During the past year, there have been major implosions of robot startups, such as with Jibo, Anki and Rethink Robotics. They all raised substantial amounts of capital from top-tier investors and had strong teams.
So why the failure? One of the main reasons is the extreme complexities of melding software and movable hardware. As a result, the technology often does not live up to expectations.
Even with the strides in AI – such as deep learning -- there is still much to do. “Deep learning and robotics is difficult for a variety of reasons,” said Carmine Rimi, who is a Product Manager for AI at Canonical, “For instance, simultaneous localization and mapping (SLAM) in unknown environments, while simultaneously keeping track of an agent's location within it in tractable time, is a challenge. In real-time it is at least a magnitude more difficult. Research into advanced algorithms, that deliver better accuracy faster and at lower power consumption, along with quantum-like parallel states and processing, are some of the areas that will help. And this part of why it is difficult.”
But there is much more than this. Dr. Alex Wong, who is the Chief Scientist and co-founder of DarwinAI, has this to say: “One of the primary difficulties with AI in this context is that learning to manipulate physical objects with a high level of dexterity in dynamic and ‘noisy’ real-world environments is extremely challenging, as it must take into account an incredible number of environmental factors to make complex decisions in real-time. Additional complexities in this area are issues associated with ‘data sparsity’ and training speed.” [Read more]
This is an excerpt of an article originally published on the Forbes website.