What are the biggest challenges in artificial intelligence research today?
In the coming years, Artificial Intelligence will indefinitely be one of the biggest things to hit the technology industry. Although it has huge potential, it also has its challenges. These challenges and possibilities are not small, this is why it is important to recognise and work towards their resolution. Resolving these problems can help further propel artificial intelligence’s rapid growth.
Common Challenges in AI:
1. Computing Power
Machine Learning and Deep Learning are the stepping stones of Artificial Intelligence as they seem to be most promising. These techniques require a huge number of calculations to be made very quickly and an ever- increasing number of cores and GPUs to work efficiently.
These power-hungry algorithms use huge amount of power, these factors keep most developers away. They require computing power of supercomputers, and of course, supercomputers aren’t cheap. Although, the availability of Cloud Computing and parallel processing systems make it possible for developers to work on AI systems more effectively but 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 aspect that is a cause of worry for the AI is the unknown nature of how deep learning model predict the output. It is difficult for a layman to understand how a specific set of inputs can devise a solution for different kinds of problems. Most people are not even aware of the use and existence of Artificial Intelligence and how it is integrated into those items that they interact with on a daily basis such as Smartphones, Smart TVs etc.
3. Limited Knowledge
Although, there are several places in the market where application of Artificial Intelligence is considered as a better alternative to traditional system; the real problem is the knowledge of Artificial Intelligence itself. Not until very recent times, AI has been something talked about only by science fiction writers, and worked on in the depths of university IT research labs. Apart from technology enthusiasts and researchers, there are only a limited number of people who are aware of the potential of AI. There have been comparatively few organizations willing to invest money into development of these skills, and the subject was not well- represented in industry- focused education and training curricula. With the explosion of interest in the field of AI over the last few years, the scenario has changed a bit. However there are still not enough people to enable every business or organization to unleash their vision of machine-powered progress in the world. Just as in other areas of science and technology there is a skills shortage – simply not enough people who know how to operate machines which think and learn for themselves.
A final challenge which is worth considering is that the vast majority of AI implementations in use today are highly specialized. Specialized AI or “applied AI” is created to carry out a specific task and learn to become better at it. It does this by simulating what would happen given every combination of input values, and measuring the results, until the most effective output is achieved.
Companies giving AI services might be boasting of above 90% accuracy, but humans can perform better in any given scenario. For a deep learning model to perform a similar performance would require unprecedented fine-tuning, hyper parameter 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.
The only way you can avoid doing all the hard work is 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 process is based on is the availability of data and resources to train them. Although we have data, there are chances that these data that is generated from millions of people may be misused if it falls on the wrong hands. 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.
6. 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.
7. 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 strict 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 labelled data is used to train machines to learn and make predictions. Some companies are trying to invent new methodologies and are focused on creating AI models which can give accurate results despite the scarcity of data. With biased information, the entire system could become flawed.
Why should you choose SBR Technologies?
Are you considering integrating artificial intelligence in your business? There are only a few firms that provide services in the latest tech trend- AI programming. However, finding an appropriate artificial intelligence developer can be a daunting task as there are several factors that need to be considered in advance. The world is getting smarter, and automation has grasped all the attention. AI is a critical aspect of making your application work smarter and faster. Every enterprise is investing in this to get accurate and better results. It facilitates data which can be easily later studied for a better future.
At SBR Technologies, a software development company in India, we have a team of expert AI and Engineers and data scientists to help you transform your traditionally operating system into modern and intelligent systems. It helps to build up data storing architecture which can be used for data visualization, prediction, and decision making. At SBR, we create fault tolerance and highly automated systems using AI. Our team of data scientists will guide you in developing unique and user-friendly services for your clients.