From fraud-proof BFSI applications, autonomous cars, chatbots to crime-fighting facial recognition, machine intelligence is redefining so much in the world we inhabit. But how do you know how it works? How are these applications so accurate, fast, real-time, and imaginative beyond our imagination? The answer lies in what happens inside the models that run them. The way algorithms are structured and run makes machines churn out so many decisions and insights- and so swiftly.
In a supervised mode, the input and output parameters and the data fed into them help a machine generate the answers required of them. In an unsupervised mode, the machine unlocks the patterns and interpretation from all the data fed into it. In reinforcement learning, we tap the ability of machines to learn from even bad decisions and past actions. These lessons help them to churn out insights. Here are a few real-life examples of machine intelligence:
- Voice assistants
- Fraud detection in insurance
- Algorithmic trading
- Anti-money laundering apps
- Robo advisors in BFSI
- RPA in operations
- Smart mining
- Predictive maintenance and Cobots in factories
- Geo-analytics for agriculture
- Personalized, real-time, and contextual marketing
- Self-driving vehicles
- Law and Order support – with image and speech recognition
- Smart City applications
- Traffic monitoring
- Personalized medication
- Smart drug discovery, triage, and diagnosis
- Robotic surgeons
- Predictive advertising
- Product personalization
- Climate modeling
- Natural language processing (NLP)
Why do we need to know?
Of course, machines are helping us solve many questions in our lives. But there is a flip side to this power. The quintessential ‘Black Box’ problem of AI is a real challenge when applying machine intelligence. Even if a machine is spinning out beautiful and thunder-fast answers – one has to know what is going on inside the box that is leading to these answers. It is significant to see the process of helping a machine learn anything. Otherwise, these insights can easily be prone to costly errors, bias, discrimination, false positives, and ethical problems.
According to McKinsey’s ‘The State of AI in 2021’ report, 57% cited cybersecurity as a relevant AI risk. Some companies also report personal and individual privacy as a relevant artificial intelligence risk more often. Explainability continues to be an important risk area – whether it is for emerging economies or developed ones. Both these economies also found fairness and equity as significant risk factors with AI. What is notable here is that high performers put in effort in managing these risks. For example, data professionals actively check for skewed or biased data during data ingestion (47%), data professionals actively check for skewed or biased data at several stages of model development, and high performers have a dedicated governance committee that includes risk and legal professionals.
Machines emerge as intelligent edifices that complement and elevate human efficiency, decision-making, and action. But they are learning based on data that is fed into them or which they pick on their own. The quantity, and context, of this data, can change the outcomes of a model. So humans cannot wash off their responsibility by just making data available to machines. They need to go further and be cognizant of this data’s quality, use, and application. They should know what is happening inside the machine as it processes this data, learns from it, and uses it as per the algorithm or experience it works on. Spending good time in programming, setting boundaries, and controlling measures are essential parts of machine learning. They matter a lot – even if the machine is excellent in speed.
It is advisable and critical to know how your machines learn what they are learning. In the real world out there, it can make a lot of difference – not just between a high-performer and an average runner. But between a responsible human and a clueless one.