Artificial Intelligence, Data Science and Machine Learning are quite general terms these days. They are even utilized interchangeably from time to time. However, we should mention that this is not the appropriate way to use them, and this article will explain why.
Moreover, we will reveal the essence of these terms, as well as the differences between AI, ML, and data science.
Overview of Topics Covered
Artificial Intelligence: What is It?
Artificial Intelligence (AI) is an innovation that can be used with everything: starting from game apps and ending with voice recognition systems or robots that will be able to speak and think as people do. For instance, the Siri voice assistant created by Apple understands human speech and answers pronounced requests.
What are the most widespread AI use cases?
- Natural Language Processing (NLP)
- Optimization (e.g., Google Maps laying a correct route)
- Reinforcement learning
- Game-playing principles
- Control theory and robotics (e.g., walking a robot, self-driving vehicles)
Such Artificial Intelligence frameworks as Caffe, Chainer, Pytorch&Torch, etc. help the software development teams create artificial intelligence-based apps.
What’s Machine Learning?
Machine Learning (ML) is one of AI branches letting computers behave like humans and enhance their learning abilities. You shouldn’t even write a code for using this technology — a particular algorithm will apply to your data to create its own logic.
While machines learn to code themselves, programming becomes more scalable, helping people get better results in less time.
How do businesses use such an innovation? For example, Netflix utilizes predictive analytical features to determine what films site visitors like the most and how to enhance their preferences section. It happens the following way: machine learning algorithms assess users’ tastes and ‘understand’ which films they enjoy. Then they present all the possible suggestions to the site visitors. This way, the platform encourages users to apply their services more actively.
What is the role of Machine Learning experts in such processes and similar ones? They are in charge of applying the scientific approach to commercial problems, as well as cleaning and preparing information for statistical and machine learning modeling. Moreover, they use analytical algorithms to create models that better understand data relationships, forecast situations, and turn data into commercial value.
Machine Learning specialists typically have:
- Practical experience in MALLET
- Open Source/Apache Tomcat knowledge
- C++ and Python knowledge, which is a plus.
- GraphLab Create, NetworkX, Spacy, or NLTK knowledge
The Essence of Data Science
Data Science, based on strict analytical evidence, works with processing both structured and unstructured information to receive fresh results from it.
DS encompasses all aspects of data selection, processing, and analysis. Data science experts apply to this technology to extract meaning and essential information from vast amounts of data. There is a lot to learn from processing raw data kept in data warehouses since there’s so much of it.
Mainly, DS is used for:
- Recommendation systems (like those of Netflix or Amazon)
- Tactical optimization (improving business flows)
- Analytical predictions (forecast of demand and events)
- Social science research (processing of questionnaires)
- Automated systems for decision-making purposes
Netflix, for example, searches its data mined for watching trends. It helps employees to have a better understanding of viewers’ preferences and make more accurate selections about what Netflix shows to produce next.
Top Companies that Use Data Science
A data scientist understanding data insights and seeing the numbers is in charge of DS implementation. Generally speaking, data scientists should be able to:
- Deal with SAS and other analytical software
- Have programming abilities (R, Python, SQL, RapidMiner)
- Skills in data processing and statistical analysis
Simulations and quality control, computational finance, industrial engineering, and even number theory may be required as additional experience to DS professionals.
Data Science, Machine Learning, Artificial Intelligence — What is the Difference?
Finally, let’s find out the distinctions and similarities between artificial intelligence, machine learning, and data science.
Artificial Intelligence and Machine Learning: Essence, Distinctions and More
To find an answer to the question of differences between Artificial Intelligence and Machine Learning, let’s compare their basics.
Artificial Intelligence is a vast scientific discipline focused on automating commercial operations and making computers behave like people. Machine Learning, that is a branch of AI, allows computers to draw conclusions from the received data and send such findings to Artificial Intelligence applications.
The distinctions between data science and machine learning are better visible by comparing the examples. Thus, Siri, Google Home, and Amazon Alexa are tightly connected with AI, while Spotify, Netflix, and YouTube are ML-based video and audio prediction systems.
Despite all the differences, both AI and ML can be used for customer services automation (e.g., voice assistants) and vehicle-connected operations (pilotless automobiles, for example). Such an upgrade helps people save money and manforce for other tasks that need more human attention.
Connections Between Machine Learning and Data Science
Machine learning algorithms train on information delivered by DS to become smarter and make more accurate business predictions.
However, data science encompasses far more than machine learning and collected statistics. How does DS collect data? It might be done automatically or manually (e.g., survey data might be collected the second way).
Data science includes the entire spectrum of data processing, so it is not only about the algorithmic or statistical components. Here are some of the areas that data science includes:
- Distributed architecture
- Data integration, visualization, and engineering
- Data-driven choice
- Data engineering employment in production mode, etc.
This way, while machine learning specialists are busy developing practical algorithms throughout the project’s lifetime, data scientists must be more adaptable in transitioning between different data roles based on the project’s demands.
Wrapping Up
Artificial Intelligence (AI), Data Science (DS), and Machine Learning (ML) are quite complex for a seamless implementation. First off, you should fully realize their differences and interconnections to deal with them appropriately. If you want to facilitate your work, you can apply to a skilled software development team. The specialists will give you a comprehensive consultation that will provide answers to all the disturbing business questions, help you choose a proper tech stack for work, and develop a flawless, modern, and profitable solution that will be sustainable and competitive on the market.
