Over the last millennia, technology has witnessed substantial evolution that has not only transformed the human way of living but has also led to human evolution. Starting from roadways and railways in the past to Artificial Intelligence in the present, technological evolution has only benefitted humans with an improvement in their standard of living. Even with the various technology surrounding us now, there is constant research and development going on to make human life more efficient and better.
In this blog, readers will be taken on a journey of machine learning and data science’s evolution from its inception to its present state.
Data Science and its evolution:
The term data science was first coined in the 1960s mainly to describe and interpret large data collected over time. With time data science has evolved with the use of computer science and statistics and now encompasses various fields. Data science is used to gain insight, understand trends, and make future predictions.
Data science and its roots can be traced back to statistics, but over time, data science came to include concepts such as machine learning, artificial intelligence, etc. With an increase in population and globalization, people are getting more dependent on the use of technology for everyday things. This surge in use has amassed huge data and data science is helping scientists find answers to human behaviour through this data. This new information has paved way for businesses to find a way to increase profit but making better consumer decisions and data science has also helped in the field of medicine and engineering.
Around 1962, an American mathematician J.W Tukey first coined the term as he saw the origin of data study way before computers came into the picture. After Tukey, another scientist came to believe in the potential of data science and defined it as the science of dealing with established data and the meaning behind this data is delegated to other branches of science.
Then around 1977, the International Association of Statistical Computing was formed and had the principal agenda to link traditional statistics to computer technology with individual domain experts who would help decode the data into information and knowledge.
In the 1990s data science started to take a more significant form with the formation of Knowledge Discovery in Database (KDD) and the International Federation of Classification Societies (IFCS). With time these two societies were focused on the education and training of individuals in the theory of data science and methodology. Data science also started to gain more attention with professionals starting to monetize data and statistics.
Finally, around 1994, a newspaper published the first article related to data science named “Database Marketing”, which essentially explained the process of how some businesses were collecting large data to study consumer behaviour. Their competition and how to advertise to the right audience.
In the early 2000s, data science was recognised and specialised as its own field. Data science journals started getting published and circulated and scientists continued developing and improving the potential of data science. By the late 2000s, technology evolved to provide universal access to internet connectivity and communication along with data collection.
Worth mentioning that in 2005 it was the scene for big data. Technology giants like Google and Facebook entered the market and started collecting massive data, uncovering them and developing technologies that were capable of processing them. Technologies like Hadoop, Spark and Cassandra started developing.
Leaping to 2014, the increasing importance of data science made organizations interested in finding patterns that would help them make better business decisions and demand for data scientists went on growth in various parts of the world.
Now, in 2015, machine learning, deep learning and finally artificial intelligence entered the field of data science. Highly different from the past technologies, they were an innovation on their own. Right from the personalised shopping experience to self-driving vehicles, real-life applications of AI started to be used on a daily basis.
Coming to 2018, the dangers of these evolutions were realised, and new regulations came to place. As we enter the 2020s, big data and its study have become more relevant than ever. Now coming to the specific year 2022, Artificial Intelligence and Data Science have advanced to include the following things:
- With a given text, algorithms can write articles.
- Identifying people by their face shape, skin tone and other characteristics is made easy with a facial recognition program.
- Music can be automatically generated by advanced neural systems as they are able to interpret the emotions behind the songs.
- Electronic chatbots can communicate with humans.
- Computer programs are able to beat professional players in various online strategy games.
The evolution of data science and machine learning has evolved, and these advancements can be listed as:
- Objects and faces can be identified by machine learning algorithms with 100% accuracy.
- Natural Language processing software responds to human questions with complete accuracy.
- Data science is used to develop medicines and treat diseases.
- With AI, humans are able to drive cars, fly planes and other complex tasks.
These listed advancements are only the tip of evolutions that have propelled the world in the era of new technology. The basis of this evolution is far and outreaching than comprehensible by ordinary humans.
According to Karun E S, Analyst at Quadrant Knowledge Solutions, “The Data Science and Machine Learning (DSML) Platform vendors continue to strengthen their capabilities by leveraging interoperability and integration to design, develop, deploy, monitor, and manage all the models in a unified platform. The industry is focusing on extending support for various use cases such as image processing, signal processing, optimization, anomaly detection, and more along with offering multiple deployment options like deployment on edge devices, cloud platforms, on-premises, and embedded systems.
Trends to look out for in Data Science and Artificial Intelligence in 2023:
- There will be an increase in the use of machine learning and artificial intelligence in businesses and industries.
- Businesses are taking advantage of artificial intelligence for decision-making.
- Data science is getting extensively used in finance, banking and the healthcare sector.
- With more use of data science and big data, more jobs are getting opened in this field.
Conclusion:
With further technology advancements, the collected data will enable more interaction and decision-making in the future. Human lifestyle will get more intertwined with technology and with the help of artificial intelligence, predictive algorithms and data analytics, not only humans but organizations also, will have ease in decision-making.
Author: Shinjini Sarkar, Senior Content Specialist, Quadrant Knowledge Solutions.
