Artificial Intelligence has become so integrated into the technology stack that it goes largely unnoticed. When you take a picture with a newer iPhone, for instance, the phone collects a couple of frames and analyses it at the pixel level to identify what is in the scene and balance the exposure to produce a crisp, detailed final picture.
All this happens in a fraction of the time it took you to read that sentence.
Outside of turning you into a better photographer, AI is verifying your identity and enabling all the smart solutions that make your personal admin more convenient. Need a quick loan for your business? There’s an AI engine doing an instant credit assessment to approve your application in minutes.
“When NGA first started playing with data, we made a decision to ensure that our models used ethical AI,” explains NGA CEO Mark Germishuys.
1. Ethical AI
Ethical AI is a catch-all considerations that helps eliminate bias and minimise discrimination by constantly improving the data sets used to train machine learning tools, as well as improve data handling and privacy.
On the AI bias side companies like Goldman Sachs and Amazon have faced major reputational damage for relying on AI tools that actively discriminated along gender lines when granting credit and filling vacancies.
But the company damage of choosing the wrong AI solution can be financial too, with all the funds spent on research and development evaporating with the failed project.
“Data needs controls because there’s more to it than simply scraping data,” Germishuys continues. “One of the biggest problems we see globally is false positives. Our SocialListener product is trained to look at context because it analyses sentiment and negative keywords. This helps deliver a clean data set that is free of the noise.”
2. Data anonymity
Data anonymity is another major concern for companies that are forced to lean increasingly on AI tools. Since the Protection of Personal Information Act (POPIA) came into effect in 2020 the associated limitations around data gathering have compromised legacy lead generation techniques.
NGA gained an early advantage by basing its AI on public domain data. The company’s RiskSecure solution monitors transaction behaviour in the absence of attached identity to gain a more reliable understanding of fraudulent activity patterns.
This pattern matching can then be scaled to larger data sets and serve as a prediction tool that can dynamically flag similar behaviour.
“Our sentiment score can predict market trends because we have the entire history of the internet to trawl for patterns. And these patterns do repeat themselves,” he explains. “We also have the capacity to support our products because we’ve built them from scratch using revenue we generated from other parts of the business and not impatient venture capital.”
3. Market readiness
Market readiness is a big problem Germishuys sees in the AI industry, with many vendors overpromising customer benefits and then underdelivering on the unrealistic expectations. “Don’t lie to clients about readiness, just get the MVP (minimum viable product) out,” he says.
NGA is committed to delivering excellence and working alongside customers to create unique solutions that meet their needs. The company has become a global leader in AI development because it considers international AI firms as its competition.
It can be cheaper for local clients or regional offices of multinational corporations to buy off-the-shelf from major international technology vendors, hence the NGA global approach.
Business owners need to be mindful of the state of AI technology and how they can leverage these tools to improve productivity and efficiency. These tools are becoming essential to compete in the marketplace and finding the right technology partner could be the difference between success and failure.