The Bitphase engine can extract complex causal models from raw data, by applying a variety of pattern recognition and machine learning techniques and combining the results. These models can then be queried interactively to return the probabilities of particular statements or outcomes, optionally with additional user-specified assumptions, or linked to an automated decision system. The Bitphase system supplements its library of standard analysis techniques with customised algorithms generated and refined as it analyses the data set, delivering outstanding predictive performance and efficient use of compute resources.
Our technology excels at finding and exploiting complex, conditional patterns and layered structure that simpler and more conventional data mining techniques miss. The models produced by the system are open to human inspection and unlike opaque systems can benefit from human refinement and tweaking. If you have a tough data mining application and desire a higher level of performance than existing techniques can provide, a Bitphase solution may be able to deliver the results you're looking for.
The modelling component of the Bitphase system is a valuable tool in its own right. It allows complex discrete, continuous, sharp, fuzzy or hybrid systems to be specified by using a powerful toolbox of representations that can be combined as necessary. Models can be designed using our general developer interface or a bespoke application-specific interface. Once entered, they can be refined (within user-specified limits) by the learning system, checked for compliance with available data, queried via the inference engine or visualised by standard or custom visualisation plugins. As with the learning subsystem, the modelling subsystem uses a library of standard techniques plus custom algorithms developed in real-time to implement targeted, high-performance probabilistic inference.
Bitphase-based modelling applications can greatly reduce development and simulation time, while improving both accuracy and flexibility, compared to Monte Carlo simulation or conventional statistical techniques. When necessary the system can simulate competing agents and use a game theory model to develop optimal strategies for competitive environments. For any domain in which your staff have to make accurate and timely predictions based on large amounts of hard-to-analyse data, or in which you have to move fast to adapt your business strategy to real-time information, Bitphase modelling tools may be able to greatly increase productivity.
A key technology behind Bitphase modelling applications is intelligent search, which focuses computational effort on the lines of inquiry most likely to produce useful information. This adaptive search control is not limited to working with the system's internal inference engine; it can be connected to other simulation applications and direct external search processes. The Bitphase system can act as a controller and interface to an existing application or simply as an optimiser, improving performance by prioritising the search tree. For simulations that involve trade-offs between accuracy and performance (such as protein folding), Bitphase technology can potentially optimise the resolution of the simulation in real time, extracting maximum value from every second of computing time.
If you have an application that relies on computationally intensive search, in a domain where it's hard to predict where successful results are likely to be found, Bitphase search control may be able to deliver an order of magnitude improvement in performance. Contact us today for further information or to arrange an application-specific demo.
The Bitphase platform's unique modelling, learning, planning and visualisation capabilities can be combined live feeds of mission critical data and a streamlined domain-specific user interface. The result is a decision support system that can deliver the most critical, distilled information where and when it is needed most, and support a truly interactive decision process. Bitphase solutions can outperform existing expert systems in dealing with uncertainty and changing domains, carrying out complex inference and actioning user-specified plans, as well as expanding the scope of decision support into areas that no previous expert system infrastructure could reach. Once we have analysed and discussed your specific needs, our staff can rapidly develop and deploy a solution, leveraging our machine learning technology, model development interface and flexible integration platform.
The Bitphase decision engine works with the modelling and search capability (and optionally with the learning subsystem) to allow the development of highly capable software agents. Bitphase technology can potentially automate many business processes, but our initial focus is out-performing and replacing existing rules-based decision systems. Our recent development effort has focused on automated and semi-automated trading applications, including position management in financial markets and auction-based purchasing.
Bitphase-based agents can evaluate and select actions for maximum expected utility based on complex user-supplied, self-learned or hybrid models of the target domain. The system can continually refine both short and long-term strategy and adapt to changing conditions (within user-specified limits). Capabilities including deep planning (using supplemental cost/benefit models where appropriate, such as in energy trading), catastrophic risk analysis (including market stability), modelling competing agents and where practical reverse-engineering competitor strategies for exploitable weaknesses. We are happy to analyse your business systems for capabilities we can enhance on request.
The Bitphase system's modelling, simulation and inference capabilities can be used to analyse a variety of complex systems, including software, hardware, engineering plant, biochemical pathways and even business processes. At present we are focusing on high-level analysis of information processing systems, modelling critical software, hardware and human components. The inference engine can verify that critical security, reliability and real time constraints hold on the system, via either crisp logic for software systems or probabilistic reasoning for fuzzy systems. This process allows vulnerable points, failure modes and performance bottlenecks to be identified relatively quickly and easily, even in many systems too complex to permitt such analysis by conventional means (or for which detailed modelling was formerly infeasibly expensive).
Potentially the Bitphase system will be able to build models automatically based on existing code, documents and active investigation of networked systems. However this particular capability is still under development at this time; we are currently looking for possible early adopters and partners to assist with its commercialisation. To find out more about this application area and potentially get involved in its development, please contact our business development manager.
Bitphase AI is developing a range of software engineering tools that build on our targeted code generation capability and our ability to model and analyse complex, hybrid systems. Currently in an early development stage, these tools include security and failure analysis, automated test case generation and high-level performance optimisation. Particularly valuable are our bridging tools that can automatically generate interface code to connect incompatible (frequently legacy) applications. All of these tools promise major improvements in developer productivity and software quality. We are also researching domain-specific rapid application development tools that leverage AI capabilities to dramatically cut time-to-market and deliver improved flexibility and performance.
While it will be some time before these AI-derived software engineering tools are ready for general use, we are confident that once on the market they will significantly transform the software development process. You can receive announcements about future developments, of these and other Bitphase applications, by joining our announcements mailing list