Tech addiction is the new point of discussion in mainstream media with companies like Facebook, discussing how they can make their user’s time more productive. With social apps like Facebook and Snapchat deploying addictive social feedback loops to grab people’s attention. On the other hand, smartphone users are trying to use all sorts of timers and Quantified Self tools to improve the way they spend their time. But are these timers or tracking tools enough to help people?
Intelligent time for smartphone users
Millions of people are using Quantified Self tools to help them understand their time better. Some apps are even helping people set limits to their phone usage. This does help people upto an extent but for deeper insight people need some proactive feedback, to make this lifestyle change. Artificial Intelligence and Machine Learning can be really useful to understand a person’s day better and suggest them how to use their time in a more productive manner.
Data is no longer a limitation, with smartphone sensors more accurate than ever. The challenge lies in understanding the data and making it meaningful for the user. Many apps of today are applying all this contextual data in learning algorithms and being able to provide smartphone users basic recommendations. AI is bridging the gap, by providing tracking apps with the ability to give users proactive suggestions. Apart from this Machine Learning can help provide a much deeper insight into the data tracked. A good example of this would be how Cardiogram is using Neural Networks with Heart Rate data to detect diseases like diabetes with a high accuracy rate.
Enterprises using time tracking
On the other end, big companies have been using tracking tools for many years now. Trying to make it a metric for Quantifying their employees time has been hard though. With basic data of time spent on the computer, websites visited & apps used it makes these tools seem like a ‘Big Brother’. This data makes it tough to justify an employees productivity. But with a huge dataset and some learning algorithms it can be possible to get some meaningful trends out of the basic data.
With managers judging their team performance based on their ability to complete tasks quickly and with quality, it is tough for software to make such observations based on simple Git commits or tracking time spent on the computer. Apart from human input, this brings in the need for deeper analysis in terms of trackable data for algorithms to be able spot useful trends.