Artificial intelligence is rapidly advancing to the point where it may be able to write its own code, potentially leading to significant job displacement, societal problems, and concerns about unregulated use in areas like warfare.
## Questions to inspire discussion.
Career Adaptation.
đŻ Q: How should workers prepare for AIâs impact on employment? A: 20% of jobs including coders, medical, consulting, finance, and accounting roles will be affected in the next 5 years, requiring workers to actively learn and use large language models to enhance productivity or risk being left behind in the competitive landscape.
Economic Policy.
đ Q: What systemic response is needed for AI-driven job displacement? A: Government planning is essential to manage massive economic transitions and job losses as AIâs exponential growth reaches a tipping point, extending beyond manufacturing into white-collar professions across multiple sectors.
Greater access to legal cannabis is associated with a significant drop in daily opioid use, suggesting that cannabis availability may reduce reliance on opioids for pain or other use.
How can cannabis legalization influence opioid use? This is what a recent study published in Drug and Alcohol Dependence hopes to address as a team of researchers investigated behavior connections between cannabis use and opioid use. This study has the potential to help scientists, medical professionals, legislators, and the public better understand the benefits of cannabis, including how it can help the opioid epidemic.
For the study, the researchers analyzed survey data collected from 28,069 individuals designated as people who inject drugs (PWID) during 2012, 2015, 2018, and 2022 across 13 states. The goal of the study was to compare medical cannabis and medical plus recreational cannabis use to opioid use. The respondents were asked to report their past 30-day use for both cannabis and opioids. In the end, the researchers found that users who subscribed to both medical plus recreational cannabis use compared to just medical cannabis use experienced a 9â11 percent decline in opioid use.
The study notes in its conclusions, âCannabis legalization may shape daily opioid consumption among PWID, potentially reducing drug-related harms. Differences in cannabis use following legalization may reflect disparate impact by race, due to structural racism or other factors. Future research examining whether policy attributable changes in substance use manifest health benefits among PWID is critical to developing evidence-based cannabis reform.â
Humanoid robots with full-body autonomy are rapidly advancing and are expected to create a $50 trillion market, transforming industries, economy, and daily life ## ## Questions to inspire discussion.
Neural Network Architecture & Control.
đ€ Q: How does Figure 3âs neural network control differ from traditional robotics? A: Figure 3 uses end-to-end neural networks for full-body control, manipulation, and room-scale planning, replacing the previous C++-based control stack entirely, with System Zero being a fully learned reinforcement learning controller running with no code on the robot.
đŻ Q: What enables Figure 3âs high-frequency motor control for complex tasks? A: Palm cameras and onboard inference enable high-frequency torque control of 40+ motors for complex bimanual tasks, replanning, and error recovery in dynamic environments, representing a significant improvement over previous models.
đ Q: How does Figureâs data-driven approach create competitive advantage? A: Data accumulation and neural net retraining provides competitive advantage over traditional C++ code, allowing rapid iteration and improvement, with positive transfer observed as diverse knowledge enables emergent generalization with larger pre-training datasets.
đ§ Q: Where is the robotâs compute located and why? A: The brain-like compute unit is in the head for sensors and heat dissipation, while the torso contains the majority of onboard computation, with potential for latex or silicone face for human-like interaction.
Are we chasing the wrong goal with Artificial General Intelligence, and missing the breakthroughs that matter now?
On this episode of Digital Disruption, weâre joined by former research director at Google and AI legend, Peter Norvig.
Peter is an American computer scientist and a Distinguished Education Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). He is also a researcher at Google, where he previously served as Director of Research and led the companyâs core search algorithms group. Before joining Google, Norvig headed NASA Ames Research Centerâs Computational Sciences Division, where he served as NASAâs senior computer scientist and received the NASA Exceptional Achievement Award in 2001.He is best known as the co-author, alongside Stuart J. Russell, of Artificial Intelligence: A Modern Approach â the worldâs most widely used textbook in the field of artificial intelligence.
Peter sits down with Geoff to separate facts from fiction about where AI is really headed. He explains why the hype around Artificial General Intelligence (AGI) misses the point, how todayâs models are already âgeneral,â and what truly matters most: making AI safer, more reliable, and human-centered. He discusses the rapid evolution of generative models, the risks of misinformation, AI safety, open-source regulation, and the balance between democratizing AI and containing powerful systems. This conversation explores the impact of AI on jobs, education, cybersecurity, and global inequality, and how organizations can adapt, not by chasing hype, but by aligning AI to business and societal goals. If you want to understand where AI actually stands, beyond the headlines, this is the conversation you need to hear.
