The word "leverage" is defined as the exertion of force by means of a lever. When
we speak of "leveraging technology", we are referring to the use of technology to
make significant changes for a better future
Technology leverage is the ability to create increasing value with a stable or
shrinking amount of resources, while at the same time increasing the ability to evolve
at a faster rate.
We live in an era where the ever-changing digital economy is growing at an extremely rapid pace.
The key to success in the coming decade is leveraging technology to future-proof businesses. Technology has affected the culture in such a way that what made businesses successful in the past won't guarantee future success. There's no better time to embrace digital transformation than the present. For 5 years, Tech evolution has helped many companies to thrive in this digital era. Meaningful digital transformation can be achieved by democratizing innovation, integrating applications, automating processes and embracing the potential of AI. AI can be used to tackle profoundly difficult problems and find solutions that are important to human wellbeing. These developments are generating substantial economic and social benefits.
These technologies are driving a new wave of economic progress, solving some of the world's most difficult problems and providing solutions to some of the most profound challenges in human history.
As our digital world grows, devices will have to Expand until your devices can serve you totally. This won't happen overnight. But it's time to start realizing that it will happen ultimately contributing for growth and better future.
Mental Health has been one of the biggest concerns in recent years.
Its high time we had acknowledged it.
Suicide is among the top common causes of death in the world. The pervasiveness of social media and its easy access offers a new type of data for understanding the behaviour of those who take their own lives and suggest new possibilities in its prevention.
MY TAKE ON IT:- We can use Natural Language Processing and Machine Learning to find areas of frequent mishappenings and could set up a system to estimate the risk. The creation of such a system would depend on having social media data prior to the attempt. The machine learning model should examine the data to automatically extract relevant patterns for suicide risk. The algorithms could be described in such a manner that they are able to identify people at risk from the analysis of their social media posts over a period relevant enough for intervention. The method explained above is the easiest part of a meaningful impact on suicide prevention. But it addresses a larger limitation for progress.
One of the main problem surrounding this is ETHICS. The method explained above raises a question on the trade-off between privacy and prevention.
The closest analogue to these kinds of systems is the world of advertisements. Companies use the online behaviour of their consumers/customers to build/train algorithms designed to suggest products or services that a person may need/want. We could use the same data and steer the user's behaviour away from adverse health event.
CONCLUSION:- The State-of-the-art machine learning algorithms are of sufficiently high accuracy to deal with the problem mentioned. The heart of the idea revolves around our capability to weigh the aforementioned tradeoff between privacy and prevention. In my personal opinion the government, the academia, and the advocates of the medical community should come together to assure that the right individuals are benefitting from the technology that could be put to use.