Introduction to AI:
From its humble beginnings in the 1950s to the remarkable advancements we witness today, Artificial Intelligence (AI) has grown into an awe-inspiring realm of innovation and research. Over the years, the progression of Artificial Intelligence has continually provided opportunities for innovation, and one of the most exciting developments has been the recent emergence of large language generative models like “Chat GPT.” These sophisticated models, built upon massive datasets and powered by state-of-the-art deep learning techniques, have unlocked an unprecedented level of natural language understanding and generation capabilities that we have never seen in the current age of computational systems.
The implications of this breakthrough extend far beyond the realm of research laboratories as it has swiftly permeated various industries, forever changing how we interact with machines and revolutionizing our approach to problem-solving.
A Brief History of Artificial Intelligence:
The term “Artificial Intelligence” refers to the simulation of human intelligence in machines, particularly computer systems. The developmental journey of AI is widely cited as having begun in 1956 with John McCarthy’s project “Logic Theorist,” which became the first computer program capable of proving certain mathematical theorems. This first introduced the idea of machines with the potential to rapidly grow in intelligence and eventually surpass the capabilities of humans.
The subsequent decades saw what was labeled as the first “AI boom,” with massive waves of enthusiasm that boosted funding across the industry and propelled many of the technological advancements we still use today. However, the road to a more advanced Artificial Intelligence would prove to be more difficult than originally anticipated. Faced with limitations in computer processing power and memory storage, the development of AI went through multiple extended periods of “AI Winters,” years in which enthusiasm and funding waned.
It wasn’t until the late 1990s when advancements in computational power and the access to “big data” through the internet would set the stage for the next renaissance in AI development.
Understanding Generative AI:
Artificial Intelligence is an extremely broad field that includes a wide array of applications, including Machine Learning, Natural Language Processing (NLP), Deep Learning, and Expert Systems. Although there are already many AI applications that are a part of our daily lives, the recent buzz has been about the development of large-scale generative models such as the ever-popular ChatGPT.
“GPT” models (Generative Pre-trained Transformer) broke through with the advent of “transformers” in 2017, marking a significant turning point in the development of generative models and their potential applications across industries. Transformers are a groundbreaking AI innovation pioneered by Google, revolutionizing the way large language models were trained, enabling them to understand the context and meaning within sentences, paragraphs, and even entire stories. The introduction of “transformers” into AI systems has allowed the models to leverage various AI applications in unison, enabling them to take in data, interpret it effectively, and generate original responses with impressive accuracy.
These recent innovations have sparked new excitement in the world of Artificial Intelligence, potentially marking the next “AI boom.” Although history has shown difficult periods in the development towards an advanced Artificial Intelligence, ChatGPT, at its core, embodies the remarkable ability of machines to create, synthesize, and produce content that mirrors the creative essence of human intelligence. Though still constrained by its limitations, it does appear to be a significant step in the direction of AI efficacy.
Text-based models like ChatGPT are currently at the forefront of AI’s latest advancements, and, while all these capabilities sound intriguing, can they be used today to make our lives more efficient? To gain a real understanding of how we can utilize this tool today, let us go through an example in which we ask ChatGPT to explain its most useful current applications.
Certainly! Here are the five most useful applications of ChatGPT:
- Customer Support and Service: ChatGPT can be deployed as a virtual customer support agent, offering instant and personalized assistance to customers. It can handle frequently asked questions, resolve issues, and provide helpful information, leading to improved customer satisfaction and reducing the workload on human support agents.
- Content Creation and Marketing: ChatGPT can be a valuable tool for generating high-quality content for blogs, articles, social media posts, and marketing materials. It can help content creators with writer’s block, provide fresh ideas, and speed up the content creation process, ensuring consistent and engaging content for various platforms.
- Language Translation: ChatGPT’s multilingual capabilities make it an excellent choice for language translation tasks. It can quickly and accurately translate text from one language to another, making it beneficial for individuals and businesses dealing with international audiences.
- Programming Assistance: Developers and programmers can utilize ChatGPT for code autocompletion, offering helpful suggestions and code snippets based on the context. This makes it a useful assistant for writing code and learning new programming languages.
- Educational Support: ChatGPT can be integrated into e-learning platforms to provide personalized tutoring and explanations on various subjects. It can act as a virtual tutor, helping students with homework, providing explanations, and guiding them through complex topics.
As you can see, ChatGPT was able to understand our question and quickly provide a thoughtful response that precisely met our requirements. This is where text based GPT models thrive, showcasing their ability to do this across a wide array of complex topics while maintaining a conversational format that allows the user to continuously engage the model using natural language. To illustrate further, we will provide one more example of how we continued to prompt GPT in the form of a natural conversation. This time, we asked it to elaborate on its own response, specifically focusing on point number 5, to provide us with some ideas on how GPT can assist underprivileged children who lack educational resources.
Expanding on how GPT can help kids with inadequate levels of education, here are some ideas on how this powerful language model can be leveraged in the education sector to provide support and assistance:
- Personalized Tutoring: GPT can act as a virtual tutor, offering personalized educational support to students with inadequate access to quality education. It can adapt its responses and explanations based on individual learning styles and pace, providing a tailored learning experience to help bridge knowledge gaps.
- Interactive Learning Materials: Developers can create interactive learning materials powered by GPT, such as educational games and quizzes. These engaging and interactive resources can make learning more enjoyable and effective for students who may not have access to traditional educational resources.
