Re: Different kinds of memory

perhaps checkout https://cohere.com/  &  https://lmql.ai/

IMHO there's some bigger issues.  As TimBL would put it, at the "social"
layers.  which in-turn ties back to nuanced considerations related to the
question....

NB:
https://docs.google.com/spreadsheets/d/1jDLieMm-KroKY6nKv40amukfFGAGaQU8tFfZBM7iF_U/edit?usp=drivesdk

I'll add more to it over the next few days, then perhaps flag it with
you...  The langchain stuff is useful, but it's still overall complicated
to set-up. Some of the 'agents' examples are useful too, but the approach
is different to my historical approach  - that I haven't been able to
advance very well..   I think the desire is for a pervasive
surveillance ecosystem, tracking every keystroke - then censoring the bad
stuff certain 'castes' of society engage in doing...   harming others,
whilst benefiting for doing so...  so, bit depressed atm.

i've been a bit miserable...  here's a music playlist;
https://www.youtube.com/watch?v=NucJk8TxyRg&list=PLCbmz0VSZ_vponyiYMLdoJ_gGmA-6iwG_


Whilst I've been doing some work in the area - but, I need an LLM Machine -
so, waiting on that really..  thinking I might change my life and focus on
art creation or something that leads to income; I've done alot for human
rights support, anyhow.. imho, hasn't worked out; and,  I don't want to go
into it now.

imho; one of the purposes of DIDs &
https://docs.google.com/document/d/1Fwx3-YYyKgeigaScoMVoTFc3V2p-0jVwOg0IvMr8TZs/edit#heading=h.9mam9vryntlt
(note use of HDF5 containers); amongst other things, was about
decentralising commons infrastructure from a technical perspective - using
various different DLTs (depending on the characteristics needed, different
protocols suit - also, nothing 'standard' like http, certainly not then -
and blockchains can be centralised in ways different to say - CDNs... );
therein, 'commons' could be merely between two people (ie: lifecycle of a
relationship) or far broader (ie: laws in jurisdictions); therein, the
software agent for the natural agent(s) needs to take into account the
n-dimensionality of the status of knowledge of the natural agents involved
as observers, temporally, in experiences.

i probably haven't been as clear as i could have been; noting, w3c work was
thought of as getting the royalty free patent-pool supported 'thoughtware'
tooling components, to ensure people could own the software prosthetic of
self - rather than companies, or government, or whoever else wants to have
their hooks into it - like its a new form of slavery that'll help them make
money, long before anyone knows what to do about it; at which point, they'd
be unlikely to be penalised - which - has overall been shown to be true.
I"ve made attempts to produce some basic initial tooling, as a
web-extension, basically - to support social-web foundations, but am
finding it too hard...  but that could be a way of going, perhaps with the
solid cg or indeed also, the rww cg - yet, seems to be entirely
discouraged...

so, given that's seemingly the case;  been looking at what exists and how
it works,

LLMs don't appear to understand time - so, i've been doing some experiments
using characters from films - as LLMs know much more about the worlds
described by films / tv - whether it be contagion or star-trek, which
enables a means to in-effect, engage a sort of 'pointed graph' within the
LLM - with relatively short prompts - understanding that, they've been
defined in a way that seeks to ensure they're not sued for copyright
infringement, etc...  which means outcomes to prompts, etc.  might look
like something out of the scripts of media like contagion, but have enough
differences that makes it look 'new'...  thereafter, the need to do more
research on local systems as to get a better grasp on the science of it.

I"ve tried prompting systems using RDF with directives - sometimes it
works, sometimes not - seemingly, they prefer json - can provide the
outputs if desired..

but, in a thin-client world, where people are defined via a shared private
key - in-effect, helping to pay for compute by purchasing the machinery
needed to contribute towards the systems then used by others;

https://www.youtube.com/watch?v=qZiThp3CTyw

towards a world where 'ai takes all the jobs' requiring these people to
look forward to universal basic income - as the definition of work changes,
or at least the terms associated with the notion...

