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AI vs Machine Learning: What's The Difference?

Anna Burgess Yang
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Technology
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If you have been searching for the answer to "What is the difference between artificial intelligence and machine learning?" perhaps look no further than this tweet:

If it is written in Python, it's probably machine learning.
If it is written in PowerPoint, it's probably AI.
-@matvelloso, Technical Advisor to the CEO at Microsoft

In all seriousness though, artificial intelligence and machine learning: not the same thing. These two hot concepts in technology and data science are often thrown around interchangeably and have gained traction in many industries.

But they are distinct. Both are making attempts at replacing parts of human intelligence with computer systems. The difference between the two is the approach.

By understanding the differences between artificial intelligence vs. machine learning, you can know how best to incorporate both into your business model.

What Is Artificial Intelligence?

The work in artificial intelligence (AI) is focused on mimicking human decision-making. This field of computer science builds intelligent programs that can solve problems; a skill previously thought to require human intelligence. AI has a very broad scope, casting a wide net in its application.

AI is not a system by itself but rather implemented within a system. An artificial neural network (ANN) processes information in the same way a human would. The goal is to simulate natural intelligence and use the information in decision-making.

Because AI is designed to mimic humans, the systems can respond and behave in a certain way, given certain circumstances. The human brain cannot reasonably scan through thousands, if not millions, of potential outcomes. But AI can do this and find the optimal solution.

Practical Uses for AI

To understand how deeply AI is embedded in our world, let's take a look at some practical applications.

  • AI-powered chatbots provide a competitive edge for e-commerce.
  • Intelligent virtual assistants can take care of repetitive or mundane tasks.
  • AI can deliver powerful tools and insights to HR professionals.
  • Healthcare can use AI to gather patient data and improve clinical outcomes.
  • AI can detect anomalies in computer systems to improve cybersecurity.
  • Businesses can act on AI to improve logistics and the supply chain.
  • Human traders cannot compete with AI when it comes to stocks or sports bets.
  • AI can streamline manufacturing and reduce downtime.
  • Hotels can use AI to understand their guests' behaviors and needs.
  • AI brings a curated experience to retail, from customer service to purchase history.
  • Marketing has been supercharged by AI by delivering personalized and targeted ads.

What Is Machine Learning?

Much like the human brain learns through experience, machine learning (ML) is designed with the same goal in mind. Machine learning is a subset of artificial intelligence, where machines can perform actions based on past experiences and outcomes.

ML will look at a dataset and make better decisions over time. The goal is to maximize the performance of the machine on a particular task. Self-learning algorithms help to achieve this.

Machine learning gets even more advanced with Deep Learning. Multiple layers are used to extract an even higher level of understanding. As an example, deep learning is a way to improve facial recognition technology, sifting through multiple layers of data to make a match.

Practical Uses for ML

To many people, machine learning may almost seem like "magic." However, like AI, ML is now found in many everyday functions. Let's look at some of the same AI applications and how ML enhances them.

  • An ML-powered chatbot would respond based on past interactions.
  • A virtual assistant would use ML to complete tasks based on users' personal data and user history.
  • ML-based HR tools can help HR teams efficiently track candidates through an interview process.
  • In healthcare, ML algorithms can identify cancer and diagnose images.
  • ML bolsters cybersecurity through pattern detection and penetration testing.
  • ML algorithms can forecast demand in supply and logistics management.
  • In stock trading and sports betting, ML has a strong ability in trend prediction.
  • Using ML in assembly creates smart manufacturing, increasing the precision of robotics.
  • Hotels can offer guests personalized upgrades based on previous behavior.
  • Retailers can collect customer data to optimize prices.
  • ML can help advertisers and digital marketers understand their audience better.

How AI and ML Work Together

Think of a computer program that is designed to play chess in understanding how the two can operate together. Artificial intelligence would include the explicit programming of a chess program to evaluate millions of maneuvers with each move. The computer program would use algorithms to select the best one.

Machine learning would go a step further and learn behaviors based on the opponent's moves, using predictive analytics to look past the "current move." Reinforcement learning allows ML to take the right action to maximize the reward.

Another example of how tightly integrated AI and ML can become is the rise of voice-activated home assistants, like the Amazon Echo, Google Home, and Siri. Natural language processing, considered a subset of AI, allows humans to talk to machines. Machine learning allows such devices to learn from mistakes in voice recognition to make a smarter interpretation the next time.

Now take these same concepts and apply them to business models. AI can replace some of the tasks normally completed by humans, freeing up resources and improving speed and accuracy. Machine learning algorithms can learn and improve the use of your data over time. 

Supporting Your Business With AI and ML

While these concepts may sound complex, incorporating them into your business shouldn't be. You shouldn't need custom applications or a staff of data scientists to leverage the power of artificial intelligence and machine learning. With the right tools, you can turn your data into predictive models to drive your decision-making.

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