How To Accelerate Software Development With Generative AI

In the span of just a few years, generative AI has transformed how organizations build products, create content and resolve problems. The majority of business and technology leaders using GenAI are focusing on efficiency and cost-effectiveness gains, according to a 2024 Deloitte survey, with 91% of respondents reporting that they “expect generative AI to improve their organization’s productivity.”

It’s no replacement for human expertise and execution, but it can take the lead on mundane, repetitive tasks, allowing teams to dedicate more time and energy toward critical thinking, problem-solving and collaboration.

By using GenAI tools on my own development team, I’ve discovered that they are most valuable in two key applications.

-Code suggestion and autocompletion: AI can analyze developers' code as they work, automatically generating recommendations for code snippets or complete functions based on context and input.

-Code analysis and bug detection: Generative AI can quickly review code to detect errors or bugs early in the development process.

Strategies For Implementing AI In Software Development

To maximize the benefits of AI in software development, I recommend the following four strategies.

1-Test And Evaluate Different Tools

2-Create Better Prompts

3-Review Code Carefully

4-Protect Sensitive Data

Set realistic expectations, and use AI tools strategically and thoughtfully—in conjunction with human expertise and oversight—to deliver software solutions more efficiently than ever before.

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The Evolution of Intelligence

The expert consensus is that human-like machine intelligence is still a distant prospect, with only a 50-50 chance that it could emerge by 2059. We’ve partnered with VERSES for the final entry in our AI Revolution Series to explore a potential roadmap to a shared or super intelligence that reduces the time required to as little as 16 years.

Active Inference and the Future of AI

The secret sauce behind this acceleration is something called active inference, a highly efficient model for cognition where beliefs are continuously updated to reduce uncertainty and increase the accuracy of predictions about how the world works. An AI built with this as its foundation would have beliefs about the world and would want to learn more about it; in other words, it would be curious. At the same time, because active inference models cognitive processes, we would be able to “see” the thought processes and rationale for any given AI decision or belief.

The 4 Stages of Artificial Intelligence

Here are the steps through which an active-inference-based intelligence could develop:

Systemic AI responds to prompts based on probabilities established during training: i.e.current state-of-the-art AI.

Sentient AI is quintessentially curious and uses experience to refine beliefs about the world.

Sophisticated AI makes plans and experiments to increase its knowledge of the world.

Sympathetic AI recognizes states of mind in others and ultimately itself.It is self-aware.

Shared or Super AI is a collective intelligence emerging from the interactions of AI and their human partners.

Stage four represents a hypothetical planetary super-intelligence that could emerge from the Spatial Web, the next evolution of the internet that unites people, places, and things.

A Thoughtful AI for the Future?

With AI already upending the way we live and work, and former tech evangelists raising red flags, it may be worth asking what kind of AI future we want? One where AI decisions are a black box, or one where AI is accountable and transparent, by design.

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