The Tictag App enables anyone from all walks of life to convert their spare time into rewards and contribute to AI development whilst doing so.
We all spend more time than we care to admit staring at our phones everyday. There are countless ways of wasting time in the vast digital world we have right at our fingertips. But, the free time we spend on our phones doesn’t have to be unproductive. With Tictag our users have the ability to convert their spare time into tangible rewards while having fun and making a difference in the world of Artificial Intelligence(AI) and Machine Learning(ML).
Our gamified mobile platform (The Tictag App) helps AI/ML and Data Science teams satisfy all their data annotation needs within record time. Their data gets converted into games and tasks that are later distributed amongst the Tictag App users. Users of our platform/application, or the Tagger Community as we like to call them, enjoy completing these tasks and playing games on the go.
Our Taggers
With different use cases from our clients/partners, our taggers have the pleasure of playing unique mini-games on the platform. The constant introduction of these new tasks keeps the taggers actively engaged.
“The kind of Taggers one will find on the platform come from all walks of life; students, experts and even the elderly and specially abled.”
What brings them all together is our easy to use mobile UI and simple to understand application. We believe in giving back to the community and our way of doing so is by working with special care organisations that connect us with the elderly and specially abled. Through our app, we are able to provide them with a source of entertainment and also side-monetary value/benefits.
Ensuring labelling accuracy
Through facilitated engagements, we interact with our Taggers on the specifics of each micro labelling task. Our two-way communication with them allows us to understand any limitations or feedback they might have and solve it immediately.
At the end of every task, Taggers are given coins as incentives. These coins are not awarded for just completing the task, but rather for completing the task with accuracy and efficiency. With every increasing level of accuracy and efficiency obtained, Taggers are able to collect more coins. This helps us ensure that all data labelled by Taggers is accurate and of high quality.
So, what do the taggers get in return?
Taggers are able to collect large amounts of coins by constantly playing games on the Tictag app. One might ask the question - So what?
Their pool of collected coins opens up a vast variety of rewards options they can redeem! From a wide variety of online to offline vouchers, to more highly sought after lifestyle products, such as the PlayStation 5, we are constantly giving Taggers more reasons to keep playing games and completing tasks on the app! A few more dedicated Taggers even get the option to cash out as an added incentive. “Data sets used for training need to be carefully labelled and annotated before it is passed into the computer so it can learn.”
Of course, if the data is inaccurately labelled it causes the AI to make incorrect assumptions and conclusions when met with ambiguous situations. This is why accurately labelled data is vital to creating AI.
The process of data labelling and annotation is simpler than it sounds, but that doesn’t make it a small task. For a data set to be useful in training an AI it usually needs at least one thousand data points and can go up to tens, hundreds of thousands or millions of data points to enable more complex problem solving. More data points, when annotated and used correctly, leads to better and more accurate AI models.
“Choosing to implement AI/ML models into your business processes is a big decision. At Tictag we are ready to annotate data based on your scale and requirements with our wide community of Taggers working together on the Tictag app.”
Through our innovative crowdsourcing solution, we solve data annotation challenges and bring benefits to our clients AND our community!
Don’t sweat it, Tag it!
