Top 6 Challenges in Artificial Intelligence
Concepts, once thought too ahead of their time, like robotic implants in human brains are now rapidly becoming a part of reality. News of an individual wearing this kind of enhanced functionality implant went viral some time ago, and the technology is expected to improve over time. The time when humans can fully control and operate electronic devices without touching them is not far off.
However, artificial intelligence faces considerable challenges that it needs to overcome to continue on this path of growth. We’ll talk about that in a second. If you’re thinking about getting into artificial intelligence, now is an excellent time to get started. Moreover, you can leverage technology like the internet to learn more about different topics in detail.
Make sure that your internet connection is fast and reliable. For this, consider getting CenturyLink Internet at your home so you can stay productive without experiencing any lag. Contact CenturyLink Customer Service to learn more about pricing and plans. Reverting to the topic at hand, here are some of the biggest challenges faced by AI currently. Read on to know more.
- Computing power
Artificial intelligence models are made up of a lot of algorithms which the AI model quickly evaluates and executes to process inputs and outputs. The processing power required for machine learning and deep learning algorithms is crazy and these machines have to have high-speed cores and GPUs to work efficiently.
While computing power is not a limiting factor when it comes to small-scale applications, large-scale applications need computing power equal to a mini supercomputer to process all the data. And you can guess why supercomputers aren’t cheap or readily accessible. Admittedly, cloud computing and parallel processing systems have made it a bit easier for companies to deploy AI solutions, but it’s still far from mainstream adoption.
- Trust Deficit
There is a term called the “black box” in the fields of machine learning and artificial intelligence. This, in the words of Google’s CEO, is when the developers can’t figure out why the AI model generated a specific response or how did it reach that conclusion. There’s still a lot to learn as it’s in the developmental stages. The problems these companies face are also unique and require a lot more resources to devise a solution, as no solution exists before that.
Understandably, this makes some people uncomfortable, as they cannot fully comprehend it. This is one of the significant challenges AI faces in today’s world. The way AI is rapidly being integrated into different segments of our lives warrants further exploration and debate over regulatory control.
- Limited Knowledge
Unlike other fields of study, artificial intelligence didn’t exist before, which is why we don’t see many people aware of its existence. The number of people, students, scientists, and researchers working in this domain is even less. Even if some SMEs (Small and Medium Enterprises) decide to step into this field, the lack of awareness about advanced solutions like cloud computing makes it harder for them to grow.
- Human-level accuracy
If you’ve ever used AI applications or generative AI models like ChatGPT and Bard, you’d agree that AI can sometimes hallucinate. This is when the AI model generates incorrect or misleading information, which simply isn’t true. Google has added a permanent disclaimer when you use Bard that it may produce incorrect or offensive information that isn’t reflective of the company’s views.
As of now, you cannot use the information before getting it reviewed by a human for accuracy and relevance. This makes AI less dependable than it’s supposed to be, and it cannot be used for complex tasks involving human-like interpretation.
For a deep learning algorithm to work at a similar accuracy level as that of a human being, it would require massive fine-tuning, which itself is a challenge. You have to understand that human brains take years to develop and are constantly absorbing information to be able to make conscious decisions.
- Data privacy and security
Data is at the core foundation of the things that AI models need to work effectively. The amount of data used to train ChatGPT and Bard is mind-blowing, which brings us to the concerns some segments of our society have. The amount of human-generated consolidated data from millions of users worldwide is unprecedented and thus raised the concern about its security.
The potential for misuse is massive, which has caused some entities to voice their concerns. For example, there is an open letter titled, Pause Giant AI Experiments,” signed by thousands of people worldwide including some high-ranking officials and industry leaders.
- Information bias
Another big challenge AI faces on a constant basis is the information bias that can cause AI to produce biased information. Since AI is trained on large sets of data, the accuracy of outputs is directly related to the data that went into it. Another loophole is when the algorithm itself is biased and it can only generate biased responses. Then, the way the information generated by the AI model also matters. If it’s presented in a biased way, then it’s of no use.
Conclusion
To recap, these are some of the biggest challenges AI faces in today’s world. Naturally, the existence of issues is accompanied by opportunities to find solutions. Technological advancements are always welcome, but the need for stricter regulatory control to protect users’ data cannot be overstated.
