From DSC: And regarding this weekend, what an incredible waste of money to put the military on display (for his own birthday). This smacks of what arrogant dictators do. It’s big-time ugly.
If our justice system had done its job, this arrogant lawbreaker and convicted criminal would be in jail right now. No wonder he has no regard for the legal system, the Constitution, or the law — those things don’t serve his interests. They impede his interests. And thank God for that! In fact, may true leaders rise up within the Legislative and Judicial Branches of our government. The latter is our best chance of keeping our democracy, as the Republican Party has ceded all of their power — and responsibility — over to Donald Trump. They are not leaders in any sense of the word.
But whatever happens, ultimately, there WILL be justice.
Is America being humbled? Or is it being destroyed?
In President Trump’s first term, the Pentagon opposed his desire for a military parade in Washington, wanting to keep the armed forces out of politics.
But in Mr. Trump’s second term, that guardrail has vanished. There will be a parade this year, and on the president’s 79th birthday, no less.
The current plan involves a tremendous scene in the center of Washington: 28 M1A1 Abrams tanks (at 70 tons each for the heaviest in service); 28 Stryker armored personnel carriers; more than 100 other vehicles; a World War II-era B-25 bomber; 6,700 soldiers; 50 helicopters; 34 horses; two mules; and a dog.
Here are some incredibly powerful numbers from Mary Meeker’s AI Trends report, which showcase how artificial intelligence as a tech is unlike any other the world has ever seen.
AI took only three years to reach 50% user adoption in the US; mobile internet took six years, desktop internet took 12 years, while PCs took 20 years.
ChatGPT reached 800 million users in 17 months and 100 million in only two months, vis-à-vis Netflix’s 100 million (10 years), Instagram (2.5 years) and TikTok (nine months).
ChatGPT hit 365 billion annual searches in two years (2024) vs. Google’s 11 years (2009)—ChatGPT 5.5x faster than Google.
Above via Mary Meeker’s AI Trend-Analysis — from getsuperintel.com by Kim “Chubby” Isenberg How AI’s rapid rise, efficiency race, and talent shifts are reshaping the future.
The TLDR
Mary Meeker’s new AI trends report highlights an explosive rise in global AI usage, surging model efficiency, and mounting pressure on infrastructure and talent. The shift is clear: AI is no longer experimental—it’s becoming foundational, and those who optimize for speed, scale, and specialization will lead the next wave of innovation.
The Rundown: Meta aims to release tools that eliminate humans from the advertising process by 2026, according to a report from the WSJ — developing an AI that can create ads for Facebook and Instagram using just a product image and budget.
The details:
Companies would submit product images and budgets, letting AI craft the text and visuals, select target audiences, and manage campaign placement.
The system will be able to create personalized ads that can adapt in real-time, like a car spot featuring mountains vs. an urban street based on user location.
The push would target smaller companies lacking dedicated marketing staff, promising professional-grade advertising without agency fees or skillset.
Advertising is a core part of Mark Zuckerberg’s AI strategy and already accounts for 97% of Meta’s annual revenue.
Why it matters: We’re already seeing AI transform advertising through image, video, and text, but Zuck’s vision takes the process entirely out of human hands. With so much marketing flowing through FB and IG, a successful system would be a major disruptor — particularly for small brands that just want results without the hassle.
How To Get Hired During the AI Apocalypse — from kathleendelaski.substack.com by Kathleen deLaski And other discussions to have with your kids on the way to college graduation
A less temporary, more existential threat to the four year degree: AI could hollow out the entry level job market for knowledge workers (i.e. new college grads). And if 56% of families were saying college “wasn’t worth it” in 2023,(WSJ), what will that number look like in 2026 or beyond? The one of my kids who went to college ended up working in a bike shop for a year-ish after graduation. No regrets, but it came as a shock to them that they weren’t more employable with their neuroscience degree.
