AI Researcher

When it comes to recent major influences on education, there are few things as prominent as generative AI chatbots. Comprising tools like ChatGPT and Google’s Bard, generative AI has swept through schools across the country at an unprecedented rate. Their ability to find solutions to problems and create written works that can seemingly bypass plagiarism checkers (N.B. there are now AI-powered tools that can detect AI-generated work) has forced curriculums to rapidly evolve with this new “AI revolution.” It’s understandable, then, that the terms “ChatGPT” and “AI helper” have had a suboptimal reputation among teachers and instructors. But ironically enough, many educators have themselves begun to use AI tools, and for good reason as well! Many tasks required by instructors are quite tedious. From grading multiple choice answers to writing trivial emails, these duties are often interpreted as time-consuming and detract from the time that could be spent communicating with students and designing quality lesson plans. The former can be especially helpful, as much of the time, grading can be little more than a brain-numbing tedium. And given the customizable nature of AI tools, they serve as the perfect solution, especially for groups like world language instructors.

Before jumping into applications, it is first important to understand the basics of why AI tools in particular are so powerful. Unlike traditional modes of technological tools, modern AI models implement what is known as a neural network, a complex, multilayer set of connections between nodes (or “neurons”) whose nonlinearity can theoretically help identify almost any type of pattern or connection. These networks offer a sense of malleability to AI models, one which can enable the model to fit any mold required by the user. This allows for customization. For instance, if teachers properly query ChatGPT for particular grading help (using something called a “prompt”), the AI chatbot could, in theory, perform whatever grading task was asked.

So why is this particularly useful for language teachers? Well, unlike for other subjects, automatically grading language assignments often requires multiple tools for multiple levels of processing: first would have to translate the text into something the particular program would understand (e.g., English for programs designed around the English language), and only then could it perform the grading task at hand. Generative AI condenses this into one step. Chatbots like ChatGPT are trained on information scraped from all over the internet. As a result, users can expect these models to understand any language well documented online. So, the chatbot can effectively interpret the language in its native language, and depending on the prompt, output something in the same language or an entirely different one.

Michael Cai

AI as a Foreign Language Assignment Grader

LowTech AI Head of Business Development

I’ve engaged in countless conversations with individuals eager to understand what Generative AI is and how they can be using it, yet before our conversation, they hadn’t found the right point to dive in. I wanted to write an article unraveling the concept of Generative AI and illustrating how to utilize it in your lives and careers. 

Most people have been told “AI is extremely powerful”, yet still don’t know how to use it in their daily lives. By explaining what Generative AI is and how you can harness it, I believe that anyone reading this article can be made better off by taking the leap and beginning to use Generative AI.

AI, or Artificial Intelligence, is the creation of systems that can perform tasks requiring human intelligence such as learning, understanding language, recognizing patterns, problem-solving, and decision making. Generative AI is a subset of AI that has become prevalent in the past 12 months. Popularized by systems such as Chat GPT, Generative AI is AI that can generate new pieces of content. That’s it – GenAI is simply a way for computers to generate sentences and ideas that may not have existed beforehand.

Clyde Anderson

How the average person can be utilizing AI today

Diving into Gen AI

Creator of LowTech AI

Today in Cambridge MA, we brought together a brilliant group of MIT MBA and PhD students to learn about AI prompting and build tools for LowTech AI. In our short two hour event, we generated a ton of unique and useful tools applicable to work, academics, and life (

some, as expected, were entertaining but not particularly useful

). It was our first prompting event of many to come, and we couldn’t happier about the outcome.

This prompt-a-thon was a competitive event with $100 in cash prizes up for grabs! 

Jack O'Brien

LowTech AI hosted it's first prompt-a-thon!

Recap: Prompt-a-thon v0

MIT MBA

The average length of the workday has declined steadily since 1880. A Newsweek survey reports that 3 out of 4 “Gen Z” employees believe we should be working less than 6 hours per day in a 4 day work-week, in comparison to the current standard of 8 hours per day in a 5-day work week. Tim Ferriss took it a step further with his 2007 book “The 4-Hour Workweek”, which spent 4 weeks on various bestseller lists and sold over 2 million copies worldwide.

