Whether it’s scientific computing, statistics, business intelligence, or computer programming, they all work together to facilitate the multidisciplinary nature of data science. A field of study that is driving the global technological transition at the moment.
Data science enables the self-explanatory subset called machine learning(ML) which is a spiral nutshell is an application of artificial intelligence (A.I.). Understanding ML and A.I. difference are essential to understand the distinction as well as the relation between data science and A.I.
There many qualified individuals that confuse ML and A.I.as a singular field. Although they do retain some similarities, each of these fields is dedicated to separate study and knowledge.
ML is a fascinating study of training machines in order to auto-learn and self-improve to be able to execute commands they are not programmed to do so. Which is basically the application of A.I.In a bid to control time and resources that go into training machines, A.I.along with other cognitive technologies are integrated into the whole process of machines learning by themselves.
They are all interchangeable, yet they are not. Data science enable ML for its assistance, whereas ML enables A.I. for its. All these three studies have independent purposes but they need each other to execute their objectives. For instance, data science works with massive amounts of data through a specified process to extract targeted insight, for the purpose of augmenting decision making abilities to predict future events.
ML comes in the picture to assist in dealing with this massive data to develop predictive models computers use to learn from data. And A.I. makes an appearance to implement these predictive analytical models to be able to forecast the future events required by data science.
Hotchpotch alright, isn’t it? Unless you understand the gist of each of the fields, it is. So let try and understand data science and A.I. individually so we can determine the relationship between the two.
What is Data Science?
Data Science is an all-inclusive and powerful phenomenon leading the advance technological exploration and implementation around the world. It will not be too far-fetched to term the huge disruption and alteration as the fourth industrial revolution, given its influence in every industry and sector today.
It resulted from the explosion in data production and usage and the continuously increasing need of industries to depend on data in order to improve their products and services. Out of all the data in the world today, we created 90% of it in only the past two years. If that is not enough to exhibit the significance and relevance of data science, let the estimation of data amount reaching 44 zettabytes (44 trillion gigabytes) by the end of 2020 sink in.
Businesses are increasingly requiring data scientists to assist their data-driven decision making. This helps industries with performance assessment as well as enhancement by providing information to align the product and services with consumer preference acquired through behavioral analysis.
The data used throughout is of critical nature that needs all kinds of protection to prevent data breaches. Companies everywhere spend heavily to train their IT professional with up to date security programs like NetApp clustered data ONTAP certification, that teaches how best to restore, replicate, and protect critical data of the enterprise
There is not a single field out there that is not benefitting from data, whether it’s Big Data or smaller datasets. Data experts from healthcare, journalism, research and development (R&D), security, innovation & technology, marketing, etc., are comping up with one effective solution after another by using historical, current, and predictive data.
Data Science, as we discussed above, works by involving various fields like statistics, programming, business intelligence, etc. which makes the individuals running this show highly qualified and sought after people known as data scientists. They take the raw data through the processes and technologies comprised of following steps to be able to convert it in intelligible and actionable insights:
- Problem identification
- Data collection
- Data analysis and exploration
- Data modeling and visualization
- Results communication
This process culminates in forecasting future events that require data scientists to have an in-depth knowledge of ML algorithms which are in essence an A.I.
What is artificial intelligence?
A.I. in all its simplicity is referred to machine intelligence. The hype – however rightful as it may be –basically rests in the model of A.I. which is inspired by the natural intelligence humans and animals. A.I.works by using ML algorithms to execute autonomous tasks.
The traditional A.I. algorithms were dependent on explicit objectives, however, contemporary AI Algorithms with the integration of deep learning – a subclass of ML- that learns to recognize the patterns and goal embedded within the input data. Deep learning, unlike machine learning, is able to learn from unstructured data because it is stimulated by the structure of the human brain.
Today, there are many deep learning architectures integrated in A.I. that include:
- Deep neural networks
- Deep belief networks
- Recurrent neural networks
- Convolutional neural networks
All of these structures have already been applied in several fields with a stunning success rate. In some specific cases, the A.I. implementation even exceeded human performance. Following are some of the deep learning-enabled A.I. intensive fields:
- Computer vision
- Speech recognition
- Natural language processing
- Machine translation
Almost all tech giants e.g., Google, Apple, Amazon, Facebook, etc., are leveraging A.I. to build an efficient autonomous system after making the dream of self-driving cars a reality. Healthcare is another field widely incorporating A.I. with maximum accuracy e.g., neural networks have successfully detected cancer cells with a 100% accuracy rate. Now is the time to focus on workforce readiness to optimize the A.I. driven benefits for your business. The enterprise LMS type of training programs is the first step towards appropriate adaptation of a deeply technical future.
To conclude our debate lets answer the question pitched in the title that how data science and A.I. are related? Well, ML is the lynchpin of the relationship between data science and A.I. just as we have established above.
Bio: Founder Futuristic Content, I’m a technical writer with a passion to write on the subjects of cybersecurity, futuristic technologies, artificial intelligence, cloud computing, and data science.