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Why self-regulation and government intervention are at a crossroads

For some politicians and business leaders, regulation is a dirty word. The arguments generally go that regulation is bad for economies as it stifles innovation and growth. Too much red tape and bureaucracy, too much government intervention. Triumphalist, propaganda-like statements from the White House in the US last October, and a bizarre Economic Report of the President 2020 have done little to clarify the argument, either for or against. Claims that the Trump administration’s repeal of the net neutrality rules will increase real incomes by more than $50 billion per year and consumer welfare by almost $40 billion per year seem ridiculous. There is no evidence that de-regulation in the US has or will improve broadband prices or services, either now or in the future.

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We’re only human: AI ethics and the business of trust

According to figures released earlier this month from the Capgemini Research Institute, ethical AI is not just a major concern for consumers (74 percent), it could also impact customer loyalty. If a business can show ethical AI use, 62 percent of consumers would place higher trust in that company. By contrast, 41 percent said they would complain over misuse of AI and 34 percent would stop interacting with a company if its AI use was unethical.

It’s not wholly unsurprising research but it does raise the whole issue of trust. It also raises the point of what is deemed ethical, when it comes to AI use. Who decides? Businesses, consumers, governments, academics or a mash-up of all of them?

Interestingly, the Capgemini research also revealed that 74 percent of consumers want more transparency when a service is powered by AI, and over three quarters think there should be further regulation on how companies use AI.

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AI is starting to drive survival of the fastest in race for enterprise edge

“We are living in a digital Darwinist era,” says Chetan Dube, founder and CEO of IPSoft, speaking at the company’s Amelia City Lab on State Street, New York. With a view of the Statue of Liberty over his left shoulder and the iconic Staten Island ferry chugging over his right, Dube talked about the company’s latest iteration of Amelia, a digital agent he has dubbed ‘the most human AI’.

While the blonde white avatar is hardly representative of today’s diversity requirements (that’s perhaps a little unfair as Amelia is completely customisable), it is nevertheless pioneering the embryonic market of cognitive agents. It’s important to make a distinction. Amelia is much more than a chat agent. In fact, the term chatbot seems a little demeaning, especially when you get to see the depth of intelligence that Amelia can bring to call conversations.

Chatbots typically manage calls through a structured, scripted framework called a decision tree. Amelia, with the considerable help of Professor Christopher Manning, a leading machine learning, computer science and linguistics expert at Stanford University, is managing complex, contextual conversations, information requests and user verifications. More fluid, less woody.

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Are a skills gap and delusional thinking losing the AI race for Europe?

Job killer or job creator? The debate over whether or not AI will decimate or create jobs in the future has been raging for a few years now and while 2018 was a massive year for AI hype, it was also significant for its development.

There are, according to a Harvey Nash Tech Survey, four in ten organisations now using AI in a commercial way, moving beyond experimentation. A recent report from Dun and Bradstreet claims 40 percent of respondents from a survey of 100 execs from Forbes Global 2000 businesses are adding more jobs as a result of AI deployment, with only eight per cent saying they were axing jobs because of AI implementation. In January this year, research firm Gartner revealed that despite talent shortages, the percentage of enterprises employing AI grew 270 per cent in four years.

While most of this is, in all likelihood, machine learning, the intention is clear. Businesses increasingly want to use data science, analytics and automation in their products and infrastructures and are prepared to invest.

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