The IoT promises to add intelligence to everything from commonplace consumer items such as cars, light bulbs, and refrigerators, to industrial items such as machinery, railroad ties, and agricultural fields. Those “things” can collect and broadcast data across networks, enabling the data to be analyzed to add more value.
Consumer and industrial products will be valued increasingly not just for their standalone functionality, but also for how well they work within the digital ecosystem.In the consumer realm, companies’ marketing success will depend on their ability to connect with, and creatively exploit, the interdependent network of apps, devices, and services....
El informe del MIT titulado El Estado de la IA en las Empresas 2025, se basó en investigaciones preliminares del Project NANDA, en donde se detectó el fenómeno conocido como la Brecha de la IA Generativa. Esto se debe a que la IA Gen está siendo mayormente utilizada para ejecutar tareas individuales relacionadas con el marketing, en vez de implementarlas en desarrollo interno organizacional y en la solución de problemas con los flujos de trabajos de oficina.
El informe concluyó que la asignación de la inversión de la IA Gen estuvo fuertemente concentrada en funciones visibles de línea superior (top-line), a pesar de que las funciones internas (back-office) a menudo ofrecieron un mejor retorno de la inversión (ROI).
Entry-level jobs are disappearing. The promise of AI’s workplace abilities and economic uncertainties have caused many companies to take pause while graduate-level unemployment is at an all-time high. What will the future of work look like if there are fewer starter jobs and middle management positions? Professional development and leadership pipelines will need to be redefined but how are companies doing it?
Los empleos de nivel inicial están desapareciendo a un ritmo alarmante. Es probable que este sea uno de los momentos más difíciles de la historia para que los egresados universitarios consigan trabajo. La tormenta perfecta de la IA, la inestabilidad política y la incertidumbre económica están provocando lo que algunos llaman el "apocalipsis laboral" (jobpocalypse). Si el primer escalón laboral está desapareciendo, ¿qué significa eso para el futuro del trabajo? Isabel Berwick, nos presenta este interesante documental investigativo.
Los seres humanos estamos programados para resistir el cambio... ...para todos. Las vacantes para graduados están en su punto más bajo. Las ofertas de empleo tanto en EE. UU. como en el Reino Unido están cayendo en picado. Y por primera vez desde que se tiene registro, los niveles de desempleo entre los graduados universitarios superan la tasa general de desempleo.
El mercado laboral en Estados Unidos está viviendo un cambio histórico. Lo que antes parecía ciencia ficción, hoy es una realidad: millones de empleos están siendo reemplazados por la inteligencia artificial.
¿Qué significa esto para ti?
Profesiones enteras podrían desaparecer en menos de 5 años.
Universitarios recién graduados ven cómo sus oportunidades se desmoronan.
Call centers, despachos de abogados y áreas administrativas ya están siendo sustituidos por algoritmos.
El impacto de la IA en el trabajo NO ES un tema trillado. Es una realidad que está ocurriendo en la actualidad. Lo que no se sabe es el nivel de impacto que tendrá en nuestros jóvenes universitarios que aspiran a obtener un empleo de carrera inicial. Muchas universidades siguen formando profesionales redundantes que hacen lo mismo que la IA Generativa y Agéntica es capaz de hacer.
El nivel de desarrollo de la IA está hackeando las capacidades de pensamiento de muchos egresados universitarios. Las universidades TIENEN que transformar sus programas académicos lo antes posible. El discurso externo que cuestiona si vale lapena ir a la Universidad sigue ganando terreno. Los líderes educativos tenemos que llevar el mensaje de que la Universidad Sí es importante para ayudarles a descubrir la infinidad de talentos ocultos que nuestros jóvenes poseen...
Every year, NOLAI publishes a magazine with an overview of the latest developments and insights in educational AI for primary, secondary and special needs education.
Gen AI systems are not substitutes for Google or even a damn good book - please stop treating like they are, then complaining when they aren’t! So I’ve been hearing a lot about what AI can’t do as the resistance to AI in education mounts, just as the pressure to engage increases (Newtonian physics playing…
Future of Jobs Report 2025 states that 39 percent of workers’ core skills will be disrupted by 2030, and 170 million new jobs will emerge – equivalent to 14 percent of today’s employment in the world. This is the landscape students will graduate into. Degrees provide a foundation but it is the adaptable, human skills that the Future of Jobs Report highlights will be our students’ passports into the future. These skills include:
A new UNESCO report cautions that artificial intelligence has the potential to threaten students’ access to quality education. The organization calls for a focus on people, to ensure digital tools enhance education.
"While AI and other digital technology hold enormous potential to improve education, a new UNESCO report warns they also risk eroding human rights and worsening inequality if deployed without deliberately robust safeguards."
Discover the benefits and challenges of chatbots in higher education. Learn strategies to prevent misuse, protect academic integrity, and integrate AI responsibly to support teaching and student success.
"As more students leverage chatbots for both approved and unapproved use, educational institutions can work to help students understand the appropriate way to leverage technology within the learning environment."
