Overview of Artificial Intelligence Problems
Artificial Intelligence continues to bring incremental benefits to human life. As per the Mckinsey report, Artificial Intelligence is set to add $ 13 Trillion to the global economy by 2030 which is about 16% of the total global share. Notwithstanding the tangible and monetary benefits, AI has various shortfall and problems which inhibits its large scale adoption. The problems include Safety, Trust, Computation Power, Job Loss concern, etc.
Major Problems Associated with Artificial Intelligence
The following are a few of the major problems associated with Artificial Intelligence and its possible solutions.
1. Job Loss Problem
Job loss concerns related to Artificial Intelligence has been a subject of numerous business cases and academic studies. As per an Oxford Study, more than 47% of American jobs will be under threat due to automation by the mid-2030s. As per the World Economic Forum, Artificial Intelligence automation will replace more than 75 million jobs by 2022. Some of the figures are even more daunting. As per another Mckinsey report, AI-bases robots could replace 30% of the current global workforce. As per the AI expert and Venture Capitalist Kai-Fu Lee, 40% of the world jobs will be replaced by AI-based bots in the next 10-15 years. Low income and low skilled workers will be the worst hit by this change. As the AI becomes smarter by the day even the High paid, High skill workers, become more vulnerable to job losses as, given the high cost of skilled workers, the companies get better margins by automating their work. However, these issues related to Job loss and wages can be addressed by focussing on the following measures.
- Overhauling the education system and giving more focus on skills like Critical Thinking, Creativity, and Innovation as these skills are hard to replicate.
- Increasing both public and private investment in developing human capital so that they are better aligned with industry demand.
- Improving the condition of the labor market by bridging the demand-supply gap and giving impetus to the gig economy.
2. Safety Problem
There has always been much furor about safety issues associated with Artificial Intelligence. When experts like Elon Musk, Stephen Hawking, Bill Gates among various others express concern related to AI safety we should pay heed to its safety issues. There have been various instances where Artificial Intelligence has gone wrong when Twitter Chabot started spewing abusive and Pro-Nazi sentiments and in other instance when Facebook AI bots started interacting with each other in a language no one else would understand, ultimately leading to the project being shut down.
There are grave concerns about Artificial Intelligence doing something harmful to humankind. The case in point is autonomous weapons which can be programmed to kill other humans. There are also imminent concerns with AI forming “Mind of their Own” and doesn’t value human life. If such weapons are deployed, it will be very difficult to undo its repercussions. The following are the measures that can be taken to mitigate these concerns.
- We need to have strong regulations especially when it comes to creation or experimentation of Autonomous weapons
- Global Co-operation on issues concerning such kind of weapons is needed so as to ensure no one gets involved in the rat race
- Complete transparency in the system where such technologies have experimented is essential to ensure its safe usage
3. Trust Related Problem
As Artificial Intelligence algorithms become more powerful by the day, it also brings several trust-related issues on its ability to make decisions that are fair and for the betterment of humankind. With AI slowly reaching human-level cognitive abilities the trust issue becomes all the more significant. There are several applications where AI operates as a black box. Example- in High-Frequency trading even the Program developers don’t have a good understanding of the basis on which AI executed the trade. Some more striking examples include Amazon AI-based algorithm for same-day delivery which was inadvertently biased against black neighbourhood, another example was Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) where the Artificial Intelligence algorithm while profiling suspects was biased against the black community.
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Following are few of the measures that can be taken to bridge trust-related issues in Artificial Intelligence
- All the major Artificial Intelligence providers need to set up guiding rules and principles related to trust and transparency in AI implementation. These principles need to be religiously followed by all the stakeholders involved in Artificial Intelligence development and usage
- All the stakeholders should be aware of the bias which inherently comes with AI algorithm and should have a robust bias detection mechanism and ways to handle it
- Awareness is another key factor that plays a major role in bridging the trust gap. The users should be sensitized about the AI operations, its capabilities and even the shortfall that is associated with Artificial Intelligence
4. Computation Problem
Artificial Intelligence algorithm involves analyzing the humongous amount of data that require an immense amount of computational power. So far the problem was dealt with with the help of Cloud Computing and Parallel Processing. However, as the amount of data increases and more complex deep learning algorithm comes in the mainstream, the present-day computational power will not be enough to cater to the complex requirement. We will need more storage and computational power which can handle crunching exabytes and Zettabytes of data.
Quantum Computing can address the processing speed problem in the medium to long terms
Quantum computing which is based on concepts of Quantum theory might be the answer to solving computation power challenges. Quantum computing is 100 Million times faster than a normal computer we use at home. Although currently, it is in the research and experimental stage. As per an estimate by different experts, we can see its mainstream implementation in the next 10-15 years.
The aforementioned problems are certainly not impossible to solve, however, it does require rapid evolution in technology as well as human co-operation. While we are well on course in terms of rate of technological advancement but we still have a long way to go to develop principles, methodology, and frameworks to ensure that powerful technology like AI is not misused or misapplied which may result in unintended consequences.
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