Gartner’s Hype Cycle specifically focuses on the set of technologies that show promise in delivering a high degree of competitive advantage over the next five to 10 years. The 2016 report shows Machine Learning at the “Peak of Inflated Expectations.”
I’m looking forward to reading the blogs and articles off the back of this research. Who agrees? disagrees? Is Gartner’s analysis helpful? Should you make a decision based on their research? Let me know and we will publish your opinions.
Criminal injustice has been a major problem around the world, especially in the US. In order to overcome this problem, Big Data solutions have gotten major backing by government agencies. Law enforcement in the US is already making use of body cameras that utilize AI to detect whether a suspect is a threat or not.
According to Yieldify CTO Richard Sharp, Machine Learning systems can be vulnerable to discriminatory biases. This could be a major problem since ML is moving into sectors such as credit scoring, hiring, or law enforcement.
Analyst group Gartner just released their new edition of their Hype Cycle Chart and ML will be “the most disruptive class of technologies over the next ten years”. The methodology for their hype cycle can be found here.
Check out this visualisation of the history of the summer Olympics. Only 10 countries earned medals in the first modern games (1896), 100 years later we had 86 winning countries. You can also find a breakdown of the different sports.
In order to utilize machine learning to help with your decisions, you are required to trust the deployed model. But when to trust and when not to? Understanding the reasoning behind the model’s predictions can help the user decide when to trust it.
At our last CognitionX event, we were given advice on data science interviews. This article, which is the result of the analysis of hundreds of data science interviews, gives you tips and advice on the questions that could come up.