Learning AI and machine learning is one of the most important skills for the field of data science and its subsequent development in professional use. Thankfully, data science consulting has an active online community that makes it easy to access resources. Several industry experts have been pointing out how using AI and Machine learning will keep rolling into data science consulting.
However, things aren’t as easy as they look. In fact, several engineers and experts are also skeptical about the security aspects of artificial intelligence. That’s exactly what this article is to uncover. So stay tuned as you read along.
Where does AI fall in data science consulting?
According to experts, artificial intelligence in the arena of data science consulting underwriting can be utilized in two key ways. One is for the purpose of research findings, and the other is for efficiency.
The former involves a closer look at big data as well as behavior and consumer psychology. It takes into account all the collected user data patterns for the purpose of marketing or trying to get onto any early patterns that may help with the growth and development of a particular business.
On the flip side, efficiency is all about making the process of data science consulting more streamlined, time-sensitive, or simply easier to use. This can be done through the use of automation and RPA, which can take care of tedious jobs, including filing forms or other repetitive tasks that are better-taken care of by AI.
What are the disadvantages posed by the use of AI in data science?
When it comes to sensitive things like data, there are quite a few loopholes that AI has:
- Artificial intelligence is pretty much uninterpretable without constant human supervision. This is because it relies on data scraping, which means the process of discrimination is not that accurate or strong.
- Hallucinations are a major issue when it comes to any generative machine learning systems. To put it simply, AI has the tendency to make stuff up. And most of the time, this is inaccurate information.
- Security and privacy pose some of the biggest concerns for generative machine learning.
Wrapping Up
So what does that tell us? What we see is a pretty large area of development in the use of AI in a scene. While there are potentials, tehr;s also an immense amount of improvement to be made and ethical concerns to mitigate. As mentioned earlier, while there is a lot of scope for exploration as to why AI is integrated into data science is still theoretical.
So, the future of this tool in the area of data science consulting is still budding, and the only sure, tied, and tested use can be found in the field.
