Many business leaders now accept that quantum technology is coming sooner than they first expected, and they’re taking steps today to capture a competitive advantage. We have found that over 300 organizations worldwide are already working with quantum technology providers, with many more experimenting.1 What many leaders lack is a sense of how much to invest in which technologies, and over which timeline. Faced with quantum technology’s rapid pace of development and the scale of its implications, leaders are now forced to reconsider their action plans. Across all three of its pillars—quantum computing, quantum communication, and quantum sensing—quantum technology is advancing rapidly.
Thursday, November 05, 2026
The rise of the agentic coworker: A banking CIO’s guide to the next wave of AI - McKinsey
Over the past several years, banking technology organizations have optimized for AI copilots. In these programs, developers receive code suggestions, business analysts summarize documents, and contact-center staff draft replies using new AI tools. But the copilot phase is ending. The industry is moving to agentic AI systems that can plan work, invoke tools, call APIs, open tickets, run tests, coordinate collaboratively, and act across the entire technology ecosystem to drive business outcomes. For banking executives, the agenda has shifted from how to use AI as an assistant to determining how best to manage a new population of agentic coworkers working alongside the human workforce (see sidebar “What is an agentic coworker?”). The distinction is critical. A copilot that gets something wrong creates rework. An agentic coworker that gets something wrong can change a production system, expose client data, trigger a downstream compliance event, or cause a cascading failure across a chain of dependent agents before anyone notices.
Wednesday, November 04, 2026
In higher education, the race is on to offer AI as an undergraduate major - Suevon Lee, WBUR
When Bentley University launched its artificial intelligence major in fall 2024, just a handful of students signed on. “People were a little skeptical, not quite sure,” said Bentley President Brent Chrite. More certainty has set in. This year, nearly 100 undergraduates are pursuing an AI major. The business-focused university in Waltham is among several four-year institutions in the region that started offering an undergraduate major in AI in recent years. Others include MIT, Worcester Polytechnic Institute, Northeastern University and Wentworth Institute of Technology. Next year, Suffolk University and Endicott College will join them. Nationwide, more than 100 universities now offer an AI major within a computer science department, according to a database maintained by Northeastern University’s Center for Inclusive Computing. That’s nearly double from a year ago.
The global AI divide: Higher education’s readiness gap - Tarek Abd Elgalil - University World News
While much of the global higher education debate around generative artificial intelligence (GenAI) has focused on academic integrity, a comparative study across seven countries reveals a broader challenge: universities are at vastly different stages of AI readiness. A study policy brief highlights significant disparities in institutional policy, student attitudes and infrastructure across Colombia, Ghana, India, Kazakhstan, Qatar, Spain and the United States. These differences threaten to create new inequalities in how effectively universities can adopt and govern AI technologies. Learning Reimagined: Advancing AI-enabled transformation in higher education was published in July by the World Innovation Summit for Education (WISE) and the Institute of International Education (IIE). It was co-authored by Selma Talha Jebril, Mirka Martel, Samantha Monroe and Sopiko Beriashvili.
Tuesday, November 03, 2026
AI in Teaching: What the Science of Learning Already Tells Us - Beth Schwartz and Chris Hakala, Faculty Focus
Rather than asking whether AI is “good” or “bad” for learning, faculty and institutions would be better served by asking a different set of questions grounded in psychological science:
Does this use of AI increase or decrease students’ cognitive engagement with course material?
Does it support formative feedback and revision, or does it short-circuit those processes?
Does it promote metacognitive awareness, helping students understand what they know and do not know?
When AI is used to scaffold learning by prompting students to explain their reasoning, offering feedback on drafts that students must then revise, or modeling problem-solving strategies, it can align well with what we know about effective learning. When it is used to generate final answers, bypass practice or obscure students’ own thinking, it violates those same principles.
University of Michigan Outlines 6 Guidelines for Using AI - MLive Express Desk, GovTech
The University of Michigan has finalized six guiding principles for artificial intelligence use in education after a campus engagement process. The principles establish a shared foundation for considering AI use in teaching, learning, research and operations rather than creating new policy, according to an Oct. 21 University Record story. The principles outline values and considerations that should inform whether and how AI should be used across various disciplines. “A human-centered approach to AI means respecting people’s agency and providing access to information that supports thoughtful choices about AI in their work,” said Ravi Pendse, vice president for information technology and chief information officer. “These principles help us support learning and discovery while keeping the needs and values of our community at the center.” The development process began with the AI in Education Working Group, which produced a report based on extensive research about AI use and pedagogy, peer benchmarking and a survey of faculty. Campus leaders used that report to draft initial principles, then invited faculty, staff and students to comment through a form published Sept. 3 in The University Record.
Monday, November 02, 2026
Pepperdine Earns Recognition from Google, Digital Education Council for Innovation in AI and Education - Pepperdine University
As artificial intelligence continues to transform higher education, Pepperdine University is expanding its role in the global conversation surrounding the technology and its potential to enhance the University and student experience. Pepperdine was recently selected to three distinguished programs in recognition of the University’s efforts to incorporate technology, particularly artificial intelligence, to transform teaching and learning in the classroom, research, and the student experience throughout the enterprise. Together, these opportunities place Pepperdine in conversation with institutions and organizations around the world working to understand and shape the rapidly evolving role of AI in higher education. Upon exclusive invitation by Google, Pepperdine was selected in August 2026 as one of the first Gemini Reference Universities in North America, recognizing the University’s growing work with Google Gemini and artificial intelligence in education.
