What about AI?

My biggest concern with AI is which program to use for what thing.  I have sort of covered that here by naming Consensus as a great AI for student research because it only searches open-source, peer-reviewed journals.  Alternatively, something like Gemini is great for proofreading and general information.  I used it yesterday to reduce a 3000-word story down to 2500 words to meet submission requirements.  The big caveat with something like that is that you have to proofread it afterward to make sure it still makes sense.  AI is a tool, not an answer. 

There are a lot of AI programs about there.  So, for the next few weeks I am going to look at some specific AI programs, sorted by type. Remember, these are being published in August 2025.  By September, they will be out of date.  Here is my big secret though – I used AI to get this information.  I searched the general internet for this information and could not find anywhere that discussed specific brands by feature. Gemini could and did with the prompt “Which AI programs do what tasks?”  I imagine there are a lot missed, but I did look each one up so I could provide a hyperlink. At the very least, everything I include in these next few discussions actually exists.  Additionally, I removed every program that does not have some level of free use. 

Gemini tells me there are seven basic types of AI:

1. Natural Language Processing (NLP)
2. Computer Vision (CV)
3. Generative AI (Art, Music, Code, etc.)
4. Data Analysis and Prediction
5. Robotics and Automation
6. Healthcare Applications
7. Financial Modeling

Over the next few weeks, we will take an in-depth look at each of these, with a heavy emphasis on programs that will most likely help with schoolwork.  We will start next week with Natural Language Processing, the biggest group.

When Chat GPT broke over the world three years ago, I did not know anything more about AI than any other ordinary person.  I did know that it was important and it could seriously change the world as we know it. I also immediately saw its potential for students to use it to do their homework for them.  That is the primary thing I have been studying and learning since the beginning.  There are a lot of really useful ways for everyone to use AI.  Students have to remember, though, that they are learning and sometimes you just have to do things the hard way in order to make them stick. 

One of the places I get my information is a blog called One Useful Thing by Ethan Mollick.  Earlier this month, he published a blog called Against ‘Brain Damage’ exploring how AI can help or hurt the way we think.  It had a lot of useful information and I may revisit some of the topics later, but today I want to look at a specific study.

According to Mollick, researchers “at Penn conducted an experiment at a high school in Turkey where some students were given access to GPT-4 to help with homework.  When they were told to use ChatGPT without guidance or special prompting, they ended up taking a shortcut and getting answers.  So even though students thought they learned a lot from ChatGPT's help, they actually learned less - scoring 17% worse on their final exam (compared to students who didn't use ChatGPT).”

The same study surveyed the students four months later and found students who had used Chat GPT remembered less than students who did not use AI for the same assignment.  What it boils down to is that getting the answers is not the same as finding the answers.  Do the work, do the research.  I am able to pull information from papers I wrote twenty or more years ago out of my memory because I had to learn the material as I was working on it. You can (and should) too!

Did you know that you can search Consensus with a conversational (and maybe controversial) command?  

Try this:  How does immigration impact local economies?  Group together the pros and cons.

The answer was quite a bit longer than I want to include here, but you can easily see for yourself that there are a lot of possibilities with the answer.  Most importantly, it provides the source material.

Your college experience should be about exploring a variety of points of view.  Questions like this can help you see beyond what you have experienced so far.

If you are interested in current, and possibly controversial, events, try exploring Our World in Data, which provides accurate, sourced statistics on almost every topic.  As good as Consensus is, it only provides information from peer-reviewed journals.  Most of the time that is enough, but there are a few websites in the open source world that are worthy as well.

Chain of Thought (CoT) prompting is a technique used to improve reasoning by encouraging models to break down their thought process into intermediate steps.  This method helps the model articulate its reasoning, leading to more accurate and transparent answers.  

