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Tag: ai and ml courses by E&ICT Academy

How long term modeling of our future energy system can be mapped with artificial intelligence and machine learning

Posted on by Imarticus Learning

Today, technology and sustainability are the main axes of development. To secure the planet and continue the growth of industry, we are engaged in a global energy transition. Most countries have become aware that measures must be taken to address a problem that, if not curbed, will have catastrophic consequences for the environment and, of course, for human beings themselves.

However, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector. At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML).

Technology as a tool

Changing the energy paradigm of the last century will be an arduous and complicated task. That is why new technologies have a lot to say as tools to facilitate evolution. The Internet of Things, machine learning, artificial intelligence, and Big Data will be key to making the processes of change as effective as possible. Massive data analysis must become a fundamental pillar for transforming how energy is generated, transmitted, and distributed.

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it.

Data that, when properly analyzed, can provide a tailor-made understanding of the sector. The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters. In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimising the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes.

Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

In this respect, we should note that although everyone is involved in the energy transition and awareness must start in every household, the technology will be geared towards people having little to do in terms of reducing consumption and costs.

Artificial intelligence-based models and predictions facilitate and will continue to be a major advantage in mapping energy systems. What is most surprising is that this is just one of the many applications of these technologies. If you want to contribute to the change, you can sign up for AI and ML courses by E&ICT Academy, IIT Guwahati.

With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?

Posted on by Imarticus Learning

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception.

In addition to widespread use in the capital markets, artificial intelligence and machine learning are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans.

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. The question is, how are financial institutions dealing with these problems?

Bias and Transparency in the AI Context

AI systems are powered by algorithms that “train” by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases.

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population.

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias of any form eventually leads to unfairness and inequities in financial services.

Dealing With AI Bias and Transparency

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:

  • Financial institutions and firms can have appropriate controls and monitoring tools to ensure that new data entering the pool is reliable and of high quality. 
  • In addition, some organizations have developed tools to determine if a potential AI solution is biased. 
  • When building AI systems, it is wise to gather a team with domain expertise, model development skills, data engineering capabilities, and commercial expertise. 
  • Organizations can undertake impact assessments of the AI solutions to ensure they are transparent and explainable, as well as determine how the AI-based decision-making process will impact customers. 
  • When engaging with AI technologies, financial services can apply safeguards to ensure that business outcomes are achieved, and customers’ interests are protected. 
  • Another way to minimize data biases is to be open on the user data, match and align data with the target segment, and set up review cycles with legal and statistical experts. 
  • Tracking mechanisms that allow one to track the decision-making mechanism of algorithms can be put in place to eliminate bias and ensure transparency as much as possible. 
  • Lastly, it is pertinent for institutions to document their approach to handling bias and review it after every stage of development and use of the algorithm.

What to Look for in an Artificial Intelligence Course?

If you want to learn AI and ML, there are several online courses you can choose from. An AI and ML certification course that makes you future-ready will have a robust curriculum covering critical concepts related to data science, machine learning, NLP, deep learning, and computer vision.

In addition, the program should offer in-depth experiential learning through hands-on involvement with real-world projects.

How Long-Term Modelling of Our Future Energy System Can Be Mapped With Artificial Intelligence and Machine Learning?

Posted on by Imarticus Learning

Today, technology and sustainability are the main axes of development. To secure the planet and continue the growth of industry, we are engaged in a global energy transition. Most countries have become aware that measures must be taken to address a problem that, if not curbed, will have catastrophic consequences for the environment and, of course, for human beings themselves.

ai and ml courses by E&ICT Academy, IIT GuwahatiHowever, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector.

At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML). 

Technology as a tool

Changing the energy paradigm of the last century will be an arduous and complicated task. That is why new technologies have a lot to say as tools to facilitate evolution. The Internet of Things, machine learning, artificial intelligence, and Big Data will be key to making the processes of change as effective as possible. Massive data analysis must become a fundamental pillar for transforming how energy is generated, transmitted, and distributed.

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it. Data that, when properly analyzed, can provide a tailor-made understanding of the sector.

The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters.

In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimizing the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes. Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

In this respect, we should note that although everyone is involved in the energy transition and awareness must start in every household, the technology will be geared towards people having little to do in terms of reducing consumption and costs.

Artificial intelligence-based models and predictions facilitate and will continue to be a major advantage in mapping energy systems. What is most surprising is that this is just one of the many applications of these technologies. If you want to contribute to the change, you can sign up for AI and ML courses by E&ICT Academy, IIT Guwahati.