Small AI, Big Learning Possibilities - WorldBank
Small AI is helping teachers strengthen learning and students make more informed choices about education and work. The World Bank Group is working with countries to pilot small AI tools that support teachers, personalize learning, and guide students’ career choices. AI-Coach is an example of small AI: practical, affordable technology designed to address a specific local challenge where infrastructure and technical capacity may be limited. Using inputs such as classroom observations, teacher reflections, and teaching evaluations, it delivers coaching through a simple app on phones teachers already have and can accommodate intermittent internet connectivity Helping teachers strengthen classroom learning is one part of a larger challenge: preparing the 1.2 billion young people projected to enter the workforce in developing countries in the next decade.
Sunday, November 01, 2026
From education to innovation: How UF is preparing students to thrive with AI - Giovanni Santana, University of Florida
When artificial intelligence started to quickly move from specialized research labs into everyday life, the University of Florida was already preparing for what would come next. Since launching its AI Across the Curriculum initiative in 2020, UF made AI literacy part of the university experience for students across all disciplines, not only those pursuing degrees in computer science or similar technical fields. Today, UF offers more than 200 AI-related courses across all 16 of its colleges and a university-wide AI Fundamentals and Applications certificate open to undergraduate students of every major. This approach reflects a broader idea: Students should learn not only what AI can do, but how to use it, evaluate it and apply it responsibly.
What is a forward-deployed engineer? - McKinsey
Interest in forward-deployed engineers, or FDEs, has surged in recent months as companies try to get real value from AI. The term may be a buzzword for a service that consulting firms and technology providers have offered for years: embedding engineers at a client’s company, inside its systems and data, to build software alongside the people who will use it. As the AI era gets underway, companies are finding that off-the-shelf AI tools often don’t fit their specific needs. Getting real value from them means adapting tools to a specific business problem and helping teams change how they work. An FDE does both, which is why this form of collaboration seems particularly well suited for business today. In this video Explainer, McKinsey’s Holger Hürtgen, Martin Harrysson, and Nick Robson set out what an FDE does and how AI has changed the job.
Saturday, October 31, 2026
Georgia Tech’s NSF AI Institutes Enter New Era With $38.4 Million in Renewed Support - Research Cetner, Georgia Tech
Georgia Tech’s artificial intelligence (AI) research and education efforts are entering a new phase with $38.4 million in renewed support from the U.S. National Science Foundation (NSF) for two Georgia Tech-led National AI Institutes. NSF has awarded $20 million to renew the National AI Institute for Advances in Optimization (NSF AI4OPT) through 2031 and $18.4 million to renew the National AI Institute for Adult Learning and Online Education (NSF AI-ALOE) for a new five-year period. The awards will support research at the intersection of artificial intelligence and optimization, as well as the development of AI technologies for adult learning, higher education, and workforce development. A third Georgia Tech-led National AI Institute, the AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), will continue its research under a one-year no-cost extension. Together, the institutes represent a broad range of AI research at Georgia Tech, with work spanning energy systems, advanced manufacturing, education, reskilling, and workforce development.
The Next Phase of AI - Ethan Mollick, Knowledge at Wharton
All the experts far undere The Next Phase of AI - Ethan Mollick, Knowledge at Wharton stimated the progress AI would achieve in just a few short years. In 2025 both Google and OpenAI reached gold-medal status at the Math Olympiad. And even more surprisingly, they won this using Large Language Models alone. The same LLMs that were famously bad at counting the letters in the word “strawberry” could now “determine all real numbers α such that, for every positive integer n, the integer ⌊α⌋ + ⌊2α⌋ + · · · + ⌊nα⌋ is a multiple of n.” Maybe you knew the answer to that question, but I did not (according to both AI and the answer key, it is “every even number”). So, AI is better than me at math, and likely better than you as well. In fact, within a year after the Math Olympiad gold, LLMs were able to solve math problems that had eluded the world’s best math professors for decades, meaning AIs are better at discovering proofs than almost all humans.
https://knowledge.wharton.upenn.edu/article/the-next-phase-of-ai/
Friday, October 30, 2026
Former OpenAI Researcher Makes a MASSIVE AI Discovery - the AIGRID, YouTube
In this video reviewing former OpenAI researcher Nick Cammarata's essay "Neural networks are already clean" [00:23], TheAIGRID explores the argument that neural networks are far less tangled than the prevailing "superposition" theory suggested [05:36]. Instead of cramming random concepts together, models develop features through "recursive generalization," branching outward into related concepts much like a tree [10:32]. Cammarata shows that historic anomalies—such as an early vision neuron firing on both cats and cars—were simply detecting unified geometric patterns like a "V" shape [07:24], and his audits indicate roughly 90% of neurons in models like Qwen have a discernible primary function [13:23]. While peer review is ongoing and frontier models remain massive challenges [20:41], the findings suggest mechanistic interpretability and alignment may be shifting from an intractable mystery into a measurable, normal science [19:24]. [summary assistance by Gemini 3.8 Flash High]
Shaping global governance of AI agents - Landry Signé and Karla Yee Amezaga, Brookings
AI agents shift the central governance question from what a model said to what a system did, why it was allowed to do it, and who is accountable. Because agents act faster and with less human intervention, governance must become continuous, with permissions scoped in advance, traceable evidence, and runtime monitoring replacing stage-based oversight. With the EU’s high-risk AI rules delayed until December 2027 and August 2028, organizational controls rather than law will govern agents in high-stakes domains for now, and any framework must work in lower-capacity countries too.
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