Top 10 AI Trends to Look Out For

OpenAI opened up Pandora’s box with the launch of the generative AI ChatGPT on November 30, 2022. Saying nothing’s ever going to be the same ever again is an understatement of how artificial intelligence (AI) has been shaping trends since.

Hitting the milestone of 1 million users within just five days (it took Instagram over 2.5 months), OpenAI’s breakthroughs accelerated the development of AI technologies, bringing us closer to a world where artificial intelligence can accomplish extraordinary feats. Whether it’s writing articles, automating tasks, creating deep fakes, defeating world champions in chess or driving cars, AI tools and tech have surpassed our wildest expectations.

With new trends changing the fundamentals of how we carry out all sorts of tasks, it’s imperative to keep track of the emerging AI trends that will shape various industries. As a leading AI development company, staying ahead of these trends is key to maintaining a competitive edge. Let’s explore the top 10 AI trends that you should look out for in 2024.

1. Democratization of AI

AI is becoming increasingly accessible to a wider audience, with tools and platforms designed to empower non-experts in developing and implementing AI solutions. This democratization of AI is revolutionizing how businesses of all sizes harness the power of this transformative technology.

Sy Choudhury, Director of Meta AI partnerships, further emphasized this during the Snapdragon Summit 2023 in Hawaii. “If you keep these technologies only in the hands of the few, then it’s not only a disservice to many other companies that don’t have the engineers or the hardware, but it’s actually a disservice to the world,” he stated.

Examples of AI democratization

With companies leaning more and more towards AI technologies and tools, you’ll find new AI tools popping up all around the world. Here are a few platforms and tools that we feel have exemplified the democratization of AI:

• ChatGPT: OpenAI’s generative AI solution pioneered AI democratization, offering free, user-friendly tools for diverse tasks.
• DALL-E: An AI tool that generates images from textual descriptions, enabling users to create visual content effortlessly.
• Open-source frameworks: Tools like TensorFlow and PyTorch provide pre-built algorithms and models that anyone can use, significantly lowering the barrier to entry for AI development.

While the benefits of deploying AI-powered tools are significant, challenges remain. Organizations must ensure that employees receive adequate training to use generative AI tools effectively and responsibly. Additionally, there is a risk of bias in AI outputs if not properly managed, highlighting the need for robust governance and oversight.

2. Multimodal AI Model

What started with simple text processing in a handful of languages has now broken free from those limitations with the latest AI trends—multimodal AI. These advanced AI systems can now seamlessly integrate text, images, and audio, overcoming the limitations of their text-only predecessors. 

Multimodal AI models enable AI to better mimic human-like understanding, processing complex information from various sources with unprecedented versatility and depth.

Here’s a statistic from a recent study published by MarketsandMarkets that highlights the significance of this trend—the market for multimodal AI is projected to increase from USD 1.0 billion in 2023 to USD 4.5 billion by 2028, with a compound annual growth rate (CAGR) of 35.0% during the forecast period.

Use cases for multimodal AI models

Multimodal AI models are being utilized in a variety of innovative ways across different sectors.

• Healthcare: Multimodal models can analyze patient data, including medical images (like X-rays), clinical notes, and lab results, to assist in diagnosis and treatment planning, and in identifying potential drug candidates. This holistic approach improves accuracy and enhances patient outcomes.
• Autonomous vehicles: These vehicles use multimodal AI to process data from cameras (visual input), LIDAR (depth perception), and radar (object detection) to navigate safely and efficiently in complex environments.
• Customer service: AI chatbots equipped with multimodal capabilities can interpret text, voice, and visual inputs from users, providing more personalized and effective support. For example, it allows users to upload a photo of a product issue while simultaneously describing it in text.

The rise of multimodal AI models represents a significant advancement in the AI space, enabling more comprehensive analysis and interaction with data.

3. Regulation, Copyright, and Ethical AI Concerns

With the increasing AI adoption in our world, it’s important to acknowledge its potential threats too. While artificial intelligence offers immense possibilities, it could create significant problems if left unchecked. Key concerns include:

• Miscreants & AI manipulation: Individuals using AI for deceptive purposes, such as creating deep fakes, can mislead and manipulate the public.
• Spreading misinformation: Automated systems inadvertently amplify false narratives, making it challenging for users to distinguish fact from fiction.
• Data privacy threats: AI systems collect too much personal information, raising fears about how this data might be used or shared.
• Transparency issues: Lack of transparency in AI-driven decisions leaves individuals confused and potentially disadvantaged without recourse.
• AI bias & inequality: Biased training data leads to systems that reinforce existing societal prejudices, potentially widening social and economic gaps.

