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If Levity creates an artificial intelligence DIY boom, we’ll all be laughing

Low-code systems allow anyone to have a go at artificial intelligence, but will that allow any hacker to have a go at your insecurities?

Yext uses artificial intelligence (AI) to help companies with their digital transformation in response to the Covid-19 lockdown. Its biggest customers are mobile phone operators such as Three and Verizon.

It helps these communications service providers (CSPs) by improving the digital experience of customers. For many mobile phone users, the customer journey often starts with a search for information about one supplier and ends with them going to a rival which provided the answers more readily.

“If they want customers to help themselves, they need to structure their information so the answers are readily available,” says Jon Buss, managing director of Europe, the Middle East and Africa (EMEA) for AI company Yext.

In other words, Yext uses a branch of AI – natural language processing (NLP) – to help CSPs to get their information in order.

There is a resurgence of the have-a-go spirit emerging among people who want to use artificial intelligence, but who don’t want to be beholden to some glorified computer programmer calling themselves a data scientist.

For this reason, the demand for digital transformation has created a huge boom in DIY AI systems, according to Gartner. In February, it predicted a surge in the use of low-code development of AI systems, which is already a $13.8bn market. And these low-code systems are so simple that even I can understand them.

Berlin-based Levity launched into the UK in January in a bid to make AI less elitist and expensive. It achieves this by making it really easy to train your machine – all you have to do is gather samples of data you want the machine to analyse, and then drop them into a folder. If you want to train your machine to recognise a disease when it sees it, show it lots of picture files of the disease.

A simple framework has streamlined the repetitive steps involved in training an AI system, says Levity CTO Thilo Hüllmann. Now anyone can collect, label and store unstructured data in Levity’s cloud. It’s as easy as dragging and dropping.

I’m preparing to train a robot dermatologist to recognise pyoderma gangrenosum by showing it lots of pictures. I haven’t yet met Dr Robo-Derm, partly because it lives in the cloud, but mostly because I haven’t got onto the next stage, which is the actual training – the tricky part.  

Building blocks to digitisation

That’s the thing with DIY. Getting started is easy. The hard bit is when it all goes wrong and you have to call in a professional. This could be like the PC LAN revolution all over again. The users threw off the shackles of the IT department, but then they had to call them back when it all got tricky to manage.

There are loads of these “no-code” systems coming onto the market. Recently, Israeli AI company Noogata got $12m to popularise AI using AI Blocks to create a no-code culture. The AI Blocks are the foundation of DIY AI.

This has got to be fantastic news for the channel. First, it enables resellers and systems integrators to add AI to their service portfolio without a massive investment. More importantly, DIY is a massive demand generator for professional services, as any builder will tell you.

It won’t be just AI services you will be providing; there will be a security boom.

According to Matias Madou, CTO and cofounder of Secure Code Warrior, the transformation from no clue to no code programming could be very dangerous for the DIY AI crowd.

In terms of digital journeys, this will be the IT version of a herd of wildebeest, migrating across the digital plains of Cyber Serengeti, while mobs of hacking hyenas and online alligators lay in wait at every crossing.

There is a direct correlation between the adoption of low-code/no-code (LCNC) development platforms and the perceived security of these frameworks, according to Madou. It’s all down to how they are developed. “One vulnerability in the framework can be used to attack all apps created on that platform,” says Madou.

Confidence can only be restored by transparency over how the LCNC framework was developed. Madou says that “hopefully” this will be done with security in mind by “security-skilled developers”. Maybe he’s right. Who knows?

It’s hard not to sympathise with professional people who want to take control of their own area of expertise, rather than put it in the hands of some uppity data scientist. The upshot is that it’ll create tons of work for the channel.

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