- Homework and Assignment Help: GPT can assist students with their homework and assignments by providing explanations, solving problems, and offering guidance. This support can be particularly valuable for students who lack access to experienced educators or educational resources.
- Language Learning Support: For students in non-English-speaking regions, GPT can aid in language learning by offering translations, vocabulary practice, and conversational practice. This can empower students to improve their language skills and expand their opportunities.
- Academic Guidance and Career Counseling: GPT can offer academic guidance and career counseling to students who may not have access to dedicated counselors. By providing information about various career paths, educational opportunities, and academic planning, GPT can assist students in making informed decisions about their future.
Once again, ChatGPT demonstrated its ability to comprehend our query and deliver a response that precisely addressed our requirements. This brief example merely scratches the surface of these models’ capabilities, but we hope it exemplifies the rudimentary level of accessibility that we have to this powerful Artificial Intelligence tool.
Staying Aware of the Risks:
As the world continues to embrace the mass adoption of generative Artificial Intelligence, it is essential to be vigilant about the risks that come along with these powerful models. One of the primary concerns is the potential for “Model Hallucinations.” These AI language models, including ChatGPT, are trained on vast datasets collected from the internet, which may contain inaccurate, biased, or misleading information. Consequently, the AI model might inadvertently generate responses that appear confident but are factually incorrect or misleading.
While language models have made tremendous strides in understanding context and generating coherent text, they still lack true comprehension and critical thinking abilities, making them susceptible to producing plausible sounding yet erroneous content. The dark side of this powerful technology lies in its potential use by cybercriminals to enhance their malicious activities. AI offers cybercriminals new and more accessible ways to automate attacks, evade traditional security measures, and launch sophisticated phishing campaigns with increased precision.
It remains vitally important, as technology continues to evolve, to stay aware of any new potential risks that arise with it and be sure to keep up to date on all the current cybersecurity best practices.
Stock Market Frenzy:
As with previous “boom” periods in AI development, those at the forefront have seen significant boosts in funding and have been looking to capitalize on every bit of excitement. So far, the tech giants have led the way, with the likes of Apple (AAPL), Microsoft (MSFT), and Google (GOOG) on the consumer side racing to implement GPT models across their product lines, hoping to increase efficiencies and drive more revenue. On the other side, perhaps the biggest winner so far has been the suppliers of the computer chips that power these models. The market has rewarded Nvidia (NVDA) the most at the time of this writing in August 2023; we have seen its stock price jump over 300% since last October.
For an extended period now, we have seen the bulk of returns go to the benefit of the tech companies advantaged by being the hardware and software creators, often referred to as the ‘picks and shovels’ providers. Yet, a compelling argument emerges regarding the long-term effects of major technological revolutions, such as the ongoing AI boom. It is possible these advancements, like previous technological innovations, lead to a major boost in worker productivity that can be scaled across the market and have massive implications for the earnings of companies outside the tech arena. For so long, we have watched the tech companies reap the benefits, but historically that has not always been the case. The dynamic history of technological advancements has shown the overall market’s improving ability to quickly move into a state of mass adoption. Once innovative technologies are implemented across industries, they can help scale the productive capabilities of companies, leading to deflationary periods that are often offset by boosts in economic growth and corporate profitability.
As we contemplate the current AI boom, the prospects of sustained corporate profitability certainly come to the forefront. However, we must acknowledge the dual nature of this transformation. While it could undoubtedly lead to prolonged corporate prosperity, there remains a palpable risk of upheaval within the labor market and the emergence of unintended consequences—potentially catalyzing a backlash that, in turn, might trigger consequential shifts in governmental policies. This emphasizes the overall importance of being informed on the current state of technological advancements, as they will continue to present a crossroads of both promising potential and cautious concern.
In the financial sector, the impact of AI models cannot be underestimated, as they have already been embraced by some of the largest financial institutions, hedge funds, and investment firms for their extraordinary capabilities to quickly analyze vast amounts of unstructured data in real time. The flexibility of these models is the reason for much of the excitement as it enables their use across a diverse set of industries and is setting the stage for exciting innovations. There are even talks in the healthcare care industry about the possibility of speeding up new drug discovery by analyzing molecules and protein data more efficiently.
These developments have raised many questions in a year already full of uncertainty in the markets; but one thing we do know is that OpenAI’s ChatGPT has created a tech frenzy unlike anything we have experienced in years. Will productivity improve? And will profits follow? The market certainly thinks this is a game-changer. We’ll see.
Despite the awe-inspiring progress and real-world impacts we have witnessed so far, the developmental journey of AI remains ongoing. As we continue to delve deeper into the potential of generative models and other AI technologies, we can anticipate new waves of excitement and, undoubtedly, a plethora of challenges that will keep pushing the boundaries of what is possible.
This most recent “AI boom” has already been felt throughout the markets and across the world. Here at Verum, we remain optimistic about the development of Artificial Intelligence and believe the technology’s capacity to augment human capabilities and solve complex problems offers immense potential for societal progress. However, we also recognize the importance of closely monitoring the ever-changing environment to stay informed on all the potential risks as they arise. Looking ahead, we see an exciting road for AI, filled with endless possibilities and opportunities to make a positive impact. If we proceed with principles of fostering collaboration, embracing transparency, and continuous learning, we can help pave the way for a future where AI is not just a powerful technology but also a force for good.