In this episode: 00:00 Intro. 01:00 How AI evolved since Artificial Intelligence: A Modern Approach. 03:00 Is AGI already here? Norvigâs take on general intelligence. 06:00 The surprising progress in large language models. 08:00 Evolution vs. revolution. 10:00 Making AI safer and more reliable. 12:00 Lessons from social media and unintended consequences. 15:00 The real AI risks: misinformation and misuse. 18:00 Inside Stanfordâs Human-Centered AI Institute. 20:00 Regulation, policy, and the role of government. 22:00 Why AI may need an Underwriters Laboratory moment. 24:00 Will there be one âwinnerâ in the AI race? 26:00 The open-source dilemma: freedom vs. safety. 28:00 Can AI improve cybersecurity more than it harms it? 30:00 âTeach Yourself Programming in 10 Yearsâ in the AI age. 33:00 The speed paradox: learning vs. automation. 36:00 How AI might (finally) change productivity. 38:00 Global economics, China, and leapfrog technologies. 42:00 The job market: faster disruption and inequality. 45:00 The social safety net and future of full-time work. 48:00 Winners, losers, and redistributing value in the AI era. 50:00 How CEOs should really approach AI strategy. 52:00 Why hiring a âPhD in AIâ isnât the answer. 54:00 The democratization of AI for small businesses. 56:00 The future of IT and enterprise functions. 57:00 Advice for staying relevant as a technologist. 59:00 A realistic optimism for AIâs future.
In 2026, government technological innovation has reached a key turning point. After years of modernization plans, pilot projects and progressive acceptance, government leaders are increasingly incorporating artificial intelligence and quantum technologies directly into mission-critical capabilities. These technologies are becoming essential infrastructure for economic competitiveness, national security and scientific advancement rather than merely scholarly curiosity.
We are seeing a deliberate change in the federal landscape from isolated testing to the planned implementation of emerging technology across the whole government. This evolution represents not only technology momentum but also policy leadership, public-private collaboration and expanded industrial capability.
The original version of this article, published on August 25, 2016, included an incorrect immunoblot image. The anti-H3 loading control shown in Figure 2C was inadvertently duplicated from a separate project during manuscript preparation. This was an unintentional oversight.
Normally, the authors would replace the incorrect image with the correct one. However, because the experiments were performed more than 10 years ago, the original image/film could not be located. This is consistent with the authorsâ institutional data-retention policy, which requires data to be kept for only three years after a projectâs completion. After discussion, the authors and editors agreed that removing the immunoblot images from Figure 2C would prevent confusion for future readers without changing the articleâs central conclusions.
Cloudflare has shared more details about a recent 25-minute Border Gateway Protocol (BGP) route leak affecting IPv6 traffic, which caused measurable congestion, packet loss, and approximately 12 Gbps of dropped traffic.
The BGP system helps route data across different networks called autonomous systems (AS) that send it to destination through smaller networks on the internet.
The incident was caused by an accidental policy misconfiguration on a router and affected external networks beyond Cloudflare customers.
While incidence rates for central nervous system cancer remained stable from 1990 to 2021, both mortality and disability-adjusted life-years (DALYs) declined. Disparities by geography, age, sex, and sociodemographic status highlight needs for targeted health policy reforms and resource redistribution.
Findings In this cross-sectional study, analysis of the Global Burden of Disease Study 2021 data on US CNS cancers revealed that although the incidence rate remained relatively stable, both disability-adjusted life-years and mortality rates declined. However, substantial disparities persisted across geographical location, age, sex, and sociodemographic profile.
Meaning The persistent disparity in CNS cancer burden highlights the urgent need to reevaluate public health policies and redistribute health care resources to better support marginalized and underserved populations.
This paper formalizes biological intelligence as search efficiency in multi-scale problem spaces, aiming to resolve epistemic deadlocks in the basal âcognition warsâ unfolding in the Diverse Intelligence research program. It extends classical work on symbolic problem-solving to define a novel problem space lexicon and search efficiency metric. Construed as an operationalization of intelligence, this metric is the decimal logarithm of the ratio between the cost of a random walk and that of a biological agent. Thus, the search efficiency measures how many orders of magnitude of dissipative work an agentic policy saves relative to a maximal-entropy search strategy. Empirical models for amoeboid chemotaxis and barium-induced planarian head regeneration show that, under conservative (i.e.
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.
