Even though Artificial Intelligence holds enormous potential, it still has its challenges, which are not small. Therefore, we need to recognize and work towards resolutions to these problems in order to further propel artificial intelligence’s rapid growth. Below, are some of the challenges that AI faces:
1) Lack of Computer Power
In AI, the machine learning and deep learning techniques require a huge number of calculations to be made very quickly. This means they use a lot of processing power.
At the moment, we seem to have found a solution for this through cloud computing and massively-parallel processing systems. But as data volumes continue to grow, and deep learning drives the automated creation of increasingly complex algorithms, the bottleneck will continue to slow progress.
The answer is likely to lie in the development of the next generation of computing infrastructures, such as quantum computing, which harnesses subatomic phenomena such as the entanglement to carry out operations on data far more quickly than today’s computers.
Unfortunately, it’ll probably take at least 10 years to build that, since the programming models for quantum are completely different from those we use now. For this reason, there has got to be a reinvention and that’s obviously going to take time.
2)Building Trust
AI is a black box and people don’t feel comfortable when they don’t understand how a certain decision was made.
For example, algorithms used by banks are mainly linear mathematics and it’s very easy to explain the process through which the solution was found– for example, ‘I denied your mortgage application because you don’t have a job’.
With multi-layer neural networks, the average human doesn’t understand. So, since we’re now making predictions based on things that people don’t comprehend, then this is going to make them uncomfortable. For instance, would you agree to trust your medical diagnosis to a black box that only responds “yes” or “no”? Most likely not!
This may lead to revolts (most likely, under the form of social media campaigning and boycotts) which are a hurdle that could derail attempts to drive progress.
However, growing consumer awareness of the increasing number of decisions made by machines, using our own personal data, has prompted lawmakers to tackle the problem from the consumers’ point-of-view. One example is the GDPR, which came into force across the EU in 2018 and affected anyone dealing with the private data of EU citizens, wherever they are in the world. A part of the regulation suggests that citizens could have the right to have an explanation for decisions which are made about them by AI. So, for example, under a very strict interpretation of the GDPR, one can demand Netflix to explain why it recommended that specific movie to that specific person.
3) Not Able to Multitask
As human beings, we are able to multitask: we have a number of cognitive functions that allow us to simultaneously talk, listen and devise a strategy. Everything happens instantly within our brain at any given moment. However, it’s really hard for a machine to replicate this set of functions.
Some current research is focusing on developing artificial general intelligences (capable of mimicking more than one cognitive function), but no general public applications have been produced so far.
Bibliography:
https://www.groupeonepoint.com/en/insights/current-challenges-ai https://www.forbes.com/sites/bernardmarr/2017/07/13/the-biggest-challenges-facing-artificial-intelligence-ai-in-business-and-society/#592626292aec
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