You will find people mostly using the terms artificial intelligence and ML interchangeably. You will often find this during discussions about big data and predictive analytics. It is easy to see why since AI and ML are quite closely connected. But even then, there are differences between AI and ML in terms of their use cases and scope. AI is a broader concept. ML is just a subset of it. ML focuses on teaching machines how to get better. If you want to start your career in this field, learning the differences will let you apply them better. In this blog, we explore in detail what sets AI and ML apart, and how they can be used together to reap the benefits.
What is Artificial Intelligence?
AI is a wider term for creating machines and computers that can imitate human-like thinking. AI is able to understand spoken and written language, analyze information, even make situation-relevant suggestions, and more.
What is Machine Learning?
ML is a subset of AI that allows systems to learn and improve automatically through experience. Instead of manually giving specific instructions, ML relies on algorithms to examine data to understand patterns and get better over time. The results produced by these algorithms are called ML models. These represent what the system has learned from its training data.
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How Are AI and ML Different?
Now that you know how AI and ML are connected, let us see the main differences between ML and AI. This will form the basis for understanding how you can use these techs as a professional in your job.
Artificial Intelligence (AI):
- AI enables machines to solve problems similarly to humans.
- The aim is to develop systems that can handle intricate tasks autonomously.
- AI has a wide range of uses across various fields.
- It uses various technologies to imitate human decision-making.
- AI can work with all kinds of data – structured to unstructured.
- AI systems utilize logic and decision trees to learn, reason, and adjust themselves.
Machine Learning (ML):
- ML enables machines to learn from past data without explicit programming.
- The goal is to enhance output accuracy through data-driven learning.
- ML trains machines to perform defined tasks and achieve reliable results.
- It has a narrower scope of applications compared to AI.
- ML employs algorithms that learn from data to create predictive models.
- ML primarily uses structured and semi-structured data.
- ML systems depend on statistical methods to learn and can adapt when new data is introduced.
Benefits of Using AI and ML Together
Combining AI and ML gives us significant benefits, especially for businesses. They can unlock new opportunities even when data volume and complexity continue to grow. Automated and intelligent systems are helping businesses streamline operations and increase profits. Here are some key benefits of using AI and ML together:
- Wider data ranges: Helps in the analysis of a broader spectrum of data sources (both unstructured and structured). This gives results with much richer insights than what humans could do.
- Faster decision-making: Businesses that use this tech have quicker processing times and at the same time have fewer human errors. They benefit from faster (and correct) decision-making.
- Efficiency: Businesses today are leveraging AI and ML to improve their operational efficiency and reduce costs at the same time.
Due to these benefits, businesses are always looking for skilled people in this field who can unlock the power of AI and ML. Hence, if you are planning to kickstart your career, there will never come a better time than now. Even if you are a company looking to train your current employees, you can do so easily through the right corporate training courses from providers like EducationNest, currently helping corporates across India train their workforces in various skills.
Applications of AI and ML
When asked about the applications of artificial learning, the first thing that comes to mind is probably chatbots. We think of how big companies like H&M, Domino’s Pizza, Mastercard, etc have started using chatbots. But that is just the tip of the iceberg of the use cases of AI and ML. They can be used in the simplest of tasks to the most complex business problems. Let us see some unique applications that many of you probably would not have thought about so much:
Healthcare
It has been well known that the future of healthcare lies in personalization. But till now, it has not been possible to implement this on a large scale specifically because of cost concerns. Personalized medicine is known to be extremely costly. AI systems can help bridge this gap with more affordable treatment plans by analyzing patient-specific information at a fraction of the cost. Genetic information, lifestyle factors, and environmental factors can be quickly analyzed by AI models to tailor treatment plans for people.
AI application in healthcare is revolutionizing the way radiologists interpret imaging reports. Advanced algorithms are now detecting anomalies in X-rays, MRIs, and CT scans with a level of precision that often surpasses human experts. AI can identify early-stage tumors that might be missed during a manual human review by doctors.
Finance
Traditional credit scoring models often fail to capture the complete financial picture of an individual. This is an amazing use case where AI can correctly perform credit assessment by analyzing a larger range of data sources. Instead of relying on just repayment histories, AI can also go through transaction histories, social media behavior, etc. to give more accurate credit scores and better risk management for lenders.
The applications of ML in fraud detection have already garnered a lot of praise worldwide. Financial institutions are now leveraging ML algorithms to monitor transactions in real time. By closely analyzing spending patterns, ML can now correctly identify unusual behavior that may be a sign of fraudulent activity.
Transportation
Another core application of AI is self-driving cars. These cars use a combination of sensors, cameras, and AI algorithms to interpret their surroundings correctly and navigate safely. As this technology evolves, it holds the potential to greatly reduce accidents.
AI systems can even help monitor vehicle health to tell you in advance when your car needs maintenance scheduled before failures occur. This minimizes downtime and reduces maintenance costs for fleet operators.
Public Use Cases
Smart city initiatives use ML to analyze traffic patterns and optimize signal timings. By predicting congestion and dynamically adjusting traffic lights, cities can reduce wait times and improve overall traffic efficiency.
Conclusion
Today, AI and ML are everywhere and helping businesses everywhere make smarter decisions through real-time recommendations. It is fascinating to realize all these benefits are just the tip of the iceberg and there are even more to come as and how these algorithms get better (something like Jarvis!) For businesses, they mean more efficiency, more profits, and better operations. This is why businesses today are actively seeking ways to either hire experts or build their in-house teams with the relevant skills.
If your company is also starting to think this way, the right choice would be to go for big data training programs for corporate to build your own team. EducationNest being one of the top corporate training providers in India for employees in domains like soft skills, leadership, cybersecurity, etc. also provides expert-led training programs on big data analytics.
