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               “The mind that opens to new ideas never returns to its original size.”
                                                      – Albert Einstein


                               THE FUTURE OF KNOWLEDGE TRANSFER

KEY POINTS

1. The future of human knowledge transfer is (A)Intelligent.
2. Humans and AI will collaborate to improve knowledge transfer.
3. To be human-centered, AI knowledge has to be based on knowledge created by humans.
4. Knowledge created by machine (AI) learning has to be trusted, i.e. matching human knowledge, to be used by humans in knowledge transfer.
5. Knowledge “augmented” by human-AI collaboration will be pivotal for the progress of human society in the Cognitive Age.

Education and Work in the Future
Today’s education and learning started as “walk and talk” in the Ancient world, went through “chalk and talk” in the Medieval ages, and printed pages after Johannes Gutenberg invented the movable type in publishing. Today it meets the digital knowledge transfer (we call e-learning) of the Information Age forced by the requirements of the growing knowledge-based economy. The further “intelligent” advancement of information technology (we call today artificial intelligence - AI) will bring new possibilities and challenges to humanity caused by inevitable “cooperation” between humans and AI at the workplace and in everyday life.

The Need for Change
The AI-driven automation in business and accelerated advancements in remote work, gig, and knowledge economy require innovative approaches to keep the workforce relevant to fast-changing requirements for the almost constant need for knowledge updates and upgrades. Obviously, the “learning” (knowledge acquisition) culture has to change accordingly to keep relevant to fast-changing (intelligent) Industry 4.0 and business in the “AI Age” based on the Cognitive Revolution (merging human and artificial intelligence as collaborators).

Knowledge transfer (KT) has to change to meet all those fast-coming requirements:
- Better knowledge quality–delivered by the best-of-the bests subject matter experts (SMEs)
- More efficient process–done with the least investments (time, effort, and financing)
- Just-in-time–on-demand initiated by the learners
- Do-It-Yourself (DIY)–at the learners' own pace and place
- 24/7 personalized intelligent online support, guidance, and fast reference if needed.

Intelligent Technologies in Knowledge Transfer

The primary application areas of AI (i.e. intelligent technology) are in the automation of complex information systems and processes that require control and creativity of human-like intellect. The knowledge transfer (KT) systems/platforms and processes belong to this group of complex systems and processes.
As was presented already in the KT section of this blog, such systems include a lot of complex components (among them humans–the most complex of them all), processes, and operations that require constant real-time control so the system can function at its highest efficiency.

   Integrating AI-based technologies in KT systems will automate many processes taking part in these kinds of systems. Among those are:
- AI-powered personal KT assistants to guide both knowledge contributors and seekers to perform correctly their communication and interaction with the system
- Creating human-centered human-machine interfaces (voice and visual) for bidirectional communication
- Personalization–detecting knowledge gaps and failures of the learner in real-time and suggesting additional knowledge paths to correct them; creating personalized knowledge structures/maps and paths on them following the personal preferences of the learner; synchronizing the KT process with the user’s style, etc.
- Automation of monitoring, collection of data (history of KT process for each user), applying metrics, analytics, and creation of feedback data for presenting to both users and
   e-tutors/mentors for further (personalized) guidance of the learner.

      The only tools that can help KT complete this task are the latest advancements in IT applications focused on improving knowledge transfer and acquisition–the knowledge transfer technologies (KTech). As was already presented in the previous blog section, KTech combines both–“traditional” IT and AI technology to improve the cognitive process of knowledge building in the brain. The intelligent KT technologies will “take” all the “jobs” for “intelligently” automating and supporting all processes external to the brain, such as dynamic knowledge representation and KT process control by applying the required metrics, analytics, and feedback in real time.

