Real cases usage of AI in daily life and business - Where you use it?

Surprisingly, there are still few real-world applications of AI in life and business.

Or am i wrong?

4 Likes

I use it every day in one form or another. Gemini, GPT , Stable Diffusion, etc.

I code with GPT4, I was an early tester on ChatGPT-4 and since then I didn’t go back even once to writing boilerplate code and never will. So now I do manual work only on the difficult, exciting, and complex problems which is what I enjoy the most. It helped me a lot with ADHD as well, as writing boring code used to ruin my focus / flow completely, now I just shoot it at ChatGPT and done. The only problem I ran into 2-3 months in was me getting lazy occasionally and wasting hours asking it to do a specific thing that it couldn’t do, and I could’ve easily written it in 10 minutes… I eventually learned to avoid this trap:)

5 Likes

Ok, there are my use cases:

  • Since Microsoft Copilot come, im almost stop using Google (even i though there are cases when Google is very useful, using MC is much faster) Im also using Gemini. It can solve a lot of problems for me…

  • Coding. Im using GPT-4 to write a code a lot. I still have to do a lot of coding work, but with GPT-4 its MUCH more effective and faster.

  • Writing a texts for websites and social platforms. I still have to do a lot of work here (basically currate), but since you can fed assistant with a lot of source/inspiration text, its much more effective.

  • Brainstorming ideas

  • Image creations/Video creations

1 Like
  • Even the OG term “Googling” something has been using AI for many years. So if you ever solved a problem using a Google result, you were using AI. [Machine Learning, Natural Language Processing]
  • If you ever solved a problem finding a relevant video on YouTube, you used AI. [Machine Learning]
  • If you’ve recently wanted to know the weather in the next few days, you’ve used AI. [Machine Learning]
  • If you have recently received a package with USPS, UPS, FedEx, AI likely helped get it there. [Machine Learning, Optimization]
  • If you’ve ever ordered DoorDash or Uber, you’ve used AI. [Machine Learning]
  • If you ever unlocked your phone just by looking at it, you’ve used AI. [Computer Vision, Machine Learning]
  • If you’ve ever used a home security system with a camera and get notifications, you’ve used AI. [Computer Vision, Machine Learning]
  • If you’ve ever used Bixby, Cortana, Siri, Alexa…you used AI. [Natural Language Processing, Machine Learning]
    The list could go on and on…the point is, it’s more places than it isn’t. When people say I’m not ever going to get wrapped up in that AI non-sense…well, that’s a rather quite hard thing to do.
1 Like

IMHO, there’s a lot of AI already deployed, but there’s also a long way to go. Development and commercialisation takes time, we could continue for decades deploying just the tech we have today - forget AGI or any future advances!

Coding and writing: I use Copilot autocomplete in VSCode on a daily basis. It’s amazing, even in plain text. Other Copilot features less so, I find them a bit hit-and-miss, so I supplement with ChatGPT.

Research: I’m eager to use Perplexity more in my research, when I’m looking for reference data or information that needs citation.

Learning new fields: I turn to GPT4 when I need to quickly get up to speed on a field. For example, if I need to support a pipe at 1200°C somehow, ChatGPT tells me I’m looking for a ‘silicon nitride bearing’. Or, when I’m researching a new industrial process, GPT4 can tell me the basic principles and history to get me started.

Data Analytics: GPT4 + Code Interpreter can do pretty much anything here. ‘Draw me a graph of X, rescale it, overlay Y, fit it to a curve…’ - anything Excel or a Jupyter notebook can do, ChatGPT can do (slightly more error-prone but much quicker and easier!)

2 Likes

This discussion is incredibly pertinent and one of the main reasons I was drawn to Wes Roth’s YouTube channel and subsequently to this forum. My journey into the AI domain has been fueled by a desire to unearth practical applications of AI in business and evaluate how they can be implemented effectively. Despite the expanding landscape of AI, much of what I’ve encountered feels like a repackaging of existing models, reminiscent of the app store’s redundancy.

Within the film industry, where I spend a significant portion of my career, there are intriguing instances of AI use, such as the application of tools like Scriptbook and Cinelytic for script analysis and financial forecasting. Surprisingly, these innovations seem to fly under the radar, with few in the industry aware of their existence or potential.

The real estate sector and syndication business, where I also engage, present a different set of challenges and opportunities for AI integration. While some property management systems employ AI, their applications feel rudimentary and don’t significantly enhance market analysis, underwriting, or deal flow processes. This gap is equally evident in CRM systems within the syndication space.

My exploration of AI extends beyond these industries. I am currently leveraging ChatGPT for proofreading and structuring a book I’m writing. Without any prior coding experience, I’ve delved into coding through ChatGPT and experimented with photo recognition tasks, such as identifying specific objects within images.

Additionally, I’ve utilized Runway 2 and Lumen 5 for creating Standard Operating Procedure videos and found OtterAI invaluable for transcribing Zoom meetings. One of my goals is to discover AI tools capable of conducting blueprint analysis for construction projects, including cost and labor estimates within specified time frames, which is crucial for my role in film set construction budgeting and management.

Perhaps my challenges in finding more impactful AI applications are due to looking in the wrong places or a misunderstanding of the current AI landscape. Nevertheless, the journey continues, and I remain hopeful for breakthroughs that can transform these sectors.

  1. Aggregate relevant statistical information to form an informed decision (I highly recommend Perplexity|Opus for this).

  2. Brainstorm ideas: Explain the context to GPT-4 and request a list of seven ideas. Some of them will likely be excellent.

  3. Tone adjustment and grammar correction for emails/presentations - As a non-native English speaker, I find GPT-4 to be an order of magnitude better than Grammarly

  4. Translation: GPT-4 is an order of magnitude better than Google Translate. When comparing the results of the translations to assessments by native speakers, most did not recognize that it was AI-generated.

  5. Overcoming writer’s block in academic writing: Many researchers, myself included, find it difficult to start writing a complex article or review. Explaining the context to GPT-4 and providing it with several other article PDFs from your lab can produce an excellent skeleton to start writing from. I cannot emphasize enough how much this helps those with a tendency to procrastinate.

  6. Learning new topics by generating AI mind maps, using Whimsical diagrams, for example. I used to make these manually years ago - a very effective technique but time-consuming. Now, I can produce these in a fraction of the time, significantly enhancing my learning speed.

  7. Describing health symptoms to GPT-4 to get an educated opinion on whether you should be concerned, and if so, what kind of specialist you should consult. Very helpful for a casual first assessment.

  8. If you aspire to be an investor but lack a business background, feeding GPT-4 the earnings reports PDFs of prospective companies can provide a decent value analysis of the stock, offering insights about growth and risks for the business.

  9. Obtaining fair and educated price estimations for different gig works.