Are you secretly wondering if ChatGPT wrote this article—or if DALL·E 2 generated the images? Is an AI voice generator calmly reading this to you? If so, you might be an AI geek and you’re probably very interested in learning more about the top AI trends for 2023.
The emergence of a new breed of generative AI tools, however, has made it clear that AI is no longer something that is only important for AI geeks or those in the realm of academic research or Silicon Valley. AI is for real—and for real people.
Some people may be interested in the democratization of Artificial Intelligence and AI in the healthcare industry. Still others are concerned how global events such as Russia’s war against Ukraine affects critical infrastructure and energy grid security—as well as the emergence of smart grids.
Whatever your particular business, political, or cultural interests are, nobody can deny the ascent of AI and its effects on daily life—even if you don’t realize it. Has Netflix recommended a program for you? Has Google Maps shown you a faster route or your car beeped when it drew too close to another car? Then you’ve seen how AI is used.
Let’s take a look at the top AI trends for 2023—focusing on AI applications that’ll truly affect everybody—and perhaps even get some help from our new friend, ChatGPT.
After sitting on the shelf for many years, AI is once again at the center of most political, economic, and socio-cultural debates. The numbers also back it up. Worldwide spending in the AI market is predicted to breach the $500 billion mark during 2023, according to a new IDC report.
And whether or not you believe that AI is taking us toward a dark dystopian future or a shimmering utopia—one thing is clear—the democratization of artificial intelligence is accelerating.
Many people think of AI as something restricted to secluded labs in MIT, but it includes people who do not necessarily have expertise in data science, machine learning, or computer programming. The goal is to empower more people to use AI tools, algorithms, and models to solve problems, make decisions, and automate tasks.
Yet, at the same time the demand for more business applications is not being met—and about 70 percent of digital transformations fail. The value proposition of disruptive low-code AI technology not only puts AI in more people’s hands—it gives organizations the flexibility and agility to adapt to our fast-changing world.
Low-code and no-code AI are terms simply used to describe tools that allow anyone to create AI applications without having to get their hands dirty writing technical code. Low-code AI platforms democratize AI by giving users a drag-and-drop interface that creates applications with minimal coding.
No-code AI platforms, meanwhile, help more people use AI by implementing natural language processing (NLP) and AutoML to build AI models with absolutely no coding at all.
The democratization of AI also benefits society by:
By reducing the barriers to entry and enabling more people and organizations to use AI tools—the full potential of AI can create new opportunities for innovation and growth and help reduce social ills.
What is Generative AI?
Boston Consulting Group defines Generative AI as a groundbreaking form of artificial intelligence that uses a type of deep learning called generative adversarial networks (GANs) to create novel content. ChatGTP, meanwhile, adds that Generative AI “can create new content, such as images, videos, or text, without explicit instructions from humans. In other words, it can generate new data on its own—based on patterns it has learned from existing data.”
DALL-E is an artificial intelligence (AI) model developed by OpenAI, which generates images from textual descriptions. It is a sibling model to GPT-3, both of which are part of the OpenAI family. DALL-E was first introduced in January 2021 and is named as a portmanteau of the famous artist Salvador Dalí and the Pixar character Wall-E.
When given the prompt—A more creative way to say: ChatGPT has taken the world by storm—ChatGPT responded with:
“ChatGPT has unleashed a paradigm-shifting wave across the world, transforming the way we communicate and interact.”
That is not hyperbole.
ChatGPT—an AI chatbot developed by OpenAI and launched in November 2022—has been putting AI into the hands of millions at a record pace. It only took the app five days to reach 1 million users—a number that took Instagram 2.5 months and Twitter two years to hit— according to a recent Morgan Stanley report.
As with most new technology, ChatGPT has been met with enthusiastic early adopters and those Chicken Little alarmists who love to decry its negative, disruptive qualities. Yet, as scores of non-tech-savvy individuals are leveraging its capabilities for unforeseen purposes—ChatGPT has become the ultimate proof that generative AI is not just a buzzword.
Here are a few examples:
ChatGPT is also extremely popular with coders and even with Wall Street. Morgan Stanley strategists have argued that AI is a “serious contender” for the “key theme” of 2023—and that ChatGPT mania isn’t just another investment fad.
We Asked ChatGPT to Write a Creative 🧑🎨 List of 5 AI Trends for 2023. Here’s What it Generated:
As an AI language model, I don’t have a sense of humor, but I can provide a list of unconventional AI trends for 2023 that are purely for entertainment purposes:
While some of these trends may seem far-fetched, who knows what kind of AI innovations may emerge in the coming years!