I read this book this past summer, and it had a profound impact on my outlook on life and work. Ferriss covers how he used company automation and intentional lifestyle design to win his life back, going from 14-hour high-stress days to 4 hours of work per week, all with increased outcomes for his companies. As much as I loved his book, the life he described and some of his action items felt unattainable to someone like me who lives a very structured “big corporation” life. Thankfully it’s now 2023, and AI can make this dream life accessible to everyone.

How AI can be used to adopt Tim Ferriss’s principles

Priya Sarkar

The Four Hour Work Week With AI

If you’ve encountered text written by a computer in the past year (you definitely have), it was probably generated by a large language model, AKA an LLM.

LLMs are the AI technology behind Gmail autocomplete, ChatGPT, and 

, but what does that term actually mean? As these systems get more integrated with our work and lives, it’s worthwhile to understand the basics. Here’s the absolute bare necessities you should know.

First off, let’s go through the meaning of each word in “Large Language Models.”


If you do work that’s on a computer, LLMs will be a part of your life. I think it's important to learn the basics.

What’s a Large Language Model?

Over the past ten weeks, we’ve explored the applications of AI in the legal field and dove into the specifics of how one may leverage AI tools to expedite and streamline legal work.

AI will continue reshaping industries and open new doors in legal work, transforming how legal professionals handle their work. Staying updated and informed in this technological revolution is of utmost importance. To that end, here are 10 useful prompts one may use on generative AI tools for legal work.

Wonju Lee

Top 10 Prompts for Legal Professionals

In this article, we will review several legal professional-specific artificial intelligence tools. Artificial intelligence has single-handedly transformed legal professional work and continues to improve across multiple platforms.

With the development of the state-of-the-art language model ChatGPT by OpenAI, other AI tools have been designed with various machine learning architectures. The current GPT 4 model utilizes the transformer model, widely accepted as a robust natural language processing architecture from the paper “Attention is All You Need,” published in 2017. The transformer architecture utilizes the concept of self-attention, which has proven to excel in processing sequences, particularly natural languages, and is adopted in other architectures, such as long short-term memory (LSTM) networks. Further explaining such is beyond the scope of this article, though it is important to note that technical innovations in ML training and inference have and will continue to evolve.

How can legal professions, including lawyers and paralegals, choose the right AI tools for legal practices?

Elevate Your Legal Practice: Picking the Right AI Legal Tool

The dawn of artificial intelligence has ushered in numerous tools and techniques to enhance every facet of our lives. Over the past ten weeks, we delved deep into the world of AI, exploring how it dovetails with the realm of fitness. Let's take a moment to count down ten of the best tools we've covered, offering readers actionable steps to apply AI to their fitness routines. The tools are ranked in no particular order, but have been divided into 3 categories:

: tools that you can experiment with for interesting results; may not always be accurate or useful.

: these tools can be used for a vast range of purposes beyond fitness, you can modify them to use however you want.

: These tools take your input and give you something really unique and applicable.

Rhiaan Jhaveri

Counting down 10 of the best AI tools for fitness. The last article in our 10-week series.

The 10 Best AI Tools for Fitness

In an era where AI is reshaping industries, marketers can't afford to miss out. AI is opening new doors in marketing, from content creation to data analysis. Staying updated is crucial. To that end, I created a simple set of 10 user-friendly tools to help marketers leverage AI effectively.

These prompts streamline tasks like content creation, SEO optimization, and trend analysis. No technical know-how required – just input and output.

Whether you're a seasoned prompt engineer or new to AI, this toolkit empowers you to harness AI's potential. Dive in and embrace the future of marketing.

Professional Copywriting for Successful Marketing

Danyal Jawed

Unlock the Power of AI with this Marketing Toolkit

AI Researcher, CS Teacher

Entrepreneurship is a vast pursuit that requires much more than ten artificial intelligence prompts for success. With its complexity and multifaceted nature, starting a company requires hard work, dedication, and perseverance for success.

Nevertheless, throughout the history of humanity, we as a species have been particularly adept at inventing tools that assist us to tackle challenges far greater than we could tackle on our own. Fast forward to 2023, humans have developed the newest tool in our arsenal: generative AI.

