Would be naive to think that it’s just our personal lives that are dramatically enhanced by the recent progress in artificial intelligence. Technology is changing the way we interact with the world and no question that machine learning will definitely change how our nine-to-fives look like in a few years.
As a founding partner at a venture studio we embrace these trends and try to help startups we work with to focus on the future rather than how it works today, since the technology we create will definitely create new opportunities and markets tomorrow.
And the best way to be prepared for the change is to understand what is driving it and which trends are the most distinguished in a current strive for technological singularity.
More and more big companies are trying to outsource their processes to machines or at least break them apart into pieces that can be automated, which may be a major concern to an unskilled layer of workers but can also be a stimulus to spend those saved costs on creating more qualified specialists in the missing areas instead.
The part that we still don’t get is how machines make certain decisions, even Google engineers are out of touch how their algorithms work now thanks to the complexity of a given system, which is a serious concern and a stopping point for us to completely trust AI.
To clear up this issue Defense Advanced Research Projects Agency (DARPA) recently funded a research to make new a kind of artificial intelligence algorithms that can properly explain themselves and their conclusions, something similar to what we see in a Westworld TV show.
And what’s a better way to know something for a human being than ask someone directly? This is a technology that has been creeping it’s way to the solution for many years and seems now we are finally reaching the state when it becomes usable to some extent.
The booming cloud infrastructure of corporate giants is becoming widely available for anyone to experiment with machine learning for almost nothing, things that took researchers years and millions of dollars back in 2005 cost almost nothing now. And this opportunity is open to anyone, which creates a huge pool of homegrown talent driving more and more accomplishments in the field.
One of the main breakthroughs recently was a mainstream application of deep learning as a method to discover more intimate relationships between undiscovered in advance aspects of data, to the extent of eliminating human teacher from the equation.
This is how Google Translate tool accidentally created it’s own language that describes all human languages in a single feature field, which makes it super efficient at translating any language.
The key to building intelligent machines is to have a proper data on it to learn, without that we are stuck to building dumb machines that can only play checkers.
That’s why more and more companies are becoming focused on data harvesting, especially in the major work processes, to craft better algorithms that can actually improve those.
And most of the time this aspect heavily relies on human actions as an input, which again proves the fact that humans won’t be majorly extinct from the day to day operations for a while.
We are not fully aware but for the last few years our digital presence we had a glimpse of how controlled environment may look like, where all our actions somehow influence our further actions or actions of others through algorithms.
This is one of the major trends that keeps accelerating us to a state where we hardly even have to make a decision but just execute. Taking on-demand economy (Uber, Postmates etc) as an example perfectly illustrates that machines already tell us where to go, when to go, how much to pay and even what to eat.
We’ve seen signs of that once companies like Amazon started recommending products to buy or Facebook suggesting whom to friend, but now it’s getting even more substantial to our day to day lives and future.
The promise of creating a truly smart machine or a niche specific intelligence that can disrupt the industry is definitely an exciting venture to start and be a part of.
As a startup studio we are seeing an exponential growth of new startups aiming to revolutionize how we build intelligent machines and how we communicate with them. To which availability of funding makes it possible to actually build and take their solutions to the market. And of course, investors are inclined to take part in a truly disruptive venture leading the artificial intelligence to real industry applications.