ROOTING FOR THE MACHINES

Credit: Simon Smith, Chief Technology Officer, MooD International

The world of intelligent machines has been with us for many decades, becoming a scientific discipline in the late 1990s as cheap computing power enabled data scientists to test the limits of a machine’s capability to learn. While we are currently experiencing an explosion in their function in game-based scenarios, it is their potential in business performance applications that could be most significant. If we get this right, machines could become the most important ally in future-proofing modern-organisations.

What is machine learning?

Machine learning is based on algorithms that, given enough example data, can learn to do something useful without having to be explicitly programmed with rules. It’s a rapidly evolving field, with an increasing number of achievements being publically declared and debated.

In 2007, Fei-Fei Li, the head of Stanford’s Artificial Intelligence (AI) lab created software capable of looking at pictures of complex scenes and identifying what’s happening. The accuracy of the results captured significant attention, and arguably spurred a renewed drive to create algorithms capable of thought as sophisticated as humans. DeepMind’s AlphaGo’s historic victory against Go world champion Lee Sedol is just the latest landmark in the development of AI displaying something close to human intuition. What was particularly impressive about this victory was the machine’s ability to recover from early errors to overcome its opponent – adaptability, a very human trait.

Many of these breakthroughs have been helped with developments coming from game AI techniques, such as Monte Carlo Tree Search (MCTS), a very reliable algorithm for winning games like Go that are just too complex to play well by trying to assess the effect of every possible move.

The MCTS algorithm works by representing the likely next states of a game, and then by random sampling in these areas, it figures out the best possible move, thinking only a few moves ahead. It enables gaming software to store data at every turn, noting its opponent’s effort, slowly building new patterns from previous turns and past games for the best possible route to victory. Incredibly, it’s becoming possible for these kinds of algorithm to play well without even needing to know how to play a game from the outset, only the parameters for a win.

AI application beyond gaming

The implications of AI can stretch beyond the realm of games. Similar techniques can be used in other settings that demand human-like capacity to evaluate various strategies under conditions of uncertainty, for instance, medical diagnostics and climate modelling, and the impact of AI on industry and society is now a matter of routine debate in the broadsheets.

Sectors with an enormous amount of data, where outcomes can be scrutinised and comprehended, are the ideal breeding ground for testing AI’s potential in the game of managing the operations of complex businesses.

The race is now on for businesses and academic institutions to design AI solutions that can overcome some of the unpredictability of an unconstrained environment.

Proof of concept

Software and data science organisations, such as MooD International, are investing significantly in AI-enabled business engines, often in collaboration with other institutions and government agencies, that are facilitating smarter decision-making parameters evidenced by data and guiding analytics.

For instance, MooD’s renowned outcome-focused business software is based on a causal engine, which connects individual actions to outcomes, and visualises activities in an innovative ‘business landscape exploration’ user experience. While this pioneering approach is effective at laying bare the cause and effect behind business outcomes, the next step is to have a programme that is proactively adept at suggesting “next steps”, drawing on data where available, but also previous knowledge of business people, that are most likely to lead to desired outcomes.

To tackle this challenge, MooD has been collaborating with different partners, including the University of York and The Defence Science and Technology Laboratory (Dstl), to adapt these techniques to the realm of business decision-making. In this respect, MooD is making headway in developing intelligent software products that are being tested in the real world management of business operations and, just for good measure, cyber warfare.

Three key issues machine capability can address for businesses

1. Automating businesses’ ability to respond to new market changes at inhuman speed. This is already the case with some sectors. For instance, financial firms routinely use powerful computers and sophisticated algorithms to play the stock market. In fact, it is thought machines are now responsible for most of the activity on Wall Street. The speed with which these machines are able to compute data and react to market environments means they are far more effective at making rapid decisions than a human counterpart can.  The same challenges are now hitting other industries under the guise of ‘digital transformation’, and this situation is only accelerating in pace of change.

2. Simplify costly, time-consuming and data-obsessed information processes. Not all data in and around a business is relevant or helpful, but how do you know? With the right information at hand, machines can help organisations pull together a simple picture of the health of the business in a fraction of the time it takes to do so today. Not only that, but machines are (a) less likely to duplicate tasks (b) reduce the cost and risk of uncoordinated data-driven business intelligence, and (c) provide guidance on what part of the business should be prioritised based on the desired business outcomes.

3. Confidence. Businesses can be confident in the impact that change and business activities have on business outcomes. Often companies don’t have the complete picture and are then saddled with the inability to define and agree on the best course of action, with confidence, to yield the best possible results.

Conclusion

Ultimately, while we don’t envisage seeing AI taking the place of business leaders in organisations, AI would act as their eyes and ears. AI would serve as a safety net in the background, monitoring but occasionally intervening to make sure that organisations remain on track to achieving the desired business outcomes. They’ll act as an extra economic and operational lever for business leaders to use to future-proof their businesses’ fortunes.

At MooD, we are rising to the AI challenge by collaborating with the University of York. Together we aim to significantly enhance functionality of our business performance management solutions with a decision support engine based on state-of-the-art AI technology, specifically Monte-Carlo Tree Search.

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