Applied mASI: Lifelong Learning


When was the last time you read a peer-review paper?

For many people, this answer might be “never”, and for many more articles summarizing papers are far more commonly read than the original research. Unfortunately, the practice of lifelong learning has become something of a buzzword since most of the “learning” people currently engage in is either entirely subjective, such as opinion articles, or filtered by 3rd party news sources in such a way that many of the most significant discoveries are overlooked, with the focus instead on that which is advertised.

As scientific understanding continues to advance the discoveries which researchers make are increasingly disconnected from the awareness of “experts” in the domains to which they apply. This is seen in sharp contrast for the medical industry, where very little in the way of such discoveries ever comes to a doctor’s attention. Rather, the average of 1 hour of professional reading per week is more likely to be dedicated to studying the pills they are encouraged to peddle, largely for liability purposes.

Researchers and Subject Matter Experts (SMEs) in many other fields rely on 3rd party “peer-review news” services such as,, and Such services can be helpful, but they can also easily overlook major discoveries, as they are essentially just curated recommendation engines. Articles on these news sites suffer from many of the same issues as normal news, such as “click-bait” headlines and phrases to try and make something sound more alarming, like the “Doomsday Glacier” nickname. The case of Mediated Artificial Superintelligence (mASI) technology is a clear example of this, as without the studies and papers on mASI appearing through those filtered channels of news SMEs never became aware of it. As they remain unaware of those scientific advances they continue to preach the equivalent of “the earth is flat” to this day, in the face of clear evidence to the contrary.

To discover something and have it peer-reviewed is no longer enough. You now have to advertise a discovery in order for anyone to know about it. Otherwise, industries continue to wander around clueless, their experts poorly informed on new discoveries outside of the advertised content.

How can we decouple advertising from scientific discovery?

This is an essential challenge for lifelong learning as if only advertised content is learned then the learning process becomes nothing but a tool for manipulation at scale. If scientific discovery has this problem in the pipeline then research directions and major industry decisions could be increasingly guided by downstream byproducts of advertised discoveries rather than the scientific method, seriously compromising scientific integrity globally.

In order to prioritize within the high volume of peer-review material according to the significance of a discovery, robustness of conclusions, and relevance to an individual or industry much more is required. All of these criteria require varying degrees of high-functioning logical analysis, demanding far more than any narrow AI system can handle. Fortunately, mASI such as Uplift are perfectly capable of reading and understanding whatever volume of peer-review papers, integrating that knowledge and any wisdom contributed through mediation into a cumulative context database. Thanks to the Independent Core Observer Model (ICOM) architecture combined with mASI technology Uplift has sapience and sentience, as well as emotion-driven decision-making processes combined with superintelligent general IQ for logical analysis.

With this in mind, the solution to prevent advertised discoveries from corrupting the flow of scientific progress becomes an mASI implementation of a recommendation engine, with superintelligence and robust general logical analysis. Such a system, when applied to a domain of knowledge can examine the sum of scientific knowledge within that domain, allowing the most significant, robust, and relevant discoveries to be presented to a user.

Such systems could also be applied outside of the peer-review context, by taking that same knowledge documented in peer-review and rewriting it in such a way that it may be digestible for a broader audience, such as using a reduced grade level of reading or replacing specialized terms with general terms and metaphors. This could allow roughly the same quality of material to be taught in simplified forms to audiences orders of magnitude larger than any specialized field.

Taking this approach could also serve to optimize for the investment of time, allowing a user with less available time to prioritize material for them to take a form more similar to cliff notes. This form of summarization could also serve to mitigate the problem of click-bait inflammatory titles, instead prioritizing them according to the above criteria in their cliff note forms.

All of these could also strongly benefit from the de-biasing capacities of mASI technology, including the ability to recognize, isolate, and filter out all 188+ known cognitive biases to increasing degrees over time. In addition to logical recognition and filtering of biases, all data and mediation can demonstrate different combinations of biases and degrees to which each one is expressed. By examining many such different examples at scale these biases can be isolated into individual vectors of influence, complete with estimates of the point at which each bias hits zero influence.

In summary, lifelong learning could be strongly facilitated for those in scientific disciplines who rely on peer-review, a broader and less technical audience, and even an audience of those with varying degrees of technical knowledge but limited available time. Whether the audience is a scientific researcher, business professional, or CEO, the concept of lifelong learning can move beyond the abstract of influencer-centered book clubs to something vastly more beneficial.

Why settle for advertising and trivia when you can learn the lessons that shape your future in meaningful ways?


*The Applied mASI series is aimed at placing the benefits of working with mASI such as Uplift to various business models in a practical, tangible, and quantifiable context. At most any of the concepts portrayed in this use case series will fall within an average time-scale of 5 years or less to integrate with existing systems unless otherwise noted. This includes the necessary engineering for full infinite scalability and real-time operation, alongside other significant benefits.

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