5 min
It was only recently that Facebook put an end to its ambitious digital assistant product, M, which the social media giant unveiled two years ago as the company’s answer to Apple’s Siri and Google assistant. The Messenger assistant which was offered to close to 10,000 people in the San Francisco area could perform actions like booking restaurant reservations, changing flights, sending gift, etc. Brought into life with an intention of developing artificial intelligence technology that could fully automate M’s task, it failed to do so!
Hence M was shut down on 19th Jan 2018, owing to problems like it couldn’t perform tasks that it was asked to do, didn’t justify the machine learning capabilities that it was supposedly bestowed with and demanded human intervention. According to reports it could never surpass 30 percent automation, and Facebook claimed that it had launched it to learn people’s expectation from an AI assistant, and that they would use it to power other AI projects.
While this came as an easy justification for the company, there are several others that fail to keep up to high rising complexities and challenges such as lack of funding, lack of innovation, failure to produce a scalable product, amongst others, prompting them to call it quits.
AIM list down few such instances from the analytics, data science, AI and IoT world, where tech companies were forced to succumb under various crunches.


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