Understanding AI types and subsets — and their applications in business

Such algorithms are programmed to perform a specific task — e.g., play chess or convert handwritten messages to editable text. They neither possess memory nor predict the future; instead, they merely respond to a current situation. An excellent example of a reactive machine is the IBM Deep Blue supercomputer that narrowly beat Garry Kasparov, a reigning world chess champion, back in 1997. It reportedly took IBM scientists 12 years to create algorithms that could explore up to 200 million chess positions per second.

When making decisions, limited memory AI algorithms consider both the information they’re gathering from the outside world in real time and the data they’ve been trained on. That’s how autonomous vehicles maneuver around objects and obey speed limits.

Scientists working on the Theory of Mind artificial intelligence aim to create algorithms that possess cognitive and social skills and thus glean people’s emotions from video data, voice recordings, or text messages. DeepMind’s ToMnet, a computer program that uses three neural networks to anticipate other agents’ needs, is perhaps the closest we’ve got to Theory of Mind so far.

Such algorithms acknowledge their existence. Besides interpreting the feelings of others, they can develop their own beliefs and desires, including a sense of self-preservation, which might lead to a Skynet situation in the not-so-distant future. Luckily for us, self-aware artificial intelligence remains a sci-fi concept.

Supervised algorithms lean on labeled data to find patterns and make intelligent predictions. Human engineers know in advance what the model’s output is likely to be. Supervised learning is best for classification tasks — for example, predicting house prices based on the square footage of a house, number of bedrooms, availability of a garden, etc.

Unsupervised models are exposed to unlabelled data, meaning engineers leave it up to smart algorithms to structure information and spot trends. Unsupervised machine learning is used in clustering tasks like identifying customer segments in CRM data.

In reinforcement learning, algorithms do not simply guess outputs based on the received data. Instead, they interact with the environment to find the best way to solve a problem, learn from mistakes, and get rewards when delivering accurate results. Some practical examples of deep learning include stock trading bots and computer applications that summarize long texts.

According to McKinsey’s Global AI Survey, manufacturing and supply chain management companies reap maximum benefits from feeding operational data to machine learning models and neural networks. The insights obtained from raw, unstructured data help businesses improve equipment uptime, reduce energy consumption, and optimize throughput. But it doesn’t mean companies from other industries cannot leverage AI to become more efficient. A couple of years ago, Google turned to deep neural networks to manage a cooling plant at one of its data centers. The moonshot project began as a recommendation engine that would come up with random suggestions to help human engineers optimize energy use. As the system absorbed new data, its performance improved significantly. Google let the smart algorithms make the cooling plant tweaks on its own and registered a whopping 40% reduction in the facility’s energy use.

While today’s reactive and limited memory machines display poor results without human supervision, AI-powered systems confidently take over some repetitive and time-consuming jobs on the factory floor, in the office, and at call centers. Since 2019, Amazon has been using packaging robots that box up 700 orders per hour. Humana, a leading US healthcare insurance company, automates 60% of its call center activities using voice assistants and AI-driven robotic process automation (RPA) technology. A global retail company created an AI platform to get a 360-degree view of all the data and documents accumulated in its technology systems — and automatically update or delete inaccurate information. And Shimizu Corporation is currently testing the OpenSpace artificial intelligence platform to analyze photographs taken at construction sites and track progress.

Besides answering customer questions at the help desk and helping users navigate online catalogs, AI systems can dive deeper into social and purchase history data to come up with relevant product suggestions. 80% of movies and TV shows people stream on Netflix, for instance, are discovered through the company’s smart recommendation engine. Another example comes from Walmart, which uses AI cameras to monitor inventory levels and timely restock items that customers buy the most. And with voice assistants, thriving consumer electronics and automotive brands take user experience to a totally new level — just ask Alexa to book you a flight with Ryanair or dial your friend’s number without taking your hands off the steering wheel to see the difference!

While we saw two years’ worth of digital transformation in the first two months of the COVID-19 pandemic, cyberattacks targeting businesses of all shapes and sizes grew by 273% during the same period. As more companies shift apps and data to the cloud and adopt remote work policies, it is essential to look beyond antivirus software and hardware-enforced protection of OT and IT networks — and here’s where AI comes in useful. As an alternative to passwords and PINs, enterprises may restrict access to corporate devices and applications using biometric authentication systems. Facial and voice recognition features steadily infiltrate corporate messengers, including WhatsApp for Business. And machine learning-powered platforms like Robust Intelligence could help companies monitor network traffic and detect over 100 types of cyberattacks.