The journalist and academic Nicolas Lemann has observed that when the internet came along a quarter century ago, “just about everyone, including liberals, assumed that an unregulated Internet would be a good idea.” Well, policymakers saw what that produced: tech markets dominated by a handful of giant companies, privacy invasions of truly staggering proportions and a blizzard of hate speech and disinformation that no amount of self-regulation seems to control. In my forthcoming book, Regulating Digital Industries, I argue that the U.S. should now establish a digital regulator empowered to set competition, privacy and content moderation rules for companies operating in core digital industries, which include search, ecommerce, social media, the mobile app infrastructure and ad tech. With the negative example of the internet in front of them policymakers have decided that they can and should do better in the case of artificial intelligence. They are seeking to get out in front of this promising yet dangerous technology and establish rules that will ensure that its use will be safe and trustworthy and will lead to economic growth and progress, not discrimination, inequality, loss of human control, technological unemployment, and a host of other potential harms. But how should policymakers do this? Despite the sense among the public that AI suddenly happened with the release of Chat GPT late last year, AI has been making steady progress for a decade or more and this progress has already prompted a policy response.

Full opinion : How To Regulate Artificial Intelligence.

Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.

AI Discipline Interdependence: There are concerns about uncontrolled AI growth, with many experts calling for robust AI governance. Both positive and negative impacts of AI need assessment. See: Using AI for Competitive Advantage in Business.

Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies

Emerging NLP Approaches: While Big Data remains vital, there’s a growing need for efficient small data analysis, especially with potential chip shortages. Cost reductions in training AI models offer promising prospects for business disruptions. Breakthroughs in unsupervised learning could be especially transformative. See: What Leaders Should Know About NLP