It can make correlations, draw differences and similarities with in-depth analysis of volumes of data — it’s the next step for Big Data. For example, when sensors collect data and analyze it, AI enabled cognitive system can derive inference related to the user preferences and behaviors. This itself is the key to drive into an insightful future.
Are machines as creative as humans — well almost. Well, we have started to believe that if you can imagine something, chances are that Artificial Intelligence can help you achieve it. There are a lot of companies which are excited about AI as it can change the future and the way they work. Is it all easy and fancy as it sounds? There are roadblocks that are stopping AI from realizing its full potential. The biggest challenge is that of data.
In the last few years, there have been some remarkable development in AI; there have been major developments in deep learning and machine learning. Deep learning is a subset of machine learning and it gives the power to systems to learn. The ability to learn, however, comes from huge amounts of clean data sets. With the data protection noose tightening, feeding data to machine learning algorithms is obviously is not as easy as it sounds. The engine to deep learning is huge amounts of data that is required to perform even the most rudimentary tasks.
The biggest concerns related to AI is that it is not regulated. People don’t feel confident about the security of their data being fed into AI software that is not required to meet any compliance or regulatory requirements.
What are the main hurdles to obstruct data from being fed into AI systems?
· IP Protection
· Access issues
Enterprises face the challenge, even if they have the data, to turn it into something that can be consumed by AI systems.
What are some of the characteristics that data must have to be useful?
· Format and structure
Let’s discuss some of these characteristics in details. We’re discussing only the most important ones.
Data quality is a major issue. Machines need to be fed high-quality data to learn to make accurate decisions based on the existing data. The data quality projects are still done manually and therefore there are more chances of errors. There is a lot of time and energy that is wasted to improve data quality.
Data timeliness is a challenge. If your company receives data periodically, it may not be necessarily in a form that can be instantaneously used by AI system. If you need usable data, it must be harmonized in the desired format. If there’s a lot of time spent on cleaning and harmonizing data, there’s a lot of time that is lost and data becomes less and less relevant.
The challenges are getting bigger with regulations getting stricter. Enterprises need to have a strategy to manage changing requirements of regulations like GDPR. They must ensure that they meet are compliant and storing data securely.
Data analytics is complicated and there’s a dearth of data scientists. Additionally, machine learning and data mining requires multidisciplinary skills like knowledge of Python and Java, statistics, software engineering, and in-depth knowledge of platforms for advanced analytics, one of them being Hadoop.
Bringing data together is tedious. Data resides in multiple systems and lives in silos. The stakeholders who own data need to integrate their systems so that it can work seamlessly on a real-time basis. At times, it makes sense to involve an AI partner who can get the required stakeholder buy in.
To effectively and successfully implement AI empowered solution, you need a team of AI experts. They must drive the full value of AI implementation with a complete knowledge of overcoming the challenges that impede it. If you wish to chat with some of AI experts at ISHIR, you can click here.
Source: Deep Learning on Medium