPT that I had created around enhancing prompts. Since then, I have been actively using my Prompt Enhancer GPT to much more effective outputs. Last week, I did a series of mini-talks on generative AI in different parts of higher education (faculty development, human resources, grants, executive leadership, etc) and structured it as “5 tips”. I included a final bonus tip in all of them—a tip that I heard from many afterwards was probably the most useful tip—especially because you can only access the Prompt Enhancer GPT if you are paying for ChatGPT.
Effectively integrating generative AI into higher education requires policy development, cross-functional engagement, ethical principles, risk assessments, collaboration with other institutions, and an exploration of diverse use cases.
Creating Guidelines for the Use of Gen AI Across Campus— from campustechnology.com by Rhea Kelly The University of Kentucky has taken a transdisciplinary approach to developing guidelines and recommendations around generative AI, incorporating input from stakeholders across all areas of the institution. Here, the director of UK’s Center for the Enhancement of Learning and Teaching breaks down the structure and thinking behind that process.
That resulted in a set of instructional guidelines that we released in August of 2023 and updated in December of 2023. We’re also looking at guidelines for researchers at UK, and we’re currently in the process of working with our colleagues in the healthcare enterprise, UK Healthcare, to comb through the additional complexities of this technology in clinical care and to offer guidance and recommendations around those issues.
My experiences match with the results of the above studies. The second study cited above found that 83% of those students who haven’t used AI tools are “not interested in using them,” so it is no surprise that many students have little awareness of their nature. The third study cited above found that, “apart from 12% of students identifying as daily users,” most students’ use cases were “relatively unsophisticated” like summarizing or paraphrasing text.
For those of us in the AI-curious bubble, we need to continually work to stay current, but we also need to recognize that what we take to be “common knowledge” is far from common outside of the bubble.
Despite general familiarity, however, technical knowledge shouldn’t be assumed for district leaders or others in the school community. For instance, it’s critical that any materials related to AI not be written in “techy talk” so they can be clearly understood, said Ann McMullan, project director for the Consortium for School Networking’s EmpowerED Superintendents Initiative.
To that end, CoSN, a nonprofit that promotes technological innovation in K-12, has released an array of AI resources to help superintendents stay ahead of the curve, including a one-page explainer that details definitions and guidelines to keep in mind as schools work with the emerging technology.
As college costs soar and demand for skilled labor rises, programs that prepare students for well-paid work are gaining popularity. About 85 percent of high school graduates in 2019 had taken at least one course in career and technical education, or CTE. In 2018, Congress increased annual funding for CTE, which now exceeds $1.4 billion. And in 2022, 36 states enacted policies promoting career training for high schoolers, college students, and adults, according to Advance CTE, a professional organization for state CTE leaders.
Yet many states struggle to answer a basic question: Is career education working?
Why are we doing this work? Over the past two years, the U.S. Department of Education has been committed to maintaining an ongoing conversation with educators, students, researchers, developers — and the educational community at large — related to the continuous progress of Artificial Intelligence (AI) development and its implications for teaching and learning.
Many educators are seeking resources clarifying what AI is and how it will impact their work and their students. Similarly, developers of educational technology (“edtech”) products seek guidance on what guardrails exist that can support their efforts. After the release of our May 2023 report Artificial Intelligence and the Future of Teaching and Learning, we heard the desire for more.
Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and work while maintaining a sense of caution for the vast array of risks AI-powered technologies pose. To aid in these efforts, we present this inaugural EDUCAUSE AI Landscape Study, in which we summarize the higher education community’s current sentiments and experiences related to strategic planning and readiness, policies and procedures, workforce, and the future of AI in higher education.
Educational administrators should not worry about every AI development, but should, instead focus on the big picture, as those big picture changes will change the entire world and the educational system.
AI and related technologies (robotics, synthetic biology, and brain-computer interfaces) will continue to impact society and the entire educational system over the next 10 years. This impact on the system will be greater than anything that has happened over the last 100 years, including COVID-19, as COVID-19 eventually ended and the disruptive force of these technologies will only continue to develop.
