Artificial Intelligence is no longer fiction. With voice assistants like Alexa and Siri or personalized recommendations on social media platforms, AI has become an integral part of daily lives.
Moreover, AI continuously evolves with advancements in Machine Learning and Deep Learning algorithms, making it hard to predict the future. However, based on recent developments and industry trends, we have listed the top 10 AI trends to watch in 2023.
While many are aware of Artificial Intelligence technology, some might still be unaware of the basics. So, before we move on to the top 10 AI trends, let’s define AI.
Artificial Intelligence, aka AI, is the human intelligence stimulation so machines can perceive, synthesize, and conclude information. AI systems work by ingesting labeled data in a large amount, analyzing the data for patterns and correlations, and using analysis results to make informed predictions.
AI programming is focused on three intellectual skills:
AI is popular because it reduces the time to complete data-heavy, detail-oriented tasks while delivering consistent results. Some industries where AI has made its way include healthcare, education, banking, finance, corporate, law, manufacturing, transportation, and security.
Google Search, YouTube, Netflix, Amazon, Siri, Cortana, Alexa, Self-driving Cars, and ChatGPT are real-life examples of artificial intelligence performing specific tasks.
Top AI Trends You Must Know About
With a better understanding of AI, it’s time to glance at the top AI trends you should be aware of:
- Generative Models: The Future of AI-Generated Content
- Explainable AI: Bringing Transparency and Trust to AI Systems
- Human-Centric AI: Designing AI for Human Interaction and Collaboration
- Edge AI: Moving Intelligence Closer to Devices
- AutoML: Automating the Machine Learning Process
- Quantum AI: Bridging the Gap Between Quantum Computing and AI
- Federated Learning: Collaborative Machine Learning Across Devices
- AI-powered CyberSecurity: Addressing the cyber risks with AI
- Sustainable AI: Reducing carbon footprints AI
- Autonomous AI: Self-Learning and Self-Optimizing AI Systems
Now, let’s look at each of these AI trends in detail.
Generative AI is machine learning’s sub-field that is used to generate new content using existing data such as photos, videos, code, text, or sound. ChatGPT, DALL-E, ArtBreeder, and Pikazo. Generative AI aims to create original results by processing large data sets using unsupervised or semi-supervised learning.
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the commonly used generative models.
Generative Adversarial Networks (GANs) comprise generative and discriminative neural networks. While the generative network creates outputs on request, the discriminative network tries to differentiate between real-world and fake data to improve the content quality.
On the other hand, VAE encodes data into a low-dimensional representation that apprehends essential features, structure, and relationships of data by training a single machine learning model.
Explainable AI refers to the process and methods that allow humans to comprehend and trust machine learning algorithm-generated outputs. It is used to describe potential biases and impacts of AI models.
For AI decision-making to be successful, businesses must know how the AI decision-making process works, with model monitoring and accountability of AI.
In simple terms, XAI helps humans to understand the predictions and decisions made by the AI model. It differs from the “black box” machine learning, where even its designers can’t explain why the AI reached a decision. Instead, Explainable AI helps signify an AI-based decision's accuracy, transparency, fairness, and outcomes.
Human-Centric AI is an emerging practice aimed at creating AI systems that amplify and augment instead of replacing human abilities. HCAI will preserve human control while ensuring AI meets our needs by working transparently, respecting privacy, and producing unbiased outcomes.
Simply put, HCAI combines artificial intelligence, machine learning, and human-centered design to transform how businesses use, operate, and take advantage of data acquired without creating new algorithms for new jobs.
The blend of artificial intelligence and edge computing is what we call Edge AI. Edge AI is an AI technology that brings the computational power of AI algorithms to the edge of the network rather than relying on a centralized cloud computing infrastructure.
The AI models used in edge AI are typically lightweight and optimized for a specific task, such as object detection, speech recognition, or natural language processing. As a result, edge AI improves the response time speed, increases privacy and security, reduces network latency and bandwidth usage, and makes edge computing cost-efficient.
Automated machine learning refers to the processes and methods that improve Machine Learning efficiency and make it available to non-ML experts. AutoML is used to apply the ML model to solve real-world problems through automation. It makes ML processes user-friendly and provides more accurate output than manually written algorithms.
Data preparation, Feature engineering, Ensembling, Model Selection, Hyperparameter Optimization, Pipeline Selection, Problem Checking, and Result Analysis are some ML processes that can be automated through AutoML.
As the name suggests, Quantum AI combines quantum computing and artificial intelligence. By using quantum AI, scientists can achieve results that conventional computers would not be able to achieve. The aim of quantum AI is to create algorithms for decision problems, learning, searching, game theory, etc., that work better than classical ones.
Federated Learning focuses on training machine learning algorithms across multiple decentralized data servers or edge devices without changing them. Multi-actor machine learning models can be built using Federated Learning without sharing data, which allows it to address critical issues such as data privacy, security, and access rights.
With the growth of cyber threats in complexity and volume, AI can help security operations analysts stay ahead by curating threat intelligence from millions of news stories, research papers, and blogs on cyberattacks. In addition, AI can provide insights to improve response times drastically. What makes AI-powered cybersecurity a trend is its ability to continuously learn, identify threats faster, and remove time-consuming tasks.
Sustainable AI is devising energy-efficient, highly accurate, and explainable machine learning algorithms that support data processing from distributed sources.
In simple terms, it refers to the use of AI technologies that are socially and environmentally responsible. Sustainable AI aims to create AI systems that can reduce carbon emissions on the environment while empowering long-term viability and ethical use.
Moreover, to achieve sustainable AI, developers must consider various factors such as energy efficiency, data privacy, bias and fairness, and transparency.
Autonomous AI refers to creating AI systems that can operate autonomously and make independent decisions without human intervention. These AI systems can learn and adapt using data analysis and machine learning algorithms to improve performance. Some popular examples of Autonomous AI systems are self-driving cars, robots, and drones that don’t require human intervention.
We live in an era of artificial intelligence. Several industries are about to be revolutionized by artificial intelligence, which is advancing exponentially. Needless to say, AI will continue to evolve, integrate more seamlessly into everyday life, and transform how we work, live, and interact.