A recap of the “AI Race”, and how it could change the way we do things.

Author: Leonard Lacson |

Blog by Jenoir International Inc

Special thanks to Leonard Lacson for this blog entry which reviews the advancement of artificial intelligence with a focus on business and other applications and his own experience with using AI tools. Leonard earned his bachelor’s degree in Information Technology from York University in Toronto, Canada. He has been working as a software developer since 2017. Leonard has completed consulting and development work for clients such as Audi, The Bank of Montreal, John Deere, and Mitel. Leonard currently works as a software engineer at a telecommunications startup based in New York City.

While recently bursting onto the “mainstream” scene,” the field of artificial intelligence has been around for quite some time with continuing improvement and application to countless real-world scenarios. Now, however, “artificial intelligence” and “machine learning” seem to be thrown around as ubiquitous buzzwords — and for good reason. AI has the potential to empower businesses — and its users — to do more.

Due to its complex nature and high cost, the initial adoption of AI and machine learning was slow and quite limited. Well-funded companies and academic institutions were often the only ones able to afford to build their own infrastructure and dedicated research teams to work on this rapidly-emerging technology. For some time, exploring AI for personal use would be rare, typically reserved only for individuals with the proper background or technical resources.

Since then, however, the adoption of AI technology by the general population has grown, primarily due to the tremendous leaps in computer processing combined with large tech companies investing in and offering several AI platforms for the masses. Businesses and their employees are now able to leverage AI tools through cloud services such as Amazon Web Services, Microsoft Azure, Google Cloud, IBM Watson, and Oracle Cloud to automate and improve their processes, reduce or eliminate bottlenecks, and help with identifying and addressing customer pain points.

Outside of enterprise AI services, there is a specific focus on AI that is currently the hot topic of conversation for the industry: Large Language Models, also known as the technology behind the latest AI chatbots. The two major players in this field are OpenAI (an AI research company backed by Microsoft), and Google. In November 2022, OpenAI released ChatGPT (which stands for Chat Generative Pre-trained Transformer) — a chatbot that outputs detailed and well-articulated answers to questions on a wide variety of topics. This chatbot went viral for its groundbreaking ability to provide answers in a conversational manner, even retaining previous prompts to keep the discussion going and make it sound as natural as possible.

The popularity and practical usage of ChatGPT meant that OpenAI (and Microsoft) are the ones paving the way for language-based artificial intelligence, which means that the way we look for and consume information could change forever. When it comes to searching for information, we have often relied on online search engines for the fastest way to get results. Google has dominated amongst all global search engines by holding over 90% market share as of mid-2022 (according to kinsta.com). With its control on the information search space at stake, it was only a matter of time until Google responded with its own product. In February 2023, Google announced its ChatGPT rival: Bard.

The news of Google’s chatbot was somewhat unexpected, with Google CEO Sundar Pichai announcing Bard the same week Microsoft was supposed to host another event on ChatGPT. The showcase of Bard’s capabilities were marked by a highly publicized “blunder” as it gave an inaccurate answer when asked about the James Webb Telescope. This mishap, combined with Microsoft’s announcement of their own search engine Bing (which comes second on the search engine market share) integrating ChatGPT, put OpenAI and Microsoft ahead of the competition, at least for now. However, this does not mean that ChatGPT has won the race. The chatbot has a few quirks of its own, such as “meltdowns” and initiating strange conversations. After all, this piece of technology — regardless of who created it — is at its infancy and training the platform to be perfect will take time, if at all possible.

Aside from ChatGPT and Bard, there is another type of AI tool that has gained traction over the past year. AI Image Generators is a technology that is now directly affecting the creative community. Major advancements in language models meant that users can now type in “prompts” and receive detailed images as an output. The only requirement from the user is to be as descriptive as possible with the prompt and wait for the AI generator to output multiple image options, based on the description. DALL-E, Midjourney, and Stable Diffusion are three of the more popular AI Image Generators currently available.

These AI image generators have caused a variety of debates online, some of which relate to whether or not “AI art is real art” due to the fact that no creative work needs to be done to generate the images, aside from the description on what the image should look like as provided by the user. It is now possible to create visuals using solely one’s imagination. This has raised pushback as the AI could only be trained and generate images based on existing data. In fact, a group of artists have filed a lawsuit for plagiarizing existing artwork without their permission. Therefore, while it is an extremely powerful tool, its impact on the art community is something to be cautious of. I have asked several artist and designer colleagues on how they felt about AI image generators, with all of them questioning the necessity of the tool; agreeing that although this could be used for generating repetitive and rather trivial design content, its potential to create any content based on a prompt offers endless possibilities. They fear that one day, once this technology is completely out of its early “trial” stages, their roles could be obsolete, which I believe is a common fear against AI regardless of one’s profession.

Much like the experience of my friends in the design space, the progression of these AI technologies has also caused concern in the tech industry. I first heard of GPT-3 back in 2020, which was 3 years into my software development career. As someone who was new in the industry, hearing about the potential to generate functioning blocks of code by solely using a prompt was terrifying. However, despite the massive improvements of these AI tools, I now see these technologies as a helping hand in my daily tasks rather than a threat. This makes the future of AI seem less intimidating.

In my career as a software engineer, there have been multiple instances where AI tools such as GitHub Copilot and ChatGPT have helped me (and others) save several hours on completing minor tasks such as writing boilerplate code and unit tests. There have even been discussions about using the professional/enterprise versions of ChatGPT to assist with daily development tasks. After all, using these tools to our advantage could help release features faster and keep up with — if not exceed — our goals as a company. Despite this, I still believe that it is normal to feel some anxiety and uncertainty wondering how this type of technology can — and will — dramatically influence or change how we work.

These current advancements are just the beginning with other big names in tech also joining the AI race. Amazon is partnering up with Hugging Face, a machine learning company, to develop next-generation AI models and make AI more accessible. Similarly, Meta has announced their own large language model called LLaMA (Large Language Model Meta AI), aiming to make their model available to the public (in contrast to Google and OpenAI’s models, which are private). With these new players having a common goal of opening up AI to public use, it will be interesting to see whether OpenAI, Microsoft, and Google will make similar moves to accelerate the advancement of AI.

With so much attention and money being invested into this industry, the real-world applications and ramifications of AI are bound to grow quickly. It is only a matter of time until these tools completely change the “normal” way of working and living (though it could be argued that it has already begun doing so). These AI models can make our lives easier by rendering tedious tasks effortless, giving instant access to information, eliminating bottlenecks, and streamline business processes. But they are also creating unwanted or unintended consequences and have spawned counter measures especially in the field of education. Increased use of these tools will no doubt assist with identifying problems and “training” the models, though the concern over use by bad actors remains and calls for regulation become more insistent.

While there is no doubt that many of these issues will continue to pre-occupy the world of technology and beyond, it will be interesting to see what these tools will be capable of in a year’s time. Will the “AI race” between big tech companies help drive the adoption of AI by businesses, and will it benefit the wider population? I am looking forward to providing thoughts on these questions in due course!

Leonard Lacson holds a bachelor’s degree in information technology and has been working as a software engineer since 2017. He currently works at a telecommunications startup based in New York City.

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