What are the political requirements for 'memory'?

When considering the social factors - there's a lot that the general
public, the people who vote as distinct to other cohorts - are expected to
forget - if standards are sought to be defined in this area, what would the
characteristics of it be?  as the natural considerations about
consciousness - don't appear to be treated very respectfully;  seems the
focus is on social influences; a limitless volume of parallel universes
computationally engineered to be applied upon people to live in; with, the
opportunity, perhaps, for people to make their own artificial realms, that
might be happier than those offered by others, for profit, power or
immunity to the consequences of wrongs...  which is hard to consider being
a new problem: https://www.youtube.com/watch?v=UkjyCPuTKPw

in anycase;

From 2016:
https://docs.google.com/presentation/d/1RzczQPfygLuowu-WPvaYyKQB0PsSF2COKldj1mjktTs/edit?usp=sharing

video is: https://www.youtube.com/watch?v=k61nJkx5aDQ

Numenta https://github.com/numenta/htmresearch-old  has been doing - what I
consider to be great work - in the area - re: sparsity.

But I don't see how it can work in a dishonest environment; where, the
spatio-temporal n-dimensional identifiers are aggregated and relabelled to
IP harvesters.

Bit more complex than the characterisation of problems associated with
http-range-14 or cooluris...

therefore; in consideration,

Rather than 'digital twins' or similar; the functions that appear desirable
is for 'artificial colleagues', where there's effectively - software
defined 'robots' that have different functions; whether it be the community
dj, or a researcher or a financial / administrative assistant, etc..
 therein, the process being similar to HR, defining the characteristics of
these 'colleagues' and their characteristics and qualities, access
privileges, etc.

older example is:
https://docs.google.com/spreadsheets/d/1VixKXjZL31bZRXQS9J1FmvPyDzdkgE8B2-3fzPmRYNc/edit?usp=sharing


but that list was produced to try to get people to think about the
characteristics of their 'ai assistant' / ai agents that they're
developing; whereas, more recently - i think...  telling an LLM to go into
'monty python' mode works.  similarly boston legal, or other examples that
have a lot of information (far more than can easily be provided by a
prompt) in the existing models...  and, perhaps also, more direct?  perhaps
that's part of how the scenario response frameworks actually function..  as
noted, earlier.

but what's likely to happen; is that, the means to define personal
assistants for VIPs / PEPs, etc.  will end-up requiring access to their
diary, health information, etc..  but, perhaps then it'll be easier to
understand the importance of broader ecosystems works that are define
natural agents in terms broader than shared private / public keys, in a
wallet...  idk...  also, the question of whose asset is - the asset rather
than the principal?

The commodification methodologies are highly evolved.  alternatives, not so
much. not sure if there's much interest, indeed, seems as though there
isn't really...  at least, not at the moment.

The other aspect, was - in langchain like methods - to have another bot,
like a supervisor bot, that checks the output of the bot process - as to
instigate corrections, where required.

So, overall,

https://github.com/Mintplex-Labs/anything-llm
https://medium.com/openlink-software-blog/introducing-the-openlink-personal-assistant-e74a76eb2bed

https://community.openlinksw.com/t/llamaindex-based-retrieval-augmented-generation-rag-using-a-virtuoso-backend-via-sparql/4117


and, I'll update the spreadsheet provided above with the other links I've
got, but haven't put into a public resource somewhere yet...

Yet, i hope to learn more about how these sorts of things fit into the
generation of artificial realms, whether it be generating game like
experiences - say, from a book or series of books; or, creating linear
media, again, from a book or similar - but - i don't think a language
taxonomy exists, across different 'large learning model' fields (llms) to
standardise the command structures, in-effect..

