Generative AI is a collaborator, not a replacement
A cold open or teaser action sequence, like in a film or TV, is as follows:
As the world struggles with rising healthcare costs, a new method of skin cancer diagnosis promises to make treatment cheaper and more effective. Artificial Intelligence (AI) has revolutionized many aspects of our lives, from how we drive cars to how we diagnose diseases. A recent project by Duc Haba demonstrates the potential for AI to diagnose skin cancer using Deep Learning. Skin cancer is one of the most expensive medical conditions in the United States, costing over eight billion dollars annually. Early detection can significantly reduce these costs as well as improve patient outcomes. This project uses two Deep Learning models to predict malignant and benign tumors and identify different skin cancers. The dataset used was provided by International Skin Imaging Collaboration (ISIC), which included images from three separate datasets.
The first model predicts between malignant and benign tumors with an impressive F1-score of 92%. The prediction result makes it compatible with a human dermatologist’s predictions. The project is for research purposes and should not use for any medical diagnoses without further review or endorsement from a professional dermatologist.
The second model identifies seven types of skin cancer, including Bowen Disease AKIEC, Basal Cell Carcinoma, Benign Keratosis-like Lesions, Dermatofibroma, Melanoma, Melanocytic Nevi, and Squamous Cell Carcinoma. Both models are available on the HuggingFace website. Users have access to a user-friendly interface that allows them to upload their photos and get results back in two delightful donut graphs summarizing predicted outcomes for each type of tumor analyzed.?
This project demonstrates how AI can provide valuable insights into complex data sets related to healthcare applications like diagnosing skin cancers. Duc Haba does an excellent job exploring this concept through his Deep Learning projects, inspiring others worldwide to use technology for good causes! Check out his work at the HuggingFace website (https://huggingface.co/spaces/duchaba/skin_cancer_diagnose) if you’re interested in learning more about this exciting research!
Is the above article amazingly well written? After considering that a Generative AI writes the summarization based solely on one URL input, are you impressed now?
Welcome
Welcome new friends and fellow readers to a new article in the Demystified AI series. It is the first of many articles written with a Generative AI as a collaborator.?
Fun fact: The image above is generated by the Stable Diffusion Generative AI (Duc Haba forked version) from feeding the title of this article.
Sensational headlines such as:
“Programmers [writers, artists, managers, and so on] will be obsolete in 3 years.”
Are they true? Absolutely not. We naturally fear what we don’t know. As an AI scientist and a solution architect focused on Deep Learning, I found Generative AI a superb and non-judgemental collaborator. I am not afraid of AI, as accountants do not fear Excel spreadsheets or artists terrified of Photoshop.?
In this article, I want to dive deep into “how” I use Generative AI. Many of my colleagues and popular articles warned that Generative AI would take over your job, from writers, artists, analysts, managers, and even programmers. In particular, I will cover the following topics:
Collaborative versus replacement
Part of my paying job is researching, reading, and summarizing articles or technical papers. It is what Solution Architects do. The topic differs from project to projects, such as healthcare or banking, to specific, like GraphQL versus REST API. Thus, would Generative AI replace me in three years?
To be blunt, why hire Duc with a senior title and high pay??
The Generative AI wrote an excellent and accurate summary of the published Skin Cancer Diagnose Using Deep Learning article with the live interactive demo on HuggingFace.?
I verified that the Generative AI article is accurate and that relevant and essential concepts are included in the AI summarization. It is because I wrote and published on HuggingFace the original article.?
Thus, is it an open-and-shut case for replacement??
Figure 1.1 is from Stable Diffusion-- Generative AI (Duc forked version)-- with the input text “CFO from big corporations are drooling at the prospect of cutting costs from high-paying staff members by replacing them with Generative AI.”
Fun fact: After two dozen or more repeatedly run, the 30 images only have men as “CFO from the big corporation.” Why not women? I can sense biases in my AI model. Furthermore, our CFO does not look handsome as “Don from the Mad Men TV show.” It is a more egregious bias.?
