What are the Biggest Challenges for AI and ML

Categories: Artificial Intelligence (AI) and Machine learning(ML)

What are the Biggest Challenges for AI & ML?

The impact of AI on people and the economy has been astounding. By 2030, AI might add $15.7 trillion to the global economy. To put that in perspective, that's around China and India's combined economic output today. Artificial Intelligence (AI) and Machine Learning (ML) have helped alter businesses and tackle global issues. 

Artificial Intelligence (AI) is now being used in factories, healthcare, and security, as well as eCommerce, social media, and mobile application platforms. Want to know more about AI and ML? Why not take an AI and Machine learning course in Chennai. 

However, as AI and ML technologies advance and solutions are developed, additional variables emerge concerning how we do things and if present resources will be enough to meet people's ever-changing requirements. As a result, along with unsolved problems, obstacles feel interminable, and we're nowhere near perfecting our systems. Here are the biggest AI and ML challenges in today's world:

1. Computing Capability

Most developers are turned off by the amount of power these power-hungry algorithms consume. Machine Learning and Deep Learning are the building blocks of modern Artificial Intelligence, and they require an increasing number of cores and GPUs to function properly. We have concepts and skills to implement deep learning frameworks in a variety of disciplines, including asteroid monitoring, healthcare deployment, cosmic body tracing, and much more.

They necessitate the computing capacity of a supercomputer, yet supercomputers are not cheap. Although the availability of Cloud Computing and parallel processing systems allow engineers to work more successfully on AI systems, it comes with a cost. With a growth in the intake of enormous amounts of data and rapidly expanding complex algorithms, not everyone can afford that.

1. Lack of Trust

One of the most significant causes of concern for AI is the uncertain nature of how deep learning models forecast output. For a layperson, understanding how a certain set of inputs might design a solution for many types of problems is tough.

Many people throughout the world are unaware of the use or presence of artificial intelligence, as well as how it is integrated into common objects they interact with, such as smartphones, smart TVs, banking, and even automobiles (at some level of automation).

1. Inadequate Knowledge

Although there are many areas in the industry where Artificial Intelligence can be used as a better alternative to traditional systems. The fundamental issue is a lack of understanding of Artificial Intelligence. Apart from technology enthusiasts, college students, and academics, only a small number of individuals are aware of AI's potential.

Many SMEs (Small and Medium Enterprises) can, for example, have their work scheduled or learn novel ways to enhance production, manage resources, sell and manage products online, learn and understand consumer behavior, and respond to the market effectively and efficiently. They are also unaware of service providers in the computer industry such as Google Cloud, Amazon Web Services, and others.

1. Human-level

This is one of the main reasons why academics are looking for AI services in enterprises and start-ups. These companies may claim over 90% accuracy, yet people can surpass them in all circumstances. Let our model guess whether the image is of a dog or a cat. A human can almost always predict the correct outcome with above 95% accuracy.

Deep learning models require exceptional finetuning, hyperparameter optimization, a large dataset, a precise algorithm, as well as robust computing resources. That sounds like a lot of work, and it is.

Using a service provider who can train specific deep learning models using pre-trained models is one possibility. They are trained on millions of photographs and fine-tuned for maximum accuracy, but they still make mistakes and fail to perform at human levels.

1. Data Security and Privacy

Data and resources to train deep and machine learning models are essential. Yes, we have data, but because it is generated by millions of users globally, it may be misused.

Assume a medical service provider serves one million people in a city, and a cyber-attack exposes all one million consumers' personal data to anyone on the dark web. This data includes diseases, health difficulties, medical history, and more. Worse, we now have data on planet sizes. With so much data flowing in from all directions, data leakage is nearly certain.

Some businesses have already started to think outside the box. Because the data is learned on smart devices, it is not returned to the servers; only the trained model is.

1. The Bias Issue

The quality of an AI system is determined by the amount of data on which it is trained. As a result, the ability to obtain good data is the future solution to good AI systems. However, the everyday data that organizations collect is weak and has no meaning on its own.

They are biased and only define the character and specifications of a small group of people who share common interests based on religion, ethnicity, gender, community, and other racial prejudices. Only by establishing some algorithms that can efficiently track these challenges can genuine change be brought about.

1. Scarcity of Data

With large corporations such as Google, Facebook, and Apple facing charges for unethical use of user data, countries such as India are enacting rigorous IT regulations to limit the flow. As a result, these organizations are now faced with the challenge of employing local data to construct global applications, which would result in bias.

Data is a critical component of AI, and labeled data is used to teach robots to learn and predict. Some businesses are attempting to develop new approaches and are focusing on developing AI models that may provide reliable results despite a lack of data. With skewed data, the entire system may become defective.

Conclusion

Although these AI issues appear to be exceedingly gloomy and destructive for humanity, we may effectively bring about these improvements through the collaborative effort of humans. According to Microsoft, the next generation of engineers must upskill in these cutting-edge new technologies in order to work with future organizations, and in order to prepare you, upGrad has been offering programs on these cutting-edge technologies, with many of our students working in Google, Microsoft, Amazon, Visa, and many other Fortune 500 companies.