Top Emerging Trends for AI Platforms in 2024 and Beyond
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“It’s hard to overstate how big of an impact AI and machine learning will have on society over the next 20 years” – Jeff Bezos.
In this age and probably in the next century, artificial intelligence (AI) will be the cornerstone for futuristic enterprises seeking to make an impact. From language translation to disease detection, AI has come a long way—the global AI market is projected to touch $1,811.8 billion by 2030. [1]
Businesses are increasingly investing in AI to boost productivity, scalability, and security. And at the core of this journey is AI platforms—sophisticated frameworks that help organizations adapt, learn, and automate.
The landscape of AI platforms
AI or artificial intelligence platforms, are integrated software solutions that provide a framework for developing, deploying, and managing various artificial intelligence applications. These platforms typically include a combination of tools, libraries, and services that facilitate the implementation of AI algorithms, machine learning (ML) models, and other cognitive computing processes. AI platforms offer a centralized environment for data processing, analytics, and the creation of intelligent applications.
In practice, these platforms can benefit organizations in multiple ways – from implementing dynamic pricing models at retail stores to detecting complex diseases in healthcare, quicker fraud detection in insurance, and optimizing baggage management systems in airports – the possibilities are never-ending. As AI evolves, these platforms will continue to play a pivotal role in driving innovation across diverse industries.
Spotlight on emerging AI trends and the new AI platforms
In tandem with the dynamic landscape of AI platforms, it is crucial to delve into the emerging artificial intelligence trends that are poised to shape and redefine their capabilities. Here are 11 emerging trends surrounding AI that are gaining popularity:
- Augmented AI
Augmented AI or augmented intelligence integrates artificial intelligence (AI) capabilities with human intelligence to enhance and complement human decision-making and problem-solving. This type of AI platform uses deep learning and ML along with AI to improve collaboration, expedite data processing, and enable automation and personalization. For instance, at HTCNXT, we’ve designed MAGE, our enterprise AI platform for advanced medical imaging, diagnosis and personalized product recommendations.
Looking ahead, businesses will see an integration of this augmented potential with Augmented Reality (AR) and Virtual Reality (VR)—leading to a highly immersive and interactive world that brings together the digital with the physical. For instance, AI-driven AR applications could enhance the travel experience by providing real-time information about landmarks, historical sites, and points of interest for self-guided tours.
- Quantum AI
Quantum Information Science (QIS), AI/ML, and deep learning use qubits to solve complex problems by speeding up data processing and analysis. This can help improve ML algorithms, thus making everything faster – from logistics to supply chain management.
In personalized medicine, for example, quantum AI can expedite the drug discovery process by simulating molecular interactions at unprecedented rates, thereby advancing the pace of research.
- Edge AI
With Edge AI, the deployment happens directly on local devices or edge devices, rather than relying on a centralized cloud-based system. This results in real-time visibility and enables instant decision-making.
With Edge AI, enterprises can yield multiple benefits, like how a pioneering healthcare firm has created a non-intrusive glucose monitoring device leveraging edge AI technology. Through real-time analysis of a user’s glucose levels, this device enables more effective diabetes management without the necessity for uncomfortable finger-pricking.
- AI-driven automation:
Automated Machine Learning, or AutoML, leverages ML, NLP (Natural Language Processing), and other AI capabilities to automate processes, significantly saving time, expediting efficiency, and enhancing productivity. The MAGE platform, for instance, helps enterprises use AutoML to power faster drug discovery in healthcare and automate underwriting in insurance.
AI-driven automation will see significant advancements in the coming year, especially in the wake of a more consumer-centric business ecosystem. In travel, for instance, AI-powered chatbots are becoming more popular for optimizing customer experience with instant and informed support — 87% [2] of users express a willingness to engage with AI chatbots if it saves them time and money.
- Sustainable AI:
Sustainable AI refers to the development and deployment of artificial intelligence systems with a focus on minimizing their environmental impact and promoting long-term ecological sustainability. The goal is to create AI technologies that contribute to environmental conservation, energy efficiency, and overall ecological responsibility.
