Skip to main content

The seven dimensions of AI

(Image credit: Image Credit: Razum / Shutterstock)

Research shows that 40 per cent of companies claiming to use artificial intelligence (AI) lack tangible evidence of its application. Without true understanding or legitimate practice the web of confusion in this field grows more and more complex, thanks to a range of buzzwords in the mix including machine learning, data mining, and deep learning.

And inevitably, with confusion comes underperformance. To fully extract the benefits of AI – and therefore the total value of this technology – companies must learn what components make up an effective AI solution. It is crucial for businesses to understand that one underperforming area of an AI pipeline can undermine the total influence and value of the respective solution entirely.

The seven dimensions of AI

Proper understanding of AI and ensuring its accurate execution is especially important among start up businesses. Those that are labelled as being “in the field of AI” can attract 15-50 per cent more in their funding rounds than other tech companies, demonstrating the power of its significance.

The challenge for start-ups is their inherent nascency, especially considering that AI directly thrives from the extent of data it collates and is exposed to. In the same way that a child grows smarter the older they get, and the more they learn; algorithms also rely on experience – namely being exposed to large volumes of data and incremental rounds of improving – to continually evolve and deliver accurate results.

Larger companies including Microsoft are trying to set an example in this realm, and have subsequently been looking at new ways to use AI to keep up the momentum of surging profits. And while not every tech business has the scope of Microsoft, there are seven dimensions of AI that all companies in the field should recognise and explore in order to achieve the same-targeted levels of power, ethics, and trust:

Data freshness

In an increasingly fast-paced world, data can become outdated and, as such, less useful quickly. Successful AI therefore needs an algorithm that is fed with real-time signals and is able to adapt quickly. In the best case, the result is the algorithm’s ability to adapt to its environment almost immediately.

Data volume

AI must span across the whole ecosystem in question, and should be able to crunch vast amounts of data signals in order to capture the patterns driving success in the finest grain.

Data diversity

An algorithm must be trained from as many dimensions and inputs as possible. Much like the importance of a sufficient volume of data, diverse sets of data ensure more powerful and broad-based decision-making capabilities as it captures the bigger picture.

Data quality

It sounds like a given, but quality takes precedence over both volume and diversity in a world prevalent with fraudulent activity, inaccurate reporting, and misinformation. The significance of authentic traffic, high-quality data and, AI being trained according to an industry’s best practice, is critical.

Data capacity

While accessing a significant volume of data is vital, the infrastructure, technological sophistication, and expertise in handling those volumes is equally as important. Investing in advanced facilitation technologies to provide the necessary data capacity and accessibility is key from this perspective.

Data results

Encapsulating all of the above, performance and results are arguably the most important characteristic. Culminating as a contributor to – and as a consequence of – quality, diversity, capacity, volume, and freshness. If an algorithm cannot outperform a human and deliver superior results (or similar ones at significantly higher efficiency), then the whole infrastructure becomes redundant.

Algorithms

Finally, just as some human brains possess a wide range of capabilities, selecting and testing the most suitable algorithms is crucial. It is also vital to hit the optimal spot of exploration in the unknown space, and exploit already known success factors – as downstream there is only a limited window of opportunity in which to deliver maximum impact. Striving for perfect results is only successful if it can be done within the right timeframe. Therefore, the Pareto principle still rules; algorithms should be optimised to accumulate results within the relevant time window.

Exceeding all other innovations

Ultimately, adherence to each of these individual dimensions contributes to the most extensive and accurate results, which yields continuous improvements. The value is undeniable, but companies should always remember and ensure that the path chosen is a true reflection of their own ethics, values, and regulations. Subsequently, AI will deliver business results and improve the human condition at a scale that will far exceed all other innovations from the past few decades combined – however, it will take time, as adhering to above dimensions will still need human guidance, strategically and operationally. 

And, the AI revolution will not happen overnight so it is important that companies make sure they are not left behind on this evolutionary path. The evolution to artificial intelligence will depend on how businesses embrace these seven dimensions of AI within their industry and domain.

Jochen Schlosser, CSO, Adform