Matthew Collins

These are the best 10 prompts you need to help you create a startup

Top 10 Prompts for Startup Founders

Over the past year, Open AI’s revolutionary generative AI chatbot, ChatGPT, undoubtedly took the world by storm. In less than a week, the chatbot had acquired a remarkable 1 million users. By January 2023, it was estimated that ChatGPT had 100 million monthly active users, setting the record for the fastest-growing user base. Following the massive success of ChatGPT and Open AI, which was now backed by 10 billion dollars worth of investments from Microsoft, we began to see the emergence of several AI tools, namely Google’s Bard, which looked to compete with ChatGPT. In a matter of days, artificial intelligence, prompt engineering, and machine learning quickly became popular topics to discuss in many fields as people began to acknowledge AI’s potential, and it’s only a matter of time before these tools make their way to the workplace.

For several weeks, we’ve covered how AI tools could be implemented to assist video game developers in various aspects of their careers. However, the possibilities with AI are limitless and, at times, overwhelming, making it difficult for those new to or unaware of the concept to grasp and utilize its capabilities thoroughly. After all, AI can do much more than summarize text and answer questions – it can spearhead communication and learning, coding and developing, marketing and data analysis. It can even help you land a job as a video game developer! That’s why we’re shifting gears for the final week and covering a list of prompts that could be used to kickstart your career or journey in the field of video game development.

Before we start diving into each of the prompts in-depth, here’s a list of all the prompts covered in this article, so you can immediately jump right in and start playing around with the endless capabilities of artificial intelligence. Note that this list isn’t exhaustive, and these prompts aren’t arranged in any particular ranking or order. And, as always, feel free to fork any of the prompts listed here to alter their functionality to meet your specific needs!


Waseem Ahmad

Kickstart your AI journey with our hand-picked AI tools for video game developers!

Chloe Yang

Explore how AI is revolutionizing communication in mental health and neurodiversity. From doctor-patient interactions to workplace dynamics, discover how AI-driven insights and personalized messages are transforming support and understanding.

Unlocking the Potential of AI for Improved Communication

In today's fast-paced digital era, creating a strong web presence is extremely important for marketing success. With millions of websites competing for visibility and user attention, Search Engine Optimization (SEO) has become a crucial aspect of digital marketing strategies. A well-executed SEO approach can significantly impact a brand's online presence, website traffic, and overall business success.

Search engines, like Google, regularly update their algorithms to deliver more relevant and valuable results to users. As a result, marketers must stay agile and adaptive in their SEO practices to maintain high rankings and engage their target audience effectively. In this dynamic environment, understanding the importance of SEO and how it fits into the broader marketing picture is paramount for businesses seeking online success.

By understanding the vital role of SEO, marketers can leverage its potential to effectively reach their audiences, boost organic traffic, and achieve their broader business objectives.

Increase Your Web Presence With AI

The 21st century has seen a recent boom in AI-related products and discoveries. From new transformer-centered model architectures to Dall-E to ChatGPT, new innovations are now rapidly expanding the horizon of what is possible for artificial intelligence and its applications. Recently, natural language processing (commonly referred to as NLP) has been in the forefront of this so-called “AI revolution,” and for good reason too. NLP products and services (ChatGPT and other generative AI chatbots, for instance) have brought to many people’s attention the practical, everyday uses AI can have. Moreover, creations centered around NLP have fundamentally altered — and will more likely than not continue to alter — many fields, particularly the field of education.

Although almost every educational subject would benefit a great amount from these innovations in NLP, this article will focus on applications in world language education. This is because NLP and current generative AI technologies pair very well with themes present in foreign language learning. The process of learning a language strikes a good balance between black-and-white, almost “binary” logic and fluid writing which offers more leeway: firstly, a main aspect of picking up a new language is applying logical rules (such as grammatical laws) that have a relatively well-defined set of correct and incorrect answers. Indeed, this aspect is similar to qualities present in math, science, and other STEM subjects. That said, the language classroom rarely explores the nitty gritty of logical relations present within these fields, ones that can be quite complex and may result in glaring issues for an AI model not trained to solve a specific problem. Language learning offers leeway in this sense because within the context of fluid writing, logical errors (which are bound to happen for any complex and nuanced subject) would be less detrimental.