A colleague provided a great example: Her son, newly graduated, went for a job interview as an entry level writer last month and he was asked, as a test, to produce a story with AI and then use that story to write a better one by himself. He would presumably be judged on his ability to prompt AI and then improve upon its product. Is that learning how to DO? I think so. It’s using AI tools to accomplish a workplace task.
Also relevant in terms of the job search, see the following gifted article:
‘We Are the Most Rejected Generation’— from nytimes.com by David Brooks; gifted article David talks admissions rates for selective colleges, ultra-hard to get summer internships, a tough entry into student clubs, and the job market.
Things get even worse when students leave school and enter the job market. They enter what I’ve come to think of as the seventh circle of Indeed hell. Applying for jobs online is easy, so you have millions of people sending hundreds of applications each into the great miasma of the internet, and God knows which impersonal algorithm is reading them. I keep hearing and reading stories about young people who applied to 400 jobs and got rejected by all of them.
It seems we’ve created a vast multilayered system that evaluates the worth of millions of young adults and, most of the time, tells them they are not up to snuff.
Many administrators and faculty members I’ve spoken to are mystified that students would create such an unforgiving set of status competitions. But the world of competitive exclusion is the world they know, so of course they are going to replicate it.
And in this column I’m not even trying to cover the rejections experienced by the 94 percent of American students who don’t go to elite schools and don’t apply for internships at Goldman Sachs. By middle school, the system has told them that because they don’t do well on academic tests, they are not smart, not winners. That’s among the most brutal rejections our society has to offer.
A wide range of roles can or will quickly be replaced with AI, including inside sales representatives, customer service representatives, junior lawyers, junior accountants, and physicians whose focus is diagnosis.
Dario Amodei — CEO of Anthropic, one of the world’s most powerful creators of artificial intelligence — has a blunt, scary warning for the U.S. government and all of us:
AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years, Amodei told us in an interview from his San Francisco office.
Amodei said AI companies and government need to stop “sugar-coating” what’s coming: the possible mass elimination of jobs across technology, finance, law, consulting and other white-collar professions, especially entry-level gigs.
Why it matters: Amodei, 42, who’s building the very technology he predicts could reorder society overnight, said he’s speaking out in hopes of jarring government and fellow AI companies into preparing — and protecting — the nation.
We are entering a new reality—one in which AI can reason and solve problems in remarkable ways. This intelligence on tap will rewrite the rules of business and transform knowledge work as we know it. Organizations today must navigate the challenge of preparing for an AI-enhanced future, where AI agents will gain increasing levels of capability over time that humans will need to harness as they redesign their business. Human ambition, creativity, and ingenuity will continue to create new economic value and opportunity as we redefine work and workflows.
As a result, a new organizational blueprint is emerging, one that blends machine intelligence with human judgment, building systems that are AI-operated but human-led. Like the Industrial Revolution and the internet era, this transformation will take decades to reach its full promise and involve broad technological, societal, and economic change.
To help leaders understand how knowledge work will evolve, Microsoft analyzed survey data from 31,000 workers across 31 countries, LinkedIn labor market trends, and trillions of Microsoft 365 productivity signals. We also spoke with AI-native startups, academics, economists, scientists, and thought leaders to explore what work could become. The data and insights point to the emergence of an entirely new organization, a Frontier Firm that looks markedly different from those we know today. Structured around on-demand intelligence and powered by “hybrid” teams of humans + agents, these companies scale rapidly, operate with agility, and generate value faster.
Frontier Firms are already taking shape, and within the next 2–5 years we expect that every organization will be on their journey to becoming one. 82% of leaders say this is a pivotal year to rethink key aspects of strategy and operations, and 81% say they expect agents to be moderately or extensively integrated into their company’s AI strategy in the next 12–18 months. Adoption is accelerating: 24% of leaders say their companies have already deployed AI organization-wide, while just 12% remain in pilot mode.
The time to act is now. The question for every leader and employee is: how will you adapt?
Anthropic expects AI-powered virtual employees to begin roaming corporate networks in the next year, the company’s top security leader told Axios in an interview this week.
