In the coming decade, artificial intelligence will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works. From image generation to natural disaster detection, here are the six AI trends driving that transformation in 2023.
Conversational artificial intelligence is the ability of technology to carry a conversation with humans. But the capabilities of conversational AI platforms 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.
The launch of ChatGPT proved to be a pivotal moment in the AI industry. 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.
While ChatGPT took the world by storm with generative text creation, an equally revolutionary change was happening with generative image creation.
Text-to-image generators use natural language processing and neural networks to translate users’ text prompts into digital images in seconds. The rising popularity of these tools has created plenty of controversy, including copyright lawsuits, art contests won by AI images, and the virality of a deep fake image of the Pope wearing a puffer jacket.
But the growth of AI-image generation has also raised many existential questions. How will AI image generation impact the careers of visual artists? When will these tools become sophisticated enough to create photo-like images? And will tech companies manage to create equally sophisticated text-to-video generators?
While there are now dozens of image generation tools out there, applications like Midjourney, Dall-E2, and Dream Studio have garnered the most attention.
AI & Cybersecurity
The first moments after a cybersecurity attack are the most crucial. Early detection and response can make the difference between a successful defense and a security breach. To improve their response plans, security teams are turning to machine learning.
With nearly every technical discipline implementing machine learning, its role in cybersecurity shouldn’t come as a surprise. In 2022, machine learning was the most in-demand technical skill in the world.
Machine learning plays a key role in cybersecurity automation by executing key actions in response to a threat. These include:
- Event detection
- Data leak detection
- Firewall updates
- Legal team engagement
While cybersecurity automation is a powerful tool for enhancing security protocols, it’s still not (and will likely never be) a total replacement for cybersecurity teams. Security systems still require ongoing development, monitoring, maintenance, and oversight to be successful.
That said, in 2023, we can expect to see more advanced AI-powered cybersecurity tools being developed, which will use machine learning algorithms to analyze network traffic and identify potential threats in real-time.
Natural Disaster Detection
Natural disasters can be devastating, but AI can help to predict and mitigate their impact. In 2023, we can expect to see more AI-powered natural disaster prediction tools that can analyze weather patterns and geological data to provide accurate predictions. This will help authorities to save lives, minimize damage, and improve the response of emergency services.
Exciting applications of AI in disaster detection include early warning systems, image analysis, social media monitoring, data sensor analysis.
Marketing Content & Analytics
Marketing teams have been using AI for some time to unlock customer insights, customize user journeys, and conduct market research. But the spread of generative tools like ChatGPT has introduced AI to the world of content marketing. Now, marketing teams are using conversational AI to produce content for marketing campaigns, such as blog posts, social media posts, and email newsletters. And they’re also using text-to-image generators to create campaign visuals in seconds.
As generative-AI companies mature, they will likely identify marketing teams in every industry as a key vertical. Tech companies might also start developing generative AI tools or platforms designed for more specialized marketing use cases.
Regulation of AI
Now that AI has exploded in popularity, it’s easy to predict that data scientists and machine learning engineers will create more and more sophisticated AI models. What’s more challenging to anticipate is how humans will react to the creation of intelligence that will eventually compete with our own.
Governments around the world are already responding to the proliferation of AI by passing legislation to regulate its use. Notable examples include:
- The European Union’s Artificial Intelligence Act
- The White House’s Blueprint for an AI Bill of Rights
- FTC enforcement and regulation of companies relying on AI
- New York City’s regulation of AI hiring algorithms
- Local governments in the US banning facial recognition software