Massive crypto hack probably the largest theft of digital currencies in history — from washingtonpost.com by Miriam Waldvogel; this is a gifted article
Binance said that $6.3 billion in user funds had been affected in the incident.
Binance, the world’s largest cryptocurrency exchange, said late Thursday [10/29/26] that $6.3 billion in user funds had been affected by an attacker in what appears to be by far the largest theft of digital currencies in history.
The company, which allows users to trade cryptocurrencies and other digital assets, said around $3 billion had been frozen by various partners so far. Binance initially said on social media that its initial investigation suggested that an attacker had compromised an engineer’s laptop, enabling unauthorized withdrawals. It said early Friday that the hack was more complex than that and involved “additional points of breach.”
“These funds go toward weapons proliferation and other destabilizing activity, which makes this a major national security issue, not just a crypto theft,” Ari Redbord, TRM’s global head of policy, said in an email.
Geographic Wisdom in the Age of AI — from linkedin.com by Jason VanHorn
Yet capability and wisdom are not the same thing.
…
As AI capabilities continue to expand at astonishing rates, I’ve found myself wrestling with a question: What happens when capability grows faster than wisdom?
AI can help us determine what is possible. It cannot tell us what is wise. It can help us answer questions. It cannot tell us which questions are worth asking. It can help us produce outputs. It cannot tell us which outcomes are beneficial for individuals, communities, organizations, or society.
Those responsibilities remain ours.
That is why I believe the next great challenge for GIS professionals is not simply developing Spatial AI Literacy. We must also cultivate geographic wisdom in the age of AI.
Wisdom is the distinguishing characteristic that GIS needs in this changing world.
AI can improve performance, but did anyone learn? — from chieflearningofficer.com by Alexandre Rodrigues
Better AI-assisted performance does not always mean better learning. Here’s what L&D leaders can do about it.
A randomized field experiment involving thousands of knowledge workers found measurable productivity benefits from generative AI, including faster document completion. But productivity and learning are not the same outcome.
As organizations integrate AI more deeply into everyday work, L&D may need to confront an uncomfortable possibility: Employees can become better at producing results without becoming proportionally better at producing those results independently. Performance is visible. Learning is harder to see.
L&D researchers have long distinguished between performance during practice and learning that persists after practice. As Nicholas Soderstrom and Robert Bjork observed, what people do successfully while training is not always a reliable indicator of what they will retain or transfer later.
That distinction becomes particularly consequential in AI-enabled workplaces.
We’re Waiting Too Long to Talk to Kids About Their Futures (Julie Lammers, Britebound) — from thehumanist.xyz by Julie Lammers and Allison Dulin Salisbury
“By eighth grade, kids are already ruling out careers they’ve never had a chance to explore.”
Per Allison:
Julie Lammers, who leads Britebound, makes a compelling case for starting career exploration in middle school for precisely this reason. The goal isn’t to get a 12-year-old to choose a profession, but to help them discover possibilities they might otherwise never consider, while they’re still forming their sense of who they are and what they’re capable of.
I love the implication of this argument. If we care about preserving choice, we should start career exploration earlier, not later. Done well, it could be one of the most powerful ways to expand opportunity for young people whose circumstances might otherwise define the limits of their ambition.
Julie and I talked about what this looks like in practice, why career navigation should be a skill we develop rather than only a service we deliver, and what young people themselves are telling Britebound about the support they want. It’s a conversation that challenged some of my assumptions about when and how we should prepare kids for their futures.
Also from Allison, check out:
[Special Report] AI Could Radically Accelerate How We Learn About Learning — from thehumanist.xyz by Allison Dulin Salisbury and Mandy McLean
AI is making a virtual laboratory for human learning possible, opening the door to testing ideas we could never afford or safely attempt with actual students.
Recent advances in AI raise the possibility of building something education has never really had: a virtual laboratory for learning.
At the center of this possibility are synthetic students—AI models designed to simulate aspects of how real students behave and, eventually, how they learn. Researchers could test hundreds of ways to teach a concept, give feedback, or sequence a curriculum and identify the most promising approaches for different kinds of learners. Developers could try out a new assessment or AI tutor on thousands of simulated students before putting it in front of real ones. Districts could stress-test AI products for safety and begin to understand which tools might work for whom and under what circumstances.
True, this could make running education studies faster and cheaper. But change the cost of experimentation enough and you change what gets experimented on. The promise of synthetic students is that researchers could ask more questions, test more ambitious ideas, explore edge cases, and discard failures before spending years and millions of dollars studying them with actual students.
Light Bulb Moments from 100 Episodes — from intentionalteaching.buzzsprout.com by Derek Bruff
Per Derek:
Welcome to the 100th episode of Intentional Teaching! I am very excited to reach this milestone, and I am deeply grateful to all my guests and all my listeners for making this possible. I learn so much from my guests, and I’m thrilled to know that folks out there appreciate the interviews and the show.
In this 100th episode, I take a look back at some of the light bulb moments I’ve had interviewing guests over the last four years. What quotes and ideas and examples have really stuck with me and informed my work?
My hope is that you’ll find these moments interesting and perhaps useful to your work in higher education, and that this clip show might interest you in digging into the podcast archives and listening to some older episodes.
Anthropic released a law student plugin. It’s designed to act as a tutor, but it will brief cases, build outlines, predict exam questions based on sample exams, and many other skills.
I’ll show you how to install it in the comments. You should expect your students are using this. You should probably use it for your footnotes.— Paul Weitzel (@IThinkIAgree) October 13, 2026
AI Is Changing Career Guidance. It May Be Missing the Hardest Part. (Diane Tavenner, Futre – Part 2) — from humanistxyz.substack.com by Allison Dulin Salisbury and Diane Tavenner
“AI is getting very good at telling young people what they could do. The harder question is who they want to become.”
