Let’s stipulate that you can find an article, tweet, post on generative AI almost everywhere. Everyone’s weighing in – McKinsey, Microsoft, TechTarget, Accenture The New York Times and of course OpenAI – among so many others way too numerous to mention here.
It’s a reporting frenzy, for sure.
Is all the coverage warranted?
I asked ChatGPT “why generative AI is so important”:
“Generative AI refers to a type of artificial intelligence (AI) that is able to generate new, original content based on a set of input parameters. This type of AI has the potential to revolutionize a number of fields, including:
“Overall, generative AI has the potential to significantly impact a wide range of fields by enabling the creation of new content and ideas at a faster rate than is possible with human effort alone. This has the potential to drive innovation and progress in a variety of areas.”
I then asked: “is generative AI just another technology fad?”:
“It is difficult to predict whether generative AI will turn out to be just another technology fad or a lasting and significant technological advancement. Generative AI, which refers to a type of artificial intelligence (AI) that is able to generate new, original content based on a set of input parameters, has the potential to revolutionize a number of fields, including the creative industries, product design, medical research, and marketing and advertising.
“However, it is important to note that generative AI is still a relatively new and rapidly evolving field, and it is likely that the capabilities of these systems will continue to improve over time. It is also possible that generative AI may face challenges or limitations that could limit its widespread adoption and impact.
“Overall, it is difficult to predict the long-term future of generative AI. It is important for businesses and organizations to stay up-to-date with developments in the field and to carefully consider the potential benefits and risks of adopting this technology.”
Machine Learning Vs Generative AI
I asked ChatGPT: “what’s the difference between machine learning and generative AI?”
“Machine learning is a method of training artificial intelligence (AI) systems to perform tasks by exposing them to data and allowing them to learn from it. It involves training a model on a dataset and then using the trained model to make predictions or decisions based on new inputs. Machine learning can be supervised, in which the model is trained with labeled data and the desired output is provided, or unsupervised, in which the model is not given any labeled data and must find patterns and relationships in the data on its own.
“Generative AI is a type of AI that is focused on generating new content, such as text, images, or audio. It does this by learning the patterns and characteristics of a particular type of data, and then using that knowledge to create new, similar examples. Generative AI can be trained using various machine learning techniques, such as neural networks or probabilistic models.
“One key difference between machine learning and generative AI is that the latter is specifically designed to generate new content, whereas machine learning can be used for a wide range of tasks, including prediction, classification, and optimization. Additionally, generative AI often relies on unsupervised learning, as it is generating content that may not have a clear “correct” output.”
Why Should We Care?
“Why should we care about generative AI?”
“There are several reasons why generative AI may be of interest or importance:
“Overall, generative AI has the potential to bring significant benefits and advances in a wide range of fields and industries. However, it is also important to consider the potential risks and ethical implications of this technology, and to ensure that it is developed and used responsibly.”
There wasn’t much for me to do here.
What do you think?
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