Gatfol for Talking Fridges

Talking fridges (and any other conversing appliances) have a major problem…

If I ask…

“Any beer in the fridge?”

“Any beer left?

“Got beer?”

What is the beer situation?”

“Do we have beer?”

“Out of beer?”

“Beer finished?”

“No more beer?”

the appliance language software has to semantically equalise my different ways of asking the same thing.

Currently this is extremely difficult outside of simple one-to-one synonym replacement.

Gatfol is the world leader in multi-word inline-, real-time semantic equalisation.

 Gatfol makes human-machine conversation work

Why Gatfol Makes Machine Based Sentiment Analysis Easier…

“I really hate that more people are unimpressed by the lack of low quality of this restaurant”

Is this a positive or negative sentiment statement ?

The mere aggregation of positive and negative expression words is insufficient to determine opinion.
Even hierarchically ranking sentiment words in terms of semantic contribution
to final statement
opinion only brings marginal efficacy increases.

Acceptance levels of more than 80% are extremely hard to achieve.

Gatfol applies an innovative and powerful approach to complicated sentiment analysis.

Multiword synonyms iteratively replace groups of statement words
in semantic progression from complication to simplicity:

“I really hate that more people are unimpressed by the lack of low quality of this restaurant”

“I am unhappy that more people are unhappy with the quality”

“I want less people to be unhappy with the quality”

“I want more people to be happy with the quality”

“I am happy with the quality”

Even though almost all of the original sentiment phrase words are negative –
through Gatfol simplification iteration – the final sentiment result is positive.

Gatfol Semantic Firewall


Gatfol has developed a router and network switch hardware-based natural language semantic firewall for deployment in enterprise data streams to control data leakage.

Developing hardware-based semantic firewalls is difficult :

Language permutation combinations in n-gram format are too many for router-based RAM storage         

Gatfol multiword-to-multiword firewall instances do not require static databases for signature retrieval. The trillions-upon-trillions of natural language permutations needed to effectively process multiword groupings of up to 20-words in input phrase sizes of up to 200-words overwhelm even the largest commercial databases today. Gatfol performs semantic equalisation between multiword groups fully in RAM employing several layers of heuristic filters – developed over 9 years – to bound permutation volume.

Language permutation iterations take too long with non-parallel processing

Even without static database retrieval, the amount of processing permutations at throughput volumes of gigabytes per second is too large to provide microsecond input-output delivery. Parallel processing of multiword groupings is required. Programming for parallel processing on single- or dual chip hardware is difficult. Gatfol utilises a simple multiple EXE architecture and massively scalable proprietary developed local Hadoop master-and-slave technology to let the OS take care of parallel processing. 

The same set of algorithms must work seamlessly between all natural languages

A semantic firewall must be able to filter any base language dynamically. Gatfol technology uses no language-specific grammar- or other processing rules. At embedded level, Gatfol runs on binary patterns and can process any system of repetitive symbols efficiently. Gatfol is functional in base English, -Chinese, -Arabic and any other natural language.

Semantic processing ontologies and definition lists normally require huge disk storage resources

The limited memory processing storage space on router- and network hardware prevents usage of very large ontologies, -word linkage repositories and -definition lists normally required for language semantic processing. Gatfol uses compact 2-gram, two-dimensional word linkage matrixes read fully into RAM combined with simplified Markov chain analysis to provide large permutation power. Total disk space required for even the largest Gatfol firewall deployment is only around 100 MB.

Guarding against false positives in multiword synonym equalisation is difficult

Multiword synonym replacement technology cannot work efficiently without grammar linkage verification. Most dictionaries list “detest, hate, loathe and abhor” as synonyms, but only grammar link filters show up usage frequency discrepancies when each of these words are used with i.e. the term “pizza”. Gatfol uses grammar linkage verification at both word linkage matrix building as well as input-output processing to ensure synonym equivalence quality.

Reflecting web concept relationship changes in real time is difficult

Concept linkages on the web can change unpredictably and abruptly. A representative semantic firewall must mirror linkage changes in real time. All Gatfol concept matrixes update dynamically from locally connected proprietary RSS crawlers to reflect rapidly changing patterns in web language within seconds after actual changes anywhere in the world.

Semantic firewalls can never be “offline” during housekeeping processes

Deployment of semantic firewalls running continuous packet inspection on local hardware is sub-optimal when signature updating requires human intervention at any stage in the update process. Gatfol multi-modular functioning at hardware level is fully hands-free and requires no human intervention of any kind.

