A Brave New World! Machine Learning… where are we headed?

It’s not long now. We’re finally racing towards a future that we dared to dream of in the 1980s. While we don’t quite have flying cars ala Back to the Future II, we do now have driverless cars, autonomous agent personal assistants with Siri and Cortana, and can even print full buildings and even food with 3D printers.

These first two concepts are part of a range of software that involves machine learning: the software learns from the input that it’s given. We no longer have to program in all the inputs, it can actually learn via the use that it’s getting.

Not only is Siri adjusting to learn your specific voice, she is actively learning from millions of requests around the world, each and every day. Driverless cars are gathering information from their surroundings all the time. So what does this mean for us? Does it mean in the new world we won’t need programmers at all? Will software simply write itself?

Well, the answer to this, at least for the foreseeable future is no. These sorts of software are very hard to write, and require the most brilliant engineers to work on them. If your company is interested in building software that involves machine learning, you’ll need to hire extremely strategically and likely have to dedicate a lot of time and money to your project.

You’ll notice how only the world’s biggest brands are incorporating machine learning into their products. This is because currently they are the only ones who have the resources. If you are a software developer, then you would do well to focus on machine learning if you want to get noticed by top software houses and command a high salary.

The future looms large for machines to learn as they go along. You may have heard of Google projects where they can now make up their own songs and draw pictures. Although the software still isn’t brilliant, it’s passable for human construction. In 10 years, these sorts of programs will be well advanced. Will we need musicians, artists, drivers? Will we need script writers, chefs, builders?

It is important that we ponder these sorts of questions going forward. While there will always be a desire for human actors in these positions, perhaps as a nod to nostalgia, it may well be likely that it isn’t the norm. We’re likely to see whole industries transformed by clever machines and clever programs. If you thought Uber was disruptive to the taxi industry, just wait until driverless cars are used everywhere – which is why Uber have planned their own fleet, as they’ve seen human driver obsolescence on the horizon. Having your own car will become redundant.

Here are six real-life examples of how machine learning is being used right now:

Image recognition

Image recognition employs deep learning which is an advanced form of machine learning. Machine learning works by taking data as an input, applying various ML algorithms on the data to interpret it, and giving an output.

Speech recognition

Machine learning methods like deep learning and neural networks are common in advanced speech recognition software. These systems use grammar, structure, syntax and composition of audio and voice signals to process speech.

Medical diagnosis

The use of Artificial Intelligence, or AI, is growing rapidly in the medical field, especially in diagnostics and management of treatment. To date there has been a wide range of research into how AI can aid clinical decisions and enhance physicians’ judgement.

Statistical arbitrage

Statistical arbitrage is a trading strategy class that uses statistical and econometric techniques to exploit historically related financial instruments’ relative mispricings. Statistical arbitrage trading strategies still work as new instruments, exchanges, and financial markets create trading opportunities.

Predictive analytics

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modelling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.


Data extraction refers to the process of procuring data from a given source and moving it to a new context, either on-site, cloud-based, or a hybrid of both. There are various strategies employed to this end, which can be complex and are often performed manually.

Are you involved in any machine learning programs?

Perhaps looking to create your own, or learn more about it? At genesis IT we can help with finding top developer and architect talent or help to launch you in your machine learning career. Make sure to give us a call if you’d like to find out more information.