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Top 12 AI Technology Trends For 2024
As 2024 unfolds, Artificial Intelligence and its related applications evolve at a rapid pace. Following the same pathway, this year promises groundbreaking advancements across various sectors. From the creative spark of AI-powered art generation to the ever-growing sophistication of quantum-enhanced algorithms. In this blog, we will discuss top AI technology trends in 2024.
Emerging AI Technology Trends To Follow In 2024
1. Multimodal AI For Diverse Content Creation
Unlike traditional AI models, which focus on a single text-only modality, multimodal AI integrates information from multiple sources like audio, video, images, and more. In 2024, various AI multimodal learning applications are already functional. A few of them are:
DALL-E: It is responsible for generating images from textual descriptions. It possesses a remarkable talent for generating images of objects and scenes that donât exist, infusing AI with a creative flair.
GPT-4: It introduces a new dimension by processing and generating text and images, unlike its predecessor. Its comprehensive comprehension results in outputs that are more contextually relevant, bringing us closer to AI systems that perceive the world in a manner similar to humans.
Sora: Itâs the most advanced text-to-video multimodal AI that is developed by OpenAI. It is capable of generating stunning high-definition videos only from text prompts.
Here are a few usages of multimodal AI in daily life:
Generative AI: Multimodal AI is powering up various software applications to make them more innovative and user-friendly. For example, Microsoft Designer, which is a graphic designer app, is now powered by multimodal AI. It can now generate images based on text prompts. It also enables custom neural voices that are helpful for people with vocal disabilities.
Revolutionizing Content Moderation: Social media platforms can leverage Multimodal AI to identify and moderate harmful content more effectively, analyzing text, images, and audio for potential violations.
Personalized Customer Experiences: E-commerce platforms use multimodal AI to analyze usersâ search queries and understand customer preferences and interests through product images and videos. This helps e-commerce platforms display relevant products, leading to highly personalized product recommendations.
Next-Gen Search Functionality: Search engines can integrate Multimodal AI to deliver richer results based on a combination of text queries and visual information.
Emotion Recognition: It is a groundbreaking use of multimodal learning, where AI models assess an individualâs emotional state by analyzing facial expressions, voice tones, and speech content. For example, Affectivaâs Emotion AI is employed by companies in sectors such as automotive and media testing.
2. Small Language Models Are Trending Because Of Cloud Costs
Since we are discussing AI trends, we assume you are already familiar with Large Language Models(LLMs) like Gemini and ChatGPT. However, these models are so large that they require a massive chunk of significant computing resources to run, requiring the need for Small Language Models(SLMs). The requirement and development of SLMs are among the top AI trends in 2024.
Smaller models are getting popular because they are resource-intensive. These models are compact versions of their larger counterparts. Small Language Models (SLMs) are increasingly appealing to smaller businesses operating on limited budgets. They are typically simpler to train, adjust, and put into operation, making them more cost-effective to operate as well.
Also, training smaller models on big data yields better performance than training larger models on less data. Recently, better models with fewer parameters from 3 to 70 billion were made. They were mainly built upon LLaMa, Llama 2, and Mistral foundation models. These improvements proved that smaller models can perform almost as well as larger ones.
SLMs can be used to create chatbots with limited capabilities that can handle basic customer concerns and resolve them as needed. Almost all big companies and banks use SLMs on their websites to power chatbots.
SLMs are enhancing offline translation functionality. As you communicate with your device in your language, it will translate the same into the desired language in real-time, allowing for seamless communication.
SLMs can be trained to identify suspicious patterns and check spam or phishing content. Various email and messaging service providers are already using this
3. Edge AI Implementation For Faster Response Times
2024 is witnessing a surge in Edge AI, a powerful combination of Artificial Intelligence (AI) and edge computing. Edge AI formulates machine learning tasks directly on interconnected edge devices. It has been made possible by placing AI algorithms directly on IoT devices like sensors, drones, or even smart appliances. This situation eliminates the need to constantly send data back and forth to the cloud, enabling real-time decision-making and faster response times.
The combination of edge computingâs localized processing power with AIâs analytical capabilities unlocks many possibilities.
Predicting Equipment Safety:
With edge AI, machine sensor data can be analyzed locally to predict equipment failures before they occur, preventing costly downtime.
