Digital Literacy, Data Privacy, and Algorithmic Bias (sorting the data)
Understanding AI and digital technologies for information and service access.
Ensuring privacy and mitigating biases in AI algorithms.
2-5 years of integration with business processes.
Models start to distribute across cloud and devices to find their correct scale fit.
The NEXT Phase
Biome AI, AI Ethics & Safeguarding, and Ambient Education
Integration of AI in biological lives and everyday environments.
Developing robust ethical frameworks for AI.
Creating ambient Education and AI environments facilitated by AI. Education is too slow and monolithic for this to happen sooner.
5-10 years.
The LATER Phase
Fully Autonomous Agents and AI as lifelong companions
AI operating without human supervision in various sectors. Value globally is arbitraged by these agents in near real-time. A renaissance of the idea of smart efficient markets?
Global access to hyperlocal models which match context and personal choices, through personal devices which need not be cloud connected.
AI understanding human emotions for psychological, contextual, and business support.
No idea… I have no idea…
Human flourishing and expression
Celebrating Human Diversity: Now to Later
AI understanding and adapting to human experiences and identities.
AI supported creativity: Soon to Next
AI assisting and leading in art, music, and literature creation.
Algorithms managing financial transactions and negotiations.
Ubiquitous multi-modal UX: Next to Later
Rise of integrated displays and interfaces for AI interaction.
Intentional UX for accessing diverse networks of information: Later
Sectors
Generic Business Efficiency and Productivity Predictions
Generative AI enhancing business productivity and efficiency through domain-specific models, optimizing workflows, and integrating multimodal applications.
Advantage for employees who intersect with AI tooling, increasing engagement and productivity… but for the benefit of whom Social contract and jobs
Most time benefit from things “close to the metal” such as Dev Ops, coding, macros, light financial work, etc. Thing someone “looked up” then did.
AI-driven project management which doesn’t sound like much but… Diagrams as Code
Enhanced virtual collaborative environments(think reduced travel for the classic complex visual tasks)
Remote working; Metaverse and Telecollaboration (notes, minutes, knowledge management tools like this one). This includes radically improved document understanding
Business to business mixed reality. I have been doing this since 1997. It’s always going to be big “SOON”, but the signs are improving and I can’t discount it as easily this time.
Concentrate on the lowest hanging fruit, most people are not using this, you’ll get huge advantage. Protect your data, let the market develop. If you have a clear business case then do get a consultant and legal advice (£1000/hr). Make a risk matrix, use my GPT if you like.
Consumer Services and Personal Use
AI agents managing emails and consumer services, offering personalized and intuitive user experiences.
In a future where intentional programming user experience (UX) evolves to its next stage, we could envision a scenario where multimodal and language models interface directly with bytecode, driven by semantic instructions from product owners. This approach would represent a significant leap from current programming paradigms, emphasizing a more intuitive, less syntax-heavy interaction with software development.
1. Semantic Instruction and Bytecode Manipulation:
In this future, product owners or non-technical stakeholders could provide instructions in natural language or through other intuitive interfaces.
These instructions would be semantically analyzed by advanced language models, capable of understanding the intent and context of the request.
The language models would then translate these semantic instructions into bytecode – the lowest-level code executed by the computer’s processor.
This process bypasses traditional programming languages, allowing for more direct and efficient creation or modification of software functionalities.
2. Multimodal Interfaces:
Multimodal interfaces, incorporating voice, text, and possibly visual or gestural inputs, would make the process more accessible and intuitive.
These interfaces would cater to a diverse range of users and preferences, allowing instructions to be given in various formats.
3. Enhanced Collaboration and Iteration:
By enabling product owners to directly communicate their requirements to the software, the gap between idea conception and implementation narrows significantly.
This direct communication loop would facilitate rapid iterations, as changes can be implemented and reviewed in real-time or near-real-time.
4. Automated Testing and Validation:
The system would incorporate sophisticated automated testing mechanisms.
As soon as the bytecode is generated or modified, a series of automated tests could run to validate the functionality, ensuring that the changes meet the specified requirements and do not introduce bugs or vulnerabilities.
Product owners could also perform their own tests on the resultant functionality, using user-friendly testing tools integrated into this system.
Less Optimistic
This is taken from Sam Hammond who I have discovered recently. All his stuff is summarised and linked here.