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Disruptive innovation can be described as a new technology that is inferior in certain respects to existing ones, but has other desirable attributes (The Economist, 2014). These specific innovations can challenge incumbents and eventually destroy their existing business models.  The term ‘disruptive innovation’ is often misunderstood and even misapplied in different situations. The theory of Professor Clay Christensen states that every successful business will be affected by disruptive innovations and eventually be overtaken by them. Customer preferences vary in each market, some customers demand a high level of performance from a certain technology, while others just expect basic needs (Itonics, 2016).

The success of Netflix can be considered a great example of a disruptive innovation. Its business model pushed an incumbent of the industry, Blockbuster, into bankruptcy (Ostrower, 2011). Netflix targeted the segments of the population that were overlooked by its competitors and offered the customers an alternative that was both inferior and lower in price (McAlone,2015). Initially, Blockbuster and Netflix were not even competing for the same customer segments. Eventually, Netflix wins over the mainstream customers and managed to move to the top, leading to the collapse of Blockbuster (McAlone, 2015).

According to Christensen, not every innovative company is considered a disruptive innovation. He explains this using Uber as an example. As stated above, it is important to focus on the overlooked segments of the population (McAlone, 2015). Uber did not target the overlooked segments, nor did it provide alternatives for a lower price. It provided their customers with a more convenient taxi system, and therefore attacks the competitors’ core business from the beginning. Christensen explains that Uber did the opposite of a disruptive company by shifting more downmarket, as they began to focus on overlooked segments in a later stadium. It is fair to call Uber innovative, but according to Christensen it is not disruptive (McAlone, 2015).

 

 

References

The Economist. (2014, September 6). Pardon the disruption.

Itonics (2016). https://medium.com/datadriveninvestor/why-uber-isnt-disruptive-but-netflix-is-disruptive-innovation-explained-198d250f4db0

1. McAlone (2015). The father of ‘disruption’ theory explains why Netflix is the prefect example and Uber isn’t. https://www.businessinsider.com/the-father-of-disruption-theory-explains-why-netflix-is-the-perfect-example-and-uber-isnt-2015-11?international=true&r=US&IR=T
2. Ostrower (2011). Netflix Instant Video Streaming: A Disruptive Innovation That’s Disrupting Netflix. https://www.altitudeinc.com/netflix-a-disruptive-innovation-thats-disrupting-netflix/

 

 

 

 

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Full autonomy is closer than ever, using artificial intelligence will allow vehicles to react to the real-world by taking data inputs of different sensors. This seems like something promising, but it will take some time. The autonomous driving capabilities encounter different challenges, ranging from moral considerations to technical issues (Brandom, 2018).

However, the Society of Automotive Engineers (SAE) defined many benefits of autonomous driving capabilities, especially with the regard to life-long mobility and a decrease of accidents. It is therefore important to integrate artificial intelligence capabilities and focus on further development of automation (Reser, 2018).

As stated above, sensors will enable vehicles to react to its environment by developing a representation of the real world. Developers need to focus on the identification and control of the input parameters send by the sensors. A sensor consists of various radars, ultrasound and cameras. It is however a challenge to discover correlations between AI scenario’s, deep learning, the physical signals and the real world impact a decision will have in real traffic situations (Reser, 2018).

Other researches argue that fully autonomous cars are further in the future than most people realize. For deep learning to work properly, it will require a lot of training data to integrate almost every possible scenario. For algorithms to recognize different scenarios, engineers can use their creativity for the source of data and its structure. However, there is a limit when it comes to how far a certain algorithm can go. This recognition process, also called ‘generalization’, requires certain skills. Research has shown that deep learning is not very good in doing this generalization process, leaving the autonomy companies with many concerns (Brandom, 2018).

 

 

2018. Brandom. 2018. Self-driving cars are headed towards an AI roadbloack. https://www.theverge.com/2018/7/3/17530232/self-driving-ai-winter-full-autonomy-waymo-tesla-uber
2019. Reser. 2018. How AI will help pave the way to autonomous driving. https://www.electronicdesign.com/test-measurement/how-ai-will-help-pave-way-autonomous-driving