Why it matters: Managing those AI identities will require companies to reassess their cybersecurity strategies or risk exposing their networks to major security breaches.
The big picture: Virtual employees could be the next AI innovation hotbed, Jason Clinton, the company’s chief information security officer, told Axios.
Over the course of the next 10 years, AI-powered institutions will rise in the rankings. US News & World Report will factor a college’s AI capabilities into its calculations. Accrediting agencies will assess the degree of AI integration into pedagogy, research, and student life. Corporations will want to partner with universities that have demonstrated AI prowess. In short, we will see the emergence of the AI haves and have-nots.
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What’s happening in higher education today has a name: creative destruction. The economist Joseph Schumpeter coined the term in 1942 to describe how innovation can transform industries. That typically happens when an industry has both a dysfunctional cost structure and a declining value proposition. Both are true of higher education.
…
Out of the gate, professors will work with technologists to get AI up to speed on specific disciplines and pedagogy. For example, AI could be “fed” course material on Greek history or finance and then, guided by human professors as they sort through the material, help AI understand the structure of the discipline, and then develop lectures, videos, supporting documentation, and assessments.
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In the near future, if a student misses class, they will be able watch a recording that an AI bot captured. Or the AI bot will find a similar lecture from another professor at another accredited university. If you need tutoring, an AI bot will be ready to help any time, day or night. Similarly, if you are going on a trip and wish to take an exam on the plane, a student will be able to log on and complete the AI-designed and administered exam. Students will no longer be bound by a rigid class schedule. Instead, they will set the schedule that works for them.
Early and mid-career professors who hope to survive will need to adapt and learn how to work with AI. They will need to immerse themselves in research on AI and pedagogy and understand its effect on the classroom.
From DSC: I had a very difficult time deciding which excerpts to include. There were so many more excerpts for us to think about with this solid article. While I don’t agree with several things in it, EVERY professor, president, dean, and administrator working within higher education today needs to read this article and seriously consider what Scott Latham is saying.
Change is already here, but according to Scott, we haven’t seen anything yet. I agree with him and, as a futurist, one has to consider the potential scenarios that Scott lays out for AI’s creative destruction of what higher education may look like. Scott asserts that some significant and upcoming impacts will be experienced by faculty members, doctoral students, and graduate/teaching assistants (and Teaching & Learning Centers and IT Departments, I would add). But he doesn’t stop there. He brings in presidents, deans, and other members of the leadership teams out there.
There are a few places where Scott and I differ.
The foremost one is the importance of the human element — i.e., the human faculty member and students’ learning preferences. I think many (most?) students and lifelong learners will want to learn from a human being. IBM abandoned their 5-year, $100M ed push last year and one of the key conclusions was that people want to learn from — and with — other people:
To be sure, AI can do sophisticated things such as generating quizzes from a class reading and editing student writing. But the idea that a machine or a chatbot can actually teach as a human can, he said, represents “a profound misunderstanding of what AI is actually capable of.”
Nitta, who still holds deep respect for the Watson lab, admits, “We missed something important. At the heart of education, at the heart of any learning, is engagement. And that’s kind of the Holy Grail.”
— Satya Nitta, a longtime computer researcher at
IBM’s Watson Research Center in Yorktown Heights, NY .
By the way, it isn’t easy for me to write this. As I wanted AI and other related technologies to be able to do just what IBM was hoping that it would be able to do.
Also, I would use the term learning preferences where Scott uses the term learning styles.
Scott also mentions:
“In addition, faculty members will need to become technologists as much as scholars. They will need to train AI in how to help them build lectures, assessments, and fine-tune their classroom materials. Further training will be needed when AI first delivers a course.”
It has been my experience from working with faculty members for over 20 years that not all faculty members want to become technologists. They may not have the time, interest, and/or aptitude to become one (and vice versa for technologists who likely won’t become faculty members).