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Not so fast. The under-the-hood answer is that it is collaborative work between a human and the AI. the “Who writes what?” section will explain it.?
Who writes what?
The Generative AI writes a beautiful and insightful summarizing article, but a human edits it. The actual process is as follows:
Thus, the true answer is that it is a collaboration between a human and three AIs.
Before we conclude, the following is the unabridged AI version (warts and all):
As the world struggles with rising healthcare costs, a new method of skin cancer diagnosis promises to make treatment cheaper and more effective. Artificial Intelligence (AI) has revolutionized many aspects of our lives, from how we drive cars to how we diagnose diseases. A recent project by Duc Haba demonstrates the potential for AI to diagnose skin cancer using Deep Learning. Skin cancer is one of the most expensive medical conditions in the United States, costing over eight billion dollars every year. Early detection can significantly reduce these costs as well as improve patient outcomes. This project uses two separate Deep Learning models to predict both malignant and benign tumors as well as identify different types of skin cancers. The dataset used was provided by International Skin Imaging Collaboration (ISIC), which included images from three separate datasets. The first model predicts between malignant and benign tumors with an impressive F1-score of 92%. This makes it compatible with a human dermatologist’s predictions but should not be used for any medical diagnoses without further review or endorsement from a professional dermatologist. The second model identifies seven different types of skin cancer including Bowen Disease AKIEC, Basal Cell Carcinoma, Benign Keratosis-like Lesions, Dermatofibroma, Melanoma ,Melanocytic Nevi and Squamous Cell Carcinoma. Both models are available on Hugging Face Space where users have access to a user friendly interface that allows them to upload their own photo and get results back in two delightful donut graphs summarizing predicted outcomes for each type of tumor analyzed. This project demonstrates how AI can provide valuable insights into complex data sets related to health care applications like diagnosing skin cancers. Duc Haba does an excellent job exploring this concept through his deep learning projects which are inspiring others around the world use technology for good causes! Be sure check out his work at Hugging Face Space if you’re interested in learning more about this exciting research!
The conclusion is as follows.
Conclusion
We have talked about collaboration versus replacement, and I have shown you step-by-step “who writes what.”
The answer has to be a collaboration between humans and AI. We should not fear what we just begin to understand. Forty years ago, accountants embraced electronic spreadsheets, and twenty years ago, artists welcomed photo editing applications like Photoshop.?
Humans [writers, artists, programmers, analysts, managers, influencers, and many others] will NOT be obsolete in the next 3 years because of Generative AI.
The last time I checked, there are plenty of jobs for accountants and artists available today. As a matter of fact, it would be difficult for you to get an accounting high-paying job if you don’t go to college and learn Microsoft Excel spreadsheets or Photoshop if you are an artist.?
The bottom line is that it would take me 8 to 32 hours of reading, researching, and writing a summary of a whitepaper or article. Using Generative AI to summarize the “Skin Cancer Diagnose Using Deep Learning” article takes 8 to 32 minutes.?
Fun fact: Writing this article takes about 6 hours spread out over 2 days. Creativity and organizing thoughts are human domains.
If any of my colleagues or my boss from YML reads this article, the answer is that it still takes me 1 to 4 working days. Duc would have more time to research deeper topics for the clients, be involved in more team-building or mentoring activities, and be happier spending more time with his family. In other words, happier workers equal a higher quality of work, faster client delivery, and increased company profit. :-)
The lesson learned is that you will thrive if you adapt and learn how to effectively use Generative AI, or you, not your job, will be obsolete.?
I am looking forward to reading your feedback. As always, I apologize for any unintentional errors. The intentional errors are mine and mine alone. :-)??
Have a great day, and I hope you enjoy reading it as much as I enjoy writing it. Please give the article a “thumbs up, like, or heart.”
#AI, #GenAI, #ML, #DucHaba
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Links to #DucHaba articles: https://www.dhirubhai.net/in/duchaba/recent-activity/articles/