Being an avid learner, community member, and mentor in this esteemed community, I am excited to share my perspectives on the “AI for Everyone” course.
A few years back, when this course was introduced, I enrolled in it, right after completing the Deep Learning specialization. This course significantly enhanced my comprehension of AI concepts, which proved invaluable as I delved deeper into my learning journey.
Beyond academic enrichment, this course also played a pivotal role in my professional growth. It empowered me to address numerous inquiries about AI, thereby enriching my career journey. Engaging more within this community and evolving as an AI aficionado has been a rewarding experience.
It’s vital to note that while learning is essential, and advancing in your career is crucial, staying updated with the latest in AI technology is equally important. Dedicating time to reading about new AI technologies and keeping abreast of emerging advancements in the field is imperative.
I trust you will find this course to be a remarkable starting point on your journey with AI.
I actually think this module is really helpful since I am thinking about the business diligence for my work, it could give me that kind of perspectives.
Thanks for sharing your perspective having finished the course. I am in week 3 of the course and was just reflecting about what I miss about learning in this manner as opposed to learning alongside other students at the same pace - I don’t get the chance to test my assumptions. I make assumptions or links between my existing knowledge and the knowledge in the course material and if those links are incorrect I could utterly fail to comprehend the learning…
So I was just wondering if I could float my assumption/reflection to this thread/the community… that AI at this point in time isn’t so much “artificial intelligence” as computer programming by exception - I still see the “if this, then that”, input a to result b, mechanisms at play that I learned in basic programming. An artificial neural network comprises multiples of inputs where their outputs may interlink in order to ultimately give the result, but every step of that process is still human engineered. “AI” is really just that we are using computers to provide better suggestions based on given data, or to complete repetitive tasks we might previously have had a human do every time (or if not complete, then process a greater volume of the data more efficiently than individual humans might do - eg in manufacturing, “AI” might do image recognition for quality control up to 80%, with fewer humans still employed to check for false positives beyond the scope of the AI).
Am I very wrong, do I just need to carry on with the course?
Opinion: The state of the art in AI is not yet at the level of its own hype.
The recent boom in generative AI (i.e. large language models, automatic image generation) has swamped the normal progress of development.
What’s classic “machine learning” is that we can create a general architecture (i.e. a neural network), feed it some data, and it can create a model that is useful for making predictions. Feed it a different data set, and you get different model that is suitable for a different purpose.
To me that’s more than computer programming, but it’s not “AI” either.