Artificial Intelligence (AI) vs. Machine Learning (ML): What’s the difference?

You’ve heard the phrases artificial intelligence (AI) and machine learning (ML) tossed around — but what do they really mean? The two terms are often used interchangeably, but while they’re interconnected, they are also different.

First, let’s define them.‍

AI is a broad term, referring to making computers behave in ways that mimic human intelligence. But the parameters of that description are unclear and always changing. For example, the ability to play a game of chess was considered a form of AI several decades ago. Today, nearly every computer’s operating system has that capability, and typical examples of AI are much more complex (like self-driving cars (https://www.hologram.io/blog/what-are-self-driving-cars)). ‍

A subset of AI, ML “is the study of computer algorithms that allow computer programs to automatically improve through experience,” according to computer scientist Tom M. Mitchell (https://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html). Closely related to data analytics (https://www.hologram.io/blog/what-is-iot-analytics), ML looks at datasets to identify patterns and “learn” from recurring events. One common example of ML in action is the recommendation algorithms used by many streaming movie and music services. Once the system knows what kind of entertainment you enjoy, it can suggest other songs or movies that you might like.‍ (https://www.hologram.io/blog/deep-learning-vs-machine-learning)

Deep learning (https://www.hologram.io/blog/deep-learning-vs-machine-learning), also known as deep structured learning, is a subcategory of machine learning. It uses neural networks (https://bdtechtalks.com/2019/02/15/what-is-deep-learning-neural-networks/) without the domain-specific feature engineering required by other types of machine learning. The “deep” qualifier refers to multiple layers of neural networks, which are often used in applications such as computer vision and chatbots.

Now let’s consider a few key differences between AI and ML. ‍

Key Differences Between Artificial Intelligence (AI) and Machine Learning (ML)

1. AI is a broad term, while ML is more narrow

AI is a wide open concept that covers a lot of territory — and ultimately lacks clear parameters. Most computer scientists use it as an umbrella term under which several other technologies fall: machine learning, deep learning, IoT sensors (https://www.hologram.io/iot), computer vision, and autonomous robots, among others.

2. All ML is AI, but not all AI is ML

All ML applications fall under the heading of AI, but not every example of AI uses machine learning. ML relies on the computer’s ability to analyze data (http://about:blank) to make predictions or improve process efficiencies. That’s certainly a manifestation of AI, but doesn’t represent its full scope.‍

Applications for Artificial Intelligence in Cellular IoT

As devices become more complex and edge computing more common, there are a multitude of potential applications for AI in the realm of cellular IoT. Let’s take a look at a few examples.‍

Autonomous Vehicles

Most new vehicles today are part of the IoT because they’re connected to the Internet in some way — and many have some automated support features, such as adaptive cruise control and lane keep assist. These, and the more advanced forms of autonomous capabilities, depend on AI to help them review data and make pivotal decisions about directing the vehicle. For example, an autonomous car’s Light Imaging Detection and Ranging (LIDAR) system creates a model and sends it to the car’s computer. The computer uses AI (and in this case, ML) to analyze the data, predict possible outcomes, and make split-second decisions, just like a human driver.‍

Industrial IoT

Many applications of industrial IoT (https://www.hologram.io/blog/what-is-iiot) (IIoT) involve AI functionality. For example, computer vision video systems stationed on assembly lines observe products as they roll by and compare those images against established standards to check for quality. When the system detects an anomaly, it sends out an alert to prompt human (or robotic) intervention. Free-roaming industrial robots are another manifestation of AI in IIoT settings. For example, Mobile Industrial Robots (https://www.mobile-industrial-robots.com/en/) makes rolling robots designed to carry loads up to 1,000 kg (around 2,200 lbs), allowing factories to automated and optimize internal transportation of heavy items and pallets. Using AI, the robots evaluate possible routes to their destination and choose the most efficient one available, maneuver safely around obstacles, and stop if a person blocks their path.‍

