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Top 12 AI Technology Trends for 2024

Top 12 AI Technology Trends for 2024 | Logic Square

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 don’t 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: It’s 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 users’ 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 individual’s emotional state by analyzing facial expressions, voice tones, and speech content. For example, Affectiva’s 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 computing’s localized processing power with AI’s 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 ‘Maps’ 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 vehicle’s 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 executives’ 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, “TensorFlow Quantum.” 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, Grover’s 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.

7. Growth In Ethical AI Concerns With Common Adoption of AI  

Multimodal capabilities have revolutionized AI, making it a fantastic tool for generating diverse content. However, these capabilities have opened up doors for a new threat – online abuse. Though online abuse isn’t a new thing, the unethical usages of AI have added fuel to the fire.

Since machine learning relies on human intervention to shape its thinking, it raises concerns about potential biases toward specific groups. Instances of AI involvement in racism have been observed, such as a study revealing significant racial bias in Amazon’s facial recognition software, leading to misidentification rates of nearly 35% for Black women compared to near-zero errors for white men. Similarly, Google’s AI Gemini generated a paragraph accusing PM Modi of fascism, sparking outrage, while remaining neutral on similar questions about other leaders. These incidents highlight the risk of bias in training algorithms due to human intervention, emphasizing the need for checks and balances.

As AI adoption becomes increasingly common, the need for ethical considerations to be woven into its development and deployment is paramount. This convergence of ethical AI and widespread adoption presents exciting opportunities to create a future that benefits everyone. Here are the benefits or Ethical AI adoption:

  • Mitigation of Bias and Discrimination: Ethical AI development removes bias and ensures fairness in decision-making algorithms. This will create trust in AI systems across all demographics, leading to wider acceptance and adoption.
  • Economic Growth: Responsible AI fosters innovation and job creation in new sectors. Ethical considerations can ensure that AI complements and augments human capabilities, leading to a more productive and efficient workforce.
  • Privacy Protection: Ethical AI frameworks incorporate safeguards to protect individuals’ privacy rights, preventing unauthorized access or misuse of personal data. This builds trust in AI technology to safeguard individuals’ autonomy. 
  • Trust Building: Transparency in AI decision-making builds trust between users and AI systems. This fosters a more responsible and accountable development process, ensuring AI serves humanity.
  • Transparency and Accountability: Ethical AI practices prioritize transparency in how AI algorithms make decisions, enabling stakeholders to understand and challenge outcomes. This fosters accountability among developers and users, reducing the likelihood of harmful or discriminatory actions going unchecked.

8. AI-Backed Algorithm For Decision Making

One major emerging trend in AI is the utilization of advanced decision-making algorithms. These AI-powered algorithms are becoming great tools for making decisions.

The entire AI-based decision-making process usually comprises multiple stages, from data collection and preprocessing to analysis and prediction. AI algorithms then utilize these analyses for predictive modeling and provide recommendations, allowing end-users to determine suitable courses of action. These decision-making approaches can be categorized into two types: learning-based and rule-based. Learning-based systems use machine learning algorithms to analyze data and learn from patterns to make predictions/ decisions. 

There are abundant examples illustrating the diverse applications of AI in decision-making and problem-solving, with the field continually expanding. A significant portion of these applications revolves around back-office automation. A few applications include healthcare, finance, marketing, agriculture, energy production, and logistics. For instance, 

  • Healthcare: It aids in forecasting patient outcomes and tailoring treatment protocols.
  • Finance: It facilitates fraud detection, enhances investment decisions, and optimizes portfolios. 
  • Marketing: It enables customer segmentation and customizes marketing campaigns.
  • Logistics: AI can optimize supply chain operations by optimizing routes and enhancing delivery efficiency.
  • Energy: AI-based algorithms can be used to minimize energy consumption and predict energy demands per season. 

9. AI-Powered Online Search

If we are talking about AI trends, there is no way you can skip virtual assistants like Siri, Alexa, or Google Assistant. Gone are the days when we used virtual assistants just for fun. Today, virtual assistants are now being powered by artificial intelligence. This exciting trend is revolutionizing how we interact with information. Pioneering efforts like Google’s AI model, Gemini, which powers features like Bard, and Microsoft’s Windows Copilot search functionality demonstrate the potential of AI-powered search. 

These advancements enable a more intuitive and user-centric search experience. Instead of simply delivering links, AI can understand the intent behind a search query, anticipate user needs, and provide comprehensive, contextually relevant answers. This can take the form of summarizing complex topics, generating creative text formats, or even translating languages on the fly.  

As AI-powered search continues to evolve, we can expect it to become an increasingly seamless and personalized experience, fundamentally changing how we used to access and interact with information on our devices.

10. Rise of XAI ( Explainable Artificial Intelligence)

The emergence of “XAI” signifies the increasing significance of Explainable Artificial Intelligence. XAI, a subset of AI, aims to enhance transparency and comprehension of AI models through specific techniques. It is one of the prominent AI technology trends unfolding in 2024, shaping artificial intelligence.

