📨 Weekly digest: 18 2024 | AI: what are the use cases?
This time it’s different. Seriously? | AI this week in the news; use cases; for the techies
Hello friends, and welcome to the weekly digest, week 18 of 2024.
This week I share with you a thought that keeps returning to my mind: AI, what are the use cases?
No matter how good the tech is, you have to think of the use-case.
Imagine a factory worker churning out a single product with painstaking precision. That's where many AI applications are today – powerful but limited in scope. Now, picture that same factory, humming with a hundred machines, each churning out the product with the same precision. That's the power of scaling AI.
Scaling AI is critical to maximizing its impact on our businesses, and large-scale AI deployments can significantly improve efficiency, cut costs, and boost decision-making. However, challenges exist and will probably persist for a long time.
Why? We’ve had ChaGPT for the past 18 months and are still trying to figure out where that leads us.
Now what? What is it for? What’s next? Will it last?
Is it sufficient to have advanced algorithms ruling agriculture, healthcare systems, autonomous vehicles, or robots? But what if we cannot really rely on them, as for facial recognition?
Training powerful AI requires vast amounts of data and processing power, which can be expensive. Additionally, complex AI models, while powerful, can be difficult to scale efficiently. But what for? What are the use cases?
Even though we first need to ensure AI is fair and unbiased, we must build models to explain their reasoning.
But again, how much can be automated? Where is the balance between the human capacity to complete tasks and the need for automation? How do we know?
Even with advanced AI, human judgment remains crucial in defining use cases and ensuring ethical implementation.
Thinking about use cases is vital for any technology, whether AI or something that doesn’t exist yet.
However, AI's adaptability and learning capabilities introduce additional considerations when defining its purpose and ensuring responsible application.
What do you think?
If you haven't already, you can start with our workbook, Building a data-driven organization.
I am looking forward to reading your thoughts in a comment.
Happy days,
Yael et al.
🦾 AI elsewhere on the interweb
Teacher arrested, accused of using AI to falsely paint boss as racist and antisemitic on NBC News.
“The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks.” an Apple paper.
“We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 […]” a Microsoft paper.
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