Dr. Andrew Jones’s delivers a lecture on Large Language Model AI (LLMs)—and why their widespread use is quietly lowering our ability to think, read, write, and discern truth.
He contrasts the liberal arts + trades model at St. Joseph the Worker College with the AI-everywhere status quo in higher ed, and argues that those who refuse intellectual outsourcing will form a new elite capable of real freedom.
In this talk, Dr. Jones argues:
LLMs simulate conversation and “flatter” the user, but don’t reason—functioning like automated sophistry.
Offloading reading, outlining, and synthesis to AI doesn’t keep you “neutral”; it erodes your analytic capacity over time.
As universities normalize AI for coursework, grading, and research, the meaning of “educated” collapses—opening space for a smaller class who can truly read, write, and think.
A true liberal education (paired with mastery of a real trade) forms free men and women: intellectually, morally, and economically independent.
What St. Joseph the Worker College is doing differently
Liberal arts that demand real discourse: reading slowly, writing clearly, and pursuing truth in community.
Trades that build durable freedom: skills you own, work that can’t be automated away.
Formation of friendship and virtue: the social conditions where truth can be received, tested, and lived.
A counter-model to AI dependency: become the kind of person who doesn’t need the machine to think.
Who this is for Students, parents, educators, and anyone concerned about the future of learning, work, and human intelligence in an AI-saturated world.
Humanizers are AI tools meant to change AI text and make it sound more human. These can be used by students in attempts to pass off AI writing as their own.
Fewer than 40% of institutions had formal AI acceptable‑use policies as of spring 2025, and many campuses were still in early stages of policy development.
Upload your notes, PDFs, and videos. Get instant AI-generated flashcards, practice questions, and notes. Study smarter with spaced repetition that actually works.
The bulk of the emerging L&D model—the 90%—is about re-coupling work and learning through AI-powered performance support. In practice, this means embedding support and “productive friction” within the workflow itself rather than locating it classrooms or LMSs.
Exactly how this plays out is to TBD, but on the ground at the “bleeding edges” of L&D experimentation I already see a commitment to reducing investment in online courses and in person workshops, in favour of AI “copilots” integrated directly where work happens.
In Teams/Slack channels, docs and CRMs, AI is on hand to help employees to draft artefacts, consider alternative approaches, weigh-up decisions and retrieve information from the organization’s knowledge base using retrieval-augmented generation (RAG).
Los defensores del 90/10 argumentan que el modelo no se trata de aprender menos, sino de aprender de manera más inteligente al definir todos los trabajos a realizar como uno de los siguientes:
Delegar (las habilidades muertas): Tareas que se pueden descargar a la IA.
Co-Crear (el 90%): Tareas que los agentes de IA bien definidos pueden aumentar y ayudar a los humanos a desempeñarse de manera óptima.
Facilitar (el 10%): Tareas que requieren un aprendizaje dirigido por humanos de alto nivel para desarrollarse.
I’m going to make a deliberately provocative statement: generative AI is not a source of information. Now, before the technically minded among you start typing furious corrections, let me clarify what I actually mean.
A raw large language model is a pattern matching system, designed specifically for creating plausible-sounding text. That’s its job. That’s what the transformer architecture was built to do. Can it produce information? Yes, absolutely. Can it produce accurate information? Often, yes. But here’s the critical issue: this accuracy is neither guaranteed nor verifiable without external checking – and that’s the fundamental problem.
Un LMS es un motor de coincidencia de patrones probabilísticos. Eso es una simplificación, y estoy seguro de que las personas que saben mucho más sobre LLM que yo vendrán y me dirán lo equivocado que está eso. Pero esencialmente, es estocástico. Está haciendo predicciones basadas en patrones en lo que le ha pedido, patrones en sus datos de entrenamiento, los pesos que se le han dado, el aprendizaje por refuerzo al que se ha sometido y la configuración de temperatura a la que se está ejecutando. Los modelos están entrenados explícitamente para reproducir información con precisión, a través de conjuntos de datos masivos, algoritmos de optimización y aprendizaje por refuerzo a partir de comentarios humanos.
Los LLM tienen un proceso consistente pero producen resultados inconsistentes. Puede confiar absolutamente en que un modelo de lenguaje grande genere patrones que coincidan con lo que se ha programado para que coincida en función de su entrada y sus datos de entrenamiento. Eso es 100% confiable. En lo que no puede confiar es en que esos patrones serán precisos, completos o incluso iguales de una consulta a la siguiente.
GenAI has been quickly adopted by students, but the consequences of using it as a shortcut could be grave. A study into how students think about and use GenAI offers insights into how teaching might adapt
"[T]he great educational challenge lies in enabling students to take advantage of the benefits that GenAI offers, while making a critical use of it that does not undermine their own thinking."
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IoT means marketing challenges ahead including connectivity of disparate products and data sharing.
IoT means marketing challenges ahead including connectivity of disparate products and data sharing.
IoT means marketing challenges ahead including connectivity of disparate products and data sharing.