There are a lot of reasons to be excited about what AI has to offer. Increasing digitization, breakthroughs in AI, and advances in computing have led an ever-increasing interest in deriving value from huge volumes of data.
The hype around AI has produced many myths which prevail from mainstream media to board meetings. The believers imagine robots like R2D2 and C3PO to be the future. The cynics treat it as just a buzzword or at best a glamorous persona to automate your mundane tasks.
The truth is somewhere in the middle. In today’s day and age, it is essential to separate the reality from the myths.
In 1950, Alan Turing put forward a question: “Can machines think?” and presented the Turing Test – a deceptively simple method to determine if a machine can demonstrate human intelligence. If a machine can engage in a conversation with a human without being detected as a machine, it has demonstrated human intelligence. The Turing Test was quite clever because there was no need to define “intelligence” – which itself is the most intriguing question!
Beating the Turing Test has remained elusive for AI systems. Meanwhile, the mechanism by which AI solutions work is still mysterious to many business leaders investigating the potential of AI.
There is a big difference in knowing what AI has to offer and actually implementing AI to create business value. AI technology is now at a point where solutions for many real-world problems are forthcoming.
However, while an increasing amount of data is getting collected, it remains mostly unmined and un-monetized. Organizations either do not know what and how much data to collect, or they stop at analytical observations. Then they often struggle to use these observations to make smart decisions that can create business value.
Successful AI initiatives depend on the effectiveness of translating the theory into practice. You can harness the true power of AI by separating myth from reality and creating an AI strategy that best suits your preferences and constraints.
Over the next several weeks, we will publish a series of blog posts to present our experiences in unraveling this mystery, deriving insights from data, and turning AI theory into practice. They’re intended to enlighten readers as well as give them a perspective into how our offerings are built. This series of blog posts will appear once a week, written by different members of our top-notch team of data scientists.
Here are some of the topics we’ll explore over the next several weeks:
1. The Law of Large Numbers
2. Moore’s Law
3. Small Data
4. The Four V’s of Big Data
5. The Curse (and Cure) of Dimensionality
6. The Cognitive Quotient – Quantifying Machine Intelligence
If you’re interested in any other topics relating to artificial intelligence, data science, and their uses in the enterprise, let us know. We may be able to address your question!
