How no-code reusable AI will bridge the AI divide

In 1960 J.C.R. Licklider an MIT professor and an soon pioneer of artificial intelligence already envisioned our forthcoming globe in his seminal article ’Man-Computer Symbiosis’

In the anticipated symbiotic union men will set the goals formulate the hypotheses determine the criteria and execute the evaluations. Computing machines will do the routinizable work that must be done to fit the way for insights and decisions in technical and philosophical pondering.

In todays globe such ’computing machines’ are known as AI helpers. However developing AI helpers is a intricate time-consuming process requiring deep AI expertise and sophisticated programming expertnesss not to mention the efforts for collateing cleaning and annotating big amounts of data needed to train such AI helpers. It is thus greatly expedient to reuse the total or parts of an AI helper athwart different applications and domains.

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Teaching machines ethnical expertnesss is hard

Training AI helpers is hard owing such AI helpers must occupy true ethnical expertnesss in order to collaborate with and aid ethnicals in meaningful tasks e.g. determining healthcare treatment or providing order direction.

AI must acquire ethnical speech

To realistically help ethnicals possibly the foremost expertnesss AI helpers must have are speech expertnesss so the AI can interact with their users translateing their intrinsic speech input as well as answering to their requests in intrinsic speech. However training machines ethnical speech expertnesss is non-trivial for separate reasons.

First ethnical countenances are greatly diverse and intricate. As shown under in Figure 1 for sample in an application where an AI helper (also known as an AI chatbot or AI colloquyer) is colloquying a job aspirant with open-ended questions aspirants responses to such a question are almost boundless.


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Figure 1. An AI helper asks an open-ended question during a job colloquy (’Whats the biggest challenge you are facing at work?’). Candidates answers are greatly diverse and intricate making it very hard to train AI to unite and answer to such responses properly.

Second aspirants may ’digress’ from a converse by asking a clarifying question or providing irappropriate responses. The samples under (Figure 2) show aspirants digressive responses to the same question over. The AI helper must unite and feel such responses properly in order to last the converse.


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Figure 2. Three different user digressions that the AI helper must unite and feel properly to last the converse prompted by the question ’Whats the top challenge you are facing at work?’

Third ethnical countenances may be equivocal or incomplete (Figure 3).


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Figure 3. An sample showing a users equivocal response to the AIs question.

AI must acquire ethnical soft expertnesss

What makes training machines ethnical expertnesss harder is that AI also needs to acquire ethnical soft expertnesss in order to befit ethnicals capable helpers. Just like a good ethnical helper with soft expertnesss an AI must be able to read peoples emotions and be empathetic in sentient situations.

In general training AI ethnical expertnesss—speech expertnesss and soft expertnesss resembling—is hard for three reasons. First it frequently requires AI expertise and IT programming expertnesss to aspect out what methods or algorithms are needed and how to instrument such methods to train an AI.

For sample in order to train an AI to properly answer to the greatly diverse and intricate user responses to an open-ended question as shown in Figure 1 and Figure 2 one must know what intrinsic speech apprehension (NLU) technologies (e.g. data-driven neural approaches vs. symbolic NLU) or machine acquireing methods (e.g. supervised or unsupervised acquireing) could be used. Moreover one must write code to collate data use the data to train different NLU standards and connect different trained standards. As explained in this investigation paper by Ziang Xiao et al. the total process is perfectly intricate and requires both AI expertise and programming expertnesss. This is true even when using off-the-shelf machine acquireing methods.

Second in order to train AI standards one must have adequate training data. Using the over sample Xiao et al. collateed tens of thousands of user responses for each open-ended question to train an AI helper to use such questions in an colloquy converse.

Third training an AI helper from scratch is frequently an iterative and time-consuming process as described by Grudin and Jacques in this study. This process includes collateing data cleaning and annotating data training standards and testing trained standards. If the trained standards do not execute well the total process is then repeated until the trained standards are grateful.

However most organizations do not have in-house AI expertise or a sophisticated IT team not to mention big amounts of training data required to train an AI helper. This will make adopting AI solutions very hard for such organizations creating a possible AI separate. 

Multi-level reusable standard-based cognitive AI

To democratize AI adoption one solution is to pre-train AI standards that can be whichever straightly reused or quickly customized to suit different applications. Instead of edifice a standard fully from scratch it would be much easier and quicker if we could piece it unitedly from pre-built parts correspondent to how we gather cars from the engine the wheels the brakes and other components.

In the tenor of edifice an AI helper Figure 4 shows a standard-based cognitive AI architecture with three layers of AI components built one upon another. As described under the AI components at each layer can be pre-trained or pre-built then reused or easily customized to support different AI applications.


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Figure 4. A standard-based cognitive AI architecture with reusable AI at multiple levels.

