Today, data science, artificial intelligence, and machine learning are buzzwords. We hear a lot about them, but what do they stand for? Is there a difference between data science, AI and ML? Are they connected and how?
In this guide, we'll figure out what data science, AI, and ML mean and how they are being used. Plus, talk about the difference between these technologies – and how they’re connected.
What is Data Science?
At the core of data science is getting new results from data. There are tons of raw data stored in warehouses, and we learn a lot by mining it. Data science uses information in creative ways to add business value. It's all about uncovering hidden information that helps companies make better choices. Here's what data science is often used for:
- Tactical optimization (aimed at improving business processes)
- Predicted analytics (forecasting the demand on products or services)
- Recommendation systems (like the ones of YouTube or Spotify)
- Automatic decision-making systems (like face recognition systems)
- Social research (for processing of questionnaires)
Let’s review a few use cases:
- Netflix uses data mines to figure out what its customers like (and what content Netflix should make next).
- Target tries to define major customer segments and their shopping preferences.
- Procter&Gamble looks towards time series models to understand future demand for their products.
But big data means nothing if you don't know how to turn it into actions. There's a human behind the technology – a data scientist who understands the insights and sees the figures.
Here's what data scientists are skilled at:
- Cloud tools like Amazon S3
- Big data platforms (Hive&Pig, Hadoop)
- Python, Perl, Java, C/C++
- SQL databases
- SAS and R languages
And that's just the beginning. Data science specialists have expertise in data mining, munging and cleaning, data visualization, and reporting techniques. This list keeps changing – just like data science does.
What's AI and How to Use It
At the core of artificial intelligence is imparting human intellect to machines. AI focuses on creating smart devices that act as humans do. They're trained to solve problems and learn from that. AI can relate to anything from speech recognition systems like Siri or Alexa to Amazon's delivery robots. Or even AI in logistics.
How do people use AI? Here are a few use cases:
- For game-playing algorithms
- Robotics and control theory (like motion planning)
- Optimization (when online maps are building the fastest rouse)
- Natural Language Processing
The standard example of AI application is self-driving cars. Let’s review a case study. In 2018, Waymo launched a commercial taxi service that uses driverless technology. It now works in Phoenix, Arizona. The next step for Waymo would be making self-driving trucks. The company is now testing its self-driving technology on Class 8 trucks. They've conducted road tests of Waymo's trucks in California, Atlanta, and Arizona.
Another example: Rolls-Royce has been working on autonomous ships since the 2010s. Two years ago, they launched at Intelligence Awareness system on a passenger ferry. The system recognizes nearby objects in the water, monitors engine condition, and picks the fastest routes.
Machine Learning: What Is It and Why Use ML?
Machine learning is plainly and simply a branch of AI. Not all AI has to do with machine learning, but all machine learning has to do with AI. The idea of ML is about computers learning things – without being programmed to do that. Instead of writing code, engineers feed information to a generic algorithm. Then it creates logic based on that data.
Machine learning makes programming scalable and helps to get better results in less time. If programming is called 'automation,' we can call machine learning 'double automation.' How is machine learning used? In data science, machine learning has been used to create systems that predict future trends. ML is used in medicine, robotics, security systems, and even spam filters for emails are based on machine learning and recognition models.
ML vs. AI vs. Data Science
So what's the actual difference between ML and AI, what data science has to do with them? Here's a hint:
Let’s take a closer look. ML and statistics are parts of data science. Machine learning algorithms train on data collected by data science; that's how they become smarter. So ML algorithms rely on data – they won't learn anything otherwise. But data science applies to much more than machine learning. Information may be collected manually, like survey data. Sometimes it has nothing to do with learning. The difference is that data science covers the whole range of data processing; it’s not limited to the algorithmic or statistical aspects.
What about AI vs. data science?
Data science is a process that involves analysis, visualization, and prediction uses different statistical techniques. AI is the use of a predictive model to forecast future events. It uses logic and decision trees. We use data science to create models that use statistical insights. While artificial intelligence works with models that make machines act like a human.
AI vs. machine learning?
As I've said, machine learning is a subset of artificial intelligence. ML consists of methods that let computers draw conclusions from data and provide them to AI applications. AI is a broad field working on automation processes and making machines work like humans. Machine learning is pushing data science into the next level of automation. AI is about human-AI interaction gadgets like Siri, Alexa, Google Home, and many others. But we call video and audio prediction systems (like those of Netflix, Amazo, Spotify, YouTube) ML-powered.
How Do Data Science, AI, and ML Work Together?
Let's say you're making a self-driving car and want it to stop at stop signs. You would need all three to make it possible. Machine learning. To make the car recognize stop signs using cameras, you'll need to create a dataset with streetside object pictures and train an algorithm to recognize those with stop signs on them.
Artificial intelligence. When the car recognizes the sign, it should hit the brakes right in time, not too early and not too late. That's an issue of control theory.
Data science. Let’s imagine that while running the test, we see that the car doesn't react to stop signs sometimes. What do we do?
Analyze the test data and find out the reason. Maybe, it's the time of the day. The car misses stopping signs at night cause the training data only has objects in daylight. We add a few nighttime photos and get back to testing.
And that’s it! That's how the whole ML vs. AI. vs. data science correlation works. As you see, they all go hand in hand: machines won't learn without data, and it's better to do data science with machine learning. And we can't use ML for self-learning or adaptive system but not use AI.
Vitaly Kuprenko is a technical writer at Cleveroad. It's a web and mobile app development company in Ukraine. He enjoys telling about tech innovations and digital ways to boost businesses.