Semantic processing systems require large combined CPU/RAM resources

The total Gatfol semantic firewall footprint is extremely small, both from viewpoints of processing power as well as storage. A full strength Gatfol firewall can run on as little as a single CPU and with only 3GB of RAM.

Language-based software applications with a statistical argument basis are never 100% accurate

Humans have an intuitive “accuracy” limit below which language product functionality is deemed inadequate. Accuracy controls inversely impact results volume. Balancing control limitations to volume depends on finely tuned static variables linked to naturally occurring patterns in language together with specific algorithmic functioning. Gatfol spent many years perfecting a proprietary multi-layered semantic intelligence filtering technology (SIFT) to maximise quality against processing volume and speed.

Gatfol Announces Successful Closing of Angel Seed Fund Round and EU Collaboration


Gatfol optimises search query input into large online industries worldwide through inline, real-time expansion of multiword keyword sets

Luqa, Malta (PRWEB UK) 21 November 2013

As part of its development initiative into Europe, Gatfol today announced the reaching of further milestones:

On reaching set technological programming benchmarks, Gatfol received final funding installments in its initial seed fund round with current investors. Gatfol will utilise the funds to set full EU development- and marketing functionality with Malta as base and operational satellites in New Zealand for Asia-Pacific reach and through government funded programs in South Africa for African continent expansion.

Gatfol founder Carl Greyling explained that development of its cloud-based application took twice as long as envisaged. The complexity of bringing product delivery speeds down to microseconds from the current milliseconds – especially for processing input exceeding gigabytes per second – proved more challenging than expected. Carl re-iterated that “…the team wants to thank our set of investors for their extreme patience and endless advice and guidance through this extended final stretch of the development path….”.

Gatfol is proud to announce finalising of its European expansion through Malta as development base: “…The massive benefits that European resource proximity bring to us – especially given the wide application range of our base technology and limited team size – contributed hugely to successful closing of our investor round as we could present a lean but realistic operational budget to funders – especially through our extremely discounted cloud subscription…” Carl said.

About Gatfol

Gatfol is the culmination of 12 years of work originating in the United Kingdom in 2001. Virtual auditing agents were developed using an intelligent natural language accounting application with neuro-physiological programmatic bases to penetrate, roam in- and report on patterns in financial data. This led to an Innovation in Software award from the European Union in 2006 and formed the basis of the Gatfol algorithms and technology currently in development.

Gatfol’s immediate aims are to improve search using semantic intelligence (meaning in data), both on the Web and in proprietary databases. Gatfol technology was provisionally patented in the USA in April 2011 and received PCT protection in 144 countries worldwide.

It is Gatfol’s vision to eventually enable humans to talk to data through all relevant interfaces and on all possible devices.

Those interested in learning more about Gatfol technology can visit Gatfol Blog For more information, contact Carl on Skype: carl_greyling.

Gatfol Europe

Gatfol is proud to announce that it is setting up an operational base in Malta Europe as outflow of an invitation from Malta Enterprise. Gatfol also joined the Microsoft BizSpark program at Sky Parks in Luqa.

Gatfol’s European presence will open up large market segments on the Continent and will add to the current strong operational development in Africa.

Gatfol is now in the final stages of quality- and speed testing across all the integrated modules on a networked 6-machine cluster flown in from South Africa. Gatfol will port- and test to Azure from MIC premises shortly and finalize a commercial-processing backed web interface with client online testing functionality across all of the Gatfol application fields.

Why Gatfol Technology is Extremely Timeous…Siri for Everybody…

General-purpose conversational assistants by design use voice
recognition technology to isolate “key terms”
to mine sources for useful results…

Here’s the problem:

Imagine any sentence of about 8-10 words…

i.e. “I really appreciate my mother in the morning”

What would happen if we replace each word with – let’s say –
ten equivalent words that fit both grammatically and semantically?

i.e. “I definitely/positively/demonstratively…” “like/admire/love my mother…”

Taking the original phrase and randomly inserting the
replacement words in all possible groupings that still make sense, we get
100 million phrases that are ALL grammatically intact and semantically equivalent
– and
we are still only saying that we feel positive about our mother some time early in the day!

