Some people think that combining artificial intelligence with big data is a natural mistake, partly because the two are actually the same. But they are different tools for accomplishing the same task. But the first thing to do is to clarify the definition of the two. Many people do not know this.
A major difference between artificial intelligence and big data is that big data is the original input that needs to be cleaned, structured, and integrated before the data becomes useful, while artificial intelligence is the output, that is, the intelligence generated by processing the data. This makes the two fundamentally different.
Artificial intelligence is a form of computing that allows machines to perform cognitive functions, such as acting on or responding to input, similar to what humans do. Traditional computing applications will also respond to data, but both responses and responses must be manually coded. If an error of any kind occurs, like an unexpected result, the application cannot react. And artificial intelligence systems constantly change their behavior to adapt to changes in survey results and modify their responses.
Machines supporting artificial intelligence are designed to analyze and interpret data, and then solve problems based on these interpretations. Through machine learning, the computer learns how to act or react to a certain result once, and knows to take the same action in the future.
Big data is a traditional calculation. It does not act on the results, but only looks for results. It defines a very large data set, but it can also be extremely diverse data. In big data sets, there can be structured data, such as transaction data in relational databases, and structured or unstructured data, such as images, email data, sensor data, and so on.
There are also differences in their use. Big data is mainly for gaining insights. For example, the Netflix website can learn about movies or TV shows based on what people watch and what content they recommend to viewers. Because it takes into account the customer’s habits and the content they like, it infers that the customer may feel the same.
Artificial intelligence is about decision-making and learning to make better decisions. Whether it's self-adjusting software, self-driving cars, or checking medical samples, artificial intelligence will accomplish the same tasks before humans, but faster and with fewer errors.
Although they are quite different, artificial intelligence and big data can still work well together. This is because artificial intelligence needs data to build its intelligence, especially machine learning. For example, a machine learning image recognition application can view tens of thousands of aircraft images to understand the composition of the aircraft so that they can be recognized in the future.
The biggest leap achieved by artificial intelligence is the emergence of massively parallel processors, especially GPUs, which are massively parallel processing units with thousands of cores instead of dozens of parallel processing units in CPUs. This has greatly accelerated the speed of existing artificial intelligence algorithms and has now made them feasible.
Big data can use these processors, and machine learning algorithms can learn how to reproduce certain behaviors, including collecting data to speed up the machine. Artificial intelligence does not infer conclusions like humans do. It learns through trial and error, which requires a lot of data to teach and train artificial intelligence.
The more data that artificial intelligence uses, the more accurate the results obtained. In the past, artificial intelligence did not work well due to slow processors and small amounts of data. There are no advanced sensors like today, and the Internet was not widely used at that time, so it is difficult to provide real-time data. People have everything they need: fast processors, input devices, networks, and large data sets. There is no doubt that there is no artificial intelligence without big data.
Some people think that combining artificial intelligence with big data is a natural mistake, partly because the two are actually the same. But they are different tools for accomplishing the same task. But the first thing to do is to clarify the definition of the two. Many people do not know this.
A major difference between artificial intelligence and big data is that big data is the original input that needs to be cleaned, structured, and integrated before the data becomes useful, while artificial intelligence is the output, that is, the intelligence generated by processing the data. This makes the two fundamentally different.
Artificial intelligence is a form of computing that allows machines to perform cognitive functions, such as acting on or responding to input, similar to what humans do. Traditional computing applications will also respond to data, but both responses and responses must be manually coded. If an error of any kind occurs, like an unexpected result, the application cannot react. And artificial intelligence systems constantly change their behavior to adapt to changes in survey results and modify their responses.
Machines supporting artificial intelligence are designed to analyze and interpret data, and then solve problems based on these interpretations. Through machine learning, the computer learns how to act or react to a certain result once, and knows to take the same action in the future.
Big data is a traditional calculation. It does not act on the results, but only looks for results. It defines a very large data set, but it can also be extremely diverse data. In big data sets, there can be structured data, such as transaction data in relational databases, and structured or unstructured data, such as images, email data, sensor data, and so on.
There are also differences in their use. Big data is mainly for gaining insights. For example, the Netflix website can learn about movies or TV shows based on what people watch and what content they recommend to viewers. Because it takes into account the customer’s habits and the content they like, it infers that the customer may feel the same.
Artificial intelligence is about decision-making and learning to make better decisions. Whether it's self-adjusting software, self-driving cars, or checking medical samples, artificial intelligence will accomplish the same tasks before humans, but faster and with fewer errors.
Although they are quite different, artificial intelligence and big data can still work well together. This is because artificial intelligence needs data to build its intelligence, especially machine learning. For example, a machine learning image recognition application can view tens of thousands of aircraft images to understand the composition of the aircraft so that they can be recognized in the future.
The biggest leap achieved by artificial intelligence is the emergence of massively parallel processors, especially GPUs, which are massively parallel processing units with thousands of cores instead of dozens of parallel processing units in CPUs. This has greatly accelerated the speed of existing artificial intelligence algorithms and has now made them feasible.
Big data can use these processors, and machine learning algorithms can learn how to reproduce certain behaviors, including collecting data to speed up the machine. Artificial intelligence does not infer conclusions like humans do. It learns through trial and error, which requires a lot of data to teach and train artificial intelligence.
The more data that artificial intelligence uses, the more accurate the results obtained. In the past, artificial intelligence did not work well due to slow processors and small amounts of data. There are no advanced sensors like today, and the Internet was not widely used at that time, so it is difficult to provide real-time data. People have everything they need: fast processors, input devices, networks, and large data sets. There is no doubt that there is no artificial intelligence without big data.
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