I think it's important to also consider how to ensure people are not
defined by others without any ability to do anything about it, when the
characterisation or purpose of any such definitions are wrong, whether, in
association to STEM (ie physics or life-sciences) or morally, otherwise....
and particularly, in a world where its assumed that people will be defined
by some app associated to a phone device related identifier only.

another idea, fwiw, in consideration of the 'social issues', was whether
these LLMs should understand RDF & thereby also, decentralised namespaces,
etc...  there's a variety of good technical reasons why this might benefit
the technology stack - as well as, having potentially meaningfully positive
attributes that could act to protect against various forms of potential
disaster, by decentralising the namespace in ways json can't do.

but idk.   There's alot missing from the stack required for what I intended
to produce re: "human centric" (ai)..  so much stuff, that's just not free
to do...

i hope something in my ramblings helps.

tim.

On Thu, 11 Jul 2024 at 00:16, Dave Raggett <dsr@w3.org> wrote:

> Unfortunately our current AI technology doesn’t support continual
> learning, limiting large language models to the datasets they were trained
> with. An LLM trained back in 2023 won’t know what’s happened in 2024, and
> retraining is very expensive. There are work arounds, e.g. retrieval
> augmented generation (RAG) where the LLM is prompted using information
> retrieved from a database that matches the user’s request. However, this
> mechanism has its limitations.
>
> For the next generation of AI we would like to support continual learning,
> so that AI systems can remain up to date, and moreover, learn new skills as
> needed for different applications through a process of observation,
> instruction and experience. To better understand what’s needed it is worth
> looking at the different kinds of human memory.
>
> Sensory memory is short lived, e.g. the phonological loop is limited to
> about one to two seconds. This is what allows us to replay in our heads
> what someone just said to us. Short term memory is said to be up to around
> 30 seconds with limited capacity. Long term memory is indefinite in
> duration and capacity. Humans are also good at learning from single
> observations / episodes. How can all this be realised as artificial neural
> networks?
>
> Generative AI relies on back propagation for gradient descent, but this is
> slow as can be seen from the typical learning rate parameters. It certainly
> won’t be effective for single shot learning.  Moreover it doesn’t apply to
> sparse spiking neural networks which aren’t differentiable.  Alternative
> approaches use local learning rules, e.g. variations on Hebbian learning
> where the synaptic weights are updated based upon correlations between the
> neuron’s inputs and output.
>
> One approach to implementing a model of the phonological loop is as a
> shared vector space where items from a given vocabulary are encoded with
> their temporal position, which can also be used as a cue for recall.
> Memory traces fade with time unless reinforced by replay. In essence, this
> treats memory as a sum over traces where each trace is a circular
> convolution of the item and its temporal position.  The vectors for
> temporal positions should be orthogonal.  Trace retrieval will be noisy,
> but that can be addressed through selecting the strongest matching
> vocabulary item.  This could be considered in terms of vectors representing
> a probability distribution over vocabulary items.
>
> A modified Hebbian learning rule can be used to update the synaptic
> weights so that on each cycle, the updated weight on each cycle pays more
> attention to the new information than to old information. Over successive
> cycles, old traces become weaker and harder to recall, unless boosted by
> replay. This requires a means to generate an orthogonal sequence of
> temporal position vectors. The sequence would repeat at an interval much
> longer than the duration of the phonological loop.
>
> The next challenge is to generalise this to short and long term memory
> stores. A key difference to the phonological loop is that we can remember
> many sequences. This implies a combination of context and temporal
> sequence.  Transferring a sequence from sensory memory (the phonological
> loop) to short and long term memory will involve re-encoding memory traces
> with the context and a local time sequence.
>
> This leaves many questions. What determines the context?  How can memories
> be recalled? How are sequences bounded? How can sequences be compressed in
> terms of sub-sequences?  How can sequences be generalised to support
> language processing?  How does this relate more generally to episodic
> memory as the memory of everyday events?
>
> I now hope to get a concrete feel for some of these challenges, starting
> with implementing a simple model of the phonological loop. If anyone wants
> to help please get in touch. I am hoping to develop this as a web-based
> demo that runs in the browser.
>
> Best regards,
>
> Dave Raggett <dsr@w3.org>
>
>
>
>