Welcome to another edition of Talsco Weekly

Security: Booby Trapping IBM i.
Data: What is a Large Language AI Model? How to use AI to improve customer service.
Development: How to Review and Refactor Code with AI.
Modernization: Technical Debt Threatens Innovation.

Security

Here is a continuation of a series of blog posts from Silent Signal as they highlight how an “attacker can abuse normal user activity to escalate privileges” on the IBM i.

“Post-exploitation is a crucial element of any attack aiming for realistic objectives, so it is no surprise that the topic is extensively researched, resulting in a trove of information that defenders can rely on to design and implement countermeasures.”

This blog post shares “a technique that not only allows attackers to escalate their privileges by abusing standard permissions, but can also be useful for defenders to catch perpetrators red-handed, and to gain a better understanding of their behavior and motives.”

Data

What is a Large Language AI Model?

We are seeing a push in the IBM i community of various data warehouse technologies, and Databricks is one of many platforms.

What is Databricks?
It is a unified set of tools for building, deploying, sharing, and maintaining enterprise-grade data solutions at scale.

“In an attempt to open up its technology to a wider audience, enterprise software company Databricks has released Dolly, a large language model, and its associated training code under an open-source license.”

You might be thinking, “What is a large language AI model?“

A large language AI model is a type of artificial intelligence that is trained on vast amounts of text data to generate human-like responses to natural language inputs.

These models use deep learning algorithms to analyze and understand the patterns and structures of language, allowing them to generate coherent and contextually appropriate responses.

These models can understand and respond to natural language inputs. They have a wide range of applications, from chatbots and customer service systems to language translation and content creation.

Regarding the IBM i community, could LLM (large language AI models) be used in a customer service solution?

This article dives into some potential use cases.

How to use AI to improve customer service

“Even if you’re not using AI to improve your customers’ lives, your rivals most likely are. Here are some key ways businesses can leverage AI to build a customer service experience that inspires loyalty and delivers value both for you and for them.”

Key Takeaways:

AI can be available to all your agents, providing real-time insights that improve customer and agent experiences and the consistency of your service.
With support from the AI, the agent needs less time to find a resolution, improving the customer experience.

In Conclusion:
“It used to be that AI was used on historical data, which was useful but retroactive. More recently, it’s become possible to use AI to make real-time decisions. The next generation of AI is forward-thinking, using data to make predictions that humans can’t come up with nearly as quickly.”

Development

How to Review and Refactor Code with AI

Everywhere you turn, it seems like you run into ChatGPT or one of the many other LLMs.

It is easy to be afraid of ChatGPT but if we look at it as a tool it

“offers developers a wealth of opportunities to improve, review, fix, and even outsource code writing. Knowing how to use these large language models during the development process will soon become an essential tool in the developer’s toolkit.”

“Ever wondered how AI could transform your coding process? In this guide, we’ll demonstrate techniques for using ChatGPT or GPT-4 to review and refactor code, as well as discuss some limitations and provide handy resources for using these LLMs in your programming workflow.”

Modernization

Technical Debt Threatens Innovation

“Technical debt is a leading obstacle that impacts nearly 70% of organizations’ ability to innovate.”

What is technical debt?

It is the accumulated cost of maintaining and supporting legacy IT systems.

“On average, surveyed organizations invest more than 30% of their IT budget and devote more than 20% of their resources just to tame technical debt.”

The linked article highlights the largest talent gaps, as it relates to technical debt, that organizations face in the areas of design thinking (37%), solution architecture (37%), enterprise agility (35%) and technical knowledge (31%).

I think what is interesting here, which often does not get enough attention, is that we always think about the skills gap as it relates to technology or programming languages, but what is missed is the gap in the “new ways of thinking” that are required to modernize our legacy systems.

What do you think?

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