Introducing GPT-4
People were waiting for months. It’s now here. Open AI has finally announced the release of GPT-4, a large multimodal model that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
GPT-4, can parse both text and image input, though it can only respond via text. OpenAI also cautions that the systems retain many of the same problems as earlier language models, including a tendency to make up information (or “hallucinate”) and the capacity to generate violent and harmful text.
Comparing the Results of the Same Prompt Given to Both GPT-4 and ChatGPT
If you’ve ever been lost or needed help in a foreign country—you know that it’s a lot easier and more natural to use your phone’s voice technology to translate than typing on a keyboard or tapping on a screen. AI-powered voice technology can also be more accessible and easier to use for people with disabilities, older adults—and others who struggle with traditional interfaces.
For example, children with Down syndrome or autism can become anxious when their routines are upset or they are separated from their caregivers. Technology such as the Echo Dot can reinforce vital routines that offer reassuring words in the voice of their caregiver.
Voice assistants also help people suffering from memory loss by delivering reliable reminders of the date, days of the week to take medication, and appointments.
While the movies may show people developing empathetic relationships with machines that can speak, we’re still in the first phase of the evolution of voice technology. These include:
Besides being a part of our living rooms and on our phones, the global Speech and Voice Recognition Market is big business. It’s projected to hit $49.79 billion by 2029, with a CAGR of 23.7 percent during said forecast period (2022–29).
Industries making headlines with voice-powered AI include:
AI is affecting the energy industry in more ways than just voice technology. Let’s take a deeper look at the new ways AI, in conjunction with global events, has been profoundly changing this vital sector of the economy.
The economic and political turmoil caused by the Russian invasion of Ukraine has amplified calls for an accelerated energy transition towards more renewables and cleaner energy, a move to smart grids, and better AI-driven management of our current energy systems.
The war and ensuing energy crisis has also tested the strength and flexibility of the prediction models that drive electricity and natural gas grids. KPMG notes that grids’ ability to function properly in generally exceptional circumstances—and their ability to handle outside interference to disrupt them—is also being scrutinized.
What Is a Smart Grid?
A smart grid is a network that integrates energy distribution and digital communication technology in a two-way flow of electricity and data. This enables utility companies to optimize the generation, transmission, and distribution of electricity. Consumers also benefit from the stories the data is telling—explaining better how they store and use energy.
ChatGPT says that, “the primary goal of a smart grid is to improve the efficiency, reliability, and sustainability of the electrical grid while also providing consumers with more information and control over their energy consumption.” Consumers can benefit from the stories the data is telling—explaining better how they store and use energy through devices such as solar panels and EV batteries.
Let’s take a look at how AI can make the energy industry smarter, safer, and cleaner:
Machine learning algorithms are known for detecting financial fraud, but can also pinpoint illegal energy fraud, the intentional misrepresentation of energy data or energy usage.
Wars may not be affecting every corner of the globe—but every region should be adopting best AI practices to make their energy supplies cleaner, safer, and more secure.
Medical imaging is a high-stakes business—lives are literally on the line. It shouldn’t come as a surprise then, that many medical professionals have expressed concerns about black box AI and the risks involved with a lack of explainability. The EU has even passed Article 15 of the GDPR which ensures the right of patients to receive meaningful information about how decisions were rendered.
The financial industry is also in dire need of proper Explainable AI (XAI). Imagine a large bank attempting to predict creditworthiness among an underserved community more accurately—leveraging dozens of variables as inputs, including never-before-used alternative data.
Expanding their market is great—as is expanding to an underserved community—but what if the model developers can’t explain how their model arrives at the credit outcomes, let alone identify which factors had the biggest influence on them.
And what about AI adoption? Imagine workers at a plant operating very heavy—and potentially dangerous—equipment. Without clear XAI, said workers may not trust that a newfangled algorithm will make their work more efficient or safe.
Besides increasing trust and adoption, a recent McKinsey report has noted that XAI can also:
Increase productivity: Does your team understand the specific features that lead to the model’s output? If yes, then technical teams can better apply them to future predictions—or determine if it was just a one-off situation or related to anomalous historical data.
In the same way, ML Studio lets users understand how models function—detect bias and fairness—with an explainability module. Get access to explainable AI and solve real-world business problems with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.
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