Artificial Intelligence (AI) vs. Machine Learning (ML)

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Technology solution providers frequently list artificial intelligence (AI) and machine learning (ML) products under an AI / ML category.

The combination of these two technological trends has generated a lot of interest in the business world, but AI and ML are not the same thing: Machine learning is a subset of artificial intelligence.

Bartowsz Wojtowicz, Machine Learning Engineer at Netguru, a machine learning and digital transformation company, offered an additional distinction:

“AI describes a general concept of creating machines, simulating human cognitive abilities – such as learning and problem solving,” Wojtowicz said. “While machine learning is currently the most promising and widely used application of AI, enabling computers to learn and improve based on experience. “

Read on to learn more about artificial intelligence versus machine learning and how these technologies work together to create business innovations:

AI and ML in Business Technology

Read also : Trends in artificial intelligence (AI) in cybersecurity

Machine learning today

Machine learning, or the training of computer algorithms to recognize sets of data and perform certain tasks based on that data, has become an integral part of new technological developments.

Machine learning is a part of AI that has many practical applications for using data at scale. Since machine learning involves the development of an algorithm that focuses on performing one data-driven task at a time, data scientists and ML specialists can program and refine these solutions for a single task over time.

Machine learning alone dominates a growing global market, as it reached an estimated value of $ 1.41 billion in 2020 and is expected to reach $ 8.81 billion by 2025, according to 360 research reports.

Machine learning applications

  • Deep learning: This type of ML includes neural networks that make it function like a human brain, allowing deep learning models to closely copy human behaviors for assigned tasks. Some common examples include chatbots and virtual assistants.
  • MLOps and automation: Machine learning models are often trained to automate back office tasks that require less specialized human skills or can benefit from machine support. This process, more commonly known as MLOps or AIOps, automates functions such as security monitoring, network audits, and network self-healing efforts.
  • Smarter data analysis: The most widely used application of ML is found in data mining and Data analysis. Once ML models are trained to browse large data sets, they can not only browse data faster than humans, but they can also provide deeper insight and generally avoid user error issues.

Learn more about machine learning: Machine Learning Market

Artificial intelligence today

Artificial intelligence is the umbrella term for algorithm-based technology designed to simulate human actions and intelligence.

Machine learning, deep learning and natural language processing (NLP) are subcategories that fall under AI.

The AI ​​software market is one of the fastest growing technology markets in the world, reaching an estimated value of around $ 62.3 billion in 2020 and is expected to grow to around $ 997.8 billion by 2020. 2028, according to Grand View Search.

The majority of AI today is considered weak AI or artificial intelligence that can simulate aspects of human behavior and intelligence. Some of the more specialized tech companies are working on solid AI development or robotic solutions that can think and act independently once programmed.

Applications of artificial intelligence

  • Natural language processing: A commonly used form of AI, NLP analyzes text, voice, and other communication data in order to make decisions and communicate with users.
  • Computer vision: This type of AI focuses on analyzing images, finding meaning, identifying people, and detecting potential dangers in a given environment. Computer vision is increasingly used by law enforcement to supplement criminal investigations.
  • Accessibility tools: Most of today’s AI innovations focus on improving accessibility and simplifying daily routines. Some common accessibility improvements that come from AI include autonomous vehicles, medical diagnostic tools, and virtual home assistants.

Check out other AI use cases here: Artificial intelligence market

ML and AI use cases

Some of the most powerful applications of smart tools occur when big data-driven machine learning information and task-oriented features are layered on the development of human-emulating AI.

Check out the following use cases to learn more about the benefits of using ML and AI together:

Conversational AI and ML

Fueled by ML and AI, the telecommunications and telephony market has evolved to offer voice, audio and video networks with capabilities for real-time conversation analysis and automated feedback. ML is the engine that dynamically delivers learning / training based information from massive data entry. AI is the intelligence that is provided as a result of ML discoveries. Without ML, there is no AI. Together, AI and ML technologies work seamlessly to deliver stronger, natural experiences between reps and customers, resulting in greater trust on both sides. ” -Greg Armor, Executive Vice President at Gryphon.ai, an AI-powered sales acceleration platform

Learn more about AI and communication: Conversational Artificial Intelligence (AI) Market

Better business intelligence (BI)

“In BI tools, you can see the AI-powered features as aids to help humans better see the patterns in their data and ML as the technology that finds and brings up those patterns. In the AutoML tools market, you see AI-powered features being used to help citizen data scientists automate data management and select the best model for the task at hand. -David P. Mariani, founder and CTO of At scale, a BI and data science software provider

Fraud and transaction monitoring

“Some people think AI and machine learning are the same things – that couldn’t be further from the truth. Machine learning is about teaching machines to learn without being explicitly programmed. It enables automation. repetitive tasks. AI is all about providing machines with the ability to perform tasks that typically require human insight, such as evaluating the nuance of a particular transaction. An effective fraud prevention solution takes accurate real-time decisions about transactions and user behavior, and it can’t do without machine learning and AI. -Liron Damri, President and Co-Founder, Forter, a fraud prevention platform

Learn more about AI technologies: Top performing artificial intelligence companies


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