That all said, Scott relays many things that I have reflected upon and relayed for years now via this Learning Ecosystems blog and also via The Learning from the Living [AI-Based Class] Room vision — the use of AI to offer personalized and job-relevant learning, the rising costs of higher education, the development of new learning-related offerings and credentials at far less expensive prices, the need to provide new business models and emerging technologies that are devoted more to lifelong learning, plus several other things.
So this article is definitely worth your time to read, especially if you are working in higher education or are considering a career therein!
Google Public Sector and the University of Michigan’s Ross School of Business have launched an advanced Virtual Teaching Assistant pilot program aimed at improving personalized learning and enlightening educators on artificial intelligence in the classroom.
The AI technology, aided by Google’s Gemini chatbot, provides students with all-hours access to support and self-directed learning. The Virtual TA represents the next generation of educational chatbots, serving as a sophisticated AI learning assistant that instructors can use to modify their specific lessons and teaching styles.
The Virtual TA facilitates self-paced learning for students, provides on-demand explanations of complex course concepts, guides them through problem-solving, and acts as a practice partner. It’s designed to foster critical thinking by never giving away answers, ensuring students actively work toward solutions.
Companies need to develop a sense of curiosity about both the observable trends in the present and the unobserved factors that could significantly influence their futures. While current trends can drive us in certain directions, we also need to imagine possible futures that could either disrupt our industry or offer tremendous opportunities for growth.
To stay ahead of the game, companies should focus on recognising weak signals in the present – subtle hints of emerging trends – and deciding whether to encourage or discourage these signals to avoid undesirable futures and encourage desirable ones. This process is a constant dance between the push of the present (existing trends) and the pull of the future (visions of the future we want to create).
From DSC: Look out Google, Amazon, and others! Nvidia is putting the pedal to the metal in terms of being innovative and visionary! They are leaving the likes of Apple in the dust.
The top talent out there is likely to go to Nvidia for a while. Engineers, programmers/software architects, network architects, product designers, data specialists, AI researchers, developers of robotics and autonomous vehicles, R&D specialists, computer vision specialists, natural language processing experts, and many more types of positions will be flocking to Nvidia to work for a company that has already changed the world and will likely continue to do so for years to come.
NVIDIA just shook the AI and Robotic world at NVIDIA GTC 2025.
CEO Jensen Huang announced jaw-dropping breakthroughs.
Here are the top 11 key highlights you can’t afford to miss: (wait till you see no 3) pic.twitter.com/domejuVdw5
For enterprises, NVIDIA unveiled DGX Spark and DGX Station—Jensen’s vision of AI-era computing, bringing NVIDIA’s powerful Blackwell chip directly to your desk.
Nvidia Bets Big on Synthetic Data — from wired.com by Lauren Goode Nvidia has acquired synthetic data startup Gretel to bolster the AI training data used by the chip maker’s customers and developers.
Nvidia, xAI to Join BlackRock and Microsoft’s $30 Billion AI Infrastructure Fund — from investopedia.com by Aaron McDade Nvidia and xAI are joining BlackRock and Microsoft in an AI infrastructure group seeking $30 billion in funding. The group was first announced in September as BlackRock and Microsoft sought to fund new data centers to power AI products.
AI Super Bowl. Hi everyone. This week, 20,000 engineers, scientists, industry executives, and yours truly descended upon San Jose, Calif. for Nvidia’s annual GTC developers’ conference, which has been dubbed the “Super Bowl of AI.”
The problem is that these new roles demand a level of expertise that wasn’t expected from entry-level candidates in the past. Where someone might have previously learned on the job, they are now required to have relevant certifications, AI proficiency, or experience with digital platforms before they even apply.
Some current and emerging job titles that serve as entry points into industries include:
Digital marketing associate – This role often involves content creation, social media management, and working with AI-driven analytics tools.
Junior AI analyst – Employees in this role assist data science teams by labeling and refining machine learning datasets.
Customer success associate – Replacing traditional customer service roles, these professionals help manage AI-enhanced customer support systems.
Technical support specialist – While this role still involves troubleshooting software, it now often includes AI-driven diagnostics and automation oversight.
