Unveiling the Top 5 AI Trends of 2024

2024 promises to be a pivotal moment in the AI journey. In this article, we count down the top five AI trends of the year.

AI Trends of 2024

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This article on AI trends of 2024 was written by Melody Zacharias with the support of Perplexity AI for a “better together” blog. 

The landscape of artificial intelligence (AI) continues to evolve at an unprecedented pace, reshaping industries, societies, and the very fabric of our daily lives. 2024 promises to be a pivotal moment in the AI journey, marked by transformative advancements that will leave a lasting impact. Here are five AI trends to watch in 2024, each representing a crucial aspect of the AI reorientation we are facing.

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AI has matured to a point where its integration into various sectors demands a comprehensive legal framework. This means AI legislation will be a crucial development to strike a balance between innovation and responsible use.

Governments worldwide are recognizing the need for clear guidelines to govern the deployment of AI technologies. Issues such as data privacy, algorithmic accountability, and potential job displacement are at the forefront of discussions. In response, legislative bodies are crafting policies to ensure the ethical and fair use of AI.

“As steps are already in process via the Biden administration’s issuance of an Executive Order in October 2023 concerning AI safety and security and the EU’s has move toward provisional agreement on an Artificial Intelligence Act in December 2023, we can expect in the near future new developments in legislation and associated regulations by many jurisdictions concerning this increasingly important technological and cultural change,” Terry Woo—Pure Storage VP, Deputy General Counsel for IP & Product

One notable development is the establishment of AI regulatory bodies. These organizations, equipped with interdisciplinary experts, are tasked with monitoring and regulating AI applications. The goal is to ensure that AI systems align with ethical standards and do not inadvertently perpetuate biases or discriminate against certain groups. 

Organizations that believe strongly in developing and deploying AI responsibly have testified before judiciary hearings in an attempt to help shape frameworks legislating AI. You can read Vice Chair and President of Microsoft Brad Smith’s comments in his article, “Developing and deploying AI responsibly: Elements of an effective legislative framework to regulate AI.”

AI legislation is also likely to address liability concerns. As AI systems become more autonomous, questions regarding accountability in the event of errors or unintended consequences must be answered. Legal frameworks will need to establish guidelines for attributing responsibility in cases where AI systems cause harm or fail to meet expected standards.

The EU’s “Artificial Intelligence Act” endeavors to acknowledge the value of the economic growth and innovation that AI can bring while also acknowledging the characteristics and concerns with regard to safety, security, and fundamental rights and protections it can bring against society. 

The AI legal frontier will undoubtedly continue to unfold and evolve, reflecting the global recognition of the need for responsible AI development and deployment.

Trend #4: Ethical AI: A Moral Compass for Artificial Intelligence

With AI infiltrating various aspects of our lives, ethical considerations are more critical than ever. Ethical AI is a concerted effort to infuse moral principles into the development and deployment of AI systems. It encompasses a range of considerations, including:

  • Fairness
  • Transparency
  • Accountability
  • Mitigation of biases 

Developers and organizations are increasingly recognizing the importance of addressing these ethical dimensions to ensure that AI benefits society as a whole.

One key aspect of ethical AI is fairness in algorithmic decision-making. Biases in AI models, often unintentionally perpetuated through biased training data, have raised concerns about the potential reinforcement and perpetuation of societal inequalities. In response, developers are working to implement algorithms that are not only accurate but also fair and unbiased.

Transparency is another key cornerstone of ethical AI. Users need to have insight into how AI systems make decisions, especially in applications such as finance, healthcare, and criminal justice. As a result, explainable AI (XAI) techniques are gaining prominence, allowing developers to create models that provide understandable explanations for their outputs. 

Accountability mechanisms are being integrated into AI systems to ensure that developers are held responsible for the performance and impact of their creations. This includes establishing clear lines of responsibility for the actions of AI systems and implementing mechanisms for addressing errors or unintended consequences.

The ethical AI trend reflects the growing recognition that the ethical considerations surrounding AI are not secondary concerns but fundamental required pillars that shape the responsible development and deployment of AI technologies.