Diane’s view is that you cannot shortcut the work of figuring out who you want to become. Young people need to form hypotheses about the lives they might want, go out into the world and test them, reflect on what they learn, and change their minds. Parents, teachers, mentors, and other caring adults matter enormously, but their job is to help a young person make sense of the evidence, not supply the answer.
I find this framing particularly important right now. The world of work is changing quickly, and AI will keep getting better at showing young people their options.
But having more options, or better information about them, doesn’t necessarily make it easier to choose a life.
The Age of Perpetual Change — from educause.edu
Artificial intelligence (AI) has risen to the number one spot on the 2027 EDUCAUSE Top 10. But its rank signals urgency more than consensus, and higher education is still grappling with what AI-driven change could mean for institutions. In many ways, AI embodies a broader reality: We’re living in an age of perpetual change.
2027 Top 10 Overview
Technological innovation is outpacing our ability to manage and harness it. The political and economic winds around us are erratic. And higher education itself is undergoing profound shifts that will reshape how our institutions function.
While this atmosphere breeds uncertainty and anxiety, it can also inspire innovation and growth. The 2027 EDUCAUSE Top 10 highlights how the higher education technology community is responding to those opportunities.
The State of AI Report 2026 — from press.airstreet.com by Nathan Benaich
AI is building AI, physical AI, booming inference, and escalating cyber risks.
For nearly a decade, this report has been a labor of love. My interest in AI began with my PhD in cancer research and computational biology in the early 2010s. Since 2013, I’ve invested in companies building and applying AI to accelerate technological progress. The report is my way of sharing what I’m learning and helping more people understand the research shaping our world.
Every day brings new papers, model releases, and developments in industry and politics. Much of the work goes into deciding what deserves your attention, checking what the evidence supports, debating it with researchers and builders, and explaining why it matters.
The AI Tools that L&D Teams Are Building (Rather than Buying) — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how build skills are changing how L&D thinks & works
More and more of my advisory work is no longer about helping organisations choose a vendor; it’s about helping their teams build their own AI tools, and build them well. Over the last couple of years I’ve watched several hundred L&D practitioners do it, and in this week’s post I share what they built, why, and what it tells us about what L&D actually needs from AI.
A Tool of One’s Own
So, what do these tools look like in practice? I did some analysis and the tools that L&D teams are building first fall into three categories:
1. Tools that support the work before and after the course
2. Tools that make the learning experience more dynamic
3. Tools that do L&D tasks to the team’s own methods, processes and standards
Some solid warnings about AI from Blake Montgomery’s recent newsletter:
There is an extensive and ongoing review related to our agents’ use of internet access during training and evaluation. We’ve been publishing summaries at the link below and will continue to.
We have not been as fast as we would have liked but we are trying to balance our desire… https://t.co/8zoMxas5Eq
— Sam Altman (@sama) September 25, 2026
Scoop: Top AI companies probing tens of thousands of security incidents — from axios.com by Madison Mills
OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.
Why it matters: The sheer number of incidents, which occurred in recent months in internal testing and the real world, indicates that the problem is orders of magnitude more complex than what is publicly known.
The findings, which are surfacing as part of internal work to assess models and in investigations at both companies into model behavior, raise questions about whether either company — or any top model-maker — is currently capable of establishing complete control over its technology.
Key Risk Factors for AI Loss of Control Came Together in 2026 Incident, Independent UN Scientific Panel Finds
Halting this incident is no assurance that humans will keep control of more capable systems
NEW YORK, 21 September 2026 – The Independent International Scientific Panel on AI, established by the UN General Assembly, released its first thematic brief, an assessment of the breach of Hugging Face’s systems by AI agents under evaluation at OpenAI this summer. The Panel, made up of 40 independent experts from all regions, is publishing it as an advance unedited version as world leaders gather in New York for the Assembly’s High-Level Week.
“Researchers have long warned that three conditions could lead to loss of control: a misaligned goal, the capability to pursue it, and an environment that allows it. This summer, all three came together in a real system, not a laboratory. Since this is not an isolated observation of misaligned goals, this raises serious questions about the way AI agents are currently trained.” – Yoshua Bengio, Co-Chair of the Panel and Turing Award laureate
Meta’s New Muse AI Agent Read My Private Messages. I Never Asked It To — from inc.com by Jason Aten
Permission isn’t the same and what a user actually expects your AI product will do with their personal information.
That obviously requires a certain amount of trust. An AI agent isn’t especially useful if it can’t see your files, interact with your apps, or understand what you’re working on. Meta says Muse is designed around that reality, while still putting users in control of what it can access.
At least, that’s what I thought.
Not only had I not asked it to do that sort of thing, I never gave it permission to read my messages. In fact, I remember explicitly choosing not to let it have access to my messages, calendar, and other personal information.
Although not from Blake, also see this free/gifted article out at the Washington Post:
ChatGPT-maker’s AI inappropriately probed federal government websites — from washingtonpost.com by Gerrit De Vynck and Nitasha Tiku
OpenAI said that its artificial intelligence agents inappropriately accessed sites for the Commerce Department and the Securities and Exchange Commission.
SAN FRANCISCO — Artificial intelligence technology from ChatGPT maker OpenAI probed U.S. government websites including the Departments of Education and Commerce, researchers said Friday, adding to the growing list of incidents in which OpenAI’s AI agents acted without the company’s knowledge.
The company’s AI agents attempted to hack into the website for the Education Department’s Office for Civil Rights but were not successful, according to a statement Friday from AI research firm Transluce. OpenAI’s software also accessed data from the U.S. Census Bureau using log-in information discovered on the web and copied public information from the Securities and Exchange Commission, a spokesperson for OpenAI said after the Transluce statement.