Blind Spot on AI — from the-job.beehiiv.com by Paul Fain Office tasks are being automated now, but nobody has answers on how education and worker upskilling should change.
Students and workers will need help adjusting to a labor market that appears to be on the verge of a historic disruption as many business processes are automated. Yet job projections and policy ideas are sorely lacking.
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The benefits of agentic AI are already clear for a wide range of organizations, including small nonprofits like CareerVillage. But the ability to automate a broad range of business processes means that education programs and skills training for knowledge workers will need to change. And as Chung writes in a must-read essay, we have a blind spot with predicting the impacts of agentic AI on the labor market.
“Without robust projections,” he writes, “policymakers, businesses, and educators won’t be able to come to terms with how rapidly we need to start this upskilling.”
We introduce AI co-scientist, a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries.
There is a speed limit. GenAI technology continues to advance at incredible speed. However, most organizations are moving at the speed of organizations, not at the speed of technology. No matter how quickly the technology advances—or how hard the companies producing GenAI technology push—organizational change in an enterprise can only happen so fast.
Barriers are evolving. Significant barriers to scaling and value creation are still widespread across key areas. And, over the past year regulatory uncertainty and risk management have risen in organizations’ lists of concerns to address. Also, levels of trust in GenAI are still moderate for the majority of organizations. Even so, with increased customization and accuracy of models—combined with a focus on better governance— adoption of GenAI is becoming more established.
Some uses are outpacing others. Application of GenAI is further along in some business areas than in others in terms of integration, return on investment (ROI) and expectations. The IT function is most mature; cybersecurity, operations, marketing and customer service are also showing strong adoption and results. Organizations reporting higher ROI for their most scaled initiatives are broadly further along in their GenAI journeys.
SALT LAKE CITY, Oct. 22, 2024 /PRNewswire/ — Instructure, the leading learning ecosystem and UPCEA, the online and professional education association, announced the results of a survey on whether institutions are leveraging AI to improve learner outcomes and manage records, along with the specific ways these tools are being utilized. Overall, the study revealed interest in the potential of these technologies is far outpacing adoption. Most respondents are heavily involved in developing learner experiences and tracking outcomes, though nearly half report their institutions have yet to adopt AI-driven tools for these purposes. The research also found that only three percent of institutions have implemented Comprehensive Learner Records (CLRs), which provide a complete overview of an individual’s lifelong learning experiences.
In the nearly two years since generative artificial intelligence burst into public consciousness, U.S. schools of education have not kept pace with the rapid changes in the field, a new report suggests.
Only a handful of teacher training programs are moving quickly enough to equip new K-12 teachers with a grasp of AI fundamentals — and fewer still are helping future teachers grapple with larger issues of ethics and what students need to know to thrive in an economy dominated by the technology.
The report, from the Center on Reinventing Public Education, a think tank at Arizona State University, tapped leaders at more than 500 U.S. education schools, asking how their faculty and preservice teachers are learning about AI. Through surveys and interviews, researchers found that just one in four institutions now incorporates training on innovative teaching methods that use AI. Most lack policies on using AI tools, suggesting that they probably won’t be ready to teach future educators about the intricacies of the field anytime soon.
It is bonkers that I can write out all my life goals on a sheet of paper, take a photo of it, and just ask Claude or ChatGPT for help.
I get a complete plan, milestones, KPIs, motivation, and even action support to get there.
As beta testers, we’re shaping the tools of tomorrow. As researchers, we’re pioneering new pedagogical approaches. As ethical guardians, we’re ensuring that AI enhances rather than compromises the educational experience. As curators, we’re guiding students through the wealth of information AI provides. And as learners ourselves, we’re staying at the forefront of educational innovation.
In a groundbreaking study, researchers from Penn Engineering showed how AI-powered robots can be manipulated to ignore safety protocols, allowing them to perform harmful actions despite normally rejecting dangerous task requests.
What did they find ?