One use case to keep an eye on: AI in the plagiarism detection space. Its role (and potential weaponization) is playing out in the academic space right now by single-handedly offering the scope and scale to mass-check published works in a sort of “scorched-earth” approach to accountability.

Trend #3: AI at the Edge: Bringing Intelligence to the Front Lines

The third trend of 2024, AI at the edge, marks a paradigm shift in how AI processing is handled. Traditionally, AI computations have been carried out in centralized data centers, but the rise of edge computing is pushing intelligence closer to where it’s needed—in real time, at the edge of networks, where larger pockets of data are being created.

Edge AI involves deploying AI algorithms directly onto devices like smartphones, IoT devices, and edge servers, reducing latency and enhancing efficiency. This trend is particularly significant in applications where immediate decision-making is crucial, such as autonomous vehicles, industrial automation, and medical devices.

One of the primary advantages of AI at the edge is the reduction in data transmission and manipulation. By processing data locally, organizations can minimize the need for large-scale data transfers to centralized servers, resulting in lower latency and improved responsiveness. This is especially critical in applications where delays could lead to severe consequences, such as in autonomous vehicles making split-second decisions.

AI at the edge enhances privacy and security. Since data processing occurs locally, sensitive information may not need to be transmitted over networks, reducing the risk of data breaches. This is particularly relevant in healthcare, where patient data security is paramount.

AI at the edge is not just a technological advancement; it’s a strategic approach to optimizing AI applications for real-world scenarios, creating a more efficient and responsive AI ecosystem.

Trend #2: Augmented AI: Elevating Human Capabilities

Trend #2 of 2024 will be augmented AI. It marks a departure from the traditional narrative of AI replacing human roles. Instead, this trend focuses on enhancing human capabilities by seamlessly integrating AI into various aspects of work and daily life.

Augmented AI leverages the strengths of both humans and machines, creating synergies that lead to more effective and efficient outcomes. The better together story will manifest in AI systems working alongside human employees, automating routine tasks, and providing data-driven insights to inform decision-making.

One key aspect of augmented AI is the development of AI systems that understand, collaborate, and complement human cognition. Natural language processing (NLP) and computer vision technologies play a crucial role in enabling seamless communication between humans and AI systems, making it easier for individuals to interact with and leverage the capabilities of AI.

In fields like healthcare, augmented AI is revolutionizing diagnostics and treatment planning. AI algorithms analyze medical data, providing doctors with valuable insights and recommendations to enhance their decision-making processes. This collaborative approach not only improves the accuracy of medical diagnoses but also allows healthcare professionals to focus on more complex and nuanced aspects of patient care.

The workplace of 2024 is characterized by humans working collaboratively with AI to weave a better together story, creating a symbiotic relationship that leverages the strengths of both to achieve unprecedented levels of productivity and innovation.

Trend #1: Integrations with AI and Multi-modal Models: The Seamless Fusion of Technology

Integration is the linchpin of AI’s transformative power. It underscores the seamless fusion of AI technologies into every facet of our lives, businesses, and societies. This trend is fueled by the growing availability of AI-powered application programming interfaces (APIs) that allow developers to integrate AI functionalities into their applications without having to build complex algorithms from scratch.

Integration with AI involves incorporating AI capabilities into existing systems and processes, creating a cohesive and interconnected AI ecosystem. This trend extends beyond individual applications and platforms, emphasizing the interoperability of diverse AI systems to maximize their collective potential.

AI is no longer a standalone technology. Instead, it’s woven into the fabric of business processes, from customer relationship management (CRM) to supply chain optimization. This integration enables businesses to harness the power of AI for data-driven decision-making, process automation, and predictive analytics. Integrations with AI are not only about incorporating AI into existing systems but also about fostering collaboration among diverse AI in all aspects of the technology stack and all vertical solutions.

Use case to watch: In the consumer space, API integrations for generative AI will be where the tech really takes off—turning queries and responses into leads and conversions. Kayak is an early example of a query about travel ideas generating not only a response but also links to flights and hotel stays.

Embrace the Future of AI with Pure Storage

We’re just scratching the surface, but one thing is certain: As these trends evolve, organizations’ infrastructures will need to evolve so they’re able to embrace change without disruption. Learn how the Pure Storage data platform can support every step of the AI journey

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