Intelligent Transportation Systems:
Recently, Google has supercharged its âMapsâ with AI. This is helping various modes of transportation with fuel-efficient routing. It also understands real-time traffic data, road elevation, and even your vehicleâs engine type to identify the route that minimizes fuel and emissions. Recent advancements suggest the speed you should maintain to ensure a smoother traffic flow, which could be a revolutionary step in traffic management.
Ensuring Safe Working Conditions in Heavy Industry:
Edge AI allows operators to control heavy machinery, especially those situated in remote or hazardous locations, through a remote platform, allowing them to do so from a secure and convenient location.
As 2024 unfolds, we can expect edge AI to become an even more prominent force where intelligent IoT devices supercharged by AI will seamlessly integrate with our lives.
4. Augmented Workforce For Enhanced Productivity
In 2024 and later, the augmented workforce will be one of the top AI trends. Augmented workforce refers to a situation where human workers work along with digital workers, collaborative robots(cobots), and smart virtual assistants. The key idea is to integrate the new AI technology into the workforce to support people. Here are a few characteristics of an augmented workforce:
Making People & Technology as Partners:
The aim is to create a team environment where both human and machine strengths are maximized. For instance, humans provide creativity and problem-solving, while machines excel at data analysis and repetitive tasks.
New skills aligned with new opportunities:
With technological evolvement, the workforce will adapt and learn new skills to work effectively with these new tools. This can open up new career opportunities.
5. AI-Driven Customer Support For Faster Resolutions
A robust customer support system is essential for the success of any business. Companies are incorporating artificial intelligence into customer support to enhance customer support experience. AI-generated responses ease customer support executivesâ workload, particularly in scenarios with limited staff. This allows them to focus on more complex issues, improving efficiency and customer satisfaction. This enhances user experience by addressing their queries at the earliest convenience. This is one of the biggest AI trends that you have noticed in a while. Here are a few ways that companies are pushing for AI-powered customer support:
Companies Are Allocating Resources To AI For Customer Service
Companies increasingly invest in AI for customer service to streamline operations and enhance satisfaction. Businesses can automate tasks, analyze data, and deliver seamless support experiences across channels by leveraging AI-driven solutions.
AI Will Improve Customer Service Roles Rather Than Replace Them
AI is poised to enhance customer service roles. By integrating artificial intelligence into support processes, businesses can augment human capabilities, streamline operations, and deliver more efficient and personalized customer experiences. Instead of replacing human agents, AI technologies empower them by automating repetitive tasks, providing data-driven insights, and facilitating quicker resolutions to customer inquiries. This collaborative approach ensures that human expertise remains invaluable in addressing complex issues and fostering meaningful interactions, ultimately leading to elevated customer satisfaction and loyalty.
6. Quantum AI For Superfast Computing
Quantum computing is a concept that remains unfamiliar to many outside the scientific community. To explain in simple terms, quantum computing uses principles from quantum mechanics to process information. Unlike classical computers, which use bits, quantum computers leverage qubits, allowing for faster calculations with potential applications in cryptography, optimization, and scientific simulations.
Recently, Google developed an open-source library, âTensorFlow Quantum.â This tool combines quantum modeling and machine learning techniques, and it is necessary to control and model natural or artificial quantum systems.
Quantum-based AI could be used to develop robots that can interact with humans more naturally and intuitively. For example, a robot could understand and respond to human emotions, providing more empathetic assistance in healthcare, elder care, and education.
What are the possibilities of quantum AI in computing?
Developing Quantum Algorithms For Learning:
AI supports the development of quantum algorithms for generalizing classical learning models. These algorithms can speed up the deep learning and training process.
Quantum Search:
Quantum search is an algorithm in quantum computing that efficiently finds a target item in an unsorted database. It utilizes quantum principles like superposition and entanglement to search through data much faster than classical methods. Notably, Groverâs algorithm is a crucial example, offering significant speed-up for data mining and optimization tasks.
Quantum Game Theory:
Classical game theory, commonly applied in AI, extends into the quantum realm as quantum game theory. This extension holds promise for addressing critical challenges in quantum communication and implementing quantum artificial intelligence.
Although quantum AI is still in development, various improvements in quantum computing have increased its potential. A few such developments are:
It is a less-error-prone and more powerful quantum computing system.
Widely adopted open-source modeling and training frameworks.
Compelling AI applications for Quantum computing.
Read full blog here - https://logic-square.com/top-12-ai-technology-trends-for-2024/