Everyday AI

[/fusion_title][fusion_text columns=”” column_min_width=”” column_spacing=”” rule_style=”” rule_size=”” rule_color=”” hue=”” saturation=”” lightness=”” alpha=”” content_alignment_medium=”” content_alignment_small=”” content_alignment=”” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” sticky_display=”normal,sticky” class=”” id=”” margin_top=”” margin_right=”” margin_bottom=”” margin_left=”” fusion_font_family_text_font=”” fusion_font_variant_text_font=”” font_size=”” line_height=”” letter_spacing=”” text_transform=”” text_color=”” animation_type=”” animation_direction=”left” animation_color=”” animation_speed=”0.3″ animation_delay=”0″ animation_offset=””]

Everyday AI is hard to ignore. As Artificial Intelligence (AI) becomes increasingly present in our everyday lives, we can see how AI technology can be used to automate mundane tasks and to make our lives easier. We are seeing AI being used everywhere, from voice-activated home assistants, to customer service chatbots, to self-driving cars. It is no surprise, then, that software companies are now turning to Everyday AI to help automate their routines.

At NLP Logix, we are actively using AI to make our workflow more efficient. One way we are doing this is by using a product called GitHub Co-pilot. This platform helps developers automate mundane but necessary steps in the development workflow, freeing up their time to focus on more complex, logical tasks. GitHub Co-pilot also allows developers to share their code with the rest of the team, review and approve changes, and even automate the process of writing code. While Co-pilot certainly saves us time in automating things like the development of test cases and standardizing repeated patterns, it is not always accurate. In fact, it is often confidently wrong. Acting as the “human in the loop,” developers must carefully review and test code that has been generated.

As developers continue to manually review and write code for new projects, the power of AI can be utilized through a Large Language Model (LLM). A LLM is an AI model trained on large amounts of data can help speed up the process of writing code, by answering questions better than other programming resources and giving developers the help they need quickly. Questions can be answered using the entirety of a LLM’s knowledge base or be based on a specified text (such as a help desk article). LLMs can also summarize code by reading sections and giving a human-like explanation of the steps the code is taking. This can reduce the amount of time a developer has to spend understanding the code that has been previously written.

What’s more, LLMs can also be used to edit articles and emails, meaning marketing-ready content can be produced faster and more efficiently. Given a well-written prompt, LLMs can generate content on most topics and even suggest ideas about topics for content.

The use of AI in our everyday lives is becoming increasingly common, and it is clear that AI can be used in many places that you wouldn’t expect. From automating code writing to editing and producing content, AI is proving to be an invaluable tool for software companies.

everyday ai