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How artificial intelligence is helping farmers and babies in the developing world

Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review’s EmTech Digital conference in San Francisco on Tuesday.

Solomon Assefa, who oversees IBM’s research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions.

This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest.

After the workers use their mobile phones to take short video clips of the infants, Wadhwani AI’s software tools can build 3D models of the babies and estimate their weight, length, and head circumference.


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Machine learning – a kind of sub-field of artificial intelligence (AI) – is a means of training algorithms to discern empirical relationships within immense reams of data.

Similar processes that refine algorithms to recognise or discover patterns in reams of data are now running right across the global economy: medicine, law, tax collection, marketing and research science are among the domains affected.

Welcome to the future, say the economist Erik Brynjolfsson and the computer scientist Tom Mitchell: machine learning is about to transform our lives in something like the way that steam engines and then electricity did in the 19th and 20th centuries.  Signs of this impending change can still be hard to see.

Might machine learning bring us full-circle in the history of economic thought, to where measures of economic centralisation and control – condemned long ago as dangerous utopian schemes – return, boasting new levels of efficiency, to constitute a new orthodoxy?

Tacit knowledge will probably remain the preserve of human beings – with implications not only for the prospect of a return of the command economy, but also for broader fears and hopes about a future powered by machine learning.

Governments could know enough to manipulate demand to smooth out fluctuations in the trade cycle – this was the Keynesian wager widely embraced after 1940 – but supply was another matter, better left to the unfettered interaction of individuals and firms.

The Russian economist and Nobel laureate Leonid Kantorovich spent six years trying to figure out an optimal price for Soviet steel production in the 1960s – far too slow to be useful in practice.

If Lange was right, and relations between supply and demand in a given market could be formulated algebraically, the exponential growth of computer power made it only a matter of time before determining price centrally by Lange’s method became feasible.

Plotting out market relations algebraically for the convenience of the central planners involved compressing the rich and complex data dispersed among individuals in a given market into a set of statistics.

Market coordination engaged the tacit dimension of human cognition innately: every manager thinking about hiring and every homemaker sizing up a side of beef drew on inarticulate know-how in making their decisions.

The inarticulate know-how that enables specialists to apply the relevant learning can be imparted only in person – that’s why, the world over, students follow doctors on ward rounds.

The apprenticeship that humans go through to learn to tell a freckle from a cancer is not simply a matter of processing piles of empirical information to discern what function of X input (symptoms with which potentially cancerous patients present) Y (cancer) happens to be.

The human student learns the equivalent skill – a sense or feel for which spots are innocent, and which spell trouble – in tandem with a whole vocation of which diagnosing melanoma forms only part.

But even if the incentives shifted to channel investment towards the design of so-called ‘general artificial intelligence’ systems – robots such as Hal in Stanley Kubrick’s film 2001: A Space Odyssey (1968) – nothing in the development of machine learning so far suggests that such investments would bear any fruit.

The kinds of AI applications that become conceivable by virtue of machine learning will frequently be able to answer specific, tightly framed questions (‘Is this mole cancerous?’) better than human beings can.

‘How many bags of rocket will this south London Tesco sell in the second week of March?’ is the kind of question that – given the right data – machine learning can answer better than any human being.

Given the right data, real-time regressions parsing relationships between output statistics and other indexes of wellbeing could be made available, giving new momentum to attempts to shift focus away from GDP.

Just as machine learning brings us no nearer the advent of ‘general’ AI systems, it does not shunt us into some new epistemological paradigm where tacit knowledge-based arguments against centralised planning suddenly lose their validity.

But even if algorithms representing the world in a storm of zeroes and ones recreated the market electronically under centralised control, the price signals that their system sent wouldn’t work as effectively as those generated now.

Tacit and otherwise unspecifiable parts of the knowledge that feed into market-based economic decisions would be lost in the translation of currently dispersed information into the new planning tsars’ digital code.

Some of this unspecifiable knowledge would survive, because the most advanced applications of machine learning – the kinds of systems that resemble human cognition in their discharge of task-specific functions – depend upon continual oversight by, and interaction with, humans for their success.

That is, they interact with human engineers, crunching huge reams of data to frame and reframe problems, and then watching humans solve them once the kinds of cognition called for exceed AI capabilities, growing more adept with each iteration.

And for as long as humans remain ‘in the loop’, centralising control of economic activity by mobilising machine learning and the AI systems it enables would also concentrate power in the hands of the humans who make the systems what they are.

By controlling access to data, companies such as Google and Facebook are monopolising a valuable resource The fact that the most promising applications of machine learning keep humans ‘in the loop’ means that, if ever the prospect of an AI-powered revival of the command economy made it out of the pages of academic journals and into mainstream discussion, there would be reason to worry about whom the algorithms really empowered.


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