Science at Uber: How AI makes Uber works 10 years ahead of the world?

You must have used Uber and you must have found it convenient. But have you ever wondered how Uber processes thousands of requests at the same time? How Uber make you meet the driver in no time? And how Uber drive the force of drivers in the uniform pattern so that demand from any corner of the city can be satisfied?

Science at Uber

One short answer to all these questions is ‘use of sophisticated AI and machine learning techniques.’ And to know the comprehensive answer, keep reading this blog. In this blog, I will share 4 use cases of AI at Uber. This will reveal the magical working method of Uber which you haven’t known yet. So, without any further due, let’s proceed.

Building a data science platform 

Given the scale that Uber is working globally, data plays a significant role. Uber relies on data so much that without it, it cannot satisfy even a single pick-up request from a city like LA. Data help Uber make a very nuanced decision and learn the local conditions of complex marketplaces.

Data also reveals the product preference of Uber’s end users. With this crucial information, Uber can easily create a magical user experience for them. But for that, Uber had to lock horns with the limitations of the data. Since data, by itself,  doesn’t disclose the meaningful relations between different attributes which later exhibit the important insight, Uber had to write the code for it or create a data science platform.

Thus, looking at gravity, one of the first platforms Uber has built is the forecasting platform. And the vision they had for the Uber forecasting platform is to make it accessible throughout all departments. This means the forecasting model provides accurate forecasts to anyone in the company with just a push of a button.

This forecasting model works on the data. An Uber employee has to give input data to the model in the form of a CSV file or through query and how far he wants to forecast out.

After that, the forecasting model, with the help of a whole suite of forecasting algorithms, and backtesting framework, prepare the forecast report and exhibits it. Here, it is worth mentioning that Uber has used many already-explored algorithms as well as their own algorithms to build a forecasting model. And with these algorithms, they have fitted backtesting framework which understands the accuracy of each forecast.

Not only this, but the forecasting model has also been deeply integrated into the business intelligence stack in order to get the real-time forecast.

Applying AI at Uber 

Uber says a tree doesn’t have a brain because the prime purpose of the brain is to movement and a tree never moves. But unlike a tree, Uber needs a brain as Uber is about movement; it moves things efficiently around the world. And out of no surprise, the brain Uber is having is AI. It provides intelligence to Uber to be able to sense the cities better to make movement efficient.

At Uber, they have a separate department called Uber AI where Uber’s scientists engage in research to push the boundaries of the AI and to serve other departments of the Uber which require the AI technology.

Uber AI does a lot of research on areas where they believe that they have to be cutting edge. These research areas include reinforcement learning, deep learning, probabilistic modeling, and evolutionary algorithms. (here, reinforcement learning is the area of research around the system that makes sequential decision to optimize something in the real world, deep learning which is a technology which does not require the predefined attributes like machine learning to predict and to identify anything, and evolutionary algorithms which can optimize their behavior through generations of improvement.)

Making a real-world impact on data science

Uber is very committed to providing the ultimate level of user experience and for Uber, allocating the nearest & fastest driver and pick-up rider in minimum time is the first step to sustain the high user satisfaction rate.

Uber actively learns the traffic patterns of the different cities and makes a model which later helps them to allocate drivers to riders more effectively. Uber compares the city traffic with the diurnal cycle of the Animal which makes animals move in a similar part of the forest at the same time of the day on different days. But to find such patterns in human movement, Uber uses data science. Using it, Uber understands why people move the way they do and uses this data to improve the user experience.

Uber does not only understand the behavior of the traffic of one part of the city, but it relates the traffic of one part of the city with other parts of the city. Moreover, Uber utilizes the spatial and temporal correlations so that Uber can give precise traffic prediction and an ETA to riders as well as drivers – Uber driver’s navigation system chooses the shortest and most efficient route.

Innovation across the digital and physical world

Uber is a very unique business as it has a presence and interaction with the physical world in a very real-time manner. And to move things in a dynamic world, Uber relies on many cutting -edge technologies.

Using these technologies, Uber tries to focus on two aspects. One is the physical real world and the other one is the virtual space which is an Uber mobile app.

Uber wants to build a link between these two aspects. In fact, they have already achieved it in the form of conversational assistants. This conversational assistance accommodates a machine learning-based feature called one-click-chat (OCC).  It aims to provide a one-click chatting experience by offering the most relevant replies.

In-built machine learning and natural language processing capabilities of the UberChat system take meaning out of the message and shows three to four most relevant suggestions so that a driver can reply rider in a very hassle-free way, even while driving.

Following is the architecture of the ML-based one-click-chat feature of the Uber chat window.

architecture of the ML-based

In the nutshell: 

In the last few years, a few groundbreaking technologies have been evolved from nowhere to aid conventional businesses. By adopting these technologies, you can leverage the potential of the market greatly.

In fact, many companies have already shifted their business processes on such technologies and experiencing exceptional growth. Uber is one of them.

Innovation through data is what makes Uber run. And for that, they are using two most cutting edge technologies – AI and ML. By using these technologies, Uber makes sure they take the user journey to the next level. AI and ML help Uber to show ETA, to find the nearest driver, to navigate, and to predict the future demand.

In this blog, we have just studied the basics of how Uber employs the right technologies. But even though, you can learn one thing for your business that using the right technologies only, you can deliver what your users are expecting.

About the Author

Vishal Virani is a Founder and CEO of Coruscate Solutions, a leading taxi app development company. He enjoys writing about the vital role of mobile apps for different industries, custom web development, and the latest technology trends.