Over the past few years, machine leaning has started to change the way we live. It has in fact been hailed as a solution to some of the most pressing problems that the world faces today. From new medicines to making online shopping platforms more customer friendly, machine learning is the future. Tech giants like Facebook, Google, Microsoft and Apple have been investing more to beef up their machine learning interface. Even brands like Amazon have been investing an excess of $228 million every year to run its machine learning platform called Alexa. So, what it is about really?
A core of Artificial Intelligence (AI), machine learning enables computers self learn without the need for human intervention (read coding/programming). If you have recently heard about Sophia, the world first robot to get a nation’s citizenship, it’s all about machine learning. These systems continuously gather new data and grow to develop by themselves. In lay man terms, machine learning allows computers to gather information from the range of connected devices and the World Wide Web. Though the concept has been worked upon for several years now, the results have recently started to show themselves. Top examples will include the Google self-driving car, online recommendation engines, cyber fraud detection and even Netflix suggesting shows based on your past likes and favorites.
Why do you need machine learning platforms?
The above examples of machine learning platforms crucially echo the role AI systems have begun to take up in the data rich network we live beside today. Machines help filtering information and we have already seen how this technology has been transforming industries. Machine learning has eased the means of data extraction and consequent interpretation. This helps us rise from the traditional ‘trial and error’ approach of data analysis. Efficient, fast and self-learning algorithms help in real time data processing and offer more precise analysis.
As the world gets digitized, these machines’s ability to learn from the connected platforms is making businesses and services more efficient, thus also saving time and money.
Obstacles to Adopting Machine Learning Platform
While all the above is true, there still lingers an apprehension. Firstly, not everyone and every business is thoroughly convinced of the powers. This has been limiting the investment that would be necessary to help machine learning being adopted worldwide. The challenges also include:
1. Lack of Understanding and Trust:
A recent study done by Belatrix Software cites “Powering the Adoption of Machine Learning’, just 18% of companies asked if they had already started a machine learning initiative in their organizations said they had done so, 40% said that they were investigating it but hadn’t started, and 43% that they had no plans to start one at all”. Many still fear things like AI and aren’t convinced that machines are ready to take over tasks that have been traditionally managed by their human counterparts. It’s like the Judgment Day theory reverberating wherein robots take over this planet! Caution comes from even personalities like Elon Musk and Stephen Hawking. Fear aside, the world is not yet ready enough to trust technology.
2. Data Inaccessibility:
Though the world of connected devices already has a huge repository of information, these databases aren’t yet programmed to be put into use. Storage of the volume of information takes up thousands of servers scattered all over and aggregating them is a complicated, expensive and time consuming task. Encryption is also something that plays it part in this limitation. The idea of a “Digital Democracy” is yet to take prominence.
3. Data Security:
Machine learning requires every bit of information, including personal preferences and details to have an online place. However, this also creates the opportunity for cyber crimes. There have been several instances when digital transformations have backfired. Security of such sensitive data is one thing that we need to start working upon.
4. Inadequate Infrastructure:
Let’s just say, we have the concept that can change the world but still lack the infrastructure that can handle the same. It’s hard to implement the opportunities with machine learning simply because the algorithm requires a whole different set of technology that we currently have today.
5. Right Business Process:
Machine learning effective requires an agile business platform. Implementing the same would require changing not only the existing infrastructure but also the mindset.
Coming to the basics, the cost of hiring a deserving data scientist is $105,000 and this was the pay scale in 2014. Not all organizations are prepared or capable enough to pay that.
7. Slow Results:
Companies and businesses need to understand that the results of machine learning will not be immediate. There’s a learning curve to it but the expectations are quite high. Companies need to work on the infrastructure, skill set and processes for long term benefits.
Artificial Intelligence has the ability to change the way of the world. Robots can take over out work, at least the basic ones that don’t require creative thinking, as of now. However, even for that, we need to be patient, be optimistic and have a precise process in place.