Rod Pardo (@Rod_Pardo) header
Rod Pardo (@Rod_Pardo) profile picture

Improving human development in the workplace. RT the best on #FutureofWork #StrategicForesight #HRM #Science #Tech

Joined July 2010
Visibilidad no es productividad. Las oficinas abiertas son un desastre ergonómico: provocan 62% más ausentismo y mayor rotación. Quienes trabajan en oficinas abiertas reportan mayores niveles de estrés y burnout. Evidencia de los últimos 15 años: ryanstephens.me/the-open-off…
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McKinsey's Global Economics Intelligence delivers a detailed monthly analysis of the world economy. Each release features an executive summary covering critical global trends and risks, alongside focused insights on the most recent national and regional developments. ➡️ This month: Central banks move to tighten interest rate policies as energy price spikes fuel inflation; growth looks sparse in some economies, but activity indicators see manufacturing and services expanding. 📈 Explore the analysis: mck.co/48LKLDz
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A team led by @MITEECS Professor Farnaz Niroui has created a new computing platform that could be used to develop intelligent and adaptive next-generation electronics. news.mit.edu/2026/nanoscale-…
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Rod Pardo retweeted
“The Effect: An Introduction to Research Design and Causality” [2nd edition] is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference: link.amazon/B0f3yyLpC
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The BRAZIL 3-disc (DVD) set, is one of the best CRITERION COLLECTION releases of all time. In 2006, Criterion released a 4th disc with THE FINAL CUT in 1.78:1 aspect ratio (in the 3-disc set it framed in 1.85:1).
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Thinking Clearly with Data — Guide to Quantitative Reasoning and Analysis: link.amazon/B0bLHiDpo Amazon Summary: 🟦 Ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields 🟥 Introduces the basic toolkit of data analysis―including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity 🟦 Uses real-world examples and data from a wide variety of subjects 🟥 Includes practice questions and data exercises
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New Cambridge Element, Geopolitics of Artificial Intelligence, by Boris Babic and Brian Wong Yue Shun @BrianWongOPR, out now! Read Open Access at cup.org/4iJk88b #cambridgeelements #openaccess #philosophy
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Google automated the research paper. Google built a team of AI agents that writes a full research paper from a scientist's rough notes. The system is called PaperOrchestra. You give it a short summary of the idea and the raw experiment results, and it returns a finished paper formatted for a conference. Five AI agents split the work. One plans the outline, one draws the charts and diagrams, and one finds related research and checks that every cited paper exists. A fourth writes the sections, and a fifth revises the draft using feedback from a simulated peer review. The team tested it on 200 papers accepted at two top AI conferences. They reduced each paper to its idea and its results, then had the system write it again. ✅ It took about 40 minutes per paper on average. ✅ An AI reviewer gave its papers simulated acceptance rates of 84% and 81%. The real human-written papers got 86% and 94%. ✅ It cited about 46 to 48 papers per manuscript. Humans cited about 59, and other AI writers cited 9 to 14. ✅ Human judges preferred its literature reviews over other AI systems' by margins of 50% to 68%. ✅ Against the human-written literature reviews, it tied or won 43% of the time. The limits are in the paper too. A second AI reviewer gave it 48% and 22%, against 71% and 63% for the human papers. When 11 AI researchers compared full papers side by side, they preferred the human-written one 81% of the time. The acceptance rates come from AI reviewers, so I'd treat them as estimates. The system still needs a human to supply the idea and run the experiments, and the authors say humans stay accountable for what the paper claims.
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Goldman Sachs Research expects steady global economic growth in 2027 amid lower energy prices and ongoing investment in AI. Read the 2027 Outlook: click.gs.com/g0kj
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OECD mining regions have 2% higher GDP per capita than rural regions, yet face challenges in economic diversification and well-being. Explore the new #OECD Guidelines for practical steps to build stronger, more resilient #MiningRegions 🔗brnw.ch/21x76Ic
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Some people can hold clear mental images in their mind—even entire movies—while others can’t do it at all. The reason why could reveal clues to how we think. sciam.com/article/why-the-br…
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Rod Pardo retweeted
Bir hedefi gerçekleştirmeye yönelik niyetiniz zayıfsa, plan yapmak çalışma sürenizde anlamlı bir artış sağlamaz.
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I died. This has to be the best one yet
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In the 1960s, an MIT scientist built the first chatbot. It pretended to be a therapist. It didn't understand a single word you said. It just kept asking you to tell it more. His own secretary asked him to leave the room. His name was Joseph Weizenbaum. The program was called ELIZA. He published it in 1966 and named it after Eliza Doolittle, the flower girl in Pygmalion who is taught to speak like a lady. Type that your boyfriend made you come here, and it answered: "YOUR BOYFRIEND MADE YOU COME HERE" That was the whole trick. His secretary had watched him build it for months. She knew it was only code. After a few sentences she asked him: "Would you mind leaving the room, please?" She wasn't the only one. He wrote that some people were very hard to convince they weren't talking to a human. When he suggested he might look at the conversations people had with it, he was "bombarded with accusations" that it "amounted to spying on people's most intimate thoughts." Then it got worse. Practicing psychiatrists seriously believed a program like this could grow into "a nearly completely automatic form of psychotherapy." Weizenbaum was shocked. In 1976 he published a book warning people about computers. He wrote that a very short time with a simple program could cause "powerful delusional thinking in quite normal people." He spent the rest of his life warning the world about what he had built. He had fled Nazi Germany with his family as a boy. In 1996 he moved back to Berlin. He died in 2008, at 85. Sixty years later, telling a chatbot your problems is normal. He never built a machine that understood people. He built the first proof that it doesn't have to. Have you ever told a chatbot something you've never told a person?
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