Technology laboratory for Enterprise Artificial Intelligence
Inferz: Rethinking AI
Our ground-breaking AI software platform enables the building of next generation AI applications which can self-learn and self-reason with an inherent intelligence to handle the multiple conflicting uncertainties and complexities of the real world. The applications are based on dynamic discovery of knowledge and common-sense reasoning, are fully transparent and are able to communicate conclusions reached by analytic and synthetic acumen.
The platform has been built bottom up on the principles of soft computing, perpetual reasoning and model driven cognition.
Our cognitive platform enables a new generation of AI applications delivering understanding and advanced problem-solving in ambiguous real-world settings
Cognition not just Recognition
Inferz is an AI Technology Laboratory. We are focussed on innovation in cognitive AI.
Our mission is to develop a new generation of symbolic/sub-symbolic AI which overcomes the limitations of existing AI. This new generation is characterised by AI which can both self-learn and self-reason (not just one or the other), is transparent (not black-box), can tolerate the multiple forms of complexity and uncertainty inherent in the real world (not failing when confronted by the unexpected, the imprecise or confliction) and can deeply understand natural language (not just statistically match or detect pattern features).
We develop new forms of representations, algorithms and technologies - unifying symbolic and sub-symbolic approaches to solving problems or synthesising solutions in pervasively uncertain situations.
The Inferz platform unifies machine learning, machine reasoning, machine-based language understanding and other capabilities.
It is based on the principles of soft computing (i.e. is tolerant of imprecision, uncertainty, partial truth, multiple contexts, approximation and likelihoods), perpetual reasoning (i.e. continuous and recurring revisions of belief) and model driven cognition (i.e. using comprehensive ontologies and knowledge to reason and learn), and employs novel forms of representations, algorithms and technologies. As a whole, the Inferz platform unifies symbolic and sub-symbolic approaches to solving problems or synthesising solutions under pervasively uncertain situations.
Inferz’s principals each have four decades practical experience of applying a wide range of AI techniques to deliver a broad spectrum of business benefits.
We are supported by a network of associates drawn from academia and business who are thought leaders and innovators in the application of Adaptive AI to complex problems.
We continue to build our network of participators and advisers who wish to be part of a community to further the cause of true cognitive AI. Please contact us if you wish to take part.
Inferz is our response to many years’ first-hand experience of implementing AI. For each wave of AI we have encountered major limitations. In particular, we believe that the current wave of Neural Network based AI has hit a brick wall because it is no more than opaque pattern matching without any ability to reason or understand. Our AI platform draws on elements of earlier waves, harnesses modern computing power (which was not available to the early waves of AI) and adds new elements (see below).
Our motivation is to build better AI. We operate as a Technology Laboratory with no plans to productise our platform.
At some point we will work with other parties to ensure that the potential of the Inferz platform is fully realised.
A mandate for an adaptive cognitive AI
Current AI is in a logjam.
The simplicity of ‘connectionist AI’ was introduced to overcome the lack of computing power required for the ‘smart AI’ of the 80s/90s. But connectionism has led AI into a simplicity tar pit because of its lack of transparency, symbolic representation and acumen. Ironically computing power has now surpassed the dreams of earlier AI practitioners and symbolic AI.
In the 90s the AI community faced a dilemma. We needed to increase the sophistication of AI capabilities to address increasingly ambitious real-world analysis/synthesis tasks, whilst being progressively thwarted by the lack of computing power. Unfortunately, and with hindsight, the AI community made a poor choice - we opted for overly simplifying the world and building an AI momentum on a cartoon of AI, one based on simple recognition technologies. This is motivated by a grossly simplified interpretation of the human brain’s neural structure (connectionism). This constrained problem solving to just detection processing and simplistic pattern learning. To handle real-world complexity AI requires much more including cognitive processing and sophisticated adaptive/dynamic reasoning. The failure to appreciate that the ‘emperor has no clothes’ is why we are now in a logjam. Non-symbolic neural recognition AI will never be able to realise cognitive computing ambitions. Replicating the mind (not just the brain) requires the power of symbolic cognition in combination with the current generation of power computing.
Such higher-level acumen needs to deal with the ‘polyform’ nature of our complex world. An AI is needed that handles the complexity of human-like learning (ML) and reasoning (MR), explicit representation, uncertainty/vagueness/probability, breadth of dynamic truth/belief. It should be capable of handling: the hyper-dimensional nature of learning/reasoning in complex worlds; the hype-associations, relations, sequences and the extra-dimensional facets of time and events; and the meta-dimensionality properties of world such as context, conjecture, hypotheses, assumption, etc. Polyform AI also recognises and handles the myriad forms and types of insight, wisdom, knowledge and information.
