Entrepreneur and Tesla cofounder Elon Musk recently sparked controversy when he labeled artificial intelligence (AI) the "biggest existential threat" to humanity.
On the flip side, according to Sandhya Venkatachalam of Centerview Capital Technology, AI can be used to fundamentally rethink how we solve the world's problems and has the potential to greatly improve health care, education, poverty, and security. (1) She predicts that AI technology can benefit society, business, and culture to the degree that the internet itself has. PricewaterhouseCoopers LLC agrees, stating that the real AI--the kind that makes life better--has arrived in the tax function, with many usable tax applications already emerging from both academic research and professional service firms. (2)
Hold on ... you mean AI can help the tax function? Can we capitalize on this new technology to gain competitive advantage? And should we worry about our own job security at the same time? Things can be confusing as the popular media presents artificial intelligence, in the same breath, as the most pressing threat to humanity and the key to improving the world.
Confusion aside, what drives these headlines grounded in future shock is that AI technology is making exponential advances. You hear about AI, but do you understand what it is? Is AI really that far out in the future, or is it already a reality we can't ignore? If you think "artificial intelligence" is just a buzzword, you might not fully appreciate how it can affect your future as a financial or tax technology executive.
Unpacking the space around AI and machine learning (or ML), which are often used interchangeably, reveals that machine learning is more applicable in the near term to the finance and tax world. While we are a long way from true artificial intelligence--which we can define as technology that understands, reasons, makes decisions, provides complex responses, and acts logically like a human--machine learning is at hand. It is an extension of advanced analytics based on the idea that machines can learn from data.
Traditional problem-solving has employed software developers to reason out and write down the steps in code to overcome a given challenge. This activity is arduous and time-consuming, as experts first must explain what the problem is and how it should be solved before technical specialists can code and test it. Enter ML, which works by feeding general-purpose software data that includes sets of input and the expected outputs that then teach the software what drives the relationships. This allows the technology to work out how best to solve the problem without the need for people to manually code software with a specific set of instructions. With ML, you feed the technology a new input and it will be able to, with a high probability, determine the right output. Of course, this prediction is based on a major assumption--that the technology was fed good, clean data to begin with. It's a predicament with which many organizations still struggle.
Although ML and AI are often used interchangeably, their meanings differ. ML can dissect past information to draw insights, whereas AI can look at data patterns identified by ML and act on them. AI can, for example, make informed financial decisions or maneuver a vehicle without human assistance in a dynamic real-world environment. ML, then, is a fundamental part of AI rather than identical to it. That said, we use them interchangeably here, without parsing the specific distinctions. (See Figure 1 on page 27, which shows how they are related.)
Artificial intelligence forges new partnerships between humans and machines. Humans can apply common sense, moral reasoning, imagination, and compassion to their decision-making, whereas AI excels at locating knowledge, identifying patterns, eliminating bias, and operating with endless capacity. However, it is important to note that, for humans, AI is more of a tool than a solution and augments human capabilities rather than replaces them.
AI has the potential to transform society, industries, and governments. It has already begun revolutionizing business processes and the way decisions are made. Market intelligence company IDC forecasts that seventy-five percent of enterprise software will include AI or ML features by 2018. "Software developers and end-user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process," says David Schubmehl, IDC's research director for cognitive systems and content analytics. (3)
Artificial intelligence and machine learning are already deeply intertwined with our day-to-day lives. They are not limited to futuristic products like self-driving cars or autonomous walking robots. There are "thinking machines," and the technology is everywhere around us.
This technology can read handwritten...