The Surprising Ways We Use Machine Learning in Our Everyday Life

The Surprising Ways We Use Machine Learning in Our Everyday Life


We use high-tech devices every day and most of the time we are not aware of the underlying technology they incorporate.



Machine learning is a subset of AI. It is a complex discipline, but implementing machine learning models have become a lot easier with Google�s open-source library TensorFlow. Nowadays the process of acquiring data, training models, solving predictions and refining results has been eased by the implementation of TensorFlow. TensorFlow has been used in many tasks, including advancing healthcare, improving our music playlists, and even in hospitality.


If you are using Google search, you are already taking advantage of TensorFlow. The moment you start typing in the search bar, you see predictions that are refined by TensorFlow. Leading tech companies, news agencies, chip manufacturers, and many others have already embraced TensorFlow. The list of famous brands and companies includes eBay, Snapchat, Airbnb, Intel, PayPal, Uber, Bloomberg, IBM, CocaCola.


TensorFlow was once a Google library that was used only inside the company for applying deep learning in a lot of different areas. Nowadays it is everywhere � from Google search to Google translate, Photos, and in the speech and face recognition. TensorFlow is making it easier for humans to use the devices that are around them.



Android Things is a fairly new embedded operating system aimed at low-power and memory-constrained IoT devices. TensorFlow can give a boost to the development and implementation of the Internet of Things with its powerful on-device processing. As developers at Google pointed out, for some operations, there is no reason to use cloud computing. For instance, if you only need to count the number of people in front of the camera, TensorFlow can do this offline and save a great amount of bandwidth by not needing to send this image anywhere. This is only one of the applications of TensorFlow on the Android Things devices.



From the moment that self-driving cars even became a thing, questions have been raised about the safety of using them. TensorFlow has been used by car companies to develop models that will allow self-driving cars on the city streets. There are numerous advantages of using an open-source library like TensorFlow, with a few to mention being the fast response time, cost efficiency, saving time to engineers by not trying to invent the wheel but actually work on real-world problems. TensorFlow 2.x natively supports training across multiple GPUs or TPUs, and it is Google AI platform compatible. The latter two features make TensorFlow a highly valued helper in building the software behind self-driving cars.


Another application of TensorFlow in the automobile industry is predicting the break-down of motors. We can attach a sensor to the motor, and with the help of a low-power, always-on TensorFlow model, we can detect motor anomalies.



Have you ever wondered how Spotify chooses the songs to show you on your Home screen in its app? Every time you go back to your home screen, it will be refreshed with the perfect recommendations for you. Behind all this customization is machine learning. In the past, Spotify used a lot of different libraries and APIs which, in combination, did a relatively good job in creating this personalized home screen for you.


Switching to TensorFlow helped Spotify improve the speed and prediction precision of user interactions. Since the switch, the randomness in predictions has decreased, accuracy in generating song recommendations that user likes have increased, and, behind the scenes, the process of finding data issues like missing data and inconsistency has been improved.



The power of machine learning with TensorFlow is helping to detect respiratory disorders. Machine learning models have been used to replace the stethoscope. The sound of a patient�s lung is recorded, and, in combination with patient records, an app gives the doctor a probability of the patient having a particular respiratory disease. Using machine learning in such a way minimizes the chances of misdiagnosis.


Another use of TensorFlow is in the field of genomics, a branch of the molecular biology that studies the structure and function of the genomes. The human genome is three billion letters long, so it is easy to see how helpful was the open-source TensorFlow library in the many computational and algorithmic challenges.


Early 2020 forecasts show that open source libraries like TensorFlow will be much in demand in the current state of healthcare and the need for fast improvement.



Airbnb has been using machine learning for pricing, search ranking, and booking prediction. One very useful feature of TensorFlow is the object recognition function. This comes handy for AirBnB developers, who, with the help of TensorFlow, were able to categorize all the photos on the platform. Airbnb features more than 5,000,000 different homes and hundreds of millions of photos of them. TensorFlow trained the model, which helped them tag the images properly into categories, like the living room, garage, kitchen, etc.

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