Artificial Intelligence Can Now Predict Ocean Current Seven Months in Advance



The quick uptake of digital technologies and the internet has considerably assisted the recent expansion of the global artificial intelligence industry. The massive research and development expenditures made by IT behemoths are continually accelerating technical development across a wide range of businesses. The growing demand for artificial technology across a range of end-use industries, including manufacturing, banking & finance, healthcare, automotive, food & beverage, retail, and logistics, is anticipated to significantly fuel the growth of the global artificial intelligence market in the years to come. The majority of sectors have traditionally placed a high priority on technological advancements.

Prediction System of the Indonesian Throughflow 

Scientists from Nanjing University of Information Science and Technology and the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) have successfully built an inference and prediction system of the Indonesian Throughflow (ITF) using the deep-learning method and realized the accurate prediction of the ITF transport. The ITF is a crucial ocean dynamic factor for the inter-basin exchange between the Indian Ocean basin and the Pacific Ocean basin. The Indonesian Seas are the only ocean conduit connecting the tropical ocean basins. The Indonesian Sea is the only ocean channel connecting the tropical ocean basins. Due to its powerful material and energy transit, the ITF is important for the Indo-Pacific Ocean's material and energy balance as well as for local and global climate change. Moreover, most ITF predictions are made using a large number of simulation techniques, including large levels of uncertainty and considerable models.

In light of this, the researchers, under the direction of Prof. HU Shijian proposed the concept of fusing satellite observations with AI techniques to build the inference and prediction system of ITF and carried out experiments with various deep-learning models. Researchers used sea surface heights between the Indian and Pacific Ocean basins to infer and predict the transit of ITF because the Indo-Pacific pressure gradient is the primary determinant of ITF. They used the vast amounts of data provided by the Coupled Model Intercomparison Project Phase 6 model and Simple Ocean Data to train the convolutional neural network (CNN).sets and reconstructed a time series of ITF transport. According to the researchers, the CNN model reproduces 90 percent of the total variance of ITF transport, showing that the system can get valid interference from ITF Transport.

In order to infer and generate the ITF time series, the researchers further coupled the system with satellite data from 1993 to 2021. They discovered that the time series was in good agreement with the ITF's well-known field observation data on an international scale. The findings of their investigation into the potential for ITF prediction using this AI system indicate that the system is capable of making a reliable prediction with a leading time of seven months. An important instrument for studying ocean circulation and climate change in the Indo-Pacific Ocean is the ITF AI inference and prediction system, which may somewhat relieve the burden of field ocean observation.

The researchers hope to keep improving the system by including more data and using more intricate machine-learning algorithms. They also want to test the system using real-world circumstances, such as predicting ocean circulation in specific regions like the South China Sea.

AI in Robotics and Various Industries

The field of robotics began developing even before AI was a reality. Robotics is presently using artificial intelligence to create more effective robots. Robots with AI capabilities are being used in a wide range of sectors and industries, particularly in the manufacturing and packaging sectors. Robots with artificial intelligence, or AI, have computer vision that enables them to move, assess their environment, and respond properly. Robots learn to perform jobs from people using machine learning, a component of AI and computer programming. The best examples of artificial intelligence in robotics are humanoid robots. Recently, the intelligent humanoid robots Erica and Sophia were produced, and they can speak and act in human-like ways.

The way we handle money in finance is changing due to artificial intelligence. AI is helping the financial sector to streamline and optimize processes in a variety of areas, including credit decisions, quantitative trading, and financial risk management. Risk evaluation, fraud identification and management, financial advising services, and automated trading are among the characteristics that artificial intelligence offers.

For instance, the AI application of facial recognition focuses on finding and learning patterns that produce quick and efficient results. Facial recognition technology captures and maps an individual's facial features as a face print. The software authenticates a person's identification by comparing a live captured image to a recorded face print using deep learning techniques. The underlying technologies of this technology include image processing and machine learning. Face recognition has attracted a lot of interest from researchers because of the human behaviors that can be seen in various security applications, such as airports, criminal detection, face tracking, forensics, etc. Face biometrics may be less reliable than other biometrics like the iris, palm print, fingerprint, etc.

The autonomous vehicle market has significantly advanced thanks to long-range radar, cameras, and LIDAR. Each of these technologies has a unique purpose and gathers information in a unique way. A lack of processing renders the data useless and prevents the extraction of any insights.

The travel and tourism sector is highly competitive. We can notice that prices are always changing and fluctuating. We can also purchase a flight ticket in advance or wait until the very last minute to obtain a better deal. Everyone struggles with that, but AI makes it easier.


The price can be forecasted using predictive analytics powered by artificial intelligence. The program can forecast pricing trends and notify users when to purchase tickets. Therefore, you can get the best deal before making flight reservations. The pricing trend is examined based on the data gathered for each route. As a result, you are informed when to book your flight. We may reserve it at the ideal time and price thanks to artificial intelligence.

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