AI is the bull in the China Shop, redefining the world and the educational system. Students writing a paper with AI is barely a poke in the educational world relative to what is starting to happen (active AI teachers and tutors; AI assessment; AI glasses; immersive learning environments; young students able to start their own business with AI tools; AIs replacing and changing jobs; deep voice and video fakes; intelligence leveling; individualized instruction; interactive and highly intelligent computers; computers that can act autonomously; and more).
verb
(of artificial intelligence) to produce false information contrary to the intent of the user and present it as if true and factual. Example: When chatbots hallucinate, the result is often not just inaccurate but completely fabricated.
Soon, every employee will be both AI builder and AI consumer— from zdnet.com by Joe McKendrick, via Robert Gibson on LinkedIn “Standardized tools and platforms as well as advanced low- or no-code tech may enable all employees to become low-level engineers,” suggests a recent report.
The time could be ripe for a blurring of the lines between developers and end-users, a recent report out of Deloitte suggests. It makes more business sense to focus on bringing in citizen developers for ground-level programming, versus seeking superstar software engineers, the report’s authors argue, or — as they put it — “instead of transforming from a 1x to a 10x engineer, employees outside the tech division could be going from zero to one.”
Along these lines, see:
TECH TRENDS 2024 — from deloitte.com Six emerging technology trends demonstrate that in an age of generative machines, it’s more important than ever for organizations to maintain an integrated business strategy, a solid technology foundation, and a creative workforce.
The ruling follows a similar decision denying patent registrations naming AI as creators.
The UK Supreme Court ruled that AI cannot get patents, declaring it cannot be named as an inventor of new products because the law considers only humans or companies to be creators.
The New York Times sued OpenAI and Microsoft for copyright infringement on Wednesday, opening a new front in the increasingly intense legal battle over the unauthorized use of published work to train artificial intelligence technologies.
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The suit does not include an exact monetary demand. But it says the defendants should be held responsible for “billions of dollars in statutory and actual damages” related to the “unlawful copying and use of The Times’s uniquely valuable works.” It also calls for the companies to destroy any chatbot models and training data that use copyrighted material from The Times.
On this same topic, also see:
? The historic NYT v. @OpenAI lawsuit filed this morning, as broken down by me, an IP and AI lawyer, general counsel, and longtime tech person and enthusiast.
Tl;dr – It’s the best case yet alleging that generative AI is copyright infringement. Thread. ? pic.twitter.com/Zqbv3ekLWt
ChatGPT and Other Chatbots
The arrival of ChatGPT sparked tons of new AI tools and changed the way we thought about using a chatbot in our daily lives.
Chatbots like ChatGPT, Perplexity, Claude, and Bing Chat can help content creators by quickly generating ideas, outlines, drafts, and full pieces of content, allowing creators to produce more high-quality content in less time.
These AI tools boost efficiency and creativity in content production across formats like blog posts, social captions, newsletters, and more.
Microsoft is getting ready to upgrade its Surface lineup with new AI-enabled features, according to a report from Windows Central. Unnamed sources told the outlet the upcoming Surface Pro 10 and Surface Laptop 6 will come with a next-gen neural processing unit (NPU), along with Intel and Arm-based options.
With the AI-assisted reporter churning out bread and butter content, other reporters in the newsroom are freed up to go to court, meet a councillor for a coffee or attend a village fete, says the Worcester News editor, Stephanie Preece.
“AI can’t be at the scene of a crash, in court, in a council meeting, it can’t visit a grieving family or look somebody in the eye and tell that they’re lying. All it does is free up the reporters to do more of that,” she says. “Instead of shying away from it, or being scared of it, we are saying AI is here to stay – so how can we harness it?”
This year, I watched AI change the world in real time.
From what happened, I have no doubts that the coming years will be the most transformative period in the history of humankind.
Here’s the full timeline of AI in 2023 (January-December):
What to Expect in AI in 2024 — from hai.stanford.edu by Seven Stanford HAI faculty and fellows predict the biggest stories for next year in artificial intelligence.
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