2023 marked a breakthrough year for artificial intelligence (AI), with its profound impact on various fields and industries. The remarkable advancements in technology in this new era have simplified many complex tasks that were previously difficult for humans to handle.
Looking ahead, AI will continue to evolve and make new strides, becoming an indispensable tool that supports human life. To prepare for this constant change, individuals, organizations, and businesses need to stay ahead of the AI trends projected to emerge in 2024. This will help them reduce uncertainty and fully harness the potential that this technology offers.
1. Neural Networks và Deep Learning
Neural Networks are a method in the field of artificial intelligence (AI), inspired by the way the human brain operates to assist computers in processing data. Neural Networks are a type of Deep Learning, utilizing nodes, also known as neurons, that are interconnected in a layered structure similar to the brain. This system enables computers to learn from their own mistakes and continuously improve, thereby solving complex problems such as document summarization or facial recognition with high accuracy.
Artificial Neural Networks simulate the functioning of the human brain by analyzing complex data, performing computations, finding patterns, and using the collected information to make predictions and classifications. The fundamental unit of a Neural Network is the neuron, or node, which helps transmit information throughout the network.
Deep Learning and Neural Networks are two crucial methods in artificial intelligence. Currently, research is focused on improving the performance and accuracy of deep learning models, as well as enhancing the automatic learning capabilities of neural networks.
One prominent application of Neural Networks and Deep Learning is ChatGPT and GPT-3. These language models have garnered significant attention in the community, not only for their ability to engage in human-like conversations but also for their potential that surpasses traditional chatbots.
ChatGPT and GPT-3 are not only useful in customer service scenarios but can also be employed to write code, create art, or make inferences on specific topics. Within seconds, these chatbots can utilize a vast amount of information to provide the most appropriate and accurate responses, demonstrating the power and versatility of AI in real-world applications.
2. Al Explainability (XAI)
AI Exploitability, or the capability to exploit AI, is an emerging field in machine learning that focuses on explaining the decisions made by artificial intelligence (AI) systems. This term is commonly abbreviated as XAI (Explainable AI).
XAI aims to examine and understand the processes and models involved in the AI decision-making process. This helps answer critical questions such as: Why did the AI system make a specific prediction or decision? Why did it not choose an alternative option? When does the AI system perform well or fail? When can we trust AI, and how can we correct any errors that arise?
XAI encompasses a set of tools and techniques designed to help organizations better understand how a model makes decisions and operates. XAI offers the following benefits:
- Best Practices: XAI employs processes and rules developed by data scientists over many years. Understanding how a model is trained and the data it uses helps identify when the model performs well and when it should not be used. Additionally, it sheds light on potential sources of bias that the model might encounter.
- Design Principles: Researchers are focusing on simplifying the construction of AI systems to make them inherently more understandable and transparent from the outset.
- Supporting Tools: As AI systems become more comprehensible, they can be improved by integrating these insights into model training and providing these tools to others to apply in their own models.
The challenge of explaining AI system decisions is a significant aspect of XAI. In the future, explaining and understanding AI processes will become a top priority, especially in fields like healthcare and finance, where transparency and risk reduction are critically important.
3. Data Replication and Reuse
AI is a combination of various technologies such as data mining, algorithms, and computational power. The development of AI is based on two main “backbones”: remarkable advancements in computational capabilities and the continuous increase in data volume. It can be said that AI is one of the most significant applications in a data-driven economy. We are at a point where economic growth and social development increasingly rely on the value generated by data.
Currently, most of the data being stored and utilized relates to consumers. However, experts predict that in the near future, data will become more diverse and rich, with a significant portion coming from industries, businesses, and the public sector.
Data plays a crucial role in the development of AI, and the replication and reuse of existing data will become an important trend in 2024. Companies and organizations will focus on storing, classifying, and leveraging data to reuse it, thereby creating new value through AI models and applications.
4. AI in Communication and Interaction
Artificial Intelligence (AI) in communication is a technology that enables software to understand and respond to human conversations through speech or text. Traditionally, interactions between humans and software were limited to predefined input, where users enter or speak preset commands. AI in communication goes beyond this. It can recognize various types of input through speech and text, mimic human interaction, and understand and respond to queries in multiple languages. Organizations use AI in communication for various use cases in customer support operations, allowing software to respond to customer queries in a personalized manner.
The advancement of AI in voice recognition, natural language processing, and human-machine interaction will continue to unlock new possibilities for AI in communication and interaction. Automated speech technology and chatbots will become smarter and provide better interactive experiences for users.
AI communication chatbots can offer 24/7 support and respond to users instantly—modern services that are preferred and expected from all online systems. Instant responses not only increase customer satisfaction but also boost the frequency of interactions with the brand.
Additionally, users can integrate previous customer interaction data with AI communication to create personalized experiences. For example, AI communication can make recommendations based on previous customer purchases or search inputs.
5. Edge Computing and AI on Mobile Devices
Edge AI refers to the deployment of AI applications on devices distributed globally. It is called “Edge AI” because the AI computation is performed close to the user at the network’s edge, near where the data is generated, rather than being centralized in cloud computing or data centers. AI algorithms are processed locally, directly on the device or on a server near the device. The algorithms use data generated by the device itself. Devices can make independent decisions in just a few milliseconds without needing to connect to the internet or the cloud. Edge AI has almost limitless potential when it comes to use cases. Edge AI solutions and applications range from smartwatches to production lines and from logistics to smart buildings and cities.
Since the Internet has global reach, network boundaries can encompass virtually any location. This could be a retail store, factory, hospital, or devices around us, such as traffic lights, vending machines, and phones.
Meanwhile, Edge Computing is a distributed computing model that brings computing power and storage closer to the source of data, which requires low latency processing and bandwidth savings.
The origin of Edge Computing lies in CDN (Content Delivery Networks)—networks created in the late 1990s to deliver web and video content from servers located closest to users. These networks have since evolved to store applications and application components on edge servers, creating the first edge computing services like real-time data aggregation.
With the advancement of Edge Computing—processing data directly at the source—AI will also be deployed on mobile devices. This enhances the robustness and processing speed of AI in mobile applications and IoT, while simultaneously reducing dependence on internet connectivity and cloud models.
Conclusion
In conclusion, many people still worry about the future of artificial intelligence (AI) and its potential to surpass human capabilities. Some are concerned that AI could bring significant changes to the job market, increasing the risk of job losses for many individuals. While no one can be certain whether AI will actually replace humans in the future, early awareness of predictions regarding AI development trends will open up new opportunities for individuals, organizations, and businesses in applying and advancing their careers.