The scope and implementation of AI is very broad, even tends to be biased because several sources have their own opinions. However, the notion of AI can be summarized into 2 main concepts, the first AI is something that computers cannot do now but will be able to do someday (because they are always learning). […]
The scope and implementation of AI is very broad, even tends to be biased because several sources have their own opinions. However, the notion of AI can be summarized into 2 main concepts, the first AI is something that computers cannot do now but will be able to do someday (because they are always learning). The second definition of AI is intelligence demonstrated by machines. Intelligence is the ability to acquire and apply knowledge and skills.
Artificial intelligence (AI) enables machines to learn from experience, adapt to new inputs and perform tasks like humans. Most of the examples of AI you hear today – from computers playing chess to cars driving themselves – rely heavily on deep learning and natural language processing. Using this technology, computers can be trained to complete certain tasks by processing large amounts of data and recognizing patterns in the data.
Humans develop machines to gain knowledge efficiently because machines can search/process information faster than humans (very basic example: calculator). However, machines cannot do things on an initiative. Humans have to give ‘what to do and how to do it’ to machines to work. The bigger hope is that humans provide a little ‘knowledge’, then machines can learn from that knowledge and develop themselves. True AI needs to be able to learn. So how do machines usually ‘learn’? we usually know this with the concept of Machine Learning.
Agency for the Assessment and Application Artificial Intelligence Adoption Technology Strengthens Weather Modification Technology
Artificial intelligence (AI) will play a very important role in the future, especially in the technology sector. The Indonesian Agency for the Assessment and Application of Technology needs infrastructure that is capable of serving all its research and innovation activities to create a center for technological intelligence, especially in the field of artificial intelligence.
The Indonesian Agency for the Assessment and Application of Technology has invested in high performance computing devices to serve artificial intelligence modeling development activities. The presence of adequate infrastructure will make application development activities that utilize artificial intelligence more developed.
The artificial intelligence named NVDIA DGX A100 is a universal system for all workloads offering compute density, performance and flexibility in the world’s first FLOPS map system. The device features the world’s most advanced accelerator, the NVIDIA A100 Tensor Core GPU.
Often the Indonesian Agency for the Assessment and Application of Technology is asked to carry out natural disaster management operations such as for example to extinguish forest and land fires during the peak of the dry season, where potential clouds have been greatly reduced, making these operations less effective and efficient.
For this reason, it is hoped that research and innovation activities can use artificial intelligence methodologies so that they can produce tools that can help make decisions at the right time for the implementation of natural disaster management. Currently, the application of disaster management for peat wetting plays a very important role in preventing forest and land fires. The application of artificial intelligence will make efforts more effective and efficient.
Modeling using artificial intelligence methodology on peatland hydrological observation data, especially groundwater level using the ETS methodology and random forest, and predicting the temporal groundwater level for the next 1-3 months. It is hoped that the forecasts in the near future, can help decision makers, to determine when it is time to carry out disaster management operations to wet peatlands before they dry out as a disaster prevention measure.
When we talk about Artificial Intelligence and Data Science, it is incomplete if we don’t add Machine Learning and Deep Learning in it. That’s because they are interrelated and dependent on each other.
MACHINE LEARNING (ML)
Machine learning is a subset of artificial intelligence. Machine learning in short is a sub-field of artificial intelligence that deals with the design and development of algorithms. Machine learning can also be defined as a technique that enables improved performance on multiple tasks through experience. The focus of machine learning is to gain insight so that it can make data-driven decisions (machine learning uses data to answer questions). In some cases, machine learning can become so complex that additional methods are needed so that machines can imitate the workings of the human brain, also known as deep learning.
Although it sounds more ‘wow’ compared to machine learning, deep learning is not a weapon to solve all data-driven problems. Deep learning will not replace all machine learning algorithms or other data science techniques. However, deep learning can indeed solve more complex problems such as computer vision (the ability of machines to recognize objects in image data), speech recognition (recognizing voice data), and natural language processing (recognizing text data) using artificial neural networks (ANN). In simple terms, deep learning imitates the workings of the human brain through a neural network whose architecture is very diverse.
Then, what do the three terms above have to do with data analytics, data science, and big data?
Big data itself is just a data processing concept whose size is very large including 7V: volume, velocity, variety, variability, veracity, visualization, and value. Big data is just a thinking concept and its understanding is also quite biased like artificial intelligence. However, it can be said that people who solve complex problems using very large data (meets 7V) through computer processes or techniques, have used the concept of big data.
On the other hand, data science has a different but related meaning to the above terms. Basically, everything a Data Scientist does includes the following three branches of knowledge:
Computer science/programming, namely the ability to write, test, repair, and maintain code on computers. Programming languages that are often used by a Data Scientist at this time are Python and R (analysis) and SQL (accessing data).
Mathematics/statistics, is the science that becomes the basic foundation of how an algorithm works. Some of the branches of science that are most often used are calculus, linear algebra, and probability. Weather modification knowledge, adapted to the background of the data and problems analyzed. This knowledge is needed by a Data Scientist to make the right decisions based on the information obtained from the analysis.