AI types and subsets

Understanding AI types and subsets — and their applications in business

By Andrei Klubnikin, Innovation Analyst
Published on

Almost 60% of companies surveyed by McKinsey last year admitted using at least one AI capability, such as natural language processing (NLP), robotic process automation (RPA), or computer vision. Yet, only 16% of businesses determine use cases for artificial intelligence before integrating AI into their workflows. Subsequently, the percentage of organizations that register a sizable return on AI investments fluctuates around 11% across different industries.

The lackluster results are partially caused by executives’ misunderstanding of artificial intelligence and the opportunities it creates. To help our clients sift through the AI noise and develop a viable AI implementation strategy, the ITRex team will publish a series of explanatory articles. This time, we’ll focus on AI types and subsets, as well as their possible applications in business.

4 types of Artificial Intelligence

Artificial intelligence is an umbrella term that defines smart algorithms capable of applying human-like reasoning to make decisions autonomously and perform miscellaneous activities.

The most rational way to classify artificial intelligence would be to compare algorithms’ cognitive abilities to those of a person. This approach allows us to single out four types of AI:

  • Reactive machines

    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.

  • Limited memory machines

    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.

  • Theory of mind

    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.

  • Self-aware AI

    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.

Some experts tend to divide AI systems into two categories: narrow artificial intelligence (ANI) and general artificial intelligence (AGI).

Artificial intelligence services and solutions
The former includes applications that outperform humans in some narrowly defined tasks, such as distinguishing malicious tumors in mammograms. ANI solutions' cognitive ability depends on the amount and quality of data you're feeding to them. When faced with an unfamiliar task or a new dataset, these systems may deliver inaccurate results.

AGI solutions, on the other hand, attempt to mirror human intelligence by applying their skills to function in different contexts. Theory of mind and self-aware AI systems belong to the general artificial intelligence realm.

Artificial Intelligence types and subsets

As we’ve mentioned earlier, artificial intelligence is a broad term that refers to machines’ ability to perform tasks that were previously handled by humans only. This term is often used interchangeably with other buzzwords like machine learning, reinforced learning, and deep learning — particularly in the business context. Here’s what’s wrong with this approach.

AI types and subsets

What is Artificial Intelligence?

Unlike humans and animals, computers and software cannot behave intelligently — unless they’ve been deliberately programmed to do so. Artificial intelligence is a multidisciplinary field of science and engineering; its goal is to create intelligent machines that emulate and ultimately exceed the full range of human cognition. Being able to mimic human reasoning and continuously learn from new data, AI-powered hardware and computer programs can simulate intelligent behavior, such as autonomous decision-making, visual perception, and speech recognition.

What is Machine Learning?

ML is a subset of Artificial Intelligence. The technique involves training algorithms on labeled data — for instance, images of puppies and bagels.

machine learning models
A fully trained ML model should correctly identify dogs or pastries in 100% of cases. This is an example of image classification algorithms in action.
Image source: Medium — The Eliza Effect

While basic AI systems often rely on if-then rules defined by human engineers, ML solutions alter themselves when faced with new information — and get better with time. To improve the accuracy of machine learning algorithms, developers calculate the bias and variance of a given model and intentionally program it to minimize errors.

Types of machine learning algorithms:

  • 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.

What is Deep Learning?

Another subset of machine learning, deep learning, uses neural networks with multiple hidden layers to extract additional insights from the input data and deliver more accurate predictions. Compared to linear algorithms, neural networks feature artificial neurons modelled after a human brain. When receiving input data, the neurons assign weights to it; the combined weight is then matched against a preset threshold, which denotes the probability or improbability of an event. To be considered “deep”, a neural network must contain at least three layers. While deep neural networks may use labeled data to inform their algorithms, they can also learn by themselves.

ai predictions
Image source: Medium — Towards data science

Some examples of deep learning systems include voice assistants like Alexa and Siri, which capture human voice, compare audio waves to the phonemes of a target language, and figure out what people say.

As of lately, deep learning has been finding its way into advanced natural language processing and computer vision systems.

The former are categorized by the ability to understand written or spoken language and generate relevant responses. The latter aim to comprehend the content of digital images; that’s the way Google Images, Face ID, and intelligent video surveillance systems work.

Practical applications of different types of AI in business

Now that we know the difference between machine learning, deep learning, and artificial intelligence, it’s time to explore how we can use different AI types to supercharge legacy enterprise applications and change the way we’ve been working for decades.

  • Uncovering insights in operational data

    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.

  • Automating digital and physical tasks

    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.

  • Improving user experience

    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!

  • Enhancing security

    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.

Through 2021, artificial intelligence is expected to recover 6.2 billion hours of worker productivity and generate $2.9 trillion in business value. And while we’re years away from human-like AI that could fully automate knowledge-intensive processes, even “basic” data classification and clustering systems promise significant time and cost savings for businesses.

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