Info World

PHP, long a popular server-side scripting language for web development, has dropped to its lowest position ever in the monthly Tiobe index of programming language popularity, ranking 17th in the April 2024 edition of the index.

When the Tiobe index started in 2001, PHP was about to become the standard language for building websites, said Paul Jansen, CEO of software quality services vendor Tiobe. PHP even reached the top 3 spot in the index, ranking third several times between 2006 and 2010. But as competing web development frameworks such as Ruby on Rails, Django, and React arrived in other languages, PHP’s popularity waned.

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We need to have a frank conversation about large language models (LLMs). At their core, LLMs are nothing more than sophisticated memorization machines, capable of reasonable-sounding statements, but unable to understand fundamental truth. Importantly and despite the fervent hopes of many, they are far from delivering or even prefiguring artificial general intelligence (AGI). The hype surrounding LLMs has reached stratospheric levels, fostering a misguided belief in their potential as AGI precursors.

We find ourselves at a critical juncture where the erroneous linkage between LLMs and AGI threatens to slow down—not accelerate—genuine progress in artificial intelligence. The clamor for LLMs to evolve into AGI solutions epitomizes tunnel vision at its finest. Consider the vast investments poured into training ever-larger models, yielding only marginal improvements in tasks that are not text-based. Let’s face it: LLMs are not learning how to do mathematics. Their forte lies in tackling statistical text tasks with finesse. It’s imperative that we recalibrate expectations and acknowledge that although LLMs excel in certain domains, they fall short in others.

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Just a few years ago, a prompt was something English teachers used for homework assignments, which filled up weekends and kept students inside on sunny days. Now it seems we’re all teachers, tasked with distributing perfect prompts that direct large language models to do our bidding. These prompts are also endowed with the power to ruin weekends, but it’s not the machines that are suffering.

The power of prompts can seem downright magical. We toss off a few words that approximate a human language and, voila! Back comes a nicely formatted, well-structured answer to whatever question we asked. No topic is too obscure and no fact is out of our reach. At least as long as it’s part of the training corpus and approved by the model’s shadowy controllers.

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There’s significant buzz and excitement around using AI copilots to reduce manual work, improving software developer productivity with code generators, and innovating with generative AI. The business opportunities are driving many development teams to build knowledge bases with vector databases and embed large language models (LLMs) into their applications.

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Many business processes involve communicating with external parties such as suppliers, logistics providers, insurance providers, and government institutions. Unlike internal communications, these inter-organizational communications usually have to follow various regulations and standards, and could introduce delays if one party doesn’t comply. Therefore, effective business-to-business (B2B) integrations are a key part of most digital transformation projects.

Let’s take the fictional example of an electronic equipment manufacturer named Smartware. Smartware supplies equipment for multiple retailers and depends on multiple suppliers and logistics partners for fulfilling orders. Their order processing workflow can be depicted as follows:

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