As a result, there is growing attention towards AI ethics, regulation, and copyright issues. Responsible AI development is crucial to ensure that AI systems, particularly those utilizing machine learning and natural language processing, are transparent, accountable, and aligned with human values.

How can we address the AI regulations & ethical concerns?

Several steps are being taken to address concerns related to AI regulation. The European Union has proposed the AI Act, which aims to establish a regulatory framework for AI, ensuring that high-risk AI systems meet specific requirements for safety, accountability, and human oversight.

Additionally, UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, provides a comprehensive framework for ethical AI use, emphasizing the importance of human rights, fairness, and inclusivity in AI systems. Their recommendation aims to guide member states in implementing policies that promote ethical AI practices and mitigate risks associated with bias and privacy violations.

By prioritizing ethical considerations and regulatory measures, policymakers, industry leaders, and researchers can create a future where AI systems, powered by machine learning and natural language processing, are transparent, accountable, and aligned with human values.

4. Smaller Language Models

Size does matter! While Large Language Models (LLM) are known for their ability to handle complex tasks, they demand significant computational power and energy. 

A researcher from the University of Washington estimates that training a single large language model like ChatGPT-3 can consume up to 10 gigawatt-hour (GWh) of energy which is roughly equivalent to the yearly electricity consumption of over 1,000 U.S. households.

These limitations of LLMs, among others, paved the wave for the rise of small language models (SLMs), a specialized subset of artificial intelligence tailored for natural language processing (NLP). SLMs offer a practical alternative to large language models —they are characterized by their compact architecture and efficient performance of specific language tasks.

The trend towards more efficient, smaller language models is gaining traction as businesses seek to optimize their AI solutions.

What are the benefits of smaller language models (SLMs)?

• Efficiency: SLMs like DistilBERT and GPT-Neo provide high performance with lower resource requirements, making them ideal for smaller organizations.
• Cost-effectiveness: By consuming significantly less power, SLMs reduce operational costs associated with energy consumption.
• Scalability: SLMs can be easily integrated into various applications, enhancing their adaptability across industries.
• Faster deployment: With their lightweight architecture and lower resource demands, SLMs can be deployed quickly, allowing organizations to leverage AI capabilities without needing extensive infrastructure.
• Enhanced data security: SLMs prioritize data security, providing robust protection for sensitive information and ensuring user privacy.
• Reduced maintenance effort: SLMs require less ongoing maintenance, allowing teams to focus on strategic initiatives rather than infrastructure management.

As the demand for AI solutions continues to rise, SLMs represent a sustainable alternative that balances performance with energy efficiency, addressing the pressing concerns associated with large-scale data center operations.

5. GPU Shortage

The AI industry is currently facing significant challenges due to hardware shortages and rising cloud computing costs. The demand for Graphics Processing Units (GPUs) and other hardware solutions has surged, primarily driven by the rapid growth of AI applications, particularly generative models like ChatGPT. The supply hasn’t quite been able to bridge the gap, leading to inflated costs and long wait times for access to necessary hardware. 

For instance, the price of Nvidia’s V100 card had skyrocketed to over $10,000 in 2023, with waiting periods extending up to six months for delivery. In recent times, while the prices have reduced significantly in comparison, the waiting periods are still around three to four months.

Strategies to overcome GPU shortage

The rapid expansion of AI has created unforeseen obstacles, but the industry is actively developing solutions. Here are three key strategies being pursued:

• Investing in specialized AI chips: Tech giants and startups alike are racing to create custom processors designed specifically for AI workloads. These chips promise to be more efficient and powerful than traditional GPUs, potentially easing supply constraints and accelerating AI development.
• Optimizing infrastructure through GPU virtualization: By allowing multiple users to share GPU resources more efficiently, virtualization techniques maximize hardware utilization. This approach not only reduces costs but also democratizes access to powerful AI computing capabilities.
• Forging collaborative supply chains: In an unprecedented move, major tech firms are joining forces to boost production capacity for AI-specific hardware. Collaboration between these firms aims to create more resilient and sustainable supply chains, ensuring a steady flow of critical components.

By tackling these challenges head-on, the AI industry is paving the way for continued innovation. These efforts promise to make AI technologies more accessible, sustainable, and powerful, benefiting researchers, businesses, and end-users alike.

6. Collaboration of Humans and Robots (CoBots)

Who doesn’t dream of a tireless assistant to make work easier? Maybe a smart robot that can perform precise tasks or lift heavy loads effortlessly. 

While we can’t summon Autobots like Bumblebee or Optimus Prime from the big screen, the next best thing is already here – the CoBot.