Healthcare IoT‍ (https://www.hologram.io/blog/healthcare-iot)

Connected healthcare (https://www.hologram.io/blog/healthcare-iot) devices and systems often depend on AI functionality. For example, medical robots perform AI surgery, smart insulin pumps gather data and automatically administer correct dosages, and smart hospital tracking systems use AI-equipped sensors to monitor the location of patients, doctors, and medical supplies, and to alert caregivers about patient mobility and falls.‍

Digital Signage

In public transportation hubs, city centers, and retail shopping areas, digital signage does a lot more than displaying pre-set information and advertisements these days. Some installations use AI computer vision to detect observers’ demographics and gender and display targeted advertisements or messaging in response. These systems can also provide advertisers and content creators with data about audience engagement — how many people stopped to look at their ad, for instance, and what their demographic was. ‍

Applications for Machine Learning in Cellular IoT

Machine learning also has many applications in cellular IoT. Let’s look at a few.‍

Smart Utilities‍ (https://www.iotforall.com/smart-grid-iot-applications)

Smart grid (https://www.iotforall.com/smart-grid-iot-applications) applications depend on IoT smart meter devices at the commercial and residential levels, combined with connected sensors at power generation centers and along the distribution routes. At the macro level, machine learning can process all this collected data and use it to glean insights that help utilities provide better customer service and improved efficiencies. For example, smart metering can help utilities spot issues such as installation problems and malfunctioning end points. The system looks for unusual patterns such as rapid or intermittent power consumption and flags those for investigation. But those indicators aren’t always reliable — when an office is closed or a family goes on vacation, for instance, power consumption is likely to drop dramatically. ML gives the utility a better filtering system for finding anomalies, because over time it “learns” to identify real problems.‍

Retail IoT

In the realm of retail IoT (https://www.hologram.io/blog/retail-iot), machine learning can power predictive analytics, a powerful tool for forecasting future sales and interpreting the possible impacts of marketing decisions. Machine learning adds extra power to predictive analytics because it is self-developing and becomes “smarter” as it gains more experience. Predictive analytics applied in the retail space can be used to assess locations for brick-and-mortar stores, anticipate product demand and needed inventory, and to help retailers make the best pricing decisions. It can also be used in marketing for assessing a customer’s longevity and lifetime value, analyzing online product reviews, and determining which marketing channels are working and which ones aren’t.‍

Asset Tracking

The field of asset tracking (https://www.hologram.io/blog/10-best-iot-asset-tracking-systems) also benefits from ML. By collecting data from RFID tags and real-time tracking systems, a company’s overarching IoT platform (https://www.hologram.io/blog/iot-platform-overview) can utilize machine learning algorithms to analyze the flow of data and make predictions about product availability, deliveries, logistics, and other supply chain issues. Companies managing a fleet of vehicles (http://about:blank) can use ML to track and optimize fuel consumption, forecast potential repair needs, and find the best possible delivery routes. ‍

Smart Factories

Smart manufacturing is another area where ML finds many applications. With the growth of edge computing, many smart factories (https://www.hologram.io/blog/smart-factories) have some ML and analytic capabilities at the edge, meaning managers can view and share reports on productivity, workflows, and maintenance issues. Data that flows to a cloud-based IoT platform can be integrated with other business data and further analyzed, with ML applications making predictions and recommendations in the bigger picture of the overall business. The ability to analyze real-time data allows manufacturers to predict future growth, notice trends, optimize productivity and workflows, and deal with problems before they become crises.‍

Cellular IoT with Hologram

For applications of AI and ML to function well, the IoT devices collecting the data need a dependable source of connectivity. Hologram’s IoT SIM card (https://www.hologram.io/products/iot-sim-card) offers seamless, global coverage for IoT devices with access to LTE/4G/3G/2G technologies. With our Hyper eUICC-enabled SIMs (https://www.hologram.io/products/hyper), you’ll gain access to new connectivity partnerships without any additional carrier negotiations, integrations, or hardware swaps.