Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required. Teachable Machine is a web-based tool that makes creating machine learning models fast, easy, and accessible to everyone.
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Mastodon and WordPress: 8 Ways to Make Them Work Together
Mastodon and WordPress seem like a pretty good match for each other. Both are open source platforms based on similar philosophies: To give users a way to build their own piece of online real estate and help democratizing publishing. Considering their commonalities, wouldn’t it make sense to find ways for the two platforms to work more closely together and integrate with each other? That’s exactly what we want to explore in this post. The following article talks about how you can use your WordPress site to verify your account on Mastodon, how to display Mastodon content in WordPress, automatically post your WordPress content to the social platform, and more.
I pulled together my notes from the last 9 months of doing sessions on Generative AI and compiled them yesterday. Shout out to Nick Baker for adding some things, editing some things and overall making it more nicely to readish. Every time I approach this issue I keep thinking that so much more and probably a lot less should be said. Every time I meet with a group of faculty or teachers on this issue we go through a few phases Boredom, kinda, as I explain what generative AI is. A bit of ‘yeah, this doesn’t apply to me, my courses…’ A demonstration where I take their actual assignments and complete them in 30 seconds using generative AI sadness. And then, hope. Hope when they realize that the only solution is good teaching. That is in no way meant to reflect a statement about all faculty or teachers. I only really get the ones who care about teaching and their students. Anyway… this is what I’ve been telling them.
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Ten simple rules to host an inclusive conference | PLOS Computational Biology
Conferences are spaces to meet and reconnect with members from a specific community, learn about advances in the field, and share recent contributions. A good conference experience can make a difference in the professional development of the participants and create long-lasting collaborations and opportunities. However, opportunities for participating in conferences are not equally available for all. Many academic and tech conferences have been spaces that reproduce systemic inequalities, by failing to overcome the barriers for participation and giving more opportunities to the most privileged individuals (typically white people from high-income backgrounds, prestigious institutions, power native English speakers with no disabilities). Here, we present a set of 10 rules based on the lessons we learned before, during, and after the organization of the useR! conference. The rules were first drafted by the core team and the members of the diversity, accessibility, and inclusion team of useR! 2021 who wrote their own 10 simple rules (see S1 Text) based on the collective experience of organizing the conference and fueled by personal experiences and literature review. A final list was proposed and then discussed and reviewed by the rest of the coauthors. The rules are organized in 3 sections: 3 foundation rules, 6 design rules, and a continuity rule (Fig 1). The foundation rules comprise key elements to conceive the work on diversity and inclusion in any conference. Rule 1 is about setting a vision of diversity and inclusion that should guide all the efforts and decision-making in the organization. Rule 2 focuses on how to create a safe and welcoming environment for all the attendees. Rule 3 highlights the importance of starting with an inclusive and diverse organizing team and provides tips on work dynamics. The design rules focus on weaving inclusion into the conference design process. In Rule 4, we introduce multiple ways to counteract bias in the conference program (keynotes, program committee, abstract selection, and thematic sessions). Rule 5 provides advice for designing an inclusive online component in virtual and hybrid conferences. Rule 6 focuses on accessibility practices to include people with disabilities. In Rule 7, we provide suggestions to account for linguistic diversity. Rule 8 offers tips for developing an inclusive communication strategy. In Rule 9, we address budgeting for inclusive practices and helping participants with affordable registration costs, scholarships, and other forms of financial support. Finally, Rule 10, the continuity rule, emphasizes the importance of self-assessment and advocates for making the conference part of a long-term commitment to inclusion and for passing the torch to future organizers.
Twitter: Far more than “what I had for lunch today” | Ars Technica
Twitter: it's an extremely simple and social microblogging tool where users can post 140 characters at a time about what's going on or in reply to someone else. While that might not sound ridiculous on its face, the service is sometimes written off as nothing more than a silly hobby or fad—narcissism masquerading as a "what I had for lunch today" diary. But people also use Twitter for much more interesting things, and we'd like to be your tour guide through the less-explored portions of the Twittersphere (yep, another "sphere" for you to be aware of). Welcome to Twitter's more interesting, more useful, and more innovative side.
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What Kind of Mind Does ChatGPT Have? | The New Yorker
Large language models seem startlingly intelligent. But what’s really happening under the hood? What kinds of new minds are being released into our world? The response to ChatGPT, and to the other chatbots that have followed in its wake, has often suggested that they are powerful, sophisticated, imaginative, and possibly even dangerous. But is that really true? If we treat these new artificial-intelligence tools as mysterious black boxes, it’s impossible to say. Only by taking the time to investigate how this technology actually works—from its high-level concepts down to its basic digital wiring—can we understand what we’re dealing with. We send messages into the electronic void, and receive surprising replies. But what, exactly, is writing back? If you want to understand a seemingly complicated technology, it can be useful to imagine inventing it yourself. Suppose, then, that we want to build a ChatGPT-style program—one capable of engaging in natural conversation with a human user. A good place to get started might be “A Mathematical Theory of Communication,” a seminal paper published in 1948 by the mathematician Claude Shannon. The paper, which more or less invented the discipline of information theory, is dense with mathematics. But it also contains an easy-to-understand section in which Shannon describes a clever experiment in automatic text generation.
