Artificial Intelligenсe (AI) is most likely to add about $15.7 trillion to the world economy by 2030. This should not as a surprise. We are already seeing AI technologies being used in virtually every sector.
The remarkable advancement in AI in the past few years has really made an impact on our lives. However this rapid progress has also brought with it some unexpected challenges that AI has still not been able to overcome.
This article delves into the major challenges being faced by AI that stand in the way of its public integration and everyday use.
Computing Power
The biggest issue AI is facing is that to run its power-hungry algorithms you need a high amount of power in the form of cores and GPUs. Both deep learning & machine learning are the foundation upon which the AI stands.
To use these tools efficiently you need super computer-like power sources which do not come cheap. Though the advent of cloud computing and parallel processing has resolved this issue to some extent still the rapidly increasing complex algorithm and the sheer amount of new data demands even powerful computing capabilities which are not yet possible for independent developers.
Data Security
Labeled data is an integral part of AI which is used in the training of machines to make predictions.
As the AI uses user-generated data and resources for training this makes the data prone to unethical usage. With the constant information flowing into AI in very large amounts there are bound to be some leaks.
There is a need to develop and train AI systems that do not send data back to servers instead they only send the trained models to the organizations to bypass the security issues.
The Bias Issue in AI Systems
Another big issue with AI is the biases and discrimination that it replicates and amplifies upon training using biased data. It has been observed that AI algorithms trained on vast amounts of data having human biases lead to discriminatory outcomes in areas like hiring and loan approvals.
The prevention of biases during the development, training, & implementation stages of AI is crucial to avoid marginalization and the reinforcement of unjust social patterns across different industries.
Limited Public Knowledge
There is also a lack of general understanding and knowledge of AI among the public. This prevents the large-scale use of AI-based systems in countless SMEs (small and medium enterprises).
These entities can use AI to manage inventories & improve production. Moreover, they can also utilize it for creating marketing campaigns and to create better work schedules.
All these applications of AI require businesses to invest more time and resources in the development and implementation of AI which is not possible with the very little Knowledge they possess about the capabilities of AI.
Ethical Dilemmas
As AI grows more autonomous it also brings about certain ethical dilemmas. The purely biased decision-making leads to several social implications and complex moral issues.
AI systems are purely objective and human values are not a priority in their decision-making mechanisms. Moreover there is another ethical dilemma surrounding AI.
Who is responsible when AI makes a decision that leads to seriously harmful consequences? Who bears the moral and legal responsibility when an AI-based model in an autonomous vehicle leads to an accident or when it makes the wrong medical diagnosis?
Robust regulation is the only way to ensure these ethical dilemmas are answered and they do not stand in the way of AI.
Human-Level Reliability
Training of AI models to behave even remotely close to humans requires careful finetuning & hyperparameter optimization. Apart from that you also need accurate algorithms and robust computing power along with uninterrupted data training and data testing.
All these processes require so much resources and hard work. These limitations make it hard for SMEs to utilize AI properly and unlock its true potential.
This struggle to reach human-level performance is what stands in the way of AI implementation on a larger scale.
Final Thoughts
AI has revolutionized the way we conduct business. It simplified the way we perform everyday task. Still, AI is facing several big challenges and without addressing them it is not possible to fully utilize it for the collective good of the human race.
There is a need for a concerted effort from researchers and developers along with key policy makers to resolve the biggest challenges that AI and its implementation face today.
This is the only way we can develop and use safe and responsible AI technologies in the near future.
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