Top 10 Challenges in Artificial Intelligence in 2024

Have you ever heard about Neuralink? It is a budding start-up company co-founded by Elon Musk that is working on some serious Artificial Intelligence integration with the human body. They have developed a chip which is an array of 96 small, polymer threads, each containing 32 electrodes and can be transplanted into the brain. 

I know what you are thinking: “This is serious science fiction”, but the answer is: no. This is happening in the real world and using this device, and you can connect your brain with everyday electronic devices without even touching them! 

Time for some serious questions: Is it really necessary? Will it be that useful? Are we ready for this kind of technology? How would it impact our lives in the future? Delving into the realm of Neuralink exposes us to the forefront of challenges in artificial intelligence, probing the ethical, technical, and societal implications of merging AI with human cognition. Let’s explore what are the challenges of artificial intelligence in India further.

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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.

Top Common Challenges in AI

There are several AI problems, and I am going to address top challenges in artificial intelligence in India and how to solve them in this blog.

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 numerous opportunities in the market where Artificial Intelligence can serve as a superior alternative to traditional systems, challenges in artificial intelligence remain a significant barrier to its widespread adoption. The real problem lies in the general understanding and knowledge of Artificial Intelligence. Beyond technology enthusiasts, college students, and researchers, awareness of AI’s potential and the challenges in artificial intelligence is limited among the broader population, hindering its full integration into various sectors.

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.

5. Data Privacy and Security

The main factor on which all the deep and machine learning models are based on is the availability of data and resources to train them. Yes, we have data, but as this data is generated from millions of users around the globe, there are chances this data can be used for bad purposes.

For example, let us suppose a medical service provider offers services to 1 million people in a city, and due to a cyber-attack, the personal data of all the one million users fall in the hands of everyone on the dark web. This data includes data about diseases, health problems, medical history, and much more. To make matters worse, we are now dealing with planet size data. With this much information pouring in from all directions, there would surely be some cases of data leakage. 

Some companies have already started working innovatively to bypass these barriers. It trains the data on smart devices, and hence it is not sent back to the servers, only the trained model is sent back to the organization. 

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Read: AI Salary in India for Freshers & Experienced

6. Lack of understanding 

When discussing the Lack of Understanding as one of the top challenges in artificial intelligence in 2024, I emphasize the importance of bridging the knowledge gap. This challenge is not just about the complexity of AI technologies but also about the misconceptions surrounding their application and potential. From my experience, many organizations struggle with unrealistic expectations from AI, largely due to a fundamental lack of understanding of what AI can and cannot do. It is critical to educate stakeholders on AI capabilities, limitations, and the nature of its learning processes. Real-life scenarios, such as the deployment of AI in healthcare for diagnostic assistance, highlight the need for clear communication between AI developers and end-users to ensure the technology meets its intended purpose without causing undue alarm or expectation. This education and clarity are vital in overcoming the hurdles AI faces today. 

7. Unreliable results 

Focusing on the Unreliable results challenge in artificial intelligence for 2024, it’s imperative to recognize the impact of inaccuracies in AI outputs. From my experience, these challenges often stem from biased datasets, overfitting models, and the complex nature of real-world data. Implementing AI solutions requires a meticulous approach to data selection and algorithm testing. For instance, a project I led involved deploying a predictive maintenance system for manufacturing equipment. Initially, the model’s predictions were inconsistent, leading to unnecessary downtime and costs. Through rigorous data analysis and model refinement, we improved its reliability significantly. This scenario highlights the importance of continuous improvement and monitoring in AI applications to mitigate the risk of unreliable results. Such experiences underline the need for professionals in the field to stay vigilant and adaptable, ensuring AI systems deliver dependable and accurate outcomes. 

8. Implementation strategy 

Navigating the implementation strategy for AI initiatives after addressing unreliable results introduces unique challenges in artificial intelligence, which will be pivotal for 2024. This involves: 

  • Strategic Alignment: Ensuring AI objectives are in harmony with overarching business goals. 
  • Infrastructure Readiness: Assessing and upgrading technical infrastructure to support AI. 
  • Stakeholder Engagement: Engaging with stakeholders early to align expectations and resources. 
  • Agile Implementation: Adopting an agile approach to accommodate rapid iterations based on feedback and evolving requirements. 

Drawing from real-life examples, such as a multinational corporation streamlining its supply chain with predictive AI, underscores the importance of a tailored strategy that addresses specific organizational needs and challenges. This approach not only enhances operational efficiency but also fosters innovation and competitive advantage in a dynamic business landscape. 

9. The Bias Problem

The good or bad nature of an AI system really depends on the amount of data they are trained on. Hence, the ability to gain good data is the solution to good AI systems in the future. But, in reality, the everyday data the organizations collect is poor and holds no significance of its own. 

They are biased, and only somehow define the nature and specifications of a limited number of people with common interests based on religion, ethnicity, gender, community, and other racial biases. The real change can be brought only by defining some algorithms that can efficiently track these problems.

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Also Read: Artificial Intelligence in Cyber Security

10. Data Scarcity

With major companies such as Google, Facebook, and Apple facing charges regarding unethical use of user data generated, various countries such as India are using stringent IT rules to restrict the flow. Thus, these companies now face the problem of using local data for developing applications for the world, and that would result in bias.