Sustainable AI platforms prioritize energy efficiency in their design and operations – a rising focus for green enterprises looking to reduce their carbon footprint. Besides, sustainable AI platforms also take into account ethical data usage practices – to balance the power and potential of artificial intelligence with environmental and societal responsibility.
- Explainable AI (XAI):
Customers will eventually want to understand AI to be able to trust it. Platforms will prioritize transparency, offering explainable AI (XAI) that allows users to comprehend the policies and the decision-making processes that enterprises have employed in their AI usage.
The outcome will be value-generating interventions and risk mitigation. Industries with heavy regulations, like healthcare and insurance, will benefit immensely, wherein fraud-detection and risk-assessment processes are simplified using XAI.
- Small Large Language Models (SLMs):
SLMs strike a unique balance between large language capabilities (like sophisticated language understanding and generation) and the efficiency and agility of smaller models—offering a more accessible and sustainable approach to AI. This simplifies research, quick training, and cost-effective deployment, to name a few.
For example, query resolution can be made easier with SLM-driven multilingual chatbots in airports across continents, thus making communication accessible using instant language translation. Another way SLMs can be used is in clinical research to help researchers derive quick insights from extensive datasets, thus speeding up discoveries.
- Federated Learning (FL):
As security concerns increase, federated learning (FL) now also involves training AI models across multiple decentralized devices or servers while keeping data localized. It offers advanced encryption, zero-knowledge proofs, secure multi-party computing, and ensures data analysis tools are privacy-aware.
With healthcare and insurance being data-driven industries, deploying federated learning platforms can help safeguard critical information and enhance credibility. For example, FL helps with collaborative research of large datasets in underwriting by securing access to more diverse data pools, thus ensuring better policies.
- AutoGen:
AutoGen frameworks simplify the processing, optimization, and automation of LLM workflows. It is powered by customizable and conversable agents that use the best abilities of advanced LLMs. It addresses LLMs’ limitations by collaborating with humans and conversing via automated chat. This makes building a complex multi-agent conversation system a minimal process of defining two things: a set of agents with specialized capabilities and roles and the interaction behavior between agents.
Amongst its many capabilities, AutoGen can develop a multi-agent coding system that can write code, implement optimized solutions, and answer user questions. This technology can then benefit industries like healthcare, where it can power data-driven functions like drug interaction checks.
- Ethical AI:
The other side of the coin to Federal Learning is Ethical AI, which refers to the development and deployment of artificial intelligence systems in a manner that aligns with ethical principles, values, and human rights. The goal of ethical AI is to ensure that AI technologies are designed, implemented, and used in ways that prioritize fairness, accountability, transparency, and respect for the well-being of individuals and society as a whole.
For example, WHO recently launched a set of guidelines for LLMs that underscore ethical considerations in healthcare, such as data security, autonomy, and privacy.
- AI Legislation:
As AI evolves, this trend will play a crucial role in ensuring that the challenges and risks associated with AI technologies, such as potential biases in algorithms, privacy concerns, and the impact of AI on employment, are addressed responsibly. The purpose of AI Legislation is to establish a legal framework that ensures responsible and ethical development, deployment, and use of AI systems.
On December 9, 2023, the EU reached a provisional agreement[3], posing itself as the world’s first set of rules on AI, paving the way forward for ethical AI adoption globally.
As these AI evolutions come into play, organizations are curious to embrace the platforms that will help them maintain the competitive edge and scale. Prominent players in this transformative space, such as Amazon Augmented AI, Google Quantum AI Lab, and Microsoft Azure AI, are harnessing these trends into their platforms to reshape the game.
Additionally, enterprise-specific AI platforms like MAGE further underscore the versatility and impact of these advanced AI capabilities, providing tailored solutions to meet the unique demands of businesses in various sectors.
What sets these platforms apart?
- Interdisciplinary integration:
AI platforms can foster collaborative ecosystems with cross-domain applications, data centralization, and predictive models. In healthcare, for instance, this enables AI platforms to increasingly merge with fields like biotechnology, neuroscience, and nanotechnology for groundbreaking innovations – from early detection of diseases like Alzheimer’s to enhancing paralytic care.
