ARTIFICIAL INTELLIGENCE IN URBAN MOBILITY

“The path for AI in Urban Mobility- the need of smart moves”

“The pace of progress in artificial intelligence is incredibly fast. Unless you have direct exposure to groups like Deep mind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year time-frame. 10 years at most.”

These words by Elon Musk are a clear indication that the rapid escalation in the growth of AI and the establishment of it’s dominance in different sectors- is a no-brainer but at the same time, its important to understand and avoid the risks associated with it. Artificial Intelligence is now being used in almost every other field. Automobile sector is no exception and it’s time for AI to establish its stronghold in this sector.

The theories of AI have existed since 1950s but it has been gaining broad functionality specially over the past few years, specifically with the rise of machine learning like technologies.

 

Despite of these changes, the automobile industry is at the beginning of AI disruption. AI applications can perform better than the humans, but only in very specific tasks. The nature of technology varies in every aspect, for instance, “Narrow AI”, it involves standard navigation systems as well as autonomous- driving errands processing one gigabyte of data per second, or a million times more data than current navigation system handle.

AI can drive a huge competitive advantage from a technological advanced improvement, and that is why, it is particularly true for automobile industry and mobile industry. And the below graphic is a clear indication of how AI is rapidly established itself in the Transport sector and how it is predicted to keep improve its reach in this sector.

ADVANTAGES OF USING ML IN MOBILITY

ML is a subset of AI,and this exciting technology can serve numerous benefits in the automobile sector.

By applying a practical knowledge,ML can help Automobile sector and mobility in the following ways-:

· Capacity to act in complex situations

· Providing the ability to cope using explicit programming.

· Improve the over time without explicit instructions.

ML is a technology, which is surely going to provide comparative advantage in the coming years, especially in fields like Autonomous Driving which otherwise are difficult to handle

The consumer response to this transition

This survey by Mckinsey is a clear indication that consumers are very much open to this technology. And it’s pretty obvious too as AI provides much more efficiency and comfort to customers.

Consumers general interest is acceptance of AI which is extending the mobility space. People are very comfortable with the application of technologies like AI and ML in this sector.This overall customer interest in AI is a great thing as it increases the willingness of companies or organizations to pay for this technological shift. Many customers believe that AI will lead to full automation in the coming 5 years.

ML is surely going to have a considerable impact on mobility and the automobile sector as a whole. It may lead to unlocking of entirely new products and or otherwise lead to enhanced productivity.

VARIOUS APPLICATION AREAS FOR ARTIFICIAL INTELLIGENCE

This graphic by Mckenzie Center for Future mobility provides us with a list of ML based applications in automotive mobility

CHALLENGES THAT NEED TO BE ADDRESSED

Incorporating AI and ML in the urban-mobility sector might seem an obvious fit but before this happens, there are a few challenges which needs to be addressed. Let’s have a look at them one-by-one:

1. Loss of jobs- AI is often directly associated with job losses and rightly so! AI moves a sector towards service-economy which means more job losses for low-skilled workers.

2. Cost – The potentially high cost that AI system comes with poses a big challenge to its implementation. Also, the cost of acquiring skilled people and business practices in order to optimize the benefits of AI is significantly high.

3. Complexity at organisational level- Many organisations do not have the appropriate infrastructure required for incorporating AI. Applying machine learning in this mobility environment, comes up with so much complexity and requires new structures and new ways and commitments of working.

4. Privacy concerns- AI and data-privacy concerns go hand-in hand. The decade has seen a stupendous rise in the number of data-thefts and cyber-attacks and for a technology like AI which literally thrives on data, its increasingly important to ensure cyber-security.

Conclusion

Throughout the article, we have discussed about the potential of AI to disrupt the automobile sector and the challenges or obstacles which needs to be addressed for it to be possible. And it’s great that companies and organisations involved in the mobility sector are already working towards it. Around 500 companies in this transportation ecosystem are working towards creating a technological know-how which would be integral in order to to claim different positions in the value chain.

Automobile requires high safety standards with accuracy. ML in combination with programmed automobile grading can assist in this regard.

In the end, we all know that future is going to be led by AI and ML, so its better to embrace the challenges and try to overcome them in order for a bright future.

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