https://steemkr.com/science/@steeminator3000/doomsday-scenarios-skynet

Anyone who has been following me on social media would know I’m crazy about blockchain. It’s the coolest thing since sliced bread (and the internet) and as with every new and shiny things, it’s the darling of the month.

There’s also another technological trend which has been gaining quiet momentum over the years. Machine Learning, an off-shoot of Artificial Intelligence, is not a new thing. It was quite the buzz word half a decade ago but without any immediate and relevant consumer-level usage, it was relegated to the realm of tech geeks and Hollywood movie scripts.

According to a tech trend survey completed by IT professionals, machine learning will receive more funding from IT companies than blockchain will in the years leading up to 2020. This shows that machine learning is set to have an explosive decade or so in growth, possibly outshining the blockchain hype, which is still in a phase of early adaptation for funding and technology.

Machine learning is set to take off this year as industries across a wide spectrum from health care, marketing, to big data analytics are all ready to use ML technology. The interesting part is that the majority of the ML projects are still about one to two years away from being deployed. Meaning the ML marketplace is like an angry adolescent pimple that’s ripe and ready to pop (or if you’re more conventional, a volcano about to erupt). This will keep investors and consumers anxiously waiting to see if some of these highly anticipated projects will perform up to expectation. According to wired.com, VCs are already shoring up the funds for investment into Blockchain and AI projects in 2018. The race is real, people.

For the uninitiated, Machine Learning, as the name implies, is about a branch of technology that actually learns from previous performance. Think Skynet in the Arnold Schwarzenegger movie, The Terminator. Apocalyptic possibilities aside, Machine learning allows for models that are exposed to new data to independently adapt to the new data. Why is this cool?

That’s because if Machine can learn and evolve using the data presented to them, it allows users — us human beings — to be more productive and gain competitive advantage in the market, at half the time. People will lose their jobs to machines. You don’t even need ML to see that technology has already made some roles redundant.

Some companies have adopted ML like how they adopted Blockchain simply because it’s cool to say you are. Consumers who don’t know any better like the idea of being associated with a company that is on the cutting edge. Beyond the hype appeal, Machine Learning has a real purpose in helping companies realise ROI in three big ways: 1) creates more revenue 2) allows staff to work more efficiently 3) lower operating costs. The main driver for companies adopting any technology would still be about the bottomline.

This just means that actual ML research and development falls onto the shoulders of specialised tech companies developing ML solutions for the masses. Decentralized Machine Learning (DML) is one of these projects that I’m involved in that is developing a ML algorithm to study data from users to produce predictive results without the actual need to touch sensitive information. Perhaps the greatest leap that DML has made especially in the big data mining landscape is in its ability to remove the need for expensive centralised data crunching server farms and instead utilise the idle power on the users’ mobile devices to run the calculation.

It’s not so much a matter of ‘if’ but ‘when’ this product is made available to public, that we should see a massive breakthrough for Machine Learning and Blockchain technology.