The advances in AI, machine learning, and robotics is astounding. Everything from social and emotional intelligence, natural language processing, logical reasoning, identification of patterns and self-supervised learning, to physical sensors, mobility, navigation and more.
What changed everything a decade or so ago was an approach called “deep learning” – an architecture inspired by the human brain.
However, unlike the human brain, such networks are “trained” on huge amounts of labeled data, then they use what they’ve “learned” to mathematically pick out and recognize subtle patterns. The advent of this disruptive technology coincided with an explosion in data and hyperconnectivity, fuelling its success.
Where a human brain tends to focus on obvious data correlations, a deep-learning algorithm, trained on an ocean of information, has the ability to discover subtle and complex connections between obscure data sets. The uses are nearly limitless, from diagnosing cancer or detecting payment fraud, to autonomous vehicles and robots scurrying about in corporate warehouses.
Algorithms can be trained on proprietary data sets ranging from customer purchases to machine maintenance records to complex business processes, all the while helping managers make better decisions. For example, AI can be used to study many thousands of bank loans and repayment rates, and learn if one type of borrower is a hidden risk for default or, alternatively, a surprisingly good, but overlooked, lending prospect.
AI is already used extensively in cyber security given its ability to spot abnormal behavior and detect payments fraud, market abuse, and rogue trading. By scouting out hidden correlations that escape the human brain’s logic, these technologies can outperform even the most seasoned of experts.
time to make the shift from standalone uses to full integration
These technologies are fast, accurate, work around-the-clock without complaining, and can be applied to many tasks and use cases. Breakthroughs in AI, machine learning, and robotics will continue to have monumental impacts on traditional business models and drive widespread economic benefits for all. We urge organizations to rapidly ramp up their efforts to understand and develop a vision for their use of these technologies going forward.
While these gifts don’t come without challenge and new risks, for example, security, privacy, data bias, and job displacement, all organizations, irrespective of size, industry, and complexity, would be well advised to position them at the heart of their innovation strategies.