An important reason to use chain of thought prompting is that LLMs are good at finding patterns in massive amounts of data, but they often struggle with complex reasoning tasks.  They might give the right answer by chance, but not necessarily because they understand the underlying logic

CoT prompting addresses this by guiding the LLM through the reasoning process step-by-step or using a chain.  This chain typically consists of intermediate steps, justifications, or evidence used to arrive at the final output.  We do this by providing examples that show how to break down a problem and solve it logically.

The user explicitly prompts the model to generate a step-by-step explanation or reasoning process before arriving at a final answer.

Try this example:  Solve this problem step by step. 

Problem: What is the value of 5+7+9-12? 

Response:

To solve the problem 5+7+9−12, we follow the order of operations from left to right:

1. Add 5 and 7:  5+7=12
2. Add the result (12) and 9:  12+9=21
3. Subtract 12 from the result (21):  21−12=9

Therefore, the value of 5+7+9−12 is 9.

The statement ‘solve this problem step by step’ is the most important factor in this process.

You will find this style of prompt creation to be particularly effective for conducting research.  CoT prompting builds on the principle that complex problems are often best tackled by breaking them into smaller, more manageable pieces.  We know this is true in any kind of research, so why not get the computer to help you.

You have to define what you are looking for, so some careful thought will have to go into the prompt.

Example:  Provide three subtopics for a paper on women soldiers’ direct combat contributions in the revolutionary war.

I tested a basic zero-shot prompt, women in war.  Consensus provided two pages of topics, any of which would make several good research papers.  It was a lot to sort through and AI is supposed to reduce your work.  

Next, I tried a CoT with clearly defined and detailed parameters “Three Subtopics for a Paper on Women Soldiers’ Direct Combat Contributions in the Revolutionary War”

• Women Disguised as Soldiers and Direct Battlefield Participation
  • Explore cases like Deborah Sampson, who disguised herself as a man to fight, and other women who directly engaged in combat or took up arms when needed, such as Molly Hays at the Battle of Monmouth.
• Overlapping Roles:  From Camp Followers to Combatants
  • Analyze how women’s roles as camp followers, nurses, and support staff often overlapped with direct combat activities, blurring the lines between support and soldiering in the heat of battle.
• Recognition, Memory, and Historical Marginalization
  • Examine why women’s direct combat contributions have been underrepresented in historical narratives, and how modern scholarship and movements have worked to recover and recognize these stories.

This response provides plenty of examples to start your research on as well as three legitimate sources for each topic. 

Few-shot prompting is a technique in artificial intelligence where a model is given a few examples of a task to improve its performance on similar tasks.  This can be used after a zero-shot prompt to increase specificity about your topic or generated right from the beginning.

For example:

“Regular exercise improves cardiovascular health by strengthening the heart and increasing circulation.  It also enhances mental well-being by reducing stress and boosting mood through endorphin release.  Now, describe another benefit of regular exercise.”

It is also an excellent way to find points of comparison or contrast in two or more items.

Enter the prompt statement and include the items to be analyzed in quotation marks.

Classify the sentiment of the third movie review.  Use the information from the first two examples: 

Review:  "This movie was a waste of time."  Sentiment:  Negative

Review:  "I couldn't stop laughing throughout the film!"  Sentiment: Positive

Review: "The special effects were amazing, but the plot was confusing.  "Sentiment:```?

WARNING:  This example does not work well with Consensus which looks at journal articles to find real data.  It did work well with Google Gemini, a more generic tool, which provided this response:

The sentiment of the third movie review is:  Neutral.

Another example is:

Write a funny limerick about a boy named Sue  This contains two ‘shots’ of information, boy and Sue.

A young boy named Sue, quite unique, 

Had a name that made everyone shriek. 

He'd say with a grin, 

"It's not where I've been, 

But the stories I've got, so to speak!"

You can try again, over and over until you get something you like

A tough boy named Sue, quite perplexed, 

When folks thought his gender was vexed. 

He'd just spit on the ground, 

"That's a man's name, profound! 

Don't you know Johnny Cash? Read the text!"