Documents artificial intelligence 716 results

"The global economy has proved more resilient than expected this year, supported by improved financial conditions, rising AI-related investment and trade, and macroeconomic policies. However, underlying fragilities are increasing. Labour markets are showing first signs of weakening despite the OECD unemployment rate steady at 4.9%, with job vacancies falling below their 2019 average in many countries and confidence softening. Risks around the outlook remain significant, including the prospect of further trade barriers, a potential sharp repricing of risk in financial markets, potentially amplified by stresses in leveraged non-bank financial institutions and volatile crypto-asset markets. Lingering fiscal concerns could lead to further increases in long-term bond yields, which may tighten financial conditions and elevate debt-service burdens, potentially weighing on economic growth."

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"Artificial intelligence (AI) is expected to reshape labor markets, yet causal evidence remains scarce. We exploit a novel Swedish subsidy program that encouraged small and mid-sized firms to adopt AI. Using a synthetic difference-in-differences design comparing awarded and non-awarded firms, we find that AI subsidies led to a sustained increase in job postings over five years, but with no statistically detectable change in employment. This pattern reflects hiring signals concentrated in AI occupations and white-collar roles. Our findings align with task-based models of automation, in which AI adoption reconfigures work and spurs demand for new skills, but hiring frictions and the need for complementary investments delay workforce expansion."

This work is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

"Russell begins by asserting that the standard model of AI research, in which the primary definition of success is getting better and better at achieving rigid human-specified goals, is dangerously misguided. Such goals may not actually reflect what human designers intend, such as by failing to take into account any human values not included in the goals. If an AI developed according to the standard model were to become superintelligent, it would likely not fully reflect human values and could be catastrophic to humanity. Russell asserts that precisely because the timeline for developing human-level or superintelligent AI is highly uncertain, safety research should be begun as soon as possible, as it is also highly uncertain how long it would take to complete such research.

Russell argues that continuing progress in AI capability is inevitable because of economic pressures. Such pressures can already be seen in the development of existing AI technologies such as self-driving cars and personal assistant software. Moreover, human-level AI could be worth many trillions of dollars. Russell then examines the current debate surrounding AI risk. He offers refutations to a number of common arguments dismissing AI risk and attributes much of their persistence to tribalism—AI researchers may see AI risk concerns as an "attack" on their field. However, Russell reiterates that there are legitimate reasons to take AI risk concerns seriously and that economic pressures make continued innovation in AI inevitable."

"As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook."