The future of Data Science and Artificial Intelligence
Data science and artificial intelligence have become integral parts of technological solutions used in many fields in the past few years. In fact, their presence is ubiquitous in several everyday applications that we use. Many global giants have already invested in both fields to improve the operational efficiency of their products and services through workflow automation.
Popular applications such as Robotic Process Automation services and spend analytics tools are revolutionising the manufacturing industry and ensuring higher accuracy, boosted productivity, and greater resilience, along with major cost savings.
Healthcare has seen significant changes with the use of technological aids such as robotic surgeries which are gaining immense popularity because of their precision and success rates. Advanced applications are seen in the use in drones for the delivery of medicines in remote areas.
E-commerce is another field where these two technologies have been visibly disruptive. Chatbots and virtual assistants, using these technologies, help us shop virtually. Such services are getting more unique and personalised continually.
The same technologies have also been used to upgrade defence services the world over. Governments, globally, have adopted AI and data science to augment and strategise defence training, surveillance, logistics, and intelligent weaponry among other things.
These are only a few of the areas of our lives that are touched by growth in data science and artificial intelligence. The field is dynamic and changes are happening at rapid paces. Let us take a quick look at what the future holds for these technologies.
Automaton of Machine Learning (ML) - ML is one of the key AI technologies that helps extract meaningful information from data through algorithms and learning models. It helps people and businesses to reduce or eliminate tedious tasks and provides data-driven insights to make better decisions. However, to avail these benefits, one would have to know ML well enough to apply it. AutoML, also called Automated ML, refers to automation of the processes while applying ML algorithms to real-world scenarios. It makes ML accessible to people who are not ML experts and helps them reap benefits from ML techniques.
AutoML, while providing speedy and more accurate results, also makes ML available to a wide audience and businesses of all sizes enabling them to unearth its vast potential. AutoML helps improve various ML model creation stages, such as data pre-processing, feature selection, extraction and engineering, algorithm selection and hyperparameter optimisation, and model deployment and monitoring. The widespread availability of autoML is expected to empower non-technical users to participate in data-oriented problem-solving, and change the data science job market in a huge way, as AutoML solutions are expected to be widely available in the next few years.
Augmented data analytics - Augmented analytics refers to automated data analytics that uses AI and ML to augment human and data interaction at a contextual level. This is touted as the future of data and analytics, though currently it is an emerging technology. It uses various tools and software to make insightful analytics available to a wider range of audience. Precise and accurate output in the form of insights, recommendations, responses to specific queries, etc, enhance business analytics solutions. Aided by various visualisation tools, augmented data analytics is expected to enhance business sustenance with speedy and credible value from their data lending agility, accuracy, efficiency and confidence to business decisions.
Enhanced natural language processing (NLP) processing - Unless data can be processed, understood and used to further business operations it is of no use. This is where NLP plays a significant role helping computers understand and interpret human or natural languages. It uses AI techniques such as ML and automatic speech recognition (ASR) to comprehend human languages and dialogue systems.
Smart assistants such as Alexa, Siri and Google assistant are examples of how NLP can work with human language. And these are just getting better every day! Chatbots, yet another example of NLP comprehending human queries, are getting quite adept at resolving basic consumer issues. NLP is also being used to detect bullying and hate speech on social media and also obtain meaningful and reliable insights to enhance the usefulness of various platforms. Predictive health analytics, sentiment analysis, cognitive analytics, etc. are few other areas where NLP is being experimented with largely. In the future, NLP will be able to power humanoid robotics to a level wherein, they will be able to understand and interpret our facial expressions and body languages to have conversations similar to the ones with humans. Perhaps, with more R&D, they will also be able to express themselves better.
Continued R&D in data science and artificial intelligence is expected to provide remarkable benefits to businesses. The transformative power of these technologies is such that it is now hard to imagine a future without them.
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