…Even the smallest body of text of even minimum complexity has trillions upon trillions of equivalent
semantic permutations.
In terms of conversational assistants – and without a Gatfol functionality-
we just do not have the
backend concept-combination multiplication power
to even begin to cover the permutation problem…

Five years ago…

 …Apple’s SIRI was not a reality…
…Augmented reality was in theoretical infancy…
…Semantic replacement technology was not on the radar…
…Apple was not promoting wearable computing devices with natural language interfaces…


…the world is starting to realise that we have to merge the immense richness
and depth of human
everyday language with the limited actionable
instructions of software programs and databases
if we want
to rely on digital machines to guide our lives…

Five years from now…

…SIRI equivalents and augmented reality will be everywhere…

 …many commercial suppliers of semantic language phrase replacement technology will exist…

…Gatfol is already the first…

Gatfol Language Semantics Announces First Integrated Prototype Installation

Gatfol is a South African origin provisionally patented (USA) Search Technology with built-in human language intelligence

Centurion, South Africa (PRWEB) April 26, 2013

Gatfol serves base technology to provide digital devices with the ability to process human natural language efficiently.

The goal of truly semantic search has not yet fully been realized. The main problem is the enormity of ambiguous word permutations of semantic equivalence in even the simplest of phrases, which up to now has processing-wise required huge structured lexicons and ontologies as guides.

Gatfol is developing its patented technology commercially to massively improve all keyword-based search in the millions of in-house and public online databases worldwide. A first fully integrated prototype has now been installed on a clustered network of twenty seven desktop computers in Centurion South Africa.

Founder and CEO Carl Greyling firmly believes that Gatfol technology is crucially needed by many digital processors worldwide. Without a Gatfol-type solution, further development in many large digital industries is difficult. These include: Online retail (Amazon, Staples, Apple, Walmart), online classified advertising (Craigslist, Junk Mail), online targeted advertising (Google, Facebook, Twitter, Yahoo), augmented reality (Google Glass), national security in-stream data scanning (FBI, CIA, most governments worldwide), abuse language filtering in especially child friendly online environments (Habbo Hotel, Woozworld), image- and video auto-tagging for security monitoring (most police forces worldwide), human-to-machine natural language interfaces (all web search engines like Google, Yahoo!, Bing, Ask) and web text simplification for disadvantaged web users.

The Gatfol operational technology comprises multiple redundant local machine-based master and slave software nodes to process input in parallel to ensure extremely high throughput speeds at very large input volumes, regardless of machine- and CPU hardware configurations. This applies to the full production cycle from web RSS-based sourcing to microsecond delivery of output to calling applications. Processing speed increases are proportional to the volume of nodes applied.

Unlike almost all competing technology available today, Gatfol provides robust parallel processing power from even simple desktops or laptops. With all data streams staying local to the processing machine, application of Gatfol technology in especially security-based environments (ie battlefield deployments) is not compromised by networking- and online processing- or data transfer exposure.

With a simulated Hadoop multiple master-and-slave node architecture built around simple but robust Windows™ executable files and with multiple fall back redundancies around both master and slave functions, as well as all nodes individually carrying full word relationship databases, reliability of throughput is ensured – especially critical in large volume streaming functionality.

With a base in ordinary executable files, Gatfol also secures legacy hardware and OS (Windows XP and older) functionality and easy portability in instances of local machine OS upgrades.

The Gatfol standalone footprint can be easily incorporated into wider distributed processing architecture including full Hadoop-, as well as cloud based environments – with corresponding scalability in throughput performance.

RSS sourcing is widely scalable. Throughput has already been successfully tested at nine terabyte of web text per month.

Current best input-output performance of a 50-100 level deep Gatfol semantic crystallization stack on a standalone desktop (Intel Dual 2.93 GHZ 3.21GB RAM Windows XP) for text throughput is 3.6mb/hour for a single Gatfol cluster instance, 11.78mb/hour for a 10-cluster instance and 98mb/hour for a 100-cluster instance.

On a standalone desktop (Intel Quad 3.30 GHZ 2.91GB RAM Windows XP) best text throughput for a 50-100 level stack on a Gatfol 1000-cluster instance is 611mb/hour.

Total text throughput for a 50-100 level stack on an ordinary desktop Microsoft Networks-linked grouping of 20 desktops (Intel Single core 2.8GHZ 768MB RAM Windows XP) each running a Gatfol 100-cluster instance is 1.9GB/hour – giving maximum text output volume of 140GB/hour.

