Lately, artificial intelligence is stealing the spotlight as the next big trend. It’s making its mark across various tech areas, from social media and manufacturing to advertising, finance, and healthcare. Given its perks, experts are shifting towards AI to enhance their processes.
One big plus is that it reduces the need for excessive staffing, ultimately saving costs for organizations. Moreover, AI outpaces traditional labor, minimizing errors while boosting speed. But, for all its achievements, AI isn’t flawless. It relies solely on the given data without truly understanding the underlying reasons.
Teaching AI to Think Beyond Patterns
Imagine a world where artificial intelligence not only predicts outcomes but comprehends why those outcomes occur. Such an AI system would not just look at a lung X-ray and detect a tumor but also understand why that anomaly signifies a potential health concern. This level of understanding, often called causal reasoning, is the key to unlocking artificial intelligence’s true potential.
AI’s Pattern Predicament
At the heart of this evolution lies a problem: AI is really good at spotting patterns in information, but it finds it tricky to understand why things happen the way they do. Let’s take online poker as an example. For an online poker AI tool to function, it calculates probability, making strategic decisions through an intricate analysis of player behavior, mannerisms, and how the cards have been distributed.
However, this AI is far from perfect; it is yet to transcend mere calculations and pattern recognition and actually comprehend the reasoning behind every winning or losing move in the game. This is true for most places where we use artificial intelligence. Only when AI can dig into the whys and hows of different results will it really make big leaps forward for the greater good?
The Quest for Causal Insight
Consider the story of Rohit Bhattacharya, a computer science Ph.D. holder; his goal was to develop a tool to identify cancer patients who would benefit from immunotherapy. While his algorithms could spot genetic patterns linked to immune response, they fell short in comprehending the intricate web of causality. AI could tell what was happening, but it couldn’t explain why.
To make better decisions, AI needs to embrace causality. You see, unlike traditional machine learning, Causal AI aims to analyze the underlying causal relationship between variables, Similar to how a human being reasons to make sense of the world, causality goes beyond correlations and provides actual insights on the driving mechanism behind a phenomenon.
Climbing the Cognitive Ladder
AI researchers like Judea Pearl have played a vital role in the world of Causal AI. Back in 2011, Pearl received the A.M. Turing Award for developing a kind of math that helps AI think about probabilities and causes. He created a three-level ladder: first, there’s ‘seeing,’ which is about recognising basic patterns—AI is really good at this. Then comes ‘doing,’ which means making a change and seeing what happens next.
This is where cause-and-effect stuff comes into play. Cause-and-effect is like connecting the dots between things. AI can use it to figure out how different things affect each other. With this knowledge, AI can predict what might happen or make choices. For example, think about marketing. Causal AI could reveal that a specific ad campaign (the cause) led to more people getting interested and buying things (the effect). This helps businesses copy good ideas and do better overall.
The Power of Imagination
At the pinnacle of this journey is imagination. Bhattacharya beautifully explains this through Robert Frost’s poem, ‘The Road Not Taken.’ Just as Frost contemplates the outcomes of choosing a different path, AI can also imagine alternate scenarios. This ‘counterfactual remorse’ could empower AI to evaluate different decisions, even those it didn’t make, and learn from them.
One such example is an AI system designed to assist doctors in making treatment recommendations for patients with a specific medical condition, such as diabetes. A counterfactual AI system would take into account patient data, medical history, and available treatments. Instead of providing a single generic treatment recommendation, it explores various counterfactual scenarios to help doctors make more informed and patient-specific decisions.
A New Era of AI
As AI strides towards causal reasoning, it transforms from a mere predictor to a genuine understanding entity. This evolution has far-reaching implications. AI’s ability to adapt to changing situations improves significantly. Imagine an AI-driven car that seamlessly adjusts to driving on either side of the road, regardless of the country it’s in. This adaptability is a game-changer in various industries.
Interesting Related Article: “The Role of Artificial Intelligence in Investment decision-making“