Scientists on Thursday unveiled the most exhaustive database yet of the proteins that form the building blocks of life, in a breakthrough observers said would "fundamentally change biological research". Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome -- the complete sequence of human genes that encode cellular life -- the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate -- the shape in which they end up, or "fold" into -- within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking.
Is your experimentation program experiencing push-back from other departments? Marketers and designers who own the brand? Product owners who've spent months developing a new feature? The reality is that experimentation programs often lose steam because they are operating within a silo. Problems arise when people outside of the optimization team don't understand the why behind experimentation. When test goals aren't aligned with other teams' KPIs. Optimization champions can struggle to scale their experimentation programs from department initiatives to an organizational strategy. Because to scale, you need buy-in.
This article was originally published on Amplero's blog. Everybody has seen the classic testing and optimization wheel on some smart art-heavy marketing PowerPoint slide. It seems so simple right? Start with a hypothesis, run the test, generate insights, and iterate. This model works great--especially if your customers only access your site or app via one device via one source on a linear transactional journey.
Phil. Trans. R. Soc. Lond. A. 1994 349 1689. Intelligence is a complex, natural phenomenon exhibited by humans and many other living things, without sharply defined boundaries between intelligent and unintelligent behaviour. Artificial inteliigence focuses on the phenomenon of intelligent behaviour, in humans or machines. Experimentation with computer programs allows us to manipulate their design and intervene in the environmental conditions in ways that are not possible with humans. Thus, experimentation can help us to understand what principles govern intelligent action and what mechanisms are sufficient for computers to replicate intelligent behaviours.
What do you do after you write out code for your idea and when the networks are training? Usually, the next thing I want to try is dependant on the result of the current run. Other than just reading papers or browsing reddit/twitter when idle, are there any tips to make the most of a Deep Learning work week?