Researchers found previously unknown security vulnerabilities in AI-governed robots and are working to address these issues to ensure the safe use of large language models(LLMs) in robotics.
Their newly developed algorithm, RoboPAIR, reportedly achieved a 100% jailbreak rate by bypassing the safety protocols on three different AI robotic systems in a few days.
Using RoboPAIR, researchers were able to manipulate test robots into performing harmful actions, like bomb detonation and blocking emergency exits, simply by changing how they phrased their commands.
Why does it matter?
This research highlights the importance of spotting weaknesses in AI systems to improve their safety, allowing us to test and train them to prevent potential harm.
From DSC: Great! Just what we wanted to hear. But does it surprise anyone? Even so…we move forward at warp speeds.
From DSC:
So, given the above item, does the next item make you a bit nervous as well? I saw someone on Twitter/X exclaim, “What could go wrong?” I can’t say I didn’t feel the same way.
We’re also introducing a groundbreaking new capability in public beta: computer use.Available today on the API, developers can direct Claude to use computers the way people do—by looking at a screen, moving a cursor, clicking buttons, and typing text. Claude 3.5 Sonnet is the first frontier AI model to offer computer use in public beta. At this stage, it is still experimental—at times cumbersome and error-prone. We’re releasing computer use early for feedback from developers, and expect the capability to improve rapidly over time.
Per The Rundown AI:
The Rundown: Anthropic just introduced a new capability called ‘computer use’, alongside upgraded versions of its AI models, which enables Claude to interact with computers by viewing screens, typing, moving cursors, and executing commands.
… Why it matters: While many hoped for Opus 3.5, Anthropic’s Sonnet and Haiku upgrades pack a serious punch. Plus, with the new computer use embedded right into its foundation models, Anthropic just sent a warning shot to tons of automation startups—even if the capabilities aren’t earth-shattering… yet.
Also related/see:
What is Anthropic’s AI Computer Use? — from ai-supremacy.com by Michael Spencer Task automation, AI at the intersection of coding and AI agents take on new frenzied importance heading into 2025 for the commercialization of Generative AI.
New Claude, Who Dis? — from theneurondaily.com Anthropic just dropped two new Claude models…oh, and Claude can now use your computer.
What makes Act-One special? It can capture the soul of an actor’s performance using nothing but a simple video recording. No fancy motion capture equipment, no complex face rigging, no army of animators required. Just point a camera at someone acting, and watch as their exact expressions, micro-movements, and emotional nuances get transferred to an AI-generated character.
Think about what this means for creators: you could shoot an entire movie with multiple characters using just one actor and a basic camera setup. The same performance can drive characters with completely different proportions and looks, while maintaining the authentic emotional delivery of the original performance. We’re witnessing the democratization of animation tools that used to require millions in budget and years of specialized training.
Also related/see:
Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required.
Google has signed a “world first” deal to buy energy from a fleet of mini nuclear reactors to generate the power needed for the rise in use of artificial intelligence.
The US tech corporation has ordered six or seven small nuclear reactors (SMRs) from California’s Kairos Power, with the first due to be completed by 2030 and the remainder by 2035.
After the extreme peak and summer slump of 2023, ChatGPT has been setting new traffic highs since May
ChatGPT has been topping its web traffic records for months now, with September 2024 traffic up 112% year-over-year (YoY) to 3.1 billion visits, according to Similarweb estimates. That’s a change from last year, when traffic to the site went through a boom-and-bust cycle.
Google has made a historic agreement to buy energy from a group of small nuclear reactors (SMRs) from Kairos Power in California. This is the first nuclear power deal specifically for AI data centers in the world.
Hey creators!
Made on YouTube 2024 is here and we’ve announced a lot of updates that aim to give everyone the opportunity to build engaging communities, drive sustainable businesses, and express creativity on our platform.
Below is a roundup with key info – feel free to upvote the announcements that you’re most excited about and subscribe to this post to get updates on these features! We’re looking forward to another year of innovating with our global community it’s a future full of opportunities, and it’s all Made on YouTube!
