Innovation | Artificial intelligence

Innovation | Artificial intelligence

Cheap prediction

AI may be best understood as ‘cheap prediction’. But while it’s a useful input to decisions, people will still be needed

Sholto Macpherson is a technology journalist and editor of DigitalFirst.com, a blog on the latest in accounting technology.

In the mid-1990s an economist called William Nordhaus had a radical idea for valuing the invention of the light bulb. How much effort, he wondered, would it take to produce a similar amount of light with a wood fire?

The answer: to produce an hour of electric light with a light bulb would require chopping wood for 10 hours a day, for six days. Nordhaus went on to create a price index going back to sesame oil-powered lamps from Babylonian times, showing that the real benefit was a dramatic fall in the cost of artificial light.

Researchers have since used this economic approach for valuing technology to examine the internet’s role in lowering the cost of search; transporting, verifying and replicating information; and in tracking behaviour.

And now three academics in Canada have followed this process to cut through the hype around machine learning, the most popular example of artificial intelligence (AI) today. ‘Digging into the technology it became clear that it was a drop in the cost of prediction,’ says Professor Avi Goldfarb, an econometrician who specialises in the science of quantitative marketing. With Professors Ajay Agrawal and Joshua Gans he has co-authored the book Prediction Machines, the simple economics of artificial intelligence.

Not everyone sees machine learning in such a reductive light. Computer science academics emphasise the potential of AI’s ability to learn, and the ramifications this holds for training robots and the like. ‘But that’s not the AI we have today. It hasn’t gone past that one thing – cheap prediction,’ says Gans, a renowned Australian economist who moved to Toronto in 2010.

Better forecasts

Prediction is the process of filling in missing information. It takes the information (or data) you have, and uses it to generate information you don’t have.

Given the great advances in AI, calling it ‘cheap prediction’ seems a little underwhelming. It suggests we haven’t yet reached a drop in the cost of intelligence; we’re only reducing the cost of one part of it. Yet this part is a critical step. Machine learning’s probabilistic model mimics our own learning process, a process that developed through millennia of evolution. Prediction, argues another author, Jeff Hawkins, is the basis for human intelligence. ‘Prediction is not just one of the things your brain does. It is the primary function of the neocortex, and the foundation for intelligence. The cortex is an organ of prediction,’ Hawkins wrote in his book, On Intelligence.

Prediction Machines explains that machine learning is not on its own a tool to replace professionals; it is merely a tool for improving prediction. And prediction is one of several inputs into the process of decision making, the authors argue. Another is that undervalued input called judgment.

‘Prediction facilitates decisions by reducing uncertainty, while judgment assigns value,’ the authors write. Luckily for accountants, value is a difficult thing for machines to assess. Machine learning may speed up the process of making predictions by categorising and sorting data and spotting patterns. But turning those lessons into business advice, and prioritising them in terms of success, requires analysing a combination of emotional, intellectual and practical considerations.

‘In economists’ parlance, a judgment is the skill used to determine a payoff, utility, reward or profit,’ the authors explain. ‘The most significant implication of prediction machines is that they increase the value of judgment.’ ‘You can appreciate what else people do to make a decision,’ Gans says. ‘They can’t just predict things. They also have to know what the trade-offs are, and these things only come from people. Then you start to understand why it’s really hard to create a fully automated thing, because we may not understand the nature of decisions that the robot needs to make.’

When will AI move beyond cheap prediction to making judgments? Few agree on the timeline for a breakthrough of that magnitude; the predictions range from imminent to almost never. ‘Someone might switch on a robot AI that works it out itself and just becomes sentient. I’m not a computer scientist so I can’t give you a probability, but my feeling is that it’s not for a long time,’ Gans says. ‘We’ve got a lot to do with the AI we currently have and that’s going to keep people occupied for the moment.’

AI onslaught

A wave of machine learning applications is breaking across the business world. One of the latest is Google’s word processor for Google Docs that automatically corrects your grammar in real time. AI is being quickly built into other programs, from SME accounting software to enterprise resource planning (ERP). But Gans cautions against believing everything you hear.

‘I wrote the book because I was concerned that people would say, “Buy my magic AI!” and it would turn out to be not that good,’ he says. ‘I don’t think there’s any need to rush to add it to your operations.’

The use case for accountants in practice is more clear-cut.

‘Accounting does have data going for it, so it’s only a step away from being put to use,’ Gans says. Forensic accounting and auditing are already making way for algorithm-driven programs that process huge volumes of transactions. These programs can pull up a shortlist of transactions to check for fraud or error (see panel).

Will accountants will replaced by machines? Goldfarb believes this is unlikely. Fifty years ago, accountants spent most of their time doing arithmetic. When the spreadsheet arrived it dramatically lowered the cost of doing arithmetic and helped customers make decisions. Before spreadsheets arrived, one would have expected the arrival of such a powerful decision-making tool to reduce the need for accountants. ‘But the numbers have remained steady,’ Goldfarb says.

‘Most of the tasks that accountants do today they will not be doing in 10 to 15 years from now. That doesn’t mean we won’t have lots of accountants, because these tools will enable accountants to better serve clients which will open up new opportunities.’

And of course, accountants should understand the capability of machine learning and other technologies to improve their clients’ businesses.


Read the CA ANZ report, Machines can learn, but what will we teach them?


 

Using AI to automate audits

Accountants are already using machine learning software to audit accounting files in minutes. And the task of poring over spreadsheets to match transactions looks like one of the first to fall under the wheels of automation.

Radlee Moller was at a partner retreat in Hawaii when he first realised the opportunity for machine learning.

Managing partner at CA firm CIB Accountants and Advisers, in Parramatta, NSW, Moller was intrigued at claims made by a software company, MindBridge Ai Auditor, that it could automate most of the legwork for auditors.

Moller invited MindBridge CEO Solon Angel to Australia and watched Angel run the ‘Pepsi challenge’. The software took five minutes to audit a file and find the four mistakes within that had taken a five-person team three weeks. It even revealed a fifth, unknown error.

Moller timed the software on other files at CIB. ‘It took 12 minutes for the biggest file in the firm,’ Moller says.
Several months later, Moller had convinced Angel to let him distribute MindBridge to Australian firms.

The startup is already making millions in revenue, has 120 customers – including the Bank of England – and is preparing for an IPO in 2021.

CIB Accountants hasn’t dropped its fees for audits, despite the time saved. ‘I tell clients there’s a software cost. We don’t pass that on; we wear it,’ Moller says.

Moller’s experience fits the prediction made by Deloitte Australia in a 2017 report. It identified auditing as the most likely role for automation.

‘Auditors will eventually veer towards the forensic accounting, accuracy, validation type of role rather than sitting with Excel spreadsheets trying to manually reconcile thousands of transactions,’ says Gavin Whyte, chief data scientist at Deloitte Australia.

Whyte has been developing inhouse algorithms that replicate MindBridge’s smarts. The Big Four firm can customise them for different clients or applications, Whyte says.