2022 is going to be a big year for data science. Let's talk about the three trends
that will emerge during this coming year. I'm Samanvay, a data scientist working in Sydney,
Australia. The first trend will be the levelling up of the online shopping experience using AI and
mixed reality. Online shopping has been steadily increasing over the past decade and it's only sped
up due to the pandemic. As we move to a culture of convenience, we all love the ease of shopping
online. As a result we crave a personalised experience to make us feel just as special
as store assistants do in physical stores. There's nothing better than receiving personalised emails
from your favourite companies with products that you will actually buy. This year we'll
see AI being used in levelling up our customer experience in all facets. One of the key uses in
this domain is providing 24/7 omni channel support to all customers across phone calls, sms, emails
and marketing campaigns. Customer support staff tend to be very expensive to hire and difficult
to find. That's why AI agents continue to be very efficient. In addition to using AI, we'll see an
increase in the use of mixed reality to provide those immersive shopping experiences especially
with the metaverse coming in. We'll see more and more physical stores popping up virtual reality
stores. Obsess is one of those platforms to make it a reality. It allows existing physical stores
to set up their own virtual stores and gives them full rein on the customization options. The next
big trend that we'll see this year is TinyML. Currently majority of ML algorithms are run
on the cloud. This is extremely intensive from an energy and resources perspective. As we move
into the future more and more of our lives will be run through IoT and small microcontroller devices.
TinyML is about using pre-trained machine learning algorithms and converting them into a low memory
form. All this ML algorithm is doing on the device is running predictions without having to train
over and over again. This can improve the speed at which you can generate these predictions
and it can happen in real time. One use of TinyML is inventory monitoring. Imagine a sensor
installed in a skateboard warehouse. Its job is to monitor the number of skateboards and alert
when the stocks are low and it might even order supplies before the stocks reach a critical level.
Another use case is forest fire detection. Imagine a drone equipped with cameras that is flying over
a forest and the drone can in real time detect whether or not there's a fire in any given area
and alert the emergency services accordingly. Hopefully from these use cases you can see
how useful TinyML will be in the future and especially this year. The final trend I'm
really excited about is AI as a service. A general trend in the AI industry is the movement toward
generalization of the most commonly used AI algorithms for the majority of use cases in
AI there exist pre-trained off-the-shelf models that can easily be adapted to a given use case
and often they can perform very well. Currently there's tools like AutoML which allows individuals
with limited engineering or machine learning experience to deploy off-the-shelf production
level AI models. Going into 2022, we'll see more products that allow you to use AI capabilities
without needing to know the underlying maths. This is part of a larger movement which is the
democratization of AI which is very important. If we want to unlock the true potential of AI
across multiple industries not to mention it is also very cost effective because this
means that companies can use off-the-shelf ai algorithms without having to hire an extensive
data science team example of ai as a service include intelligent chatbots that can simply
integrate into your website as well as nlp and computer vision apis that's it
for today if you enjoyed the video then smash that like button and i'll see you next time