Artificial Intelligence and Big Data are two of the driving factors behind a wide range of technological advancements that have created today's digital world. These two movements share the objective of maximizing the value of today's massive amounts of data. We often hear about these two terms a lot lately, perhaps to the point of confusion. In this article, let’s find out the difference between artificial intelligence, and how the combination of both leads to results beyond traditional human capability.

 

 

 

Artificial intelligence is a technology through which we can create intelligent systems that can simulate human intelligence. Artificial intelligence systems do not need to be pre-programmed. Instead, they employ algorithms that function in conjunction with their own intellect. Reinforcement learning algorithms and deep learning neural networks are examples of machine learning algorithms.

 

 

Big Data is a field concerned with the management of enormous volumes of data from many sources. When the volume of data is too huge for typical data management approaches to be effective, big data comes into play. 

 

Companies have long gathered vast volumes of data on consumers, pricing, transactions, and product security, but the volume of data gathered proved too large for humans to manually evaluate.

 

The essence of big data can be broken into “the three V’s of big data”:

 

- Volume: The amount of data being collected

- Velocity: The rate at which data is received and acted upon

- Variety: The different forms of data collected, (structured and unstructured data sources)

 

 

The big difference between artificial intelligence and big data is in their output. Artificial intelligence examines inputs in order to learn and improve its sorting or patterning processes over time, using data gathered to deliver a more accurate diagnosis.

 

On the other hand, big data is an encompassing pool of information gathered from multiple data sources and then processed by artificial intelligence. 

 

Big data and artificial intelligence are often used in conjunction with one another, but they serve quite distinct functions: one is information and the other is a treatment of that information.

 

 

Although each term is different, its existence is critical in allowing the other to function to its full potential. AI does utilize data, but its capacity to evaluate and learn from it is limited by the amount of data input into the system. Big data provides a large sample of this information, allowing it to be used to power high-end artificial intelligence systems.

 

Artificial intelligence systems can make more educated judgments, give better user suggestions, and uncover ever-increasing efficiencies in your models by leveraging large data resources. However, in order to provide the finest data possible, an agreed-upon ruleset for data collecting and data structure must be in place prior to AI deployment.

 

 

Big Data is the engine that powers AI. It is both what educates AI to become more powerful and what AI systems are eventually used to provide real-world insights. The more data AI systems can access, the more intelligent and disruptive they will be.

 

While AI as a concept has been known for more than 50 years, its progress has been stifled by a lack of organized data and technological constraints for much of that time. 

 

These improvements, however, come at a cost, notably in labor productivity, which is a measure of production per worker. On the one hand, employees will be liberated from monotonous, repetitive duties. This frees them up to focus on higher-value work that demands innovation and problem solving, resulting in greater potential productivity. 

Jobs that are primarily dependent on repeated or low-value-add tasks, on the other hand, may soon be replaced by AI systems, perhaps resulting in a reduced workforce until employees can be retrained.

 

 

 

AI will have a big impact on healthcare. According to Acumen Research and Consulting, the worldwide industry will reach $8 billion by 2026, and there is a significant overlap of capabilities in AI and big data—where information processing is optimized to assist address commercial and real-world challenges. 

 

From robot-conducted surgery, aided by integrating diagnostic imaging and pre-op medical data, to virtual nursing assistants that help with initial diagnoses and patient logistics, AI is expected to revolutionize a variety of aspects of health care.

 

In the field of healthcare, medical organizations and hospitals are now offering crowdsourcing tasks in the field of AI and big data. Companies are turning to crowdsource platforms for effective cutting-edge technologies for data collecting and analysis in order to boost production and competence. 

 

 

Artificial intelligence in the retail market is expected to grow up to 35% from 2019 to 2024. AI is already being used to produce customized suggestions to assist retailers in better interacting with their consumers and increasing revenue. However, AI may also be used to minimize expenses in operations, such as forecasting client orders, which may cut shipping, inventory, and supply chain costs.

 

 

Advances in both domains are converging to build smarter, more capable machines than ever before, with robotics representing a machine's body and AI representing a machine's mind. This implies that robots are no longer restricted to simple, repetitive jobs, but may now function more freely in unstructured areas such as warehouses or factories, and can work more closely with people on assembly lines.

 

Many vehicle manufacturers have turned to crowdsourcing to expedite the process and ensure that they have enough components on hand in a timely manner. Driver data may be given by many technologies, such as a vehicle's inbuilt sensors or telematics control unit, to enable autonomous operation, predict maintenance, and make driving safer, among other things. 

 

 

Focusing on high-quality AI training data for a better AI algorithm, we aim to become an all-in-one platform for all AI data services with tech-driven mindsets. We are currently offering crowdsourcing jobs on a global scale. Have a look at our crowd pool to find the best crowdsourcing jobs in the top growing industries now.