Many AI models, particularly complex ones like deep learning algorithms, have traditionally been considered “black boxes”. This means their decision-making process could be clearer for humans to understand. However, the XAI is evolving every single day, as developers are constantly introducing new methodologies to make learning models more interpretable. A few of them are:

  • Model-agnostic methods: These techniques, such as LIME and SHAP, can be applied to any black-box model to provide explanations for individual predictions.
  • Model-specific methods: These methods are designed particularly for a specific model type and leverage its inherent properties to provide explanations. The rise of XAI holds immense potential and scope for AI training models. It will have the following benefits. 
  • Increased Trust and Accountability: Understanding how an AI system arrives at a decision makes it easier to trust its recommendations and hold it accountable for potential biases or errors. Many times, AI models give inaccurate results and predictions, which can be harmful to the company. We have already witnessed this in the case of the Google Bard launch. Hence, XAI will help maintain transparency when training AI models. 
  • Regulatory Compliance: XAI helps organizations comply with regulations by providing explanations for AI decisions. This is crucial because unexplained AI decisions can be seen as black boxes, raising concerns about fairness, bias, and potential discrimination. XAI helps to demystify these decisions and demonstrate that AI is being used ethically.

11. AI Implementation for Robust Cyber Security

Traditionally, cybersecurity relied on signature-based detection, identifying and blocking known malware or attack patterns. However, this approach needs to evolve to keep pace with cybercriminals’ new, emerging nature. 

Today, AI offers a more sophisticated defense strategy. By identifying anomalies and unusual patterns in the data, AI can detect zero-day attacks, previously unseen malware, and other emerging threats. Here is how AI can help in increasing cybersecurity:

  • Enhanced Threat Detection: AI algorithms can now analyze network traffic patterns, user behavior, and system logs in real-time to identify anomalies that might indicate a potential attack. Machine learning models can be trained to recognize even the most subtle deviations from regular activity, allowing for proactive threat detection before significant damage occurs.
  • Automating Repetitive Tasks: Security analysts often tire of repetitive tasks like sifting through log files or analyzing suspicious emails. AI can automate these tasks, allowing human experts to focus on more complex investigations and strategic decision-making.
  • Improved Phishing and Malware Detection: AI can analyze the language patterns, sender information, and embedded links in emails to accurately identify phishing attempts. Similarly, AI is now being used to detect malware by analyzing file behavior and identifying patterns that deviate from normalcy.
  • Predictive Security: AI models can predict potential cyberattacks and system vulnerabilities by analyzing historical data. This allows security teams to take preventive measures and patch vulnerabilities before they can be exploited.

12. AI Hallucinations Are Becoming More Predominant 

AI has a new trick up its sleeve, and it’s different from what you might expect. Forget about replicating human tasks perfectly –  these machines are starting to hallucinate. AI models trained on limited datasets need more information to learn accurate patterns and relationships. This can lead the model to make up information or fill in gaps in its knowledge, resulting in hallucinations. 

While AI hallucinations are considered deviated results, they have their positive aspects, too. Overlooking those dimensions will simply ignore that anomalies in AI results can be fruitful, too. The increasing trend of AI hallucinations can benefit humans. Here are some examples demonstrating the use of AI hallucinations.

  • Reshaping Creativity: AI hallucinations can generate fresh ideas due to gaps in training models. By generating unexpected results that push the boundaries set by training models, they can inspire artists, musicians, designers, and scientists to explore new ideas and concepts they might not have considered. Hence, what we consider an error from the normal can act as ” out-of-the-box” thinking.
  • Complex Data Visualization: AI hallucinations can interpret the data sets in a new pattern. This unusual ability of AI can be used to uncover patterns in complex datasets. By exploring possibilities outside the data, AI can highlight potential trends or correlations that humans might miss, leading to more insightful data visualization and analysis.
  • Overcoming Creative Blocks: Often, the biggest hurdle in creative pursuits is overcoming a creative block. AI hallucinations can help by offering unconventional prompts or suggestions that nudge creators in new directions. This can be a valuable tool for writers, musicians, or anyone struggling to get ideas. 

13. AI Influencers Are On the Rise

One of the interesting AI trends we have seen on the rise is the emergence of AI influencers. These influencers are swiftly rising in prominence, capturing the attention of both brands and audiences.

These computer-generated personalities are crafted to emulate real individuals, sharing content, interacting with followers, and endorsing products across various platforms. This trend is underscored by the statistic that more than 52% of the entire US population follows at least one cyber-creator on Instagram.

For instance, Aitana Lopez, an entirely AI-generated influencer, boasts 303K Instagram followers and earns around $11,000 monthly from brand promotions. A few other popular AI influencers are Miquela Sousa, Imma, Kyra, Bermuda is Bae, Rozy Gram, Milla Sofia, and many more. According to Gartner’s research, the “digital human economy” is forecasted to reach a market value of $125 billion. 

The following facts back the rise of AI influencers: 

  • Cost-effective: AI influencers are generally cheaper to work with than human celebrities. 
  • Control:Brands can precisely tailor an AI influencer’s image and message to match their target audience in sync with the audience’s taste. 
  • Global Appeal: AI influencers can easily overcome language barriers which makes them appealing to international markets.

Conclusion

Artificial intelligence reigns as the ultimate game-changer in the tech world, and industries everywhere are racing to tap into its potential for revolutionary transformations!

This year, we are expected to see more AI innovations, which could surprise us in many ways. Many top AI trends anticipated for 2024 are already in development or industrial usage. From the rise of efficient small language models to the power of AI for logistic transformation, AI is rapidly reshaping industries and our daily lives. 

As we move forward, the continued development of AI holds immense potential to revolutionize sectors like healthcare, education with adaptive learning, and many other industries. For more updates on AI developments and trends, follow the blogs from Logic Square Technologies.

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