How to harness AI to augment human intelligence

Posted on: Monday 13 March 2023 | Ian Forrester - Founder and CEO of DAIVID

Advertisers should embrace AI technology but also be aware that not all AI is created equal, writes Ian Forrester, Founder and CEO of DAIVID


From AI taking human jobs in the short-term, to the singularity-induced creation of super-intelligent killer machines in the long-term, seldom a day seems to pass without AI drawing public criticism. Yet, even as our alarmist media preys on humans’ most basic fears, AI is silently improving our lives one breakthrough at a time. 

German philosopher Immanuel Kant declared that there exists a world which humans cannot conceive of, one which lays beyond our level of understanding. Now the computational power of AI is providing tantalising glimpses of that world. A perfect example is a recent MIT project in which AI discovered a new antibiotic that is now successfully killing antibiotic-resistant bacteria. 

The AI succeeded by screening more than 100 million chemical compounds in a matter of days and identifying patterns that were indistinguishable to humans. Having established the existence of these patterns, human researchers are now setting about explaining them, and in so doing are expanding the human race’s understanding of molecular cell biology.  

In this way, AI and humans are acting symbiotically, with the AI unlocking doors through which human understanding can step. It is this combination of AI computing power and human rational thinking and interpretation that provides the opportunity for an AI-powered society that is massively enhanced in many fields - and advertising is no exception. 

Advertising adopted AI technology with the aim of optimising content. Several systems have been created that can ingest content and data, and determine the most effective cut of an ad; some systems even automatically cut the ad into its “optimal” version. Yet many of these systems have left advertisers feeling short-changed; complaints that the outputs lack nuance and are one-dimensional abound. 

Herein lies the challenge of AI – an AI system is only as good as the data on which it’s based, so a system trained on shallow social data or basic media metrics will only ever produce shallow, basic results. However, when the AI is trained on deeper data, the outputs can be magical. 

The moral of the story is to check what data your AI has been trained on. Do not accept vendors’ claims at face value and always be willing to dig deeper. In particular, be wary of: 

  1. Being dazzled by studies or credentials. Many AI-based techniques are backed by academic research or created by companies with academic founders. While this can certainly add knowhow and credibility to a company, AI is advancing so quickly that core characteristics of a good AI practitioner include humility to know that they don’t have all the answers and open-mindedness as regards new techniques and data sources. Any AI practitioner without these characteristics should be treated with extreme caution.
  2. Algorithms that claim to identify a universal human truth. If an AI vendor claims that all humans everywhere respond to a certain stimulus in the same way, it is very likely because it suits them commercially to make this assertion. By claiming to have discovered a universal truth, vendors actually reveal that their model has been trained on a small and/or shallow dataset, and that they are inferring that larger populations/different cultures will respond in the same way. In the real world, they very likely won’t! 
  3. Basic outputs. When an AI system can only produce basic outputs, it means the data on which it has been trained is basic. Basic data inputs lead not only to oversimplified outputs, but inaccurate ones. Once again, buyer beware!  

In my experience the best AI systems are run by teams who continually add new training data and analysis techniques to their repertoire to increase the accuracy of their predictions. 

What training data should be used? The deeper and more granular the better. Some of the most interesting data to be collected in advertising in the last five years is attention and emotions data. By ingesting this data, AI can reveal to advertisers why their content works or doesn’t, moving far beyond simple correlations to causality. Only when this holistic understanding of content performance is achieved can advertisers learn from the past and optimise strategy for the future. 

In summary, AI is creating huge breakthroughs in just about every field of human endeavour, not by replacing human intellect, but by augmenting it. Advertisers should embrace AI technology but must also beware – not all AI is created equal. To determine whether an AI system is right for you, really get to understand the data on which the AI is built. Don’t get dazzled by academic studies, be wary of algorithms that appear to unearth universal truths and walk away when presented with basic outputs.   

Person speaking

Perspective on Trust & Effectiveness

This article is part of 'Perspectives on Trust & Effectiveness', a year-long content series spotlighting our members views across eight key areas.

Written by

Ian Forrester

Founder and CEO of DAIVID

Topics

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