In a rapid technological change, buzzwords like data science, machine learning, and artificial intelligence are no longer surprising to anyone. Nevertheless, people often find them interchangeable, which is not quite correct while they don’t refer to the same things. In this post, we’ll try to put everything in its place and help you understand data science vs machine learning vs artificial intelligence variance.
But, before we get to a comparison, let’s consider what each term means.
Simply put, data science is a blend of various tools, algorithms, and ML concepts aimed at discovering new results from the raw data.
Data we know in its traditional system is rather structured and small in size. However, the trends are changing and, today, we meet more and more semi-structured or unstructured information.
Image source Matt Carlson
These days, its volume is huge, and simple business intelligence tools can hardly sort out all the information. This is where data science with its cutting-edge algorithms and tools for data analysis come into play. It also shows good results in analyzing customers’ information to provide more precise product recommendations and is successfully employed in decision making and predictive analytics.
What else can DS be used for? Let’s find out:
Marketing — prediction of a customer’s lifetime value.
Travel — prediction of flight delay or cancellation.
Social media — sentiment analysis.
Healthcare — disease prediction.
Sales — demand forecasting.
Credit and insurance — claim prediction and fraud detection.
Automation — autonomous vehicles, drones, etc.
Automotive industry is one that makes good use of DS. Many automobile manufacturers, such as Tesla, Ford, and Volkswagen, implement ML, data science, and predictive analytics to make their autonomous cars adapt to speed limits, avoid lane change accidents, and even take the quickest route to get to the passenger’s desired destination.
Any technology cannot perform without people behind the scene. The specialists who are busy implementing data science are also known as data scientists. To be eligible to apply for this position, they should have extensive experience in certain scientific disciplines and:
understand SAS and other analysis tools;
have mastery in R and Python, SQL, and RapidMiner;
be able to process data;
have a grasp of collecting, exploring and presenting wealth of information;
be skilled in domains, like simulation and quality management.
AI makes machines act like people to address the issues and learn from experience in the best possible way. ML techniques and NLP are involved in machine training to collect and translate data into usable information, as well as distinguish and segment it to set criteria. Many examples of AI, like computers playing chess or autonomous vehicles rely on those technologies.
The importance of AI can hardly be underestimated. Other than analyzing deeper data, it also automates manual tasks, adds intelligence to already existing products, e.g. Siri, makes the most of data, etc.
Literally, every industry has hopes for AI, especially Q&A systems used for legal aid, medical research, risk notifications, and more. Other use cases cover:
Health care — preventative health care with wearables.
Retail — inventory optimization.
Banking — trader oversight.
Marketing — smarter targeted advertising.
Insurance — fraud prevention.
Security — camera with intrusion detection.
Industry — robotics, etc.
Source: Gleb Kuznetsov
One of the most prominent examples of using AI in business is Amazon. It is known not only for its voice-controlled virtual assistant Alexa but also for projects called Amazon Prime and Amazon Go. Amazon Prime’s smart robots deliver items from the warehouse to people’s doorsteps the same day. Whereas Amazon Go with built-in AI technology monitors what products you put in your bag and charges you for them using the corresponding app on your phone without forcing you to go through the checkout process.
Specialists implementing artificial intelligence in different industries should have expertise in Pytorch & Torch, TensorFlow, Caffe, Chainer, and other AI frameworks.
Machine learning is defined as a subset of AI that allows systems to get better from experience without the need of being programmed. Rather than write additional code, developers put data inside the algorithm that works on self-improvement all the time looking for data patterns. However, to let the system work autonomously, you need to provide it with examples of incoming and outgoing data.
When it comes to ML techniques, one can highlight a few of them, including learning on examples and from experience, self-learning, and deep learning. These methods are commonly employed in the analysis of media content and Big Data.
Machine learning has a huge potential and is adopted in many sectors other than finance and healthcare. Let’s consider what other industries successfully approach this technology.
Education — adaptive and personalized learning.
Retail — demand prediction and document work automation.
Advertising — media buying, customer journey mapping, and audience segmentation.
eCommerce — product suggestion.
Science — the discovery of new stable materials and prediction of their crystal structure.
CRM — predictive lead scoring.
Banking — underwriting and credit scoring, etc.
Image source Igor Kozak
One of the companies successfully adopting machine learning in their processes is the streaming service Netflix. The website makes the most of predictive analytics and recommends movies and TV shows to its users based on their watching history.
ML specialists are responsible for many things from preparing data for ML modeling to building models that would ensure a better work with data. They must have practical experience with Apache Tomcat/Open Source, C++ and Python, as well as GraphLab Create, MALLET, scikit-learn, scipy, NetworkX, Spacy, and NLTK.
Now you’ve got rid of the confusion with the terms, it’s high time to explore the difference amid them and how they relate to each other.
Because data science uses stats and machine learning to perform, there is an obvious connection between them. Data science provides ML algorithms with the information they use for training to become smarter and better informed in forecasting. That all means that ML algorithms rely heavily on data and can hardly learn without using it.
Nevertheless, data science application is not limited to machine learning. In DS, the info doesn’t have to come from the machine only, it can also be collected and processed by humans, as is the case with survey insights.
Data science sometimes has nothing in common with training. Yet, the fundamental dissimilarity is that DS is not restricted to algorithms and stats, but rather works with a full range of data processing from data integration to information-based decisions, etc.
ML specialists are responsible for creating algorithms, while data scientists should switch between data roles, which requires outstanding flexibility.
ML and artificial intelligence are often used interchangeably, so they must relate to each other. But how?
AI is a science of computers imitating people, while machine learning is a method behind how devices learn from data. Machine learning models look for stats and data and try to conclude. They are not being explicitly programmed by people. You can actually give them examples, and they learn what to do from those examples. We’d say that’s a huge difference because it’s much easier for us humans to give examples than it is for us to write code.
ML life cycle involves asking the questions, collecting the data, training the algorithm, trying it out, collecting and using feedback to make the algorithm better, and get improved execution accuracy.
Examples of AI use cases include Google Home, Siri, and Alexa digital voice assistant, whereas ML-powered projects are Netflix, Spotify, Amazon, YouTube.
Although machine learning and artificial intelligence mean different things, they still can work together to ensure automation of vehicles and customer service. Using them, companies can pass the repetitive tasks to machines, while people can deal with more urgent tasks.
Other than that, they both need granular and extremely diverse data sources of huge volumes to be able to find the patterns and learn.
Other than using machine learning to perform, data science also employs artificial intelligence to translate historical information, recognize current data patterns, and make forecasts. Data scientists, using ML and AI, get a chance to collect information and gain insight into competitors.
While DS performs making the most of analysis, visualization, forecasting, and other statistical-based methods, AI employs algorithms and forecasts future events through modeling.
So, data science lets us build up models using stats, whereas artificial intelligence can hardly perform without models that make computers as smart as humans.
To sum things up, it is worth saying that these technologies cannot be employed separately. They all go hand in hand. Machines learn using data, while data science better performs in conjunction with ML. Self-learning and adaptive systems implemented employing machine learning are not possible with AI, etc.
Apart from that, the specialists that implement these technologies should be flexible enough and have strong technical knowledge. You can find such specialists in Fulcrum outsourcing software agency. The company is dedicated to the product and uses cutting-edge technologies to develop projects and help entrepreneurs succeed. So, consider contacting the off-shore in-house agency for an assistant with your future project.