Many individuals and companies get impressed when they see the results of artificial intelligence, especially in the light of machine learning (or deep learning to be particular). You can’t dispute this fact, as recently, the news of artificial intelligence began to dominate social media and the media dramatically.
An organizational debt
Unfortunately, a considerable amount of these news articles are not even remotely accurate. Still, modern artificial intelligence represents an undeniable revolution that affects trade, governance, and many areas of great importance. Companies and government bodies must be prepared to harness the power of the technology.
Many enterprises have the authentic intention to integrate and introduce artificial intelligence as an essential element in their core business. However, the reality is that many companies are currently incapable of doing so due to many reasons, which we will cover here.
Physical documents are your boss
In the world of deep learning, intelligent and predictive models with a high degree of accuracy are based on large amounts of data that feed modern algorithms (the most famous of which are neural networks). It is unfortunate that many “reputable” enterprises do not even believe in storing data digitally to begin with. Tangible physical papers are standard all around in these enterprises, so giving up might not be easy. The culture that cares about data and other key elements is missing; AI can’t function properly without that.
Data analysis (big data specifically) requires a specialized infrastructure capable of storage and efficient processing. Startups may have much easier experiences at the beginning when adopting AI, unlike large and mature enterprises and organizations, where they’ll need a lot of time and effort to digitize their data as a crucial step; In order to benefit from it in the world of artificial intelligence, institutional maturity has a significant role in accelerating the readiness of these institutions to adopt artificial intelligence.
Garbage in, garbage out
Modern prediction models (built using deep learning) rely mainly on the quality of data fed into neural networks, so it is strange that so many people and companies expect to get great and accurate models when they’re fine making significant compromises in data quality.
Dear reader, remember that data has a story to tell. In order to be able to deal with the massive amount of data and understand the embedded stories, you will need the proper expertise and talent capable of carrying out these challenging tasks. True talent is the primary means of dealing with data, so do not forget that.
Data, talent, and its ever-evolving infrastructure are the key pillars for implementing and applying artificial intelligence in the best capacity. As a conclusion that touches reality, remember, dear reader, that no data, no artificial intelligence.