Artificial Intelligence vs. Machine Learning
While the terms artificial intelligence (AI) and machine learning are sometimes used interchangeably, machine learning is a subset of AI. We help clear the confusion by discussing how these terms came to be and how they differ.
For a good reason, the terms Artificial Intelligence (AI) and Machine Learning (ML) have generated a lot of excitement in the technological industry. They assist firms in streamlining procedures and uncovering data to make better business decisions.
Most individuals confuse the phrases artificial intelligence and machine learning and are unaware of the distinction. Even though machine learning is a subset of artificial intelligence, these two phrases refer to distinct concepts. It is possible to say that artificial intelligence is a broad field of study in which machine learning is only a minor component. Here are the main distinctions between them.
What are AI and Machine Learning?
Artificial intelligence refers to a computer system's ability to simulate human cognitive functions such as learning and problem-solving. Artificial intelligence (AI) is a computer program that mixes arithmetic and logic to simulate humans' reasoning to learn new knowledge and make decisions.
An example of an AI application is machine learning. The technique uses mathematical data models to aid computer learning without direct teaching. This enables a computer system to learn and improve depending on its own experience.
One way for teaching a computer to duplicate human reasoning is to use a neural network, a series of algorithms modeled after the human brain. The neural network aids the computer system in obtaining AI through deep learning. The debate over AI vs. machine learning is fundamentally about how AI and machine learning interact because of this close link.
Difference Between Artificial Intelligence and Machine Learning
"AI is essentially intelligence – how we make machines clever – whereas machine learning is the execution of the computing methods. In my mind, AI is the science, while machine learning is the algorithms that make machines smarter. Machine learning is thus the enabler for AI." - said Intel's Nidhi Chappell, head of machine learning.
For example, suppose an AI system is a computer that can perform a set of activities based on instructions. In that case, ML is the machine's ability to ingest, interpret, and learn from data to become more accurate or exact when performing those jobs.
Let's break down the differences.
How do AI and Machine Learning Work Together?
When attempting to understand the differences between artificial intelligence and machine learning, it is beneficial to consider how they interact due to their close relationship. The following is how AI and machine learning interact:
Machine learning and other approaches are used to create AI systems.
Machine learning models are developed by analyzing data patterns.
Machine learning models are optimized by data scientists based on trends in the data.
The process is repeated and adjusted until the accuracy of the models is enough for the tasks at hand.
Capabilities of AI and Machine Learning
The intersection of AI and machine learning reveals the new potential for businesses in almost every industry. Following is just a few of the capabilities that have been shown to help assist companies in transforming their processes and products:
By identifying cause-and-effect links in data, this capacity aids firms in predicting trends and behavioral patterns.
Natural Language Understanding and Speech Recognition
Natural language understanding recognizes meaning in written or spoken language, whereas speech recognition allows a computer system to distinguish words in spoken language.
Image and Video Processing
These abilities allow for recognizing faces, objects, and actions in images and videos and implementing features like visual search.
Companies employ data analysis to recommend products that someone might be interested in through recommendation engines.
Sentiment analysis is a technique for finding and categorizing positive, neutral, and negative attitudes conveyed by a computer system in text.
Applications of AI and Machine Learning
Most of the applications you use daily are already powered by machine learning. Your GPS navigation service utilizes machine learning to analyze traffic data, and Facebook uses it to customize users' news feeds. Your email spam filter even uses machine learning to divert undesired messages away from your inbox.
Machine learning has multiple uses in the business as well. Machine learning may help firms extract insights from enormous client data, allowing them to provide personalized services and products tailored to individual needs.
In general, machine learning and other AI techniques can give an organization more real-time transparency, allowing it to make better decisions.
- Companies in various industries use AI-powered chatbots to answer customers' questions and provide virtual assistance.
- Improve the detection of credit card fraud.
- Analyze feedback and surveys from customers.
Sales and Marketing
- Sales and marketing teams use AI and machine learning for personalized offers, campaign optimization, sentiment analysis, and predicting customer attrition.