Though AI technology has definitely impacted language learning, technologies like Quizlet or Duolingo have made large ripples in the field prior to any mass-scale wave of AI innovation. So, then, what does the future look like?

What developments in AI mean for Language Education

Security has been an issue for growing companies and professionals in finance for a long time now. And as the Internet continues to dominate the world, cyber security has become a more dominant issue for companies, especially financial firms, to deal with. In the context of financial security, generative AI has begun to be used for cyber security purposes, particularly through models such as generative adversarial networks. In the context of financial security, the application of this technology has transcended the conventional paradigms, introducing new methodologies to ensure integrity and safety. With the emergence of generative AI, there are various applications, techniques, and challenges associated with using AI for financial security.

Utilizing generative models, financial institutions are now able to simulate fraudulent activities by creating synthetic data that mirrors real-world financial transactions. This, in combination with machine learning techniques such as deep learning and random forest algorithms, provides a multidimensional approach to fraud detection. The combination between generative AI and traditional machine learning augments real-time identification of fraudulent patterns, ensuring proactive measures.

2. Risk Management and Vulnerability Assessment

Hagop Alajajian

How AI changes the game in ensuring financial and cyber security

A New Frontier in Financial Security Using AI

Transforming STEM Education: Harnessing AI for Personalized Student Learning

With the rapid advancements in Artificial Intelligence (AI), teachers have the opportunity to revolutionize the way they deliver personalized education to students. AI-driven tools and techniques can empower STEM teachers to cater to the individual needs of their students, making learning more engaging, effective, and inclusive. In this article, we explore some concrete examples of how STEM teachers can leverage AI to create personalized learning experiences for their students.

AI-driven adaptive learning platforms are designed to adjust the learning experience according to each student's unique strengths, weaknesses, and learning pace. These platforms use machine learning algorithms to analyze students' performance and behavior patterns to identify areas that need improvement and recommend personalized learning pathways.


Umar Farman Samdani

Harnessing AI for Personalized Student Learning

Ever since the release of show-stopping innovations like Open AI’s new generative AI chatbot, ChatGPT, or the company’s original artwork generator, Dall-E, artificial intelligence has been on everybody’s minds. Machine learning is still a relatively young and emerging field, and most of these mainstream technologies are all fairly new, so it’s no surprise that people have begun to gravitate towards this AI trend — after all, it shows a lot of potential. As a result, from cooking to writing essays, there have been countless AI-related topics online resources have covered, each one offering ways to use AI to its maximum potential. A vast majority of these resources concern AI prompting, the act of engineering queries to feed into AI chatbots like ChatGPT.

Now, prompting can be used in an infinite number of ways. This article covers a small pool of those methods, specifically how the act can be incorporated into language learning. Language learning is a field that AI is bound to greatly impact. In fact, apps like Duolingo have already incorporated some AI-based elements. AI and languages indeed go hand-in-hand. This is because the bulk of language learning stems from memorizing and being extremely comfortable with a set of well-defined rules. Programs like AI are quite good at identifying patterns — and in turn, rules — so they naturally excel at language learning and language translation (in fact, the field of natural language processing — teaching computers how to understand natural human languages — now mainly concerns machine learning). With this in mind, here are some of the best prompting tools to use when navigating the potentially daunting task of learning a new language from scratch.

This first section encapsulates the tools that are used in more niche scenarios; they may be powerful, but not necessarily common. When it comes to a ranking structure, these would comprise the lower five tools.

Top 10 Prompts for Learning Languages

AI is a rapidly evolving field that's transforming a multitude of industries, including the investing realm. One key aspect of AI development is the concept of fine-tuning, a technique that allows models to learn from small, specialized datasets by building on the knowledge acquired from larger, general-purpose ones. In other words, fine-tuning allows a pre-trained AI model to learn and adapt to a more specialized task, even when the available data for the specific task is limited. In the realm of investing, fine-tuning has the potential to revolutionize traditional approaches, providing finance professionals with advanced tools to assess risk, identify opportunities, and enhance decision-making. Essentially, fine-tuning helps produce more specific and desired outputs when using generative AI.  


