Genie Pal A Versatile Intelligent Assistant To Help Both Work And Personal life

Content Targeted Problems Our Approach Elaboration of Our Solution More on Focus on Web More on AI Development Team

The Targeted Problems Using a computer system is not always easy and convenient  You have to learn to use computer UI – it is not like communicating with another human being. And the learning has to occur for every new software or computer application, especially those commercial systems.  To do some slightly sophisticated job using a computer system, you often need to be trained, and you need to remember all the details in the procedure for doing the job. Missing one step, or doing it incorrectly, you can fail the job. Often, you know what has to be done, but you just don’t remember how.  There are lots of laborious, time-consuming jobs when we use computer, and many of them are essentially repetitive routines despite differences in details, but we still need to do them manually.  Siri-like human-computer interaction is great – you can tell computer what to do just as telling a human being. But Siri is designed by Apple, and cannot help you unless it has the functionality you want. This infeasibility is related to the second big issue mentioned below.

The Targeted Problems It is difficult to add functionality to a traditional computer system  Functionality and features need to be designed and implemented as code. Once done and released, code and features become fixed (until upgrade).  Design and implement an application software is hard, and requires significant cooperation between computer engineers/developers and domain experts to cover all potential use cases. Even that cannot guarantee to fully meet every customer’s requirements.  Customization and configuration is often hard, and not for normal users, let alone extending functionality and integrating features of multiple applications and systems.  Bottom line: you cannot expect a traditional computer system to do beyond what the code directly supports, and you cannot expect Apple’s Siri to help you if the features you like have not been designed in Siri.

Our Approach Make computer system to be teachable by users  We as users often know what to do, but computer systems don’t. If not all of us know, often than not, one of us in the organization would know. And even if none of us average users knows, domain experts would normally know. In any of these cases, if a system is teachable, we wouldn’t always need computer experts and engineers to give us new functionality – users or domain experts can just teach the computer.  Teaching a solution is much easier than teaching how to find a solution in general. Correspondingly, designing a system with generic problem-solving intelligence is very hard, but making it teachable is not infeasible, especially when we focus on teaching existing solutions.  Existing solutions can be taught this way: users can show or demonstrate to computer how to do a job or solve a type of problems by examples, just as how those more skillful people teach apprentices or novices in workshops or offices.

Elaboration of Our Solution Enable some basic natural language and semantic analysis capability. Introduce the capability for the system to observe user’s behavior in addition to other related things. Implement the capability for the system to imitate the user’s behavior it observes. Attach semantic meanings to observed user’s behavior, with user’s help in addition to internal semantic analysis. Allow generic domain knowledge such as concepts and their language expressions to be provided by user, so as to further facilitate semantic analysis in user-defined domain.

Elaboration of Our Solution Get abstraction of what user does in terms of the attached semantic meanings. Use learned abstract behavior models to apply to tasks with similar intentions and purposes, and adjust/adapt its actions according to different requirements and context. Verify, adjust and improve learned behavior models according to system’s own execution experience.

Elaboration of Our Solution Focus on Web application and services Prototype designed and implemented in Android mobile platform Targeted for both personal assistance usage and productivity/work assistance usage

Elaboration of Our Solution GeniePal – An Integration Of Artificial Intelligence, Web Technology, Mobile and Cloud Computing.

Elaboration of Our Solution GeniePal – System Architecture (1). GeniePal Cloud Service GeniePal Mobile Client

Elaboration of Our Solution GeniePal – System Architecture (2). User Interaction Engine Learning Engine Execution Engine GeniePal Client Knowledge Engine GeniePal Client Knowledge Database Language & Semantic Engine GeniePal Mobile Client Learning Engine Genie Mastermind Knowledge Engine Genie Mastermind Knowledge Database Language & Semantic Engine GeniePal Cloud Service ( Genie Mastermind ) Genie Mastermind Training System

Why Focus on Web The number of public web portals exceeds one billion on the Internet. The web remains the most popular Internet application platform. Web is also universally supported as an “Intranet” application platform – it has been a standard. The migration to web-based technology is largely done for commercial systems. Web is not only easily accessible, but also simple and cheap to support, so it is expected to maintain its unmatched popularity in foreseeable future. The above means that we can potentially bring Siri-like intelligence to the largest set of applications used by billions of ordinary users and commercial customers. Web capability is also very important for generic AI development. Implementing our idea is easier on Web partly because it is a public standard not controlled by any companies.

More on AI Development Our work puts more emphasis on enabling learning conditions and knowledge transfer compared with mainstream AI R&D. We deem observation and imitation capability as two essential enabling conditions for learning. The two, combined with other AI elements, could be used to construct very effective learning methods. The ability to learn existing knowledge is as valuable as (if not more important than), the ability to explore new knowledge – as human beings, most of our abstract knowledge is learned from schools and books, and they are all existing knowledge. In fact, not much new knowledge is discovered by us as individuals.

More on AI Development From a practical perspective, even if we do not aim at creating systems that can be as smart as we human beings in finding new knowledge, but only aim at systems that can learn existing solutions, it can still solve a lot of practical problems (most jobs we do everyday are not about cracking hard problems and finding new solutions). Understanding more about how existing knowledge can be transferred and learned may benefit other AI learning development, e.g. AlphaGo. Combined with other AI technology, learning can be more effective, and it won’t be limited to obtaining existing knowledge.

Who are the Developers of Genie Pal We are currently a small group of veteran computer engineers. We are not only interested in generic computer science research, such as artificial intelligence, but also eager to make real solutions and systems to solve practical problems. Our members are in Canada and US, and we work together through virtual collaboration working environment. We can be reached at