Travelling back two-three decades, the world was completely different, with less damage to earth and more nature involved climate change was unheard, which now is a reality. Weather forecasting was something that farmers and many villagers predicted without attending any school, later with the changing complexity due to industrial revolution, the computers and few devices took over. Now there are serious threats to our environment which requires complex computing for accurate prediction of weather. That takes us to the use of artificial intelligence in weather forecasting.
When floods and hurricanes personify unwanted guests that come often meteorologists tried their best to provide 100 % accuracy but couldn’t succeed much. Many big IT companies started investing more in AI, IOT and other modern tech to find a solution to tackle nature’s rampage and found some solution to get 100% accuracy.
Well, the traditional models of weather forecasting are based on statistical data measures of numeric models, and also it does not give specific answers. The data collected can be from deep space satellites (e.g.,INSAT), weather balloons, radar systems, traditional weather gauges nowcasting weather warnings and environmental analytics and sometimes from IoT based sensors. But with ever-increasing data set, climate change, changing atmospheric conditions, the accuracy of the predictions tend to fluctuate, especially for more extended time periods. This is where AI can prove an asset in augmenting the accuracy and formulating reliability of the weather forecasting as well reducing the workload on the human forecasters.
The AI predictions are fundamentally based on machine learning algorithms. By processing more complex data in a shorter timespan using linear regression principles meteorologists can make predictions with improved accuracy and thus saves lives and money. Machine learning can abet with other forecasts as well, including temperature, wave height, and precipitation. One such popular model is the Numerical Weather Prediction (NWP). The model studies and manipulates vast data sets relayed from weather satellites, relay stations, and radiosondes to deliver short-term weather forecasts or long-term climate predictions. Other AI techniques for weather predictions are Artificial Neural Network, Ensemble Neural Network, Backpropagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering.
Back in 1996, IBM was one of the first companies to use computer systems to improve predictions about the weather. After purchasing The Weather Company in 2016 and its properties, including weather.com, Weather Underground, the Weather Company Brand, and WSI, IBM plans to use Weather Company’s extensive data set with IBM Watson’s advanced cognitive computing capabilities and Cloud platform to transform the weather forecasting sector to a more advanced entity in future.
Google also have developed an AI forecast tool that is based on the UNET convolutional neural network (CNN). It allows researchers to generate accurate rainfall predictions six hours ahead of when the precipitation occurs.
The Government of India do have a successful intra-governmental SAAS ecosystem for its own use. It uses Earth’s observation and meteorological data from indigenously built remote sensing satellites for agriculture advancements in the country. Fundamental to this governmental ecosystem which supports the government is the Indian Space Research Organization (ISRO), which builds satellites and payloads and processes the satellite data. ISRO distributes volumes of state-specific satellite data to independent governing bodies within all state governments known as the State Remote Sensing Application Centres (SRSACs). The SRSACs receive vast volumes of ISRO satellite data ranging from multispectral, cartographic and radar imaging and meteorological observations and forecasting applications. The SRSACs process and analyse the satellite data and share it with all ministries and departments of state governments among others.
The weather forecasting features of AI are many times higher. This is now an important asset in disaster management to logistics and retail industry to modern agriculture. In future with advancement in tech, we can use it for radar imagery to detect storm centres, high precipitation in the world and seasons. But one should not neglect the fact that despite boosting the accuracy levels, weather forecasting can never be a hundred percent perfect. The main idea is to overcome the current limitations in the prediction and analysis process, which is where AI is filling the gaps and this process will continue all the way.
When there was weather forecasting bloomed with the help of AI there was another field which became an allrounder in terms of education, entertainment and also to economy which was none other than gaming technology. Ever wondered how gaming graphics work? How the dynamic 3D and RPG games emerged? Nerdy gamers, take a deep breath to dive into the intricacy of games!
When we catch a glimpse at the evolution of gaming, it has actually advanced over the years with as early as 1950s chess and checkers written on a computer program to the real outbreak of video games came with the game pong and space war in the early 70s. Back in the era, Discrete Logic took over Artificial Intelligence as most games involved a non-playing character (NPC), whose activity was not in the control of the user, but stored patterns which had specific outcomes. Finite State Algorithm (FSI) was used in 1990s. Monte Carlo Search Tree (MCST) algorithm was used in Deep Blue, the first computer program to defeat a human chess champion in 1997. To accomplish the demand for single player or multi player concept, AI bloomed in gaming industry. The release of Pacman marked the beginning of the gaming industry towards the cutting-edge technology. Maze games were on peak during the past.
In the current scenario, it is quite difficult to outdo computer as an opponent since it’s AI based which has been trained on data-based patterns. Therefore, AI is basically the brain of the game. The AI algorithms are primarily used to generate responses based on user inputs. Since, perspectives and actions vary from an individual to other, a set of responses with various difficulty levels are generated which would make intelligent comebacks based on them. The data collected from the user interactions with the AI is a trailblazing opportunity to understand human interactions with system through which it could be concluded how human brains respond to different simulations in various conditions which go well with the machines too. Games like AI Dungeon, Magium, The Infinite Story, Cataclysm DDA etc enable players to design their own tales and paths from apocalyptic situations and encountering thrilling adventures.
In the near future, almost all games would be based on ML algorithms. The intensity of game levels will change after every gameplay like a suspense thriller movie. Gaming experience will take a sharp turn with the further enhancement of precocious hardware tactics, AI concepts like Augmented Reality (3D graphics fit real life environment into the screen) and Virtual Reality (making the user experience an artificial environment with apparently real objects) to increase quality of user experience. Personalized aura is likely to outdo stereotyped algorithms. In the words of John Fremont, there is no one thing that defines AI. It is a tapestry of modern intelligent technologies knit together in a strategic fashion that can then uplift and create a knowledge base that is automated where one can extrapolate findings from there.
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