1.
“It is estimated that the technology firms are
likely to invest $47 billion on Artificial
Intelligence and research by 2020.”
– Wall Street Journal

2.
Here are some AI applications that we can
witness in 2019

3.
AI Chips

4.
An AI chip does the heavy lifting during functionalities
like ML-based voice assistance, AI-powered smart camera
and more.
Tech-giants like Google, Microsoft, Amazon and Intel are
also investing heavily to boost AI-based functionalities.

5.
AI & Internet of Things

6.
AI and ML with IoT can be used in capturing data from cars using sensors
and using the collected data to decide on an insurance amount.
Things are about to change in 2019 with the emergence of AI chips, as
cheaper and newer cloud-based AI services and bigger companies are
looking to invest in IoT and AI.

7.
DevOps

8.
Functions that are performed by DevOps personnel can be
automated, such as monitoring, debugging, scaling and more.
It is speculated that DevOps will slowly give way to AIOps, and
this will make things easier for coders.

9.
Healthcare

10.
Healthcare has evolved over the years. Artificial Intelligence and
Machine Learning has brought in more changes than just the
rise of personalized medicines.
China has recently claimed of creating the first gene-edited
babies. The tool used is called CRISPR-CAS9, can operate on
DNA to supply a required gene or disable the one causing
disease or illness.

11.
Social credit system

12.
China has come up with a Machine Learning application, a
ranking system for monitoring behavior of all the citizens
using surveillance cameras and online activity, to rank
them based on their “social credit“.
It is supposed to be going on floors by 2020.

13.
Surveillance

14.
Computer vision and surveillance have become a
necessity in places like malls, airports or even in city
crossroads.
Tech companies have been quietly implementing their
software in multiple places around the world so as to
spot, track and report situations when necessary.

15.
Automated Machine Learning

16.
Automated Machine Learning refers to solving repetitive tasks
through automation by applying ML to ML itself.
For instance, cleaning data before applying different algorithms
and seeing which algorithm fits the data best. These can involve
use of AutoML and its more disruptive usage.

17.
Personalized Medicines

18.
Personalized Medicines are recommended to patients based on
their lifestyles and daily habits. Better analysis can be done with
Machine Learning.
With the help of the 100,000 Genomes Project, Personalized
Medicines are expected to see big growth in 2019, even though
they might not be available publicly yet.

19.
Machine Learning-based Assistants

20.
Voice assistants like Alexa gather a lot of data from
users and are growing much more intelligent and
human-like.
Voice assistants are supposed to move on from
your living room to public places, cars,
announcement systems, ATMs, and more.

21.
Better metrics for AI machines

22.
Fixed schedules of industrial equipment and machines result in labor
wastage and the risk of sudden and unexpected equipment failures from
time to time.
When sensors are used with these machines and the data collected is fed
to ML models, it can yield better performance and more efficient
servicing schedules.
An implementation of the following is GE’s creation of “Digital Twins”
where a virtual model is built for big machines.