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Botch Artificial Intelligence, Go Out Of Business, Executives Fear

The scaled AI is generally across point solutions, such as personalization.” Awalegaonkar and his co-authors provide the following advice to bring AI out of the labs and into the mainstream of organizations: Drive “intentional” AI: Nearly three-quarters of AI leaders (71%) say they have a “clearly-defined strategy and operating model for scaling AI in place,” while only half of the lagging companies.

Tune out data noise: “After years of collecting, storing, analyzing, and reconfiguring troves of information, most organizations struggle with the sheer volume of data and how to cleanse, manage, maintain, and consume it,” the Accenture authors state.

“They recognize the importance of business-critical data— identifying financial, marketing, consumer, and master data as priority domains.” They also use the right AI tools, the report continues, “things like cloud-based data lakes, data engineering/data science workbenches with model management and governance, data and analytics marketplaces and search—to manage the data for their applications.” Treat AI as a team sport: Take AI out of the IT labs and put teams from across the organization in charge of it.

Is China gaining an edge in artificial intelligence?

'China is betting on AI and investing in AI and deploying AI on a scale no other country is doing,' says Abishur Prakash, a futurist and author of books about the effect of artificial intelligence (AI) on geopolitics.

In the last few years, Washington has toughened oversight of Chinese investments, banned US firms from doing business with certain Chinese companies and increased criminal prosecution of alleged technology theft.

The Trump administration has imposed tariffs on billions of dollars worth of Chinese goods - retaliation for 'unfair' practices it says are aimed at giving China an advantage in the field.

that we aid their mercantilist strategy through free trade and open investment in our technology sector is a choice,' US Department of Defense officials wrote in a widely cited 2018 report.

He says the US has more experience building tech companies, but China may have the advantage when it comes to AI applications relying on big data sets - and points to the medical field as an example.

Last year, Chinese investment in the US dropped to $4.8bn (£3.7bn) - its lowest level since 2011 - while US investment in China dipped from $14bn to $13bn, according to the Rhodium Group's annual report.

Mr Prakash, who works with start-ups, tech firms and governments on questions of artificial intelligence, says while many western firms continue to pursue opportunities in China, current tensions have changed the discussions.

In the meantime, he says Washington's increasingly nationalist tone risks alienating America's foreign students and researchers - many of them Chinese - who have played a critical role in US tech leadership to date.

American national plans have also called for boosting investment, reforming the immigration system and improving education, but those are much more difficult to achieve, says William Carter, deputy director of technology policy at Washington's Center for Strategic and International Studies.

At a recent conference, a US official argued that Chinese tech companies are 'de facto tools' of the state's Communist Party, saying they 'have become deeply enmeshed in Beijing's system of oppression at home and its increasingly assertive strategic ambitions globally.'

As artificial intelligence technologies drive debates over values like surveillance and privacy, free speech and censorship, conflicts between the two countries are likely to increase, says Nicholas Wright, a fellow at the New America think tank in Washington, who has worked with the US and UK governments.

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Fears about robot overlords are (perhaps) premature

In “Artificial Intelligence: A Guide for Thinking Humans,” Melanie Mitchell, a computer science professor at Portland State University, tells the story, one of many, of a graduate student who had seemingly trained a computer network to classify photographs according to whether they did or did not contain an animal.

She was accompanying her mentor, Douglas Hofstadter, a pioneer in the field who spoke passionately that day about his profound fear that Google’s great ambitions, from self-driving cars to speech recognition to computer-generated art, would turn human beings into “relics.” The author’s own, more measured view is that AI is not yet poised to be successful precisely because machines lack certain human qualities.

Posing the question “Will AI result in massive unemployment for humans?” she answers, “I don’t know.” (She adds that her guess is that it will not.) She predicts that AI will not master speech recognition until machines can actually understand what speakers are saying but then acknowledges that she’s “been wrong before.” While she’s an AI booster, Mitchell expresses a number of concerns about future implementations of the technology.

The field is currently dominated by deep learning, which involves networks training themselves by consuming vast amounts of data, and the author warns that “there is a lot to worry about regarding the potential for dangerous and unethical uses of algorithms and data.” She also points out that AI systems are easily tricked, making them vulnerable to hackers, which could have disastrous consequences where technologies like self-driving cars are concerned.

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