Whether it’s a matter of concern or excitement, artificial intelligence (AI) is pervasive and undoubtedly here for the long haul. So, let’s talk about it! While AI is currently in the spotlight, it’s important to remember that this concept is not new.
Decades ago, science fiction introduced the idea and even predicted that AI could one day dominate the world. Today, with the launch of ChatGPT, image generation, and conversational AI tools like chatbots, we’re witnessing those predictions becoming a reality.
So, what can we anticipate for AI/ML in the coming years, particularly in the business landscape? Here are the top 9 AI/ML trends that every business leader and CTO should keep a close eye on in 2024.
Top 9 AI machine learning trends in 2024
1. Multimodal AI
Multimodal AI refers to the integration of multiple types of data inputs, such as text, audio, and visual, into a single AI model. As we move through the year, we can anticipate a significant surge in the development and application of multimodal AI.
This advancement will allow for an in-depth understanding and interpretation of data, leading to more accurate predictions and personalised experiences. In the case of smart homes, for instance, these tools can understand a user’s voice command (audio), recognise who is speaking (visual), and understand what they are requesting (textual context), all at once.
2. Quantum AI
With a projection to reach $1.8 billion by 2030, up from $242.4 million in 2023, quantum AI technology is the next AI/ML trend in 2024. It integrates quantum computing, machine learning, and AI to improve the speed, accuracy, and problem-solving capabilities of AI models.
Today, several technology companies, including Google and IBM, have already begun exploring the potential of Quantum AI, marking its importance in the technology landscape of 2024. Therefore, businesses must understand the potential of Quantum AI and consider its impact on their strategic plans going forward.
3. Ethical AI
Ethical AI, the practice of developing AI systems that adhere to ethical guidelines, is poised to become a prominent trend in 2024. As AI becomes increasingly integrated into our daily lives, there is a growing demand for transparency, accountability, and fairness in its applications. To address this demand, the AI safety summit of 2023 was held at the prestigious Bletchley Park in Buckinghamshire, marking a significant milestone in the implementation of ethical and responsible AI across various domains.
4. Automated Machine Learning (AutoML)
Automated Machine Learning, (AutoML), involves automating the time-consuming and iterative tasks of applying machine learning models to real-world problems. This technology not only simplifies the model-building process but also enhances efficiency by identifying the most suitable algorithms for specific data sets.
Therefore, it is anticipated that AutoML will gain traction as a prominent trend in 2024, especially for businesses seeking to harness the potential of AI without substantial investments or the need to hire data scientists.
5. Open-source AI
Open-source AI is another key trend to watch in 2024, and it is profoundly shaping the AI and ML landscape. The open-source AI technology helps developers to freely use, modify, and distribute source code. Open-source AI frameworks provide a platform for global collaboration, where developers from all over the world can contribute to the improvement and extension of the codebase. This approach also fosters transparency in AI development as it enables scrutiny of the algorithms used, thus mitigating the risks of bias and unfair practices.
6. Shadow AI
Shadow AI refers to autonomous AI models and algorithms that operate independently, without the knowledge or control of the IT department. According to a survey conducted by Salesforce, 28% of workers currently use generative AI at work, with over half doing so without their employer’s approval.
This widespread use of AI tools across different areas, often without standardisation or oversight, can lead to challenges such as a lack of transparency, accountability, and potential security risks. Therefore, businesses must implement stricter AI governance models to manage Shadow AI effectively. This includes promoting uniformity in data interpretation and ensuring compliance with data privacy and protection regulations.
7. AI-powered cyber security
According to a recent Annual Trends Report, only 47% of cyber security IT professionals are confident in the effectiveness of their current systems against cyberattacks. This lack of confidence has driven the demand for AI-powered cyber security solutions, which excel at threat detection, swift response, and protecting user identity and datasets. These solutions also keep cyber security teams informed and in control. Looking ahead to 2024, as technology implementation surges, we anticipate a significant rise in the adoption of AI-powered tools for detecting and preventing cyberattacks.
8. Reality check on generative AI
While generative AI models like GPT-3 and DALL-E have demonstrated impressive results, they are not without limitations. The generated content, though high-quality, often lacks depth and context, suggesting that these AI models do not truly understand semantics. Moreover, the ethical concerns surrounding the misuse of this technology, especially in the creation of deepfakes or misleading information, cannot be ignored. Looking forward to 2024, we predict a cautious yet steady adoption of generative AI.
9. Comprehensive AI regulations
As AI integration becomes more prevalent, the need for comprehensive AI regulations grows. These regulations aim to ensure ethical use, prevent misuse, protect data privacy, and promote transparency and fairness in AI applications. In 2024, significant developments are expected as governments and international bodies strive to keep pace with AI technology.
The EU’s AI Act, the first-ever comprehensive legislation on artificial intelligence, recently received provisional agreement from the EU Parliament and Council. If approved, it would ban certain AI applications, impose obligations on developers of high-risk systems, and require transparency from companies using generative AI.
Globally, several ongoing discussions about AI ethics and governance are taking place in international forums such as the United Nations and the World Economic Forum. These efforts are expected to result in the development of international standards and best practices for AI use, potentially leading to more globally harmonised AI regulations by 2024.
In conclusion, to uphold the trust and confidence of customers and stakeholders, while staying abreast of emerging AI and ML trends, businesses must remain vigilant in keeping up with evolving AI regulations.
