SAP has created an AI ethics panel to guide its use of machine-learning technology. If only it had a similar committee for fraud allegations: it might have avoided the corruption scandal engulfing it in South Africa. The German ERP giant – which is accused of kicking back $2m to secure state contracts – claimed it is the first European biz to create a external artificial intelligence ethics board: a five-person committee that includes technical experts and specialists in public policy, ethics, and bioethics. However, while several of them possess solid IT credentials, there's no one with a background in AI. Rather, expertise in the evolving field will come from inside SAP.

Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies. In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked its way up the value chain by producing better and more technology-intensive goods.

Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems [1]. This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought [1]. Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period [2]. The effect of drought can be quantified based on the frequency, duration and intensity in the affected region subject to established timescales.

Machines and algorithms in the workplace are expected to create 133 million new roles, but cause 75 million jobs to be displaced by 2022 according to a new report from the World Economic Forum (WEF) called "The Future of Jobs 2018." This means that the growth of artificial intelligence could create 58 million net new jobs in the next few years. With this net positive job growth, there is expected to be a major shift in quality, location and permanency for the new roles. And companies are expected to expand the use of contractors doing specialized work and utilize remote staffing. In 2025, machines are expected to perform more current work tasks than humans compared to 71% being performed by humans as of now.

In the China model, a nation leverages its large population and low costs to build a base of blue-collar manufacturing. It then steadily works its way up the value chain by producing better and more technology-intensive goods. In the India model, a country combines a large English-speaking population with low costs to become a hub for outsourcing of low-end, white-collar jobs in fields such as business-process outsourcing and software testing. If successful, these relatively low-skilled jobs can be slowly upgraded to more advanced white-collar industries. Both models are based on a country's cost advantages in the performance of repetitive, non-social and largely uncreative work -- whether manual labor in factories or cognitive labor in call centers.

A new study finds that freshmen from 19 colleges in eight countries report symptoms consistent with a diagnosable psychological disorder. "While effective care is important, the number of students who need treatment for these disorders far exceeds the resources of most counseling centers, resulting in a substantial unmet need for mental health treatment among college students," said lead author Randy P. Auerbach, Ph.D., of Columbia University. "Considering that students are a key population for determining the economic success of a country, colleges must take a greater urgency in addressing this issue." For the study, Auerbach and his research team analyzed data from the World Health Organization's World Mental Health International College Student Initiative.

Keller Rinaudo began his career as the cocreator of Romo, a tiny toy robot. But for the past five years his work has been, well, bloodier. His company, Zipline, uses autonomous planes to deliver medical supplies--vaccines, pharmaceuticals, and blood--to hard-to-reach places. It signed its first client, the government of Rwanda, in 2016, and says it now fulfills about a fifth of the blood needs of the country's rural population. Anne Wojcicki, cofounder and CEO of 23andMe, says she was drawn to Rinaudo's "passion, dedication, and laser focus on what he wanted to accomplish."

From self-driving vehicles to smart cities, data is the driver behind AI. According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. A Brookings Institute report notes that "in this regard, the United States has an advantage over China. Global ratings on data openness show that US ranks eighth overall in the world, compared to 93rd for China". But right now, innovation in the United States is limited without a national strategy that answers questions about protocol and ownership.

NEW DELHI – Japanese companies are looking to tap India's medical market with funding and technological solutions to make health care more accessible in the world's second-most populous country. Japan-based venture capital firms like Spiral Ventures and India Japan Partnership Fund LLP are either funding local health tech startups or exploring new investment opportunities in the health care sector, and electronics giant Panasonic Corp. is offering solutions to improve rural health care. India has a huge health care gap between rich and poor and mismatches between doctors and patients. The situation is made worse by low government spending on health care at 1.3 percent of gross domestic product, the lowest among the BRICS grouping of Brazil, Russia, India, China and South Africa. Spiral Ventures has invested in four health tech startups that offer digital solutions for the local market and is scouting for more such startups in which to invest, according to a top company official.

That's how quickly brain damage happen when the cells get no oxygen in a stroke or in some brain injuries. Both can have tragic consequences -- paralysis, memory loss, speech difficulties and even death. But doctors can't start treatment without an initial diagnosis, and that requires reading a CT scan as soon as the test's completed. Unfortunately, that's not what usually happens, said Prashant Warier, co-founder of Qure.ai, a member of our Inception startup accelerator program. "Radiologists typically have a backlog of cases," he said.