The launch of ChatGPT in late 2022 was a pivotal moment for deep conversational AI, giving consumers hands-on exposure to the potential of the field. But this growing interest in the field of artificial intelligence has led to the proliferation of half-a-dozen different terms used to describe AI tools and the technologies behind them.
Machine learning, deep learning, and GPT. Large language models, chatbots, and conversational AI. But what do these terms actually mean? And what is the relationship between these technologies?
Defining these technical concepts is key to understanding this new evolution in artificial intelligence.
Conversational AI: A Definition
Artificial intelligence (AI) is the ability of a digital computer to perform tasks associated with intelligent beings. First theorized by Alan Turing in 1950, AI has become a fast-evolving discipline behind the world’s most innovative technologies. And the technology that’s generating the most headlines is conversational AI.
On a basic level, conversational artificial intelligence is the ability of technology to carry a conversation with humans. But the capabilities of artificial intelligence exist on a spectrum of sophistication. On one end are simple chatbots which can simulate a conversation based on single-line responses or parameters. On the other end are sophisticated large language models, like ChatGPT.
A large language model (LLM) is a computer system trained on huge data sets and built with a high number of parameters. This extends the system’s text capabilities beyond traditional AI and enables it to respond to prompts with minimal or no training data. But with the ability to process language, some LLMs have capabilities that go beyond carrying a conversation. These LLMs are able to create truly unique responses to complex scenarios that have never happened before.
For example, ChatGPT can write an answer to a coding question in the writing style of a specific author. Or even write rap lyrics apologizing for its own service outages. In practice, tools such as ChatGPT function like search engines or content creation systems, synthesizing billions of data points into custom responses. This functionality is controlled by a metric called temperature, which dictates the randomness, originality, and creativity of a response.
The Technologies Behind Conversational AI
LLMs have dramatically increased the capabilities of conversational AI beyond simple, low-context conversations. Behind this transformation are a number of AI disciplines, built by teams of data scientists and software engineers.
Natural Language Processing
Natural language processing, or NLP, is the branch of AI focused on training computers to understand language the way human beings do. Natural language processing relies on techniques such as big data, learning algorithms, and structured textual data.
Natural language processing is the foundational discipline behind conversational AI. Without the ability to read, write, and understand human language, a machine would be unable to hold a human-like conversation.
Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Supervised machine learning algorithms are dependent on human intervention and structured data to learn and improve their accuracy.
Machine learning is pivotal in the training of conversational AI. For example, OpenAI used supervised learning and reinforcement learning techniques to fine tune ChatGPT’s results. This technique involved a human-in-the-loop system using thousands of contractors to write human-like responses to challenging prompts as a way to continuously improve the model. Training the model to answer difficult questions improved ChatGPT’s responses at a remarkable rate.
Deep learning is a sub-field of machine learning that uses three or more neural network layers to simulate the human ability of learning by example. Deep learning is characterized by scalability, larger quantities of data, and a reduced need for human intervention. Data scientists use deep learning to train conversational AI on large, unstructured data sets to improve its accuracy.
Examples of Conversational AI
Interactive Voice Assistants
Voice assistants are perhaps the most familiar type of conversational AI to consumers. If you’ve ever spoken to or chatted with your device’s assistant, then you’ve used a conversational AI.
Voice assistants are ubiquitous, with each hardware manufacturer offering a helpful AI in their phones, computers, and smart devices. Examples of voice assistants include:
- Amazon’s Alexa
- Google Assistant
- Apple’s Siri
- Microsoft’s Cortana
- Samsung’s Bixby
While voice assistants have been helping consumers use their devices for years, their capabilities are limited compared to large language models. Unlike ChatGPT, voice assistants like Siri or Alexa aren’t able to create new content or solve complex problems. This distinction is important because it highlights just how powerful conversational agents have become.
The launch of ChatGPT in late 2022 was a key milestone for deep conversational AI, giving consumers their first hands-on exposure to the potential of the field. ChatGPT isn’t the only powerful conversational AI out there, but its viral launch has made it the most popular so far. In only five days, it surged to one million users. In just over a month, the valuation of the company behind it, OpenAI, grew to $29 billion.
The goal of ChatGPT’s developer, OpenAI, was to create a machine learning system which can carry a natural conversation with more sophistication and context than traditional chatbots. ChatGPT uses the language model GPT-3, which is built on Transformer, a neural network architecture pioneered by Google.
Google Bard is Google’s entry to the conversational AI race. Bard is a large language model, similar to ChatGPT, but with the ability to source data directly from the web. Bard is powered by the neural language model LaMDA, which is also built on the Transformer neural network.
If LaMDA sounds familiar, it might be because the AI made headlines in mid-2022 when a Google engineer claimed that the LaMDA was sentient. While most experts dispute the accuracy of the claim, the controversy did renew conversations about sentience and the ethics of artificial intelligence.
At time of writing, the potential and future of Bard is unclear. Its debut was hindered when it made an inaccurate statement about the James Webb Space Telescope during a preview demonstration. That said, if Google can manage to combine a conversational AI with its powerful search engine, the result will be a sight to behold.
Bard was only in limited availability during the first few months of ChatGPT’s reign, and will become generally available at an undisclosed date.
What’s Next for AI?
Humanity has developed technologies that can carry human-like conversations and produce unique creative works. Companies in every industry are rushing to leverage the power of tools like ChatGPT. But what comes next?
The obvious next step is that engineers and data scientists will build faster, smarter, and more human-like conversational agents with the potential to disrupt skills previously restricted to human beings. ChatGPT is already coding, writing poems, and drafting college essays. In their next iteration, the abilities of conversational AI could rise to greater heights. However, the upper limits on language models like GPT are not yet known.
In the long term, the potential of conversational AI is harder to anticipate. This writer’s prediction? Conversational agents will serve as a catalyst to inspire even greater milestones in the project to recreate human intelligence in machines. Perhaps the language processing abilities of conversational agents will evolve into the “brains” of autonomous machines.
The forms these technologies will take are limited only by our imagination. Some experts believe AI is poised to usher in the next era of human civilization, with Google CEO Sundar Pichai comparing the advancement of AI to the discovery of fire and electricity. Even the next evolution of humanity is in the works.
But this potential brings with it countless existential questions. What will be the purpose of humans in an automated world? Will we do the impossible and create sentient machines? Will we use AI to create a future of abundance? Or will our own creations become a threat of the likes from science fiction?
Indeed, AI’s potential to transform the world is limitless. In 50 years, we might look back on the rise of conversational AI as the moment that changed everything.