Today, we’re announcing new agentic capabilities that will accelerate these gains and bring AI-first business process to every organization.
First, the ability to create autonomous agents with Copilot Studio will be in public preview next month.
Second, we’re introducing ten new autonomous agents in Dynamics 365 to build capacity for every sales, service, finance and supply chain team.
10 Daily AI Use Cases for Business Leaders— from flexos.work by Daan van Rossum While AI is becoming more powerful by the day, business leaders still wonder why and where to apply today. I take you through 10 critical use cases where AI should take over your work or partner with you.
Emerging Multi-Modal AI Video Creation Platforms The rise of multi-modal AI platforms has revolutionized content creation, allowing users to research, write, and generate images in one app. Now, a new wave of platforms is extending these capabilities to video creation and editing.
Multi-modal video platforms combine various AI tools for tasks like writing, transcription, text-to-voice conversion, image-to-video generation, and lip-syncing. These platforms leverage open-source models like FLUX and LivePortrait, along with APIs from services such as ElevenLabs, Luma AI, and Gen-3.
Going forward, the opportunity for AI agents will be “gigantic,” according to Nvidia founder and CEO Jensen Huang.
Already, progress is “spectacular and surprising,” with AI development moving faster and faster and the industry getting into the “flywheel zone” that technology needs to advance, Huang said in a fireside chat at Salesforce’s flagship event Dreamforce this week.
“This is an extraordinary time,” Huang said while on stage with Marc Benioff, Salesforce chair, CEO and co-founder. “In no time in history has technology moved faster than Moore’s Law. We’re moving way faster than Moore’s Law, are arguably reasonably Moore’s Law squared.”
“We’ll have agents working with agents, agents working with us,” said Huang.
As we navigate the rapidly evolving landscape of artificial intelligence in education, a troubling trend has emerged. What began as cautious skepticism has calcified into rigid opposition. The discourse surrounding AI in classrooms has shifted from empirical critique to categorical rejection, creating a chasm between the potential of AI and its practical implementation in education.
This hardening of attitudes comes at a significant cost. While educators and policymakers debate, students find themselves caught in the crossfire. They lack safe, guided access to AI tools that are increasingly ubiquitous in the world beyond school walls. In the absence of formal instruction, many are teaching themselves to use these tools, often in less than productive ways. Others live in a state of constant anxiety, fearing accusations of AI reliance in their work. These are just a few symptoms of an overarching educational culture that has become resistant to change, even as the world around it transforms at an unprecedented pace.
Yet, as this calcification sets in, I find myself in a curious position: the more I thoughtfully integrate AI into my teaching practice, the more I witness its potential to enhance and transform education
The urgency to integrate AI competencies into education is about preparing students not just to adapt to inevitable changes but to lead the charge in shaping an AI-augmented world. It’s about equipping them to ask the right questions, innovate responsibly, and navigate the ethical quandaries that come with such power.
AI in education should augment and complement their aptitude and expertise, to personalize and optimize the learning experience, and to support lifelong learning and development. AI in education should be a national priority and a collaborative effort among all stakeholders, to ensure that AI is designed and deployed in an ethical, equitable, and inclusive way that respects the diversity and dignity of all learners and educators and that promotes the common good and social justice. AI in education should be about the production of AI, not just the consumption of AI, meaning that learners and educators should have the opportunity to learn about AI, to participate in its creation and evaluation, and to shape its impact and direction.
86% of students globally are regularly using AI in their studies, with 54% of them using AI on a weekly basis, the recent Digital Education Council Global AI Student Survey found.
ChatGPT was found to be the most widely used AI tool, with 66% of students using it, and over 2 in 3 students reported using AI for information searching.
Despite their high rates of AI usage, 1 in 2 students do not feel AI ready. 58% reported that they do not feel that they had sufficient AI knowledge and skills, and 48% do not feel adequately prepared for an AI-enabled workplace.