AI and Human Knowledge Transfer

       We may consider AI as “Intelligence 2.0” (using computer/Internet jargon) if our “natural” intelligence could be considered Intelligence 1.0. It will be an “upgrade” to our human intelligence and one of human’s peak creations aiming to “augment” our brain's “power”.
One (desired) direction of us is for AI to “serve” people thus making their lives “better”. This “vague” term “better” will probably undergo many adjustments in the process of the development of society. The direction is (more or less) clear, but the goal is still not clearly defined. It is like the answer “42” to the “ultimate question” of Life, The Universe and Everything set by Douglas Adams in his famous “trilogy” of six books The Hitchhiker's Guide to the Galaxy.
So, how can AI serve us in the future knowledge transfer of humanity?
In Part 3 of Book 1, I argue that AI is for automation. Automation of information/data processing, analyzing it, and making decisions. All other properties (functions) and the constantly growing vocabulary of functional terms attached to them today by AI developers are still for marketing purposes.
       Any human-made/designed process can (and has to) be controlled to guarantee the required quality of its outcomes and the highest possible efficiency. This requirement and rule can be also applied to the knowledge transfer process. To reach these requirements, the KT process has to take place in an especially designed environment (system) that measures and controls all the parameters of the process in real time.
In the PDF file attached to this blog section (GKP_Concepts.pdf), I present concepts for building such a system involving innovative approaches and the latest intelligent technologies for building a Global Knowledge Platform (GKP).
The purpose of such a Global Knowledge Platform is to streamline knowledge management, sharing, and transfer into a successful and efficient knowledge acquisition process for all knowledge seekers (learners) around the world. This way GKP will transform the entire learning process and experience from selecting the right knowledge source to self-evaluation of acquired knowledge done most efficiently supported by AI-powered personal knowledge transfer assistants in real time.

       Here are the GKP’s structure and functionality summarized:
- GKP automates the knowledge transfer (KT) process
- The KT process is monitored, controlled, and personalized in real time by artificial    intelligence (AI) agents (assistants)
- Knowledge to be transferred is presented by information
- Information is structured as interactive multimedia content representing and conveying knowledge
- Content is split into small portions—knowledge objects (KOs)
- Knowledge acquisition metrics and analytics are integrating into KOs thus forming active knowledge objects (AKOs)
- Combined AKOs form KT modules
- The KT modules represent specific topics
- Combined modules construct completed courses
- The courses represent knowledge subjects (in any subject area, field, and domain)
- The KT modules and courses are hyperlinked and dynamic in structure and content depending on the learner’s needs.
Such a structure and functionality of GKP makes KT highly personalized to meet the learners’ background knowledge, learning goals, and style. It keeps the KT process and its outcomes at the highest level of efficiency and quality.
GKP will pool humanity's knowledge to be transferred to the next generations living and working in the Cognitive Age.

Human-Created Knowledge for AI Systems
       Machine (AI) learning process cannot produce knowledge that matches human knowledge, because the AI systems do not interact with the environment as humans do when building experience, i.e. knowledge verified by practice in real settings. The machines also lack motivation and emotions that many times help improve the learning outcomes.
Such problems with the quality of machine learning not producing human-centered knowledge may be avoided if we “implant” into AI systems human-created knowledge modules on selected knowledge subjects.
The above-mentioned platform may be used to transfer (“implant”) human-created knowledge into AI systems with various applications, thus improving trust in the knowledge they operate. Such an approach is also presented in the attached GKP_Concepts.pdf file (the link to it is below).
So, once more time the knowledge of humanity and its efficient transfer are at the core of our success in the future. Humanity has to prepare for the coming knowledge-demanding future in time. Fortunately, new advanced intelligent technology will help us in our quest for more knowledge.

Dear Reader, this blog was aimed to inform you about the coming social change and motivate you (and society) for a timely reaction. I hope you get the message. Sure, there is more information about it in Book 1.

I don't say “Farewell and thank you for all the... reading!” to you. I hope to “see” you again at the presentation of my next books after I published them on Amazon.com. So, stay in touch (my contact info is in the next section).
If you want to know more about the author, you can continue to the next section About the Author (the link is below).

Thank you very much for your time.
Tinko Stoyanov
Your Author and Guide to Beyond the Knowledge Horizon quests.