The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. Some companies are trying to innovate new methodologies and are focused on creating AI models that can give accurate results despite the scarcity of data. With biased information, the entire system could become flawed.

How AI Can Improve Businesses In the Coming Years?

Large companies like Apple and Google have invested heavily in developing Artificial Intelligence. Beyond those businesses, AI is frequently underused in other sectors, including manufacturing, education, retail, and healthcare. 

All these businesses produce enormous volumes of data every single day, but AI is rarely used to analyze massive datasets and draw conclusions from these patterns and features of that data. The main question is, why is the issue so prominent? Lack of access, comprehension, and abilities are the cause. We have read about the top problems with AI. We need to learn what can bridge the gap between those AI problems and the profitability of a business. 

One of the biggest artificial intelligence problems is that the sophisticated and expensive processing resources needed are unavailable to the majority of businesses. Additionally, they lack access to the expensive and scarce AI expertise required to utilize those resources effectively. 

As of 2022, 37% of businesses have already employed AI services and continue to do so. According to a study, the AI industry will earn $126 billion every year by 2025. 

According to Forbes, AI will become a $15.7 trillion industry by 2030, and investments will hit approximately $500 billion by 2024. 

Here are 3 ways in which AI can help scale businesses and overcome the above-mentioned problems with AI

  • Utilize the already existing AI technologies

Businesses no longer need to train their AI from the start because there is already so much AI work being done on the cloud, which is available at large, unlike the older models with AI problems in artificial intelligence. They can profit from the already done labour by the other companies. They are able to adapt already-working AI technologies to suit their own demands. But they are unable to do so without a user-friendly, intuitive interface. 

  • Keep updating AI technologies regularly

Continuous learning and improvement is possible with AI. This is what makes it a genius form of technology. If you are a Tesla owner, you know this since a fresh software update is always available. This occurs because there are currently millions of Teslas on the road, all of which are collecting data that is utilized daily to enhance every car. With AI, this sort of learning and knowledge-sharing is required across all fields of application and industries. Constantly improving the technology will help you upscale your business like no other and also get over the AI problems

  • Take advantage of the latest technology

Even if they were groundbreaking at the time, the AI methods utilized just recently are no longer effective. There are several AI problems in artificial intelligence for older models. New, improved AI models and neural networks are always being developed, similar to how people acquire abilities while attempting to learn something new and continually grow and add new talents throughout their lifetimes; However, in order for AI users to benefit from them, new processor design and programming models that can execute both AI and non-AI algorithms are required. A new age of more useful and economically feasible AI solutions will then begin to emerge across a wide range of use cases and sectors. We will soon be able to surpass the current restrictions on power, complexity, and expense.

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Although these challenges in AI seem very depressing and devastating for mankind, through the collective effort of people, we can bring about these changes very effectively. According to Microsoft, the next generation of engineers has to upskill themselves in these cutting edge new technologies to stand a chance to work with organizations of future and in order to prepare you, upGrad has been offering programs on these cutting edge technologies with many of our student working in Google, Microsoft, Amazon and Visa and many another fortune 500 companies. 

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What are the data privacy and security concerns of AI?

The availability of data and resources to train deep and machine learning models is the most important factor to consider. Yes, we have data, but because it is generated by millions of users around the world, there is a risk that it may be misused. Let's say a medical service provider serves 1 million people in a city, and owing to a cyber-attack, all of the one million consumers' personal information falls into the hands of everyone on the dark web. This includes information about diseases, health issues, medical history, and more. To make matters worse, we're now dealing with information about the size of planets. With so much data coming in from all sides, there would almost certainly be some data leakage.

What do you understand about the ‘bias’ problem?

The amount of data used to train an AI system determines whether it is good or terrible. As a result, in the future, the ability to obtain good data will be the key to developing good AI systems. However, the data that the organizations collect on a daily basis is weak and has little meaning on its own. They are prejudiced, and they only identify the nature and characteristics of a small group of individuals who share common interests based on religion, race, sexuality, neighborhood, and other racial biases.

How much computing power is required by AI?

Most developers are turned off by the amount of energy these power-hungry algorithms consume. Machine Learning and Deep Learning are the foundations of Artificial Intelligence, and they require an ever-increasing number of processors and GPUs to function well. They necessitate the processing capacity of a supercomputer, yet supercomputers aren't cheap. Although the availability of Cloud Computing & parallel processing systems allows engineers to work more successfully on AI systems, they come at a cost.

What are the 4 main problems AI can solve?

AI can solve numerous problems, including: Automation: Streamlining repetitive tasks. Data Analysis: Extracting insights from large datasets. Personalization: Tailoring recommendations and services. Predictive Maintenance: Anticipating equipment failures to prevent downtime.

What is the biggest threat of AI?

The biggest threat of AI is losing control over its autonomous systems, which can lead to unintended consequences like biased decisions, job loss, privacy invasion, and misuse in surveillance or weaponization.

Who made AI first?

Will AI replace human jobs?

AI will likely automate some jobs but also create new ones. Its impact will depend on how societies manage adaptation and skill development in response to technological advancements.

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