No, the robots are not going to take over journalism. Yes, the machines might soon be able to do much routine journalism labour. But the reality and the potential of artificial intelligence (AI), machine learning, and data processing is to give journalists new powers of discovery, creation and connection.
The hope is that journalists will be algorithmically turbo-charged, capable of using their human skills in new and more effective ways. AI could also transform newsrooms from linear production lines into networked information and engagement hubs that give journalists the structures to take the news industry forward into the data-driven age.
Algorithms will power the systems. But the human touch – the insight and judgement of the journalist – will be at a premium. Can the news industry seize this opportunity? Read More
Daily Archives: November 20, 2019
What jobs are affected by AI? Better-paid, better-educated workers face the most exposure
Artificial intelligence (AI) has generated increasing interest in “future of work” discussions in recent years as the technology has achieved superhuman performance in a range of valuable tasks, ranging from manufacturing to radiology to legal contracts. With that said, though, it has been difficult to get a specific read on AI’s implications on the labor market.
In part because the technologies have not yet been widely adopted, previous analyses have had to rely either on case studies or subjective assessments by experts to determine which occupations might be susceptible to a takeover by AI algorithms. Read More
Brookings Institute — AI Governance
Artificial intelligence, machine learning, and data analytics are upending everything from education and transportation to health care and finance. In this series led by Governance Studies Vice President Darrell West, scholars from in and outside Brookings will identify key governance and norm issues related to AI and propose policy remedies to address the complex challenges associated with emerging technologies. Read More
Preparing the Military for a Role on an Artificial Intelligence Battlefield
The Defense Innovation Board—an advisory committee of tech executives, scholars, and technologists—has unveiled its list of ethical principles for artificial intelligence (AI). If adopted by the Defense Department, then the recommendations will help shape the Pentagon’s use of AI in both combat and non-combat systems. The board’s principles are an important milestone that should be celebrated, but the real challenge of adoption and implementation is just beginning. For the principles to have an impact, the department will need strong leadership from the Joint AI Center (JAIC), buy-in from senior military leadership and outside groups, and additional technical expertise within the Defense Department. Read More
China to Take on Leading Role in Medical Technology and Artificial Intelligence
Asia, in particular China, has been advancing significantly on its way to a key role in geopolitics, says correspondent Frank Sieren – and towards spearheading developments in medical technologies. At the same time, the healthcare market there is growing at a remarkable pace. What are the effects on our research and care? For European stakeholders from care delivery, industry, academia, and policymaking, key events such as CMEF offer the opportunity to view and evaluate new products and solutions and to exchange ideas on collaboration with Chinese market players.
First-hand experience is the best option for all who identify opportunities in this setting. Twice each year, the China International Medical Equipment Fair (CMEF) brings together stakeholders from all over the globe. Each spring, the medtech, IVD, and health IT event is part of the Health Industry Summit (tHIS) – a mega-event in Shanghai which sports about 7,400 exhibitors, 130 conferences, and 300,000 attendees. Read More
Why fair artificial intelligence might need bias
Businesses across industries are racing to integrate artificial intelligence (AI). Use cases are proliferating, from detecting fraud, increasing sales, improving customer experience, automating routine tasks, to providing predictive analytics.
With machine learning models relying on algorithms learning patterns from vast pools of data, however, models are at risk of perpetuating bias present in the information they are fed. In this sense, AI’s mimicking of real-world, human decisions is both a strength and a great weakness for the technology— it’s only as ‘good’ as the information it accesses. Read More
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