We believe that a future of consenting data will benefit both AI development and the people it is trained on. Have I Been Trained? and our API have helped artists opt-out over 1.4B images from public training datasets. Holly+ is the first project to experiment with consensual interactions around an artist AI model.
Canadian Settlement in Action: History and Future – Simple Book Publishing
The eight chapters of this book encapsulate the past, present, and future of Canadian immigration and settlement. The topics, in part, cover the history of immigration to Canada through an objective lens that allows readers to learn what transpired with the settlement of specific ethnic groups, as well as address Canada’s current policies and approaches to immigration. This leads to an exploration of the challenges that newcomers to Canada and the settlement sector are encountering today. Readers and learners of settlement studies will embark on a journey of self-reflection throughout this book as they engage in many activities, quizzes, and interactions which may be self-directed or instructor led.
Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images. When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. This pervasive and powerful form of artificial intelligence is changing every industry. Here’s what you need to know about the potential and limitations of machine learning and how it’s being used.
OpenAI's Whisper is another case study in Colonisation
The Māori language has suffered over a century of harm. It was once forbidden to speak te reo Māori. Many Māori living today have grandparents who were beaten in school for speaking their native language. Despite a growing appetite to learn te reo Māori, New Zealand is not short of racists demanding people "Speak English!" or boycotting companies like Whitakers for using te reo Māori on their packaging. The oppression of indigenous languages is why we have concerns about non-indigenous groups building tools like Whisper. A few of our data scientists tried Whisper on te reo Māori videos from YouTube. Their initial reaction was, "Wow it works!" A more critical assessment of Whisper by our Māori data experts saw that it sort of worked but it was terrible. Still, this is concerning, that a non-Māori organisation thought it was okay to create a Māori speech recognition model and open it to the public.
Students are cheating. Professors are panicking. The system is unravelling. Scenes from the AI revolution on campus
AI has made it easy for post-secondary students to fake their way to a degree. They argue that ChatGPT is just another study tool. Schools say it spells the end of university education as we know it. Maybe that’s not a bad thing. When ChatGPT first appeared, instructors and administrators saw the potential for academic grift on a massive scale, an existential threat to the norms of their institutions—and perhaps to us all. But Abhinash wasn’t convinced. His teachers had once said similar things about calculators, and anyway, people always freak out when a new technology hits the market. I teach courses on long-form journalism at the University of Toronto, and over the past year, I’ve witnessed the ChatGPT dilemma up close. Every instructor knows that the technology is a big deal. But should universities fight it with everything they’ve got, or can they somehow live with it? Is it a game ender—or just a game changer?
Why Chatbots Are Not the Future (Amelia Wattenberger)
Unfortunately for the countless hapless people I've talked to in the past few months, this was inexorable. Ever since ChatGPT exploded in popularity, my inner designer has been bursting at the seams. To save future acquaintances, I come to you today: because you've volunteered to be here with me, can we please discuss a few reasons chatbots are not the future of interfaces.
Gift a tree in someone’s name as a heartfelt and everlasting way to show your appreciation. These trees will help impact projects around the world that need some love!
The Truth About Gumption Traps. You may be in one right now. | by Devin Martin | Thrive Global | Medium
Have you ever been caught in a gumption trap? I am going to guess that you have. How did you handle it? A better question might be, did you even recognize that you had fallen into one? Put simply, gumption is your initiative, your energy to move forward and your ability to do so with commonsense and shrewdness. Gumption is the drive that pushes you to start a project and also the focus, clarity and motivation that carries you through until you finish it. When your gumption is high you will find yourself scribbling feverishly, drawing up plans, writing with acute focus, speaking with confidence and full of insights. When gumption is high you work through a project with enthusiasm and a sense of possibility. States of extremely high gumption are akin to FLOW states. In his 1974 novel Zen and the Art of Motorcycle Maintenance, Robert Pirsig coined the term gumption trap.
Generative AI in Teaching and Learning — UVA Teaching Hub
Generative artificial intelligence (AI) is transforming teaching and learning in higher education. How can you, as an instructor, leverage these tools effectively and mitigate potential challenges? This gallery is designed to support you in navigating the generative AI landscape in higher education, from what generative AI is and how you can learn more about it to what using it could look like within and across disciplines.
Looking back at the history of neural networks tells us something important about the automated decisions that define our present or those that will have a possibly more profound impact in the future. Their presence also tells us that we are likely to understand the decisions and impacts of AI even less over time. These systems are not simply black boxes, they are not just hidden bits of a system that can't be seen or understood. There is a good chance that the greater the impact that artificial intelligence comes to have in our lives the less we will understand how or why
My goal today is to provide practical, actionable advice for getting the most out of Large Language Models—both for personal productivity but also as a platform that you can use to build things that you couldn’t build without them. There is an enormous amount of hype and bluster in the AI world. I am trying to avoid that and just give you things that actually work and do interesting stuff.
Pluralistic: “Open” “AI” isn’t (18 August 2023) – Pluralistic: Daily links from Cory Doctorow
"Open AI" is a wordgame that exploits the malleability of "open," but also the ambiguity of the term "AI": "a grab bag of approaches, not… a technical term of art, but more … marketing and a signifier of aspirations." Hitching this vague term to "open" creates all kinds of bait-and-switch opportunities.
Recent Keynote: The Questions to Be AIsking (by Lance Eaton)
So I recently did a keynote talk at Husson University. As is my open-access practice when I get such opportunities, I'm not a fan of making something that is just used once... I also did something different with the resources and did an annotated slide deck with my talk and resources https://bit.ly/AI-HussonU We’re here to talk about generative AI and education and I promise you, none of this presentation was created by generative AI but there are times when I’ve used it and will make sure that’s evident.
Despite their importance and ubiquity, annual meetings faced significant criticism even before the COVID-19 pandemic, much of it focused on exposing the limitations of in-person conferences. Criticism of in-person conferences can be loosely grouped into four categories: cost, access, culture, and climate. Graduate students and early career scholars, contingent faculty, and faculty from under-resourced institutions noted that the high cost of attendance prohibited their attendance and exacerbated inequities within higher education.[4] Minoritized scholars described facing micro-aggressions, snubs, and harassment that made the boundaries and fault lines within scholarly communities all too clear while scholars with disabilities often felt excluded entirely from participating.[5] Environmentally conscious scholars have made urgent appeals for reducing the climate footprints of in-person meetings.[6] All these criticisms mirrored frustrations voiced on social media and elsewhere by scholars who believed their field’s scholarly society was stuck in its ways, insular, and inattentive to the concerns of early career scholars and with stagnant or declining society membership.[7] For the most part, these criticisms resulted in minimal changes to the conference status quo, and the overwhelming majority of conferences remained strictly in-person events.
No original work here, just summarizing how astonishingly easy it is to install and run an LLM on your own computer using Simon Willison’s fantastic llm tool. Simon has been on an absolute tear with tooling for LLMs lately, amazing work. A bit hard to keep up but between his Mastodon feed and his blog I can sort of follow along. See his list of plugins for a sense of how broad the tool is. I think this current moment in AI development is amazing and fascinating. And it’s great so many models can be downloaded for free experimentation. Simon’s tool makes this easily accessible.
At the Open Education Network, we value the power of open pedagogy to transform learning to be more equitable, inclusive, and sustainable. We have created this portal to support your efforts in open pedagogy. You can browse case studies/renewable assignments and student work product by discipline, search by keywords, or find teaching and learning resources to further your open pedagogy journey. We’re hoping to create a robust directory of open pedagogy resources, so please consider submitting your own case studies/renewable assignment, student work product, or teaching and learning resource. Thank you for your support of these efforts and for your continued partnership in making education more equitable, inclusive, and sustainable.
3 ways to get ChatGPT to write like you | Descript
Everyone wants their writing to be unique, so constantly fixing generic AI outputs to match your voice can be frustrating. The question is, can we speed up this process? I turned to best-selling professional ghostwriter Jessa Gamble to bring her expertise to this challenging task, since it's her job, as she describes it, to "make that unique voice come through in the writing." We tried three different methods to generate text in a specific voice – and evaluated how well they worked. Here are our results.
Why This AI Moment May Be the Real Deal — The New Atlantis
Call it AI’s man-behind-the-curtain effect: What appear at first to be dazzling new achievements in artificial intelligence routinely lose their luster and seem limited, one-off, jerry-rigged, with nothing all that impressive happening behind the scenes aside from sweat and tears, certainly nothing that deserves the name “intelligence” even by loose analogy. So what’s different now? What follows in this essay is an attempt to contrast some of the most notable features of the new transformer paradigm (the T in ChatGPT) with what came before. It is an attempt to articulate why the new AIs that have garnered so much attention over the past year seem to defy some of the major lines of skepticism that have rightly applied to past eras — why this AI moment might, just might, be the real deal. It is too early to say that the new AI class is an inherently antihuman technological paradigm, as social media has proven itself to be. But it is not too early to suspect that AIs will dwarf social media in their power to disrupt modern life. If that is so, we had better learn some new and unfamiliar ways of interrogating this technology, and fast. Whatever these entities are — they’re here.
Anthropic cracks open the black box to see how AI comes up with the stuff it says
The researchers were able to trace outputs to neural network nodes and show influence patterns through statistical analysis. Anthropic, the artificial intelligence (AI) research organization responsible for the Claude large language model (LLM), recently published landmark research into how and why AI chatbots choose to generate the outputs they do. At the heart of the team’s research lies the question of whether LLM systems such as Claude, OpenAI’s ChatGPT and Google’s Bard rely on “memorization” to generate outputs or if there’s a deeper relationship between training data, fine-tuning and what eventually gets outputted.