Collaborative Robots, or CoBots, bridge the gap between science fiction and industrial reality. These sophisticated machines work alongside humans, breaking free from the isolation of traditional industrial robots. Unlike their caged predecessors, CoBots feature advanced sensors and safety systems, allowing them to share workspaces with humans safely and efficiently. 

But CoBots are more than just fancy mechanical arms. Enhanced by artificial intelligence and machine learning, they’re reshaping human-machine collaboration. These robots learn, adapt, and even anticipate needs, creating more flexible and productive work environments.

AI and machine learning transform CoBots in several ways:

• Enhanced sensing: AI-driven sensors enable accurate environmental perception.
• Adaptive learning: CoBots improve their skills based on real-world experiences.
• Intelligent planning: AI optimizes actions for improved efficiency.
• Improved interaction: Natural Language Processing allows intuitive communication.
• Predictive maintenance: AI analyzes data to forecast maintenance needs.

As these technologies advance, CoBots’ potential applications continue to grow. From precision manufacturing to personalized healthcare, they’re set to play a crucial role in shaping the future of work across various industries.

7. Shadow AI (and Corporate AI Policies)

Whether you are browsing through blogs and articles, or going over that well-drafted mail from your colleague, all of us have come across AI-generated content time and again. 

Artificial intelligence technologies are more accessible to the masses than ever before, and that has given rise to a concerning AI trend known as Shadow AI, where employees use AI tools without corporate approval or oversight. 

Unsanctioned use of AI highlights the urgent need for comprehensive corporate AI policies to ensure the responsible and secure deployment of AI within organizations.

Threats and Concerns associated with Shadow AI

Shadow AI poses significant risks to organizations, including data security breaches, compliance violations, and operational inconsistencies. Employees using unauthorized AI tools may inadvertently expose sensitive data or violate industry regulations, leading to legal troubles and reputational damage. 

Additionally, the lack of coordination in AI adoption can result in inconsistent processes and workflows across different departments, hindering collaboration and efficiency.

Establishing Corporate AI Policies

Companies must develop clear guidelines and best practices for the use of AI within their organizations to mitigate all the risks of adopting AI technology. These policies should outline authorized AI tools, acceptable use cases, and data handling procedures. 

Here are some policies that’ll help you get started: 

• AI usage guidelines: Clearly define the authorized use cases for AI and the types of data that can be used as inputs.
• Security and compliance requirements: Ensure that AI tools and processes adhere to industry regulations and data protection standards.
• Employee training and awareness: Educate employees on the risks associated with Shadow AI and the importance of following corporate policies.
• Monitoring and enforcement: Implement tools and processes to detect and prevent unauthorized AI usage, and enforce compliance with established policies.

Adopting a proactive approach enables companies to make the most of AI advancements and ensure the responsible use of this transformative technology.

8. Digital Twinning

All of us have seen or have wished for a twin at some point in life. While creating an actual twin is out of the question, with the help of cutting-edge technologies, we can now create digital twins for any physical object, person, or process in a virtual environment. 

Digital twins are virtual replicas of physical systems that are revolutionizing how businesses optimize operations and predict outcomes. By creating a digital counterpart of an asset or process, you can monitor performance in real time, simulate various scenarios, and make data-driven decisions.

Applications of Digital Twin Technology

As AI-powered digital twinning technologies continue to evolve, they enable companies to innovate and enhance efficiency across a variety of sectors.

• System prediction: Imagine predicting the future with uncanny accuracy. That’s what Doosan Enerbility achieves with its wind farm digital twin. By analyzing weather patterns and turbine performance, they forecast power output, maximizing energy production and revenue.
• System simulation:  “What if?” becomes “Let’s see” with a digital twin. For instance, Siemens Mobility developed virtual replicas to iron out potential issues in their high-speed rail projects before a single wheel turned on the track.
• Asset interoperability: In the high-octane world of Formula One, Scuderia Ferrari harnesses the power of digital twins to seamlessly integrate data from their racing machines. This fusion of information fuels split-second decisions and pushes the limits of performance.
• Maintenance: Digital twins can assist in predictive maintenance, ensuring operational effectiveness. For example, General Electric deploys virtual models that act as vigilant guardians, anticipating maintenance needs and preventing costly failures before they occur.
• System visualisation: Create 3D visualizations of complex systems with digital twin technology; think Italy’s vast railway network that comes to life in the virtual realm. Ferrovie dello Stato’s digital twin of over 10,000 miles of track enables bird’s-eye management and pinpoint coordination across the entire system.
• Product simulation: Used primarily during the design phase, a digital twin can also help you simulate product behavior. Virtual prototypes allow for rapid iteration and testing, slashing development time and reducing the need for physical samples. Companies like Nike leverage digital twin technology to test new shoe designs virtually.

