MicrosoftOnTheIssues

A great foundation in statistics, math and coding is fundamental for anyone and will always help you! Even though AI has seen a significant increase in research and utilization in the last few months, the foundation of AI is still based on math and statistics that many times is 100s of years old. Even the foundation of artificial neural networks was created in 1970s and 1980s.

I teach computer science to 5 to 10-year-old students at Global Idea School, including my own children who are 9, 7, and 5 years old 😊. One effective way to demonstrate the power of AI and develop prompt engineering skills is by generating images from prompts. However, it is crucial to first establish a solid foundation in coding. I use Code.org -- it’s an excellent learning platform (and kids as early as kindergarten can use it). Once they have developed their writing skills, I introduce them to Python. Any child who can read and write has the potential to learn coding.

Hallucination is a phenomenon that occurs in large language models, which are designed to generate coherent and contextually relevant text based on the input they receive. These models work by predicting the most likely next word or sequence of words based on patterns observed in the training data.

However, because the training data is often sourced from a wide variety of texts, some of which may contain inaccuracies, contradictions, or fictional content, the model may generate text that includes factual errors or information that is not grounded in reality.

In other words, the model may generate text that is plausible and coherent based on the patterns it has learned, but that is not necessarily factually accurate or consistent with the real world. It is important to keep in mind that large language models are not capable of true understanding or knowledge, but rather rely on statistical patterns to generate text. As a result, they may produce text that is incomplete, misleading, or outright false, and it is up to humans to critically evaluate and verify the information they generate.

Here’s a LONG answer to your question (because it’s a complicated process sometimes!):

First and foremost, we look for projects that have a significant impact on society or business. This impact can vary depending on the field, but we can often compare projects to determine which will have the greatest impact.

Second, we always partner with an organization that has subject matter expertise related to the problem we are trying to solve. This helps us better understand the problem and identify the best AI solutions.

Third, we assess whether we have access to the necessary data and whether AI is a feasible solution for the problem at hand. This involves a thorough analysis of the data and the problem to determine if AI can offer a meaningful solution.

Finally, we make sure that the partner organization is equipped to use and leverage the AI solution we develop. We work closely with them to ensure a successful knowledge transfer and to enable them to continue using the AI solution after our involvement ends.

Overall, our decision-making process for choosing which problems to solve with AI involves considering impact, partnering with subject matter experts, assessing data and feasibility, and ensuring successful knowledge transfer. By following these steps, we can determine which projects are most likely to benefit from AI and demonstrate the difference it can make.

Oooh. I’m doing some research on this one – just got to get it right, because I know people will check my work!