write an essay on ten challenges and risk of Al class 6 computer
Answers
Answer:
Explanation:
The impact of Artificial Intelligence on human lives and the economy has been astonishing. Artificial Intelligence can add about $15.7 trillion to the world economy by 2030. To take that into perspective, that’s about the combined economic output of China and India as of today.
With various companies predicting that the use of AI can boost business productivity by up to 40%, the dramatic increase in the number of AI start-ups has magnified 14 times since 2000. The application of AI can range from tracking asteroids and other cosmic bodies in space to predict diseases on earth, explore new and innovative ways to curb terrorism to make industrial designs.
Table of Contents
Top Common Challenges in AI
There are several Artificial Intelligence problems, and we are going to address these challenges and how to solve them.
1. Computing Power
The amount of power these power-hungry algorithms use is a factor keeping most developers away. Machine Learning and Deep Learning are the stepping stones of this Artificial Intelligence, and they demand an ever-increasing number of cores and GPUs to work efficiently. There are various domains where we have ideas and knowledge to implement deep learning frameworks such as asteroid tracking, healthcare deployment, tracing of cosmic bodies, and much more.
They require a supercomputer’s computing power, and yes, supercomputers aren’t cheap. Although, due to the availability of Cloud Computing and parallel processing systems developers work on AI systems more effectively, they come at a price. Not everyone can afford that with an increase in the inflow of unprecedented amounts of data and rapidly increasing complex algorithms.
2. Trust Deficit
One of the most important factors that are a cause of worry for the AI is the unknown nature of how deep learning models predict the output. How a specific set of inputs can devise a solution for different kinds of problems is difficult to understand for a layman.
Many people in the world don’t even know the use or existence of Artificial Intelligence, and how it is integrated into everyday items they interact with such as smartphones, Smart TVs, Banking, and even cars (at some level of automation).
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3. Limited Knowledge
Although there are many places in the market where we can use Artificial Intelligence as a better alternative to the traditional systems. The real problem is the knowledge of Artificial Intelligence. Apart from technology enthusiasts, college students, and researchers, there are only a limited number of people who are aware of the potential of AI.
For example, there are many SMEs (Small and Medium Enterprises) which can have their work scheduled or learn innovative ways to increase their production, manage resources, sell and manage products online, learn and understand consumer behavior and react to the market effectively and efficiently. They are also not aware of service providers such as Google Cloud, Amazon Web Services, and others in the tech industry.
4. Human-level
This is one of the most important challenges in AI, one that has kept researchers on edge for AI services in companies and start-ups. These companies might be boasting of above 90% accuracy, but humans can do better in all of these scenarios. For example, let our model predict whether the image is of a dog or a cat. The human can predict the correct output nearly every time, mopping up a stunning accuracy of above 99%.
For a deep learning model to perform a similar performance would require unprecedented finetuning, hyperparameter optimization, large dataset, and a well-defined and accurate algorithm, along with robust computing power, uninterrupted training on train data and testing on test data. That sounds a lot of work, and it’s actually a hundred times more difficult than it sounds.
One way you can avoid doing all the hard work is just by using a service provider, for they can train specific deep learning models using pre-trained models. They are trained on millions of images and are fine-tuned for maximum accuracy, but the real problem is that they continue to show errors and would really struggle to reach human-level performance.