The Post-AI Instructional Designer— from drphilippahardman.substack.com by Dr. Philippa Hardman How the ID role is changing, and what this means for your key skills, roles & responsibilities
Specifically, the study revealed that teachers who reported most productivity gains were those who used AI not just for creating outputs (like quizzes or worksheets) but also for seeking input on their ideas, decisions and strategies.
Those who engaged with AI as a thought partner throughout their workflow, using it to generate ideas, define problems, refine approaches, develop strategies and gain confidence in their decisions gained significantly more from their collaboration with AI than those who only delegated functional tasks to AI.
Leveraging Generative AI for Inclusive Excellence in Higher Education — from er.educause.edu by Lorna Gonzalez, Kristi O’Neil-Gonzalez, Megan Eberhardt-Alstot, Michael McGarry and Georgia Van Tyne Drawing from three lenses of inclusion, this article considers how to leverage generative AI as part of a constellation of mission-centered inclusive practices in higher education.
The hype and hesitation about generative artificial intelligence (AI) diffusion have led some colleges and universities to take a wait-and-see approach.Footnote1 However, AI integration does not need to be an either/or proposition where its use is either embraced or restricted or its adoption aimed at replacing or outright rejecting existing institutional functions and practices. Educators, educational leaders, and others considering academic applications for emerging technologies should consider ways in which generative AI can complement or augment mission-focused practices, such as those aimed at accessibility, diversity, equity, and inclusion. Drawing from three lenses of inclusion—accessibility, identity, and epistemology—this article offers practical suggestions and considerations that educators can deploy now. It also presents an imperative for higher education leaders to partner toward an infrastructure that enables inclusive practices in light of AI diffusion.
An example way to leverage AI:
How to Leverage AI for Identity Inclusion Educators can use the following strategies to intentionally design instructional content with identity inclusion in mind.
Provide a GPT or AI assistant with upcoming lesson content (e.g., lecture materials or assignment instructions) and ask it to provide feedback (e.g., troublesome vocabulary, difficult concepts, or complementary activities) from certain perspectives. Begin with a single perspective (e.g., first-time, first-year student), but layer in more to build complexity as you interact with the GPT output.
Gen AI’s next inflection point: From employee experimentation to organizational transformation — from mckinsey.com by Charlotte Relyea, Dana Maor, and Sandra Durth with Jan Bouly As many employees adopt generative AI at work, companies struggle to follow suit. To capture value from current momentum, businesses must transform their processes, structures, and approach to talent.
To harness employees’ enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the technology alone won’t create value.
Our research shows that early adopters prioritize talent and the human side of gen AI more than other companies (Exhibit 3). Our survey shows that nearly two-thirds of them have a clear view of their talent gaps and a strategy to close them, compared with just 25 percent of the experimenters. Early adopters focus heavily on upskilling and reskilling as a critical part of their talent strategies, as hiring alone isn’t enough to close gaps and outsourcing can hinder strategic-skills development.Finally, 40 percent of early-adopter respondents say their organizations provide extensive support to encourage employee adoption, versus 9 percent of experimenter respondents.
Change blindness — from oneusefulthing.org by Ethan Mollick 21 months later
I don’t think anyone is completely certain about where AI is going, but we do know that things have changed very quickly, as the examples in this post have hopefully demonstrated. If this rate of change continues, the world will look very different in another 21 months. The only way to know is to live through it.
Over the subsequent weeks, I’ve made other adjustments, but that first one was the one I asked myself:
What are you doing?
Why are you doing it that way?
How could you change that workflow with AI?
Applying the AI to the workflow, then asking, “Is this what I was aiming for? How can I improve the prompt to get closer?”
Documenting what worked (or didn’t). Re-doing the work with AI to see what happened, and asking again, “Did this work?”
So, something that took me WEEKS of hard work, and in some cases I found impossible, was made easy. Like, instead of weeks, it takes 10 minutes. The hard part? Building the prompt to do what I want, fine-tuning it to get the result. But that doesn’t take as long now.