Some models used for fine tuning are davinci-003 and davinci. Text-davinci-003, a modern and proficient model, is specially created for tasks that require adherence to instructions. Its design empowers it to provide concise and precise responses, even when no example prompts are presented (known as zero-shot scenarios). In contrast, davinci is highly effective when fine-tuned for a specific task, given the availability of several hundred training instances. Among other models, these can be used to help determine the most suitable model for investment-related tasks. Fine tuning these models further enhances their performance and tailors them to the specific needs of investment firms.


Fine-tuning, at its core, is a transfer learning technique. Click 

 to use generative AI to get a recommendation if fine-tuning is right for your task. Fine-tuning involves taking a pre-trained AI model, usually developed and trained on a large and diverse dataset, and adapting or 'fine-tuning' it for a specific task. This is achieved by continuing the training process on a smaller, task-specific dataset. The concept significantly reduces the need for extensive computational resources and time – two vital assets that are traditionally expended in the initial training process of an AI model. The technique leverages the general knowledge the model has acquired to rapidly learn the specificities of the new task. Moreover, fine-tuning allows the application of AI in scenarios with limited data availability, which is a common occurrence in specialized fields like investment news.

How to apply fine-tuning and generative AI to investment practices

A Comprehensive Insight into Fine-Tuning for Finance

In this article, we will be covering a subspace of legal practice: trials. A trial is a form of legal proceeding typically held in courtrooms presided over by a judge or jury, depending on the case, where the facts of a case are provided, and after deliberation, a decision is made whether the defendant is guilty of the charges. Trial stages include jury selection, opening statements, cross-examination of witnesses, presentation of evidence, and more. Legal professionals, including lawyers and prosecutors, must be well prepared for their cases ahead of time.

Artificial intelligence tools are revolutionizing this process. Generative AI tools like Chat GPT have emerged as powerful tools that can empower lawyers by providing suggestions for legal argument drafting, evidence, analysis, and trial strategies. We will dive into several applications of AI in aiding lawyers with trial practice, though the prompts and examples presented in this article are merely the tip of the iceberg.

How can legal professions, including lawyers and paralegals, utilize generative AI for trial practice?

AI in the Courtroom: Enhancing Trial Strategies for Lawyers

Welcome back to this weekly series on AI. As we advance further in our exploration, we shift our gaze this week to the exhilarating field of sports analysis. In this edition, we will delve into how AI, specifically LLMs, can be employed for insightful basketball analysis and generating hypothetical game predictions. Although LLMs are trained on data up until a certain time (for example, GPT 3.5 is trained up till September 2021 as of now), it can still offer us a treasure trove of insights and predictions based on past data, enabling us to theorize and speculate about a multitude of intriguing scenarios.

This week, our journey with AI takes us courtside as we investigate three key areas—individual player performance analysis, team performance analysis, and hypothetical game predictions. Each area promises to unlock new ways of appreciating the complexity and strategic depth of basketball, harnessing the potential of AI to enhance our understanding of the sport.

To illustrate these concepts, we will use specific prompts that you, as a sports analyst, spectator, or enthusiast, can deploy to engage with AI for comprehensive basketball analysis. You will be guided step by step, just like in 

, and will be shown how to interpret chatbot responses, with examples peppered throughout the article.

Decoding AI and Hypothetical Predictions in Basketball

Understanding the interface between AI and basketball involves delving into the intricacies of predictive analysis. How do AI models like ChatGPT make these predictions, you may wonder? Unlike real-time prediction systems that utilize live data, ChatGPT relies on historical data up to 2021 to create hypothetical scenarios. It's like a seasoned basketball pundit, with years of experience and knowledge, who uses this extensive background to speculate on various situations. However, unlike human pundits, ChatGPT's predictions are devoid of personal biases, making them purely data-driven. While it can't predict real-world future outcomes, it can give us a theoretical play-by-play based on historical information.

Learning how AI can be used to interpret stats and make predictions in major sports

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