About Gatfol

Gatfol is the culmination of 12 years of work originating in the UK. Virtual auditing agents were developed using an intelligent natural language accounting system with neuro-physiological programmatic bases to penetrate, roam in- and report on patterns in financial data. This led to an EMDA Innovation in Software award from the European Union in 2006 and formed the basis of the Gatfol algorithms and technology currently in development.

Gatfol’s immediate aims are to improve search using semantic intelligence (meaning in data), both on the Web and in proprietary databases. Gatfol technology was provisionally patented in the USA in April 2011 and has PCT protection in 144 countries worldwide.

It is Gatfol’s vision to eventually enable humans to talk to data on all relevant interface devices.

Those interested in learning more about Gatfol technology can visit Gatfol Blog. For more information, contact Carl Greyling at Gatfol on +27 82 590 2993.

Gatfol Empowers The Silent Six…

What would Booker Dewitt, Jason Brody, Commander Shepard,
Master Chief, Lara Croft and Raiden say to each other when they finally meet?


 Wouldn’t it be amazing to give these guys real human language intelligence?

 Wouldn’t it be fantastic if they could understand the last dying words of their enemies?

 Wouldn’t it be incredible if they could swear like the rest of us when they pump bullets into flesh?

Gatfol provides massively scalable multiword-to-multiword
replacement technology to make gaming language AI possible…

Could Permutations Kill Ray Kurzweil’s Google AI Dream?

I remember reading somewhere online about a recent interview
(amongst many) with Ray Kurzweil – Director of Engineering at Google and the
world’s staunchest AI visionary – where he mentioned something about his plans in the line of…

to get the Google computers to understand natural language, not just do search and
answer questions
based on links and words, but actually understand the semantic content

I cannot remember where I saw the interview and will Google it…

…but I have a problem…

I know my keywords are fuzzy and I use Gatfol to show me some sample permutations on my input
…and realise that with 13 keywords and only 10 same-meaning single word replacements for each
keyword, as well as allowing for multiword to semantic equivalent multiword replacements, I am
looking at a minimum of 10 000 000 000 000 000 000 000 000 different ways to phrase my input
…all with the same basic meaning…

I also realise that I want a very specific search return that semantically equates to my input and
does not just hit and return pages corresponding to the trillions of different keyword combinations…

I quickly calculate that just by browsing my own thoughts and looking at my immediate
physical surroundings, I can theoretically come up with trillions upon trillions of meaningful
search input queries…each with trillions upon trillions of semantically equivalent phrasings…

The thought hits me that if Google wants to morph into Ray Kurzweil’s vision of a search
mind with which I can communicate and interrogate as if human – perhaps in five to ten years –
it will have to perform massively scalable multiword to multiword semantic equivalence transformations…

…Gatfol currently does this…

Gatfol – The Second Child of Internet Search Technology…

Can you see the future ?

Amit Singhal: Senior Vice President (Google Search)
…SXSW conference 2013, Austin Texas USA…

“having humanity’s knowledge on the Web is not enough, you have to understand it”

“search challenges: knowledge graph, speech recognition and natural language”

 “…the perfect search engine should know
exactly what you need and give you what you want”

“our dream is to become the star trek computer”

“people have completely come to expect search engines to work and
the questions they have been asking have gotten harder and harder”

“…voice is clearly crucial for the future”

“…a star trek computer you could type or talk
and connect to would be crucial to (search) success”

 …all good and well, but why can we easily generate millions
of semantically equivalent search query replacements while
Google still cannot return semantically equivalent result groupings?

If I ask meaning wise the same thing – just with different
keywords – shouldn’t my return results be semantically consistent?

Why do we have to laboriously guess, replace and aggregate
synonyms as keyword alternatives to improve returned search results?

“….I would really like to find the web meeting discussion where the
participants discussed help and assistance to Indian farmers to help ease
yield decreases and with immediate concern funds and finance issues on the ground….”


 “…I would actually like to get the web talk where the
participants discussed support to Indian farmers to improve yield
net gains and with the critical thought of money issues on the ground…”


 Not one of the top ten Google return
results for my first query is replicated in even
the top
hundred results of my semantically equivalent second search query…


We dream of talking to Star Trek computers but are still
stuck for optimisation in the “caveman-speak”
three word input search query universe…


Beam me up Gatfol…