- Create digital ad programs that provide clients with the offers they want.
- Using historical and market data, make more accurate forecasts.
- Improve lead scoring by updating client contact information, generating new leads, and optimizing lead scoring.
- Transportation applications benefit from AI and machine learning.
- Help organizations enhance the efficiency of their routes
- Employ predictive analytics for things like traffic forecasts.
- AI and machine learning are potent weapons in the fight against cybersecurity.
- Improve IT operations and network security.
- Protect against cyber threats by locating software vulnerabilities and viruses.
- Automate root-cause analysis.
Banking and Finance
- In the banking and finance sector, AI and machine learning can aid in fraud detection and risk prediction.
- They also provide more proactive financial advice.
- Compare market demand to inventories to improve supply-chain management.
- Create Better risk-management models.
- Automate the evaluation of contractor proposals.
- Reduce equipment maintenance by identifying abnormal behavior.
- Retailers employ AI and machine learning to improve their inventory management
- Construct recommendation engines
- Improve the consumer experience with visual search.
Benefits of AI and Machine Learning
Every business has the problem of staying competitive in the marketplace while boosting revenue and lowering operating costs. AI is the most potent tool companies utilize to make better decisions, generate new revenue streams, attract new customers, and reduce operational expenses.
"The global artificial intelligence (AI) software industry is expected to develop quickly in the future years, reaching around 126 billion USD by 2025. Robotic process automation, Natural language processing, and machine learning are just a few of the applications that make up the entire AI market." - According to Statista.
Implementing AI/ML technologies may be something to consider when planning for your organization's goals in 2022. They offer the following advantages, not just this year, but also in the future:
Increase Organizational Performance
Numerous use cases for AI-related technologies such as machine learning (ML), deep learning, and natural language processing (NLP) could generate trustworthy, relevant, dynamic, and intelligent information to aid decision-making.
One example is the employment of NLP-based chatbots in customer service, which can answer most consumer inquiries and effortlessly transfer them to a human representative in a complex query. They also provide backend API integrations with their case management or ERP systems.
Reduce Operational Costs
One of the primary areas of attention for corporations striving to restore their foothold in a post-pandemic environment would be to cut operating costs. In general, growing AI adoption can be linked-to manual or statistics-driven work automation, saving time on repetitive tasks while increasing focus on high-value jobs.
The most typical machine learning use cases that resulted in cost reductions include inventory or pricing optimization, contact-center automation, and claims processing.
Launch Innovative Products
In the post-pandemic world, every business enterprise must be competitive in the marketplace. Firms utilize AI to introduce new product lines to gain market share or establish new revenue streams.
Under Armour, a fitness business, leverages machine learning to produce personalized training suggestions by automatically identifying precise user segments. In terms of new and inventive product offers, this gives Under Armour a competitive advantage in the market.
Meet Customers' Expectations
Most automobile manufacturers have noticed that customers push environmental conservation efforts and the transition to electric vehicles.
Most major automakers have invested considerably in researching and developing autonomous vehicle technologies. This includes a variety of artificial intelligence technologies such as visual recognition, machine learning, autonomous expert systems, etc. General Motors' Cruise is one such example of a push into the self-driving automobile industry.
Introduce New Lines of Revenue
While most businesses benefit from the cost savings provided by machine learning applications, there are specific examples of ML systems that can provide new revenue streams. AGL, Australia's largest energy corporation, is one such example.
AGL has been employing machine learning in many new methods to control and evaluate the energy usage of its 3.7 million consumers for the past three years. They exploited this to enable a new product known as a "virtual power plant," allowing clients to return energy to the grid. This product has assisted AGL in increasing revenue.
Machine Learning and Artificial Intelligence may both add significant value to organizations. If correctly set up and given the relevant data, they can save money, free up human resources, increase ROI, and reduce the number of errors.
Simultaneously, these technologies are used for security duties because they can easily detect harmful patterns that could lead to cyberattacks. We hope this article has given you a better understanding of them and their relationship.