Introduction
Since the launch of Open AI’s ChatGPT in late 2022, the terms “artificial intelligence” and “machine learning” have been lodged at the very forefront of business and marketing news. With an explosion of AI platforms such as Google’s Bard and Midjourney, creators, business leaders, and marketers have been scrambling to figure out how AI affects their businesses, brands, and even their careers and livelihoods.
Although artificial intelligence seems as though it has come out of nowhere and is now everywhere, it has been around for a long time. You may be using and interacting with AI on a daily basis and are completely unaware. AI may appear complicated and intimidating, but it can offer a ton of benefits for brands and marketers and it’s not as complicated as it may appear. Of course, no innovation is without its risks, but with the right people, tools, and understanding, artificial intelligence can be a game-changer for your brand and your business.
What is Artificial Intelligence?
AI has become the comprehensive term for any platform, tool, or application that can perform tasks that once required human input. Artificial Intelligence is a form of computer and data science that focuses on systems that can reason, learn, and act. It is often used interchangeably with machine learning (ML), which is an area of artificial intelligence that is dedicated to the development of systems, processes, and algorithms that learn and make inferences from data without being explicitly programmed to do so.
The earliest applications of AI can be mapped to as far back as the 1950s, with AI researchers developing checkers and chess programs that could play against humans. Some of the most common and accessible applications of AI are within virtual assistants, like Siri and Alexa, or using chatbots, which simulate conversations and are often used for online support and customer inquiries. These are tools that businesses are using to collect, leverage and improve consumer data, while also providing an added level of accessibility to users and consumers to answer questions and provide relevant information. This helps AI platforms and applications inform their language models, which is another form of artificial intelligence that can process and generate human language.
The Evolution of Language & Learning Models
The first iterations of language models were called natural language processing (NLP) systems, which were developed to be able to understand and process human language. The next step in NLP systems was the innovation of statistical language models, which estimate the probability of a word appearing in a sentence based on the words that appear before and after it. This was used to generate new texts, translate languages, and answer questions. Think of spell checkers and predictive text.
This led to the development of what we know today as neural network language models, which can learn more complex patterns in the data. Some of the most notable neural network language models that we use today include large language models (LLM) such as Open AI’s GPT-3, Google’s LaMDA, and Meta’s Bert and RoBERTa.
Learning Models also date their origins back to as early as the 1950s but have become incredibly more sophisticated over the decades. Learning models are more general-purpose AI models that can be used to analyze and predict data of any kind.
They can be used for tasks such as image classification, fraud detection, and natural language processing. Deep learning models are also neural networks that are typically trained on large datasets of labeled data, which means that the data has been tagged with the correct answers.
Deep learning models have been shown to be very effective at a variety of tasks, such as image classification, natural language processing, and speech recognition.
The development of learning models has had a major impact on the field of AI. Learning models have enabled AI systems to learn from data and improve their performance.
AI In Marketing
The impact of artificial intelligence in marketing has begun to become very apparent, especially with the rise of AI-powered and AI-integrated marketing platforms, like SEM Rush, Midjourney, Jasper, and ChatFuel. These platforms and many like them leverage language models to engage customers in real-time conversations, analyze consumer behavior patterns, optimize advertising campaigns, predict market trends, and even create content.
Platforms like Jasper, Bard by Google AI, and Chat GPT have showcased how AI could generate high-quality, relevant content at remarkable speeds. These AI-powered content generators used intricate algorithms and deep learning models to compose articles, blog posts, and even social media captions, freeing up marketers to focus on more strategic tasks.
AI art generators like Midjourney and DALL-E 2 have shown AI’s potential in creating visually stunning and captivating artwork. This innovative approach allowed marketers to employ AI-generated visuals for branding, advertising, and campaigns, thus broadening the creative horizons of marketing professionals.
Dangers & Implications
Leveraging artificial intelligence tools and applications into your marketing tech stack can create tremendous opportunities to increase efficiencies and improve research and outputs within marketing departments and businesses, they are not without their drawbacks.
AI language and learning models have different applications, varying sizes, data sets, & architecture that perform differently depending on the specific task and the desired accuracy and performance. These neural network models are limited to what they’ve been trained on, which means that if any of the data sets contain biases and inaccuracies, then the generative results will reflect them.
Human guidance and oversight is still crucial if you are considering integrating AI-powered tools, applications and platforms into your business practices, but with proper guidance, strategy, and application, AI can offer businesses and marketers with a tremendous competitive advantage.
What’s on the Horizon?
The trajectory of AI innovation, especially within marketing, continues to be one of growth and innovation. Emerging technologies, such as General-Purpose AI (GPAI), quantum computing and more sophisticated AI architecture and applications will only further marketer’s ability to create hyper-personalized customer journeys and experiences more efficiently and effectively.
As we move forward, the role of AI as a disruptive technology in marketing will undoubtedly become more impactful and profound, propelling the industry into uncharted territories of innovation and customer engagement. From its early days as experimental technology to its current role as a cornerstone of modern marketing strategies, AI has witnessed a remarkable journey, impacting how businesses connect with their audiences.
