Machine learning (ML) is still quite an unknown to some of us, even though most of us have benefitted from it daily, especially if you are reading this article from your devices.

However, computer engineers (myself included) are not the best at explaining. Here is my attempt to break down how ML impacts us in our daily lives from Google Research 2021 blog.

More tasks can be automated better

The tasks that we do usually have a combination of text, voice, images and videos as inputs. We, as humans, take those inputs and process them based on what we are trained to do, and as a result, produce outputs.

For example, my husband was nagging me to order food for my cat (Lady Meow Meow), as her food was running out. With technology, my husband’s voice can be converted to be sent directly to our supplier. Or even better, the image of the cat food pack looks low in stock, and the order goes out automatically.

Tasks/computation can be done quicker

Advancements are made in both hardware and software to make the tedious computation work more efficient. (I am personally most excited about this, as it was my previous research area, but it is the most un-sexy trend for most.)

Also Read: How voice AI is revolutionising the fintech scene

Technology can help us live better

Our devices can help us take better pictures and videos, have clearer communication, write better and even drive more safely. There are also other interesting applications like your ML agents waiting on hold for you or screening calls automatically.

Good news for the poor photographers among us! ML can help rectify blurry pictures and safe-keep wonderful moments with loved ones. Of course, this is just one of the many applications.

The advances in computer vision help us forecast weather more accurately and provide a rapid damage assessment after natural disasters.

From the health perspective, ML can not only see the hidden characteristics of genomics data, it can also assist with diagnosis.

By optimising routing through Google maps, we can reduce fuel consumption and carbon emissions.

Build inclusive and unbiased societies

Existing ML models can be biased towards male and Western demographics because of the data that these models are trained with. This is the same problem that many medical studies face — and biases in the medical field can lead to misdiagnosis.

I hope these are helpful for you! Please feel free to reach out if you have any questions.

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