Is big data vs. artificial intelligence a fair comparison? In a way, it is, but first let us clarify the difference between them.
Artificial intelligence and big data are familiar and popular terms, but there may be some confusion. What are the similarities and differences between artificial intelligence and big data? Do they have anything in common? Are they similar? Can an effective comparison be made?
One thing these two technologies have in common is interest. NewVantage Partners' big data and artificial intelligence survey of corporate executives found that 97.2% of corporate executives said that their company is investing in, building or launching big data and artificial intelligence programs.
More importantly, 76.5% of business executives believe that artificial intelligence and big data are closely related, and the greater availability of data is enhancing artificial intelligence and cognition within their organizations.
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 don't know this.
Alan Morrison, a senior researcher at consulting giant PriceWaterhouse Coopers, said: "I found that many people don't know much about real big data or big data analysis, or just use a few prominent examples to understand artificial intelligence."
He said that one 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 there is any type of error, 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, a 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 to 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.
Artificial intelligence and big data work togetherAlthough 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.
Of course, this is an important step in data preparation. Morrison pointed out, “The data people start to use is big data, but in order to train the model, the data needs to be structured and integrated to a good enough level so that the machine can reliably identify the usefulness in the data. mode."
Big data provides a lot of data, and useful data must first be separated from a large number of complex data centers, and then do anything. The data used in artificial intelligence and machine learning has been "cleaned up", and irrelevant, repetitive and unnecessary data has been eliminated. So this is the first step.
After this, artificial intelligence can flourish. Big data can provide the data needed to train learning algorithms. There are two types of data learning: initial training can collect data on a regular basis. Once the artificial intelligence application completes the initial training, it will not stop learning. As the data changes, they will continue to receive new data and adjust their actions. Therefore, the data is initial and continuous.
Both of these calculation methods use pattern recognition, but the methods are different. Big data analysis uses sequential analysis to find patterns, sometimes cold data, or uncollected data. Hadoop is the basic framework for big data analysis. It is a batch process originally designed to run at night with low server utilization.
Machine learning learns from the collected data and continuously collects it. For example, self-driving cars never stop collecting data, and continue to learn and hone their processes. Data always appears in a fresh way and always takes action to process it.
The role of big data in artificial intelligenceArtificial intelligence has always been paid attention to. Many people still remember the plot of the movie "The Matrix" released in 1999. Humans struggled to the death with machines that became smart. But in the actual implementation process, artificial intelligence has been an edge technology until recently.
The biggest leap in 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 it obtains. 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.
Today, people have everything they need: fast processors, input devices, networks, and massive data sets. There is no doubt that there is no artificial intelligence without big data.
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