Gartner releases the Hype Cycle 2023
Gartner releases the new Hype Cycle 2023, completely AI and emerging technology-centric.
In this new model, the 2023 Gartner Hype Cycle identifies 25 emerging technologies that are expected to greatly impact business and society over the next two to 10 years.
What is the Hype Cycle
The term "Hype Cycle" originates from Gartner, a prominent research and advisory company that offers insights and evaluations of diverse technology trends. This concept entails a visual portrayal depicting the evolution, acceptance, and business influence of nascent technologies or advancements across a span of time.
I already went deep about the Gartner Hype Cycle in this post. You can find the “what, why and how” to use it extensively in the link above.
For Gartner, these technologies fit into four main themes:
Emergent AI,
Developer Experience,
Pervasive Cloud,
Human-Centric Security and Privacy.
Emergent AI
A notable instance within the domain of emergent AI is generative AI, which possesses the capacity to conceive novel iterations of content, strategies, designs, and methodologies by assimilating insights from expansive reservoirs of original source material.
This transformative potential will continue to exert profound impacts on business facets, including content and product evolution, the streamlining of human tasks through automation, and the augmentation of both customer and employee experiences.
Anticipated to gain mainstream acceptance over the next two to five years, generative AI stands poised to reshape content creation, workforce dynamics, and experiential domains.
Among the array of pivotal technologies enveloped by emergent AI are:
AI simulation: is a convergence of AI and simulation technologies to collaboratively forge AI agents and the simulated environments where they undergo training, assessment, and potential deployment.
Causal AI: extends beyond predictive models rooted in correlations, harnessing cause-and-effect associations to devise more effective AI systems capable of autonomous action.
Federated machine learning: is a methodology designed to educate machine learning algorithms sans explicit sharing of data samples, bolstering privacy and security protocols.
Graph data science (GDS): a field applying data science techniques to graph-based data structures, unveiling behavioural traits for constructing predictive and prescriptive models.
Neuro-symbolic AI: a fusion of machine learning methodologies with symbolic systems, yielding sturdier and more reliable AI models.
Reinforcement learning (RL): a subset of machine learning that exclusively trains through positive (reward) and negative (punishment) feedback mechanisms, cultivating adaptable learning systems.
Developer Experience
Elevating the developer experience stands as a paramount objective for the majority of enterprises. This thematic cluster of technologies concentrates on attracting and retaining top-tier engineering talent by facilitating seamless interactions between developers and the array of tools, platforms, processes, and individuals they collaborate with.
Illustrative of DevX technologies, the value stream management platform (VSMP) endeavours to optimize the complete product delivery lifecycle, thereby enhancing business outcomes.
Characteristically devoid of tool-specific biases, VSMPs interface with existing tools and assimilate data spanning all stages of software product delivery — from identifying customer requisites to actual value realization.
By doing so, VSMPs empower software engineering leaders to pinpoint and quantify opportunities for heightening software product performance through streamlined cost structures, operational models, technology deployment, and process refinement.
Anticipated to transition into mainstream adoption over the next two to five years, value stream management platforms are poised to reshape the software development landscape.
Additional pivotal technologies encapsulated within the developer experience realm encompass:
AI-augmented software engineering: leveraging AI technologies and natural language processing (NLP) to facilitate software engineers in crafting, delivering, and maintaining applications.
API-centric SaaS: a cloud-based application service architected with programmatic request/reply or event-driven interfaces (APIs) as the primary access points.
GitOps: a variant of closed-loop control systems tailored for cloud-native applications.
Internal developer portals: enabling self-guided exploration and access to resources within intricate, cloud-native software development ecosystems.
Open-source program office (OSPO): serving as a hub of expertise to formulate strategies for overseeing, managing, promoting, and optimizing open-source software (OSS) and open-source data/models.
Pervasive cloud
Within this theme, the spotlight rests on the evolutionary trajectory of cloud computing, positioned as a pivotal catalyst for business innovation.
These technologies are orchestrating a reimagination of cloud architecture at the periphery, rendering it more intrinsically interwoven and enabling domain-specific solutions.
Extracting the fullest value from cloud investments necessitates automated operational scalability, unfettered access to cloud-native platform tools, and robust governance protocols.
Exemplifying the realm of ubiquitous cloud are industry cloud platforms, designed to target industry-specific business objectives through the amalgamation of foundational SaaS, PaaS, and IaaS services into comprehensive product offerings, endowed with modular capabilities. These encompass an industry data fabric, an assemblage of pre-packaged business functionalities, composition tools, and other inventive platform components. By harnessing the compositional adaptability of these platforms, IT leaders can cultivate responsiveness and agility, pivotal in navigating rapid disruptions. These platforms are poised to traverse a journey of mainstream adoption over the next five to 10 years.
Further pivotal technologies within the domain of ubiquitous cloud encompass:
Augmented FinOps: an extension of conventional DevOps principles — encompassing agility, continuous integration and deployment, as well as end-user feedback — to financial governance, budgeting, and cost optimization endeavours.
Cloud development environments (CDEs): bestowing hassle-free, remote access to cloud-hosted development settings, minimizing setup and configuration complexities.
Cloud sustainability: the strategic utilization of cloud services to yield sustainable benefits across economic, environmental, and societal domains.
Cloud-native: a concept encompassing the optimal harnessing of inherent cloud attributes, enshrined within the original cloud computing paradigm, and encompassing service-oriented capabilities.
Cloud-out to the edge: an architectural paradigm wherein a centrally orchestrated cloud environment, often a hyper scale cloud, extends its service capabilities to edge contexts.
WebAssembly (Wasm): a lightweight, virtual-stack machine and binary code format tailored to support secure, high-performance applications within web contexts.
These diverse dimensions are poised to reshape the cloud landscape, carving new pathways for business innovation and technological integration.
Human Centric Security Privacy
Encapsulates technologies that revolve around organizations bolstering their resilience by embracing security and privacy strategies centred on human perspectives.
These technologies empower enterprises to foster a culture of mutual trust, nurturing a collective consciousness regarding shared risks in decision-making across multifarious teams.
An exemplary illustration within this realm is AI Trust, Risk, and Security Management (AI TRiSM), a manifestation of human-centric security and privacy practices.
This approach ensures the governance, reliability, fairness, trustworthiness, robustness, efficacy, and data protection of AI models.
Encompassing an array of solutions and techniques for model interpretability, data and content anomaly detection, AI data safeguarding, model operations, and resistance to adversarial attacks, AI TRiSM is poised to attain widespread adoption within the next two to five years.
Additional pivotal technologies in the sphere of security and privacy with a human-centric focus encompass:
Cybersecurity Mesh Architecture (CSMA): is an emerging paradigm for constructing composable and distributed security controls, enhancing overall security effectiveness.
Generative Cybersecurity AI: harnesses large repositories of original source data to generate novel iterations of security-centric and relevant content, strategies, designs, and methodologies.
Homomorphic Encryption (HE): leveraging algorithms to facilitate computations involving encrypted data, enabling data sharing without compromising privacy.
Postquantum Cryptography (PQC): alternatively termed quantum-safe cryptography, entails algorithms designed to fortify against both classical and quantum-computing-based attacks.