That’s not all, the global market for digital twins is projected to grow significantly, reflecting their transformative potential across industries. According to IoT Analytics, the digital twinning market is expanding at a projected CAGR of 30% between 2023 and 2027. These predictions underscore the increasing reliance on digital twin technology to drive innovation and operational excellence.

9. Google’s Dominance at Risk as Companies Race to Develop AI Search

Google has been reigning as the indomitable champion in the league of search engines for decades. But with the onset of AI-driven search technologies, Google’s dominance might as well be on the line if it isn’t able to keep up. 

As big tech companies race to develop innovative search capabilities, they leverage advanced technologies to enhance user engagement and improve online visibility. This shift is not just about delivering better search results; it’s about creating a more personalized and intuitive experience for users.

Several AI-driven search tools are showcasing immense potential and promise for the future:

• Google Gemini: This advanced search engine leverages natural language processing and large language models to understand conversational queries, providing more relevant and context-aware results. It allows users to pose questions in a natural manner, enhancing the overall search experience.
• Perplexity AI: Known for its conversational interface, Perplexity AI enables users to engage in a dialogue with the search engine. It not only provides answers but also allows follow-up questions, making it a powerful tool for research and information retrieval.
• Microsoft Bing with Copilot: The AI-enhanced version of Bing integrates chat capabilities, allowing users to receive summarized answers and insights without sifting through multiple links. It combines traditional search with AI-driven responses for a more efficient user experience.
• Komo: Komo’s AI-powered search engine offers a delightful user experience by suggesting related questions and providing opinions from various sources. It uses machine learning to refine search results based on user interactions.
• Arc Search: Designed for mobile browsing, Arc Search features a ‘Browse for Me’ option that generates detailed reports from multiple sources, streamlining the search process for users on the go.

As AI search capabilities continue to evolve, businesses will have more options to enhance their online presence and engage with customers effectively, making the search landscape more competitive than ever.

10. AI Across Industries: Shaping the Future

Artificial Intelligence is revolutionizing various sectors, transforming how industries operate and deliver services. From cybersecurity to healthcare, education to manufacturing, AI is driving innovation and efficiency across the board.

Cybersecurity: AI’s role in threat detection and prevention

Machine learning algorithms analyze vast amounts of data, identifying anomalies and detecting potential threats in real time. Edge computing enhances these capabilities, allowing for faster processing and reduced response times to cyber attacks.

Healthcare: Innovations in diagnosis, treatment, and patient care

The healthcare industry is experiencing a surge of AI-driven innovations. Healthcare AI is accelerating drug discovery, enabling researchers to analyze complex biological data and identify potential therapies faster than ever before. Computer vision techniques applied to medical imaging assist doctors in detecting diseases and making more accurate diagnoses, potentially saving countless lives.

Education: Personalized learning experiences and administrative automation

AI-powered adaptive learning platforms analyze performance data and adjust content accordingly. Augmented reality enhanced by AI is creating immersive learning environments, bringing abstract concepts to life and engaging students in new ways.

Manufacturing: Streamlined production processes and quality control

Machine learning algorithms enable predictive maintenance, while computer vision techniques enhance defect detection with greater accuracy than human inspectors. These emerging technologies are paving the way for smarter, more efficient factories.

Science: Accelerating research and discovery processes

When it comes to scientific research, Quantum AI is being used to simulate complex systems and solve optimization problems that are intractable for classical computers. AI is also being applied to climate change research, analyzing vast amounts of environmental data to predict future trends and impact policy decisions in the near future.

Business: Enhancing decision-making and operational efficiency

By providing data-driven insights and automating routine tasks, AI enables businesses to make more informed decisions and optimize operations. AI-powered chatbots and virtual assistants are improving customer service, providing round-the-clock support and reducing response times.

As these technologies continue to evolve, the impact of AI will only grow, transforming industries and improving outcomes across the board. The integration of AI with other emerging technologies promises to usher in a new era of innovation and efficiency.

Wrapping Up

The AI revolution is here, and it’s transforming our world at breakneck speed. As we’ve seen, 2024 promises even more groundbreaking developments that will reshape how we work, live, and interact. 

Turning these cutting-edge AI trends into practical business solutions requires expertise in AI software development services. Space-O Technologies brings this crucial skill to the table. Their team of AI specialists crafts tailored software that leverages these emerging trends and tech, helping businesses of all sizes innovate and thrive. From startups to established enterprises, Space-O’s AI software development empowers organizations to make the most of AI’s potential.

Don’t just ride the AI wave – surf it with Space-O!