A startup from the University of California-Los Angeles (UCLA) is developing an AI-based tool that analyzes spine images to inform patients whether or not they need surgery. Based on research conducted by UCLA neurosurgeon Dr. Luke Macyszyn, the startup Theseus AI aims to address the costly medical problem of unnecessary spine surgeries. Studies show that between 20 and 40 percent of spine surgeries fail to relieve pain, and there are more than 250,000 performed each year. "This is somewhat personal for me in that my own father had spine surgery twice," says Sam Elhag, CEO of Theseus AI. "To this date, it's uncertain as to whether or not it all made sense." Elhag launched Theseus to make the interpretation of spine MRIs less subjective.

Fearing that a newfangled technology would put them out of work, neighbors broke into the house of James Hargreaves, the inventor of the spinning jenny, and destroyed the machine and also his furniture in 18th-century England. Queen Elizabeth I denied an English priest a patent for an invention that knitted wool, arguing that it would turn her subjects into unemployed beggars. A city council dictated that Anton Möller, who invented the ribbon loom in the 16th century, should be strangled for his efforts. But centuries of predictions that machines would put humans out of work for good -- a scenario that economists call "technological unemployment" -- have always turned out to be wrong. Technology eliminated some jobs, but new work arose, and it was often less grueling or dangerous than the old.

Weather forecasting is notoriously difficult, but in recent years experts have suggested that machine learning could better help sort the sunshine from the sleet. Google is the latest firm to get involved, and in a blog post this week shared new research that it says enables "nearly instantaneous" weather forecasts. The work is in the early stages and has yet to be integrated into any commercial systems, but early results look promising. In the non-peer-reviewed paper, Google's researchers describe how they were able to generate accurate rainfall predictions up to six hours ahead of time at a 1km resolution from just "minutes" of calculation. That's a big improvement over existing techniques, which can take hours to generate forecasts, although they do so over longer time periods and generate more complex data.

AI has a long history. One can argue it even started long before the term was first coined; mostly in stories and later in actual mechanical devices called automata. This chapter only covers events relevant to the periods of AI winters without being too exhaustive in hope to extract knowledge that can be applied today. To aid understanding the phenomenon of AI Winters, the events leading up to them are examined. Many early ideas about thinking machines appeared in the late 1940s to '50s by people like Turing or Von Neumann. Turing tried to frame the questions of "Can machines think?" differently and created the imitation game, now famously called the Turing Test.

China is on its way to becoming the first global superpower for Artificial Intelligence. The People's Republic of China has the most ambitious AI strategy of all nations and provides the most resources worldwide for its implementation. China combines a gigantic amount of data with talent, companies, research and capital to build the world's leading AI ecosystem. In 2017, the State Council of the People's Republic of China (also known as the Central People's Government) published the Artificial Intelligence Development Plan (here you can find the original document in English). This strategy is part of the even bigger national "Made in China 2025" plan and will also be linked to the new (digital) Silk Road.

Commerce and industry minister Piyush Goyal on Monday said artificial intelligence (AI) and machine learning can help India achieve the $5 trillion economy benchmark over the next five years. "Our government believes artificial intelligence, in different forms, can help us achieve the $5 trillion benchmark over the next five years, but also help us do it effectively and efficiently," Goyal said while inaugurating the National Stock Exchange (NSE) Knowledge Hub. The hub is an AI-powered learning ecosystem for the banking, financial services and insurance (BFSI) sector. The minister referred to an Accenture report that said AI and machine learning have the potential to contribute nearly $1 trillion to the Indian economy by 2035 and said the Knowledge Hub created by NSE will fill in the gaps and help the financial sector to move into the future. Goyal said Prime Minister Narendra Modi sat through a whole day with officials from various ministries to get the sense of urgency to understand the importance of AI and how it can help put the Indian economy on the fast track.

Reproducibility has been an important and intensely debated topic in science and medicine for the past few decades.1 As the scientific enterprise has grown in scope and complexity, concerns regarding how well new findings can be reproduced and validated across different scientific teams and study populations have emerged. In some instances,2 the failure to replicate numerous previous studies has added to the growing concern that science and biomedicine may be in the midst of a "reproducibility crisis." Against this backdrop, high-capacity machine learning models are beginning to demonstrate early successes in clinical applications,3 and some have received approval from the US Food and Drug Administration. This new class of clinical prediction tools presents unique challenges and obstacles to reproducibility, which must be carefully considered to ensure that these techniques are valid and deployed safely and effectively.

Cerner was interviewing Silicon Valley giants to pick a storage provider for 250 million health records, one of the largest collections of U.S. patient data. Google dispatched former chief executive Eric Schmidt to personally pitch Cerner over several phone calls and offered around $250 million in discounts and incentives, people familiar with the matter say. Google had a bigger goal in pushing for the deal than dollars and cents: a way to expand its effort to collect, analyze and aggregate health data on millions of Americans. Google representatives were vague in answering questions about how Cerner's data would be used, making the health-care company's executives wary, the people say. Eventually, Cerner struck a storage deal with Amazon.com The failed Cerner deal reveals an emerging challenge to Google's move into health care: gaining the trust of health care partners and the public.

Retailers in Asia-Pacific are struggling to unite online and offline data and this will hinder their ability to recognise customers who engage with their brands across both channels. Furthermore, while most acknowledge the importance of artificial intelligence (AI) to their organisation's competitiveness, few have started to deploy such tools. While some retail organisations recognised the need to straddle both the online and offline channels, the biggest challenge these omnichannel retailers faced today was pulling data from both realms to establish a common view of their customers, said Raj Raguneethan, Microsoft's Asia regional business lead for retail and consumer goods. This gap hindered their ability, for instance, to recognise customers who had engaged the brand online when they walked into a physical store. To plug the gap, Raguneethan said retailers should establish a data management platform to pull together all customer information and stitch these together to provide unified profiles of their customers. With the launch of its national artificial intelligence (AI) strategy, alongside a slew of initiatives, the Singapore government aims to fuel AI adoption to generate economic value and provide a global platform on which to develop and testbed AI applications.

The White House recently released guidelines on the development and deployment of artificial intelligence technology by federal agencies. While privacy and safety concerns surround the growing use and development of AI tools, the White House seems to be taking a softer approach toward regulation of the technology. In its memorandum published Wednesday, the White House said "federal agencies must avoid regulatory or non-regulatory actions that needlessly hamper AI innovation and growth." Simply put, artificial intelligence is a branch of computer science that trains machines or software to operate and solve problems as humans would. "Artificial intelligence is building systems that do things that if a human being were to do them, you'd consider them intelligent," said Kristian Hammond, computer science professor at Northwestern University.