Artificial intelligence (AI), also known as machine learning, is a term that encompasses a variety of strategies and techniques to help machines become more humanlike. AI encompasses everything from Alexa, the smart assistant, to robot vacuum cleaners and autonomous cars. Machine learning (ML), one of many branches of AI, is a branch that includes machine learning. Machine learning (ML) is the science that involves developing statistical models and algorithms to help computer systems perform complex tasks, without explicit instructions. Instead, the systems rely on patterns. Computer systems use ML algorithm to identify patterns in large amounts of historical data. Machine learning is AI but not all AI activities include machine learning.
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What are the similarities and differences between AI (artificial intelligence) and machine learning?
Machine learning is a branch of AI that is narrowly focused. Both of these fields go far beyond simple automation and programming, generating outputs that are based on complex analysis.
Human-like problem solving
Artificial intelligence (AI) and machine learning solutions (ML/AI) are suitable for complex tasks, which require precise results based on acquired knowledge.
A self-driving AI vehicle, for example, uses computer vision to identify objects in its field and traffic laws to navigate the vehicle.
For example, a property pricing ML algorithm uses previous sales prices, the market, floor plans and location in order to predict a house’s price.
Computer Science fields
Artificial intelligence and machine-learning are computer science fields that concentrate on creating software which analyzes, interprets and comprehends complex data in a variety of ways. These fields are devoted to the development of computer systems that can perform complex tasks involving self-learning. Well-designed software can complete tasks as quickly as or faster than an individual.
Applications across industries
AI has applications in all industries. AI can be used to improve agriculture, optimize supply chains, predict sporting outcomes, and customize skincare recommendations.
The applications of ML are wide-ranging. These include dynamic pricing for travel, fraud detection in insurance, and forecasting retail demand.
AI and machine learning: Key differences
Machine learning is a branch of artificial Intelligence (AI). ML is a branch of AI with a narrower scope. AI encompasses several technologies and strategies that fall outside of the scope machine learning.
There are several key differences between them.
Objectives
AI systems are designed to automate complex human tasks. These tasks can involve pattern recognition, learning, and problem solving.
A machine is designed to analyze large amounts of data. The machine will identify patterns and generate a result using statistical models. The associated result is a probability or level of confidence.
Methods
AI is a broad field that encompasses many methods for solving diverse problems. These include genetic algorithms and neural networks. They also include deep learning, rule-based system, search algorithms, and machine learning.
ML methods can be divided into two categories: supervised and non-supervised learning. Supervised ML algorithms solve problems by using input and output values. Unsupervised learning is exploratory, and tries to find hidden patterns within unlabeled data.
Implementations
Two tasks are typically involved in the process of developing a ML solution:
- Choose and prepare the training dataset
- Select a ML model or strategy that is already in existence, such as a linear regression or a tree decision.
Data scientists choose important data features to feed into the model. The dataset is continually refined with new data and error checks. The accuracy of ML models is improved by data quality and variety.
As building an AI product can be a complex process, many people opt for prebuilt AI solutions in order to reach their goals. These AI solutions are usually the result of years of research and development. Developers make them available to integrate with products and services via APIs.
Requirements
ML solutions need a dataset with several hundred data points to train, as well as sufficient computing power to run. A single server instance, or even a small cluster of servers may be enough depending on the application and use-case.
Other intelligent systems can have different infrastructure requirements. These depend on what you are trying to achieve and how you plan to do it. For high-computing applications, several thousand machines must work together in order to achieve complex goals.
It’s important to know that prebuilt AI and machine learning functions are also available. They can be integrated into your application via APIs, without requiring additional resources.
What is required to start a machine learning and AI project?
Start by defining your research questions or problems that you would like to solve using artificial intelligence (AI). You can then determine which AI or ML technologies are best suited to solving the problem. Preprocessing the data and considering the size and type of data is important before starting.
AI can be created, managed and run using cloud-based services. Amazon Web Services Cloud allows you to create, manage, and run AI functions.
How can organisations use AI and ML to their advantage?
Most organizations can benefit from machine learning solutions.
- Customer segmentation involves segmenting customers based on their behaviors, preferences and characteristics to further your sales and marketing efforts.
- Fraud detection involves triaging and resolving unusual transactions.
- Customer feedback can be incorporated into product strategy and marketing through sentiment analysis.
Here are some artificial intelligence (AI), or AI, solutions that can be applied to the majority of organizations:
- Chatbots can be used for triaging and customer service.
- Speech recognition can be used to convert meetings into written notes.
- Computer vision is a good fit for biometrics systems.
AI and machine learning: Differences between the two
| Artificial Intelligence | Machine Learning | |
| What is it exactly? | AI is a broad term that refers to machine-based applications which mimic human intelligence. AI is not the same as ML. | Artificial intelligence is a methodology that ML uses. All ML-based solutions are AI-based solutions. |
| The best suited for | AI is the best way to complete a human task that’s complex with efficiency. | ML works best when identifying patterns within large data sets to solve specific problems. |
| Methods | AI can use many different methods such as rule-based, neuronal networks, computer vision and more. | In order to train a model, users manually extract features and weights from raw data. |
| Implementation | AI implementation is dependent on the task. AI is usually pre-built and can be accessed through APIs. | Train new or existing ML algorithms for your use case. There are ML APIs that have been pre-built. |
What AI and machine-learning capabilities can AWS provide to support your AI needs?
AWS provides a range of services that can help you create, integrate, and run artificial intelligence (AI) and machine learning solutions, regardless of their size, complexity or use cases.
Amazon SageMaker provides a complete platform for building ML solutions. SageMaker comes with a complete set of pre-built machine learning models. It also has storage and computing capabilities and a managed environment.
You can either use AWS to create your own AI solution from scratch, or you can integrate AI services that are already built into your solution.
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