But we are now overstretching ‘naïve AI’ beyond its capabilities. It is a recognition technology ill-suited to cognitive machine thinking. What is required is a new mandate to re-engage smart symbolic AI and move forward towards cognitive intelligence whilst using some of the restricted advances of non-Symbolic/sub-Symbolic AI.
Highlights from the progressive AI mandate are shown below, the full mandate can be viewed by partners using the link.
Fundamentals of adaptive cognitive AI
Soft computing is the cognitive basis for analytics and synthetics
Soft computing is fundamental to cognitive AI as it is able to deal with vague and probable forms of knowledge, situations, reasoning and learning, and to manage simultaneous multiple contexts/motives in an adaptive way. Soft computing differs from conventional (hard) computing in that soft computing is tolerant of imprecision, uncertainty, partial truth, approximation and likelihood.
The guiding principle of soft computing is: exploit the tolerance for fuzziness, vagueness, likeliness and holding multiple contradictory beliefs, ideas, or values at the same time. Soft computing is exploited by learning, reasoning, representation, retrospection and other AI capabilities. Being able to deal with the vagaries of the real world delivers tractability, high resolution, high precision and robustness in solution modelling and problem solving as required in many areas of complex enterprise systems.
A summary mandate for an adaptive cognitive AI
The following introduces each section of our mandate (with a more extensive explanation of each section available using the 'more' button). Some of the details require a user login to view the material. The full mandate is available to partners.
An important mandate concept is unified soft computing. Cognition is founded on graceful soft computation which is a merging of soft machine learning, soft reasoning, soft optimisation, soft knowledge representation and soft linguistics. These elements are interplaying throughout the soft framework technology.
In short, the Inferz mandate for AI is to replicate fully adaptive human cognitive processes: the human mind not the human brain. It is a more difficult task than the current simplistic neural technologies. Current AI has hit a road-block by merely digitally mimicking the human brain’s neural network architecture which stymies progress to General AI.
So a different approach is needed. One which is:
strongly representational and ontological
underpinned by and universally driven by an understanding of uncertainty
can deal with both recognition and high-level cognition/problem solving
explicitly based on cognitive psychological tenets and process
can cope with the polyform nature of reasoning, learning and motivation
Such an adaptive cognitive approach is built to provide predictions in situations whilst able to deal with the inherent uncertainties in solving problems. As such it is firmly based on human vague rationale and is explicitly designed to provide sufficient explanations; it can transparently clarify a situation and why it came to be.
That is the MI.ND platform.
Cognitive AI and the Machine
Problems are not all alike: problem solving has different orders of complexity and approaches
As problems become more complex and the learning of knowledge is based on a synthesis of billions of cases, humans cannot cope. Progressively human expertise needs to be replaced by machine intelligence and machine expertise, as the world becomes more instantaneous, uncertain, complex and continuous. This machine expertise needs to become based on soft computing and derived through machine learning – manual programming is just too error prone and slow.
Human experience should be restricted to monitor and critique sampled solutions, using their conscious common sense to highlight issues in the machine behaviour.
Unfortunately, this also has limits as problem/solution complexity rises. Catastrophic failure in our overly automated world is inevitable, and this will limit progress until the next paradigm shift. Enterprises will inevitably be led into the new paradigm of problem solving with soft AI applying adaptive cognitive approaches.
A capable cognitive platform for adaptive AI applications
MI.ND: an adaptive cognitive AI platform built on standard technology frameworks and running on standard consumer cloud platforms. This proprietary platform interworks the capabilities of the AI mandated needs stated above. Inferz builds elements or all of its platform as a unified suite of cloud-services available for containerised deliverables on commercial provider platforms.
Contact our team to find out more about this new form of AI.
For a more technical architecture see our marchitecture diagram.
Inferz’s proprietary methodologies and technologies unlock a new paradigm of Unified Soft Computing and
Adaptive Artificial Intelligence for the Enterprise.
Combining the best technologies from symbolic and sub-symbolic computing. Founded on the adaptive power of soft machine learning and reasoning. Replacing the black box obscurity of neural networks with a new breed of
machine learning based on polynomial fuzzy + probabilistic discovery. It removes the ‘causal chasm’ by combining symbolic and sub-symbolic representations – excluding the opaque non-symbolic black-box by modelling the mind not the brain.
Expanding the perspective of the MI.ND architecture encompasses not just deeper cognitive technologies
but also broader hyper-cognitive technologies.
Inferz’s proprietary methodologies and technologies open the new world of Unified Soft Cognitive Computing
If you need to move to smart AI - from mere recognition to the higher levels of cognition – get in contact.
Inferz is Rethinking AI