- Global accessibility:
Increasing efforts are being made to make AI platforms more accessible globally – not just across geographies, but also for the differently abled and socially vulnerable. From assistive technology to speech recognition software and enhanced medical care, AI platforms will be key to breaking down barriers for developing countries and smaller organizations.
- RAG (Retrieval-Augmented Generation):
By incorporating RAG models, AI platforms can dynamically retrieve external information for improved context understanding and efficient information retrieval. This makes them adept at supporting more accurate, contextually relevant, and data-backed exchanges, as seen in the case of humanized chatbots and suggestion-based solutions.
- The Impact of GPT-5:
AI platforms can leverage GPT-5, the latest iteration of the Generative Pre-trained Transformer model, for advanced natural language understanding and generation, enabling more sophisticated applications such as contextual chatbots, language translation, and content creation. By integrating GPT-5’s powerful capabilities, AI platforms can enhance user interactions, automate complex language tasks, and generate human-like text across various domains.
- Explainability and interpretability:
XAI platforms distinguish themselves by emphasizing explainability and interpretability, offering tools that demystify the decision-making processes of complex models. This transparency enhances user trust and aligns with regulatory requirements, addressing concerns about the “black box” nature of AI. Articulating how AI arrives at specific outcomes ensures accountability, making these platforms invaluable in industries like banking and insurance, where understanding and justifying model predictions are critical for compliance and ethical considerations.
- Customization and extensibility:
Most modern AI platforms enable customization and extensibility, thus empowering users with the flexibility to tailor algorithms, conduct feature engineering, and seamlessly integrate custom models. This adaptability caters to the diverse needs of businesses, allowing them to address specific challenges unique to their domain. For instance, Amazon’s recently introduced cloud-based AI platform, Amazon Bedrock, boasts a user-friendly interface enabling businesses of all sizes to develop customized AI models that generate new text, images, and audio according to specific industry parameters.
Applications and real-world impact
The implementation of AI platforms is increasing by the day, wherein a finance conglomerate successfully completes 360,000 hours of finance work in just seconds with the help of AI-driven automation.
Similarly, a leading automotive giant brought together generative AI and augmented AI to power voice assistance to more than nine million vehicles. At the same time, in healthcare, a specialized lab developed a ring designed to derive personalized insights into a user’s sleep patterns and overall well-being, thus showcasing the prowess of Edge AI.
The future of AI platforms is seeped in innovation more than simple adoption. As enterprises begin to experiment continuously, results will be equally rapid and creative, driven towards enriching communities, processes, and businesses.
Challenges and ethical considerations
However, with information and data in a ready-to-use format, data privacy and security, AI bias, and inequitable access become highly problematic. Many have expressed rising apprehensions about businesses using artificial intelligence in their daily applications.
HTCNXT understands this and has embarked on a conscious journey towards bringing in Ethical and Usability Principles to create Humane AI, an ethical guide to delivering Responsible AI solutions. These are tested against seven fundamental principles for responsible AI, ethics of AI, and AI governance.
Conclusion
As the potential of AI platforms unfolds, it becomes evident that these technological enablers are not merely tools but critical catalysts reshaping innovation entirely. The amalgamation of cutting-edge algorithms, scalable architectures, and integrative capabilities has positioned AI platforms at the forefront of transformative endeavors.
Looking ahead, AI platforms are set to explore and thrive in uncharted territories with limitless growth possibilities. This journey will be guided by a commitment to transparency and collaboration with room for scalability and hyper-personalization–as visible from the democratization of AI through AutoML, the growing demand for ethical AI practices, and the growth of AutoGen.
On the other hand, trends like XAI, Quantum AI, and Federated Learning have set the stage for a new dawn in which AI platforms not only augment human capabilities but become trusted partners in our collective pursuit of progress. As we stand on the precipice of this AI renaissance, the defining question is what sets these platforms apart and how they will harmonize with our aspirations, reshape industries, and create a future where the extraordinary becomes the new norm.
References
[1] https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
[5] https://www.modernhealthcare.com/technology/how-edge-ai-transforming-healthcare-faster-you-think
[6] https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-digital-trust-truly-matters
[7] https://www.forbes.com/advisor/in/business/ai-statistics/
AUTHOR
Sudheer Kotagiri
Global Head of Architecture and AI Platforms
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