According to Gartner analysts, up to 85% of big data projects fail. I suspect that for AI projects, especially if you count the ones that never start, the failure rate is even higher. What are the reasons for the high failure rate with AI projects?
Convolution is an extremely effective technique that can capture useful features from data distributions. Specifically, convolution based deep neural networks have performed exceedingly well on 2D representation learning tasks, e.g. image analysis. Given this success, it is natural to investigate how to use this concept to capture features in different settings.
A couple of weeks ago I attended the World Summit AI 2018 in Amsterdam. Coming from a non-technology background (I originally trained as a medical doctor) and as someone who had since worked as a management consultant and more recently have been working with various startups (including ConscientAI), I was keen to understand more about AI from a business point of view.
A 2017 survey of more than 3,000 executives by MIT Sloan Management Review and Boston Consulting Group revealed that almost 85% believed that Artificial Intelligence (AI) will give their companies or help them maintain a competitive advantage . Non-public AI companies raised $15.2B funding in 2017, an increase of 141% compared to 2016 .
Yes, that's correct. Well, I think so and let me explain. I think the biggest challenge of AI right now is making it an industry or an engineering field. All the other challenges were there for a while if you really think about it.
Since Ian Goodfellow first proposed the idea of GANs ( https://arxiv.org/abs/1406.2661), it has become a buzz word within ML community, simply because it works stunningly well (given that you came up with a perfect architecture). Many people, specially Yann LeCun, a who is considered as one of the giants in Deep Learning, stated at some point that GANs are a significant breakthrough in deep learning.
We were invited to attend SOCML 2018 where we could meet a diverse group of AI practitioners and share our experience.
Our paper A Context-aware Capsule Network for Multi-label Classification accepted at the ECCV 2018 workshop Brain-Driven Computer Vision
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