Artificial Intelligence, Machine Learning and Deep Learning: A Perspective on the Future

Hello Dear Reader,

I hope you will learn new skills and upgrade yourself through a lot of online courses and webinars.

Today I will give you in this article an in-depth idea about how advances in Machine Learning and Deep Learning algorithms will build new areas of AI(Artificial Intelligence). Today everyone depends on data evaluation and data statistics algorithms build around intensive data will only play a major role in the industry.

The AI, ML, and DL industry have been gaining lot of momentum of late. In this one only need to know what combination of these technologies to apply and how to apply on individuals sector of industry with effective output is most important and sometimes it also has cost-effective for mass production.  

First of all understand one of the differences between AI, ML, and DL. As shown in the below image explain in short differences between these technologies.

Industries that can use AI, ML, and DL:

The energy sector: This is one of the key sectors driving every type of industry. Optimum and efficient usage of energy is of almost importance. Once a solar system is invented there is a lot of complex problems generated in the energy sector.

Many industries have IoT systems for monitoring and control. And the IoT+AI combination is implemented in many industry sectors. For example, the best time to switch on certain lights in office premises or manufacturing shop floors can be predicted based on analyzing historical data using some well-known machine learning models. The quality of power too can be managed using AI models, which can be programmed in such a way as to provide fluctuation-free and uninterrupted power supply with optimum energy usage. The AI+IoT combination can also be used to maintain near rated voltages around 230V, constantly. The AI system considers many factors to optimize performance.

Managing smart cities using AI has become very popular and trending for automatic street lighting. If each household can optimize its light usage or save energy while operating the various power-consuming gadgets in the house, imagine how much power can be saved across the entire city.

Even in industries, smart energy initiatives along with AI+IoT can help in solving many challenges and optimizing power usage.

Media and entertainment: This sector faces a number of challenges in audio, video, and image processing, which can be addressed using advanced AI, ML, and DL techniques. Volumes of data are being generated in the media and entertainment sector every day, either video or audio-based as we see in the television news channels and other sources.Transmitting this multimedia data consumes a lot of Internet bandwidth and also hampers the transfer data rate.

Some techniques like advanced data compression can be used to make the transfer efficient and faster. IoT+ AI can be used on various data compression methodologies, which can be based on historical data for better prediction and usage. Also, by using AI, only the relevant data of the audio/video combination can be sent out. This selection is calculated independently using advanced AI algorithms, which saves a lot of bandwidth and also increases the transfer rate.

Telecom: Upcoming technologies like 5G are increasingly making use of AI for traffic shaping, congestion control and to ensure committed SLAs.Based on the type of data to be transferred, resources are allocated using AI algorithms (for prediction). The next big challenges like the seamless protocol or bulk transfer of data can be addressed using the AI+5G combination. This will help in solving most of the high-end data transmission challenges.

Manufacturing: Most manufacturing industries rely heavily on combinations of AI+ML+DL technologies including their power optimization, production quality automation especially for mass production. Even Industry 4.0 can be blended with AI technologies, for a smarter output, and to better tackle industry-specific challenges.

The challenge of robot and drone coordination can be addressed using AI techniques. Both the robot and the drone can be individually controlled or instructed to handle or predict certain outcomes in the manufacturing industry. These can be used for handling or for preventing disaster situations.

Solutions like workflow optimization and the division of labor (between machines and humans) require advanced AI algorithms and techniques. For OEM vendors of IoT (making the right choice of the device at the right time, based on prevailing parameters), the latest AI techniques can be used for deciding which combination of IoT to choose.

Finance: This is one sector that is continuously evolving and has to handle a lot of customer-related challenges, which are not only human effort-intensive but also time-consuming. For example, when granting loans to certain customers, one has to manually examine their credit histories or backgrounds. But with a combination of AI technologies, client history or credit score data can be fed to machine learning models (AI systems) and later be used to make predictions. These predictive models can be built using historical data sets.

Automotive: The use of self-driving cars is expected to become more widespread. These autonomous vehicles use many of the AI features for predicting obstacles on the road, reading signboards, gauging the speed of oncoming vehicles and their direction, etc. All these identification processes have to happen in real-time to prevent any accidents.

Many of the latest vehicles are equipped with dashboards with advanced control systems built-in, and prediction and correction units. Also, warning systems are built based on the driver’s gestures, behavior or posture. These will help in preventing many accidents.

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