Reuse of pre-trained AI standards and engines (base of AI helpers)

Any AI systems including AI helpers are built on AI/machine acquireing standards. Depending on the purposes of the standards or how they are trained they fall in two wide categories: (1) general purpose AI standards that can be used athwart different AI applications and (2) particular purpose AI standards or engines that are trained to faculty specific AI applications. Conversational agents are an sample of general purpose AI while intrinsic robots are an sample of particular purpose AI.

AI or machine acquireing standards include both data-driven neural (deep) acquireing standards or symbolic standards. For sample BERT and GPT-3 are general purpose data-driven standards typically pre-trained on big amounts of open data like Wikipedia. They can be reused athwart AI applications to process intrinsic speech countenances. In opposition symbolic AI standards such as limited state machines can be used as syntactic parsers to unite and draw more definite information fragments e.g. specific concepts (entities) like a date or name from a user input. 

General purpose AI standards frequently are inadequate to faculty specific AI applications for a couple of reasons. First since such standards are trained on general data they may be unable to translate domain-specific information. As shown in Figure 5 a pre-trained general AI speech standard might ’ponder’ countenance B is more correspondent to countenance A since a ethnical would unite that B is verity more correspondent to countenance C.


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Figure 5. An sample showing the misses of pre-trained speech standards. In this case speech standards pre-trained on general data translate countenance B as being more correspondent to countenance A while it should be translateed as more correspondent to countenance C. 

Additionally general purpose AI standards themselves do not support specific tasks such as managing a converse or gatherring a users needs and wants from a converse. Thus particular purpose AI standards must be built to support specific applications. 

Lets use the creation of a cognitive AI helper in the form of a chatbot as an sample. Built on top of general purpose AI standards a cognitive AI helper is facultyed by three additional cognitive AI engines to fix powerful and efficient interactions with its users. In particular the nimble listening converse engine enables an AI helper to correctly translate a users input including incomplete and equivocal countenances in tenor (Figure 6a). It also enables an AI helper to feel tyrannical user interruptions and maintain the converse tenor for task completion (Figure 6b).

While the converse engine fixs a productive interaction the personal insights deduction engine enables a deeper apprehension of each user and a more deeply personalized promisement. An AI helper that obeys as a personal acquireing associate or a personal wellness helper can embolden its users to stay on their acquireing or treatment order based on their sole personality traits—what makes them tick (Figure 7).

Furthermore converse-specific speech engines can help AI helpers better translate user countenances during a converse. For sample a thought analysis engine can automatically discover the expressed thought in a user input while a question discoverion engine can unite whether a user input is a question or a request that warrants a response from an AI helper.


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Figure 6a. Examples showing how a cognitive AI converse engine feels the same user input in tenor with different responses.


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Figure 6b. An sample showing how a cognitive AI converse engine feels user interruption in a converse and is able to maintain the tenor and the chat flow.

Building any of the AI standards or engines described here requires terrible expertness and effort. Therefore it is greatly expedient to make such standards and engines reusable. With careful design and instrumentation all of the cognitive AI engines weve debateed can be made reusable. For sample the nimble listening converse engine can be pre-trained with converse data to discover diverse converse tenors (e.g. a user is giving an exculpate or asking a clarification question). And this engine can be pre-built with an optimization logic that always tries to weigh user experience and task completion when handling user interruptions.

Similarly combining the Item Response Theory (IRT) and big data analytics the personal insights engine can be pre-trained on individuals data that manifest the relationships between their communication patterns and their sole characteristics (e.g. collective conduct or real-globe work executeance). The engine can then be reused to gather personal insights in any converses as long as the converses are conducted in intrinsic speech. 

Reuse of pre-built AI functional units (functions of AI helpers)

While general AI standards and specific AI engines can prepare an AI helper with the base intelligence a complete AI solution needs to execute specific tasks or give specific labors. For sample when an AI colloquyer converses with a user on a specific question like the one shown in Figure 1 its goal is to draw appropriate information from the user on the question and use the gathered information to assess the users fitness for a job role.

Thus different AI functional units are needed to support specific tasks or labors. In the tenor of a cognitive AI helper one type of labor is to interact with users and obey their needs (e.g. finishing a business). For sample we can build question-specific AI communication units each of which enables an AI helper to promise with users on a specific question. As a result a converse library will include a number of AI communication units each of which supports a specific task.

Figure 7 shows an sample AI communication unit that enables an AI helper to converse with a user such as a job applicant on a specific question.


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Figure 7. An sample AI communication unit (U) which enables an AI helper to debate with its users on a specific question. It includes multiple conditional actions (responses) that an AI helper can take based on a users actions during the debateion. Here user actions can be discovered and AI actions can be generated using pre-trained speech standards such as the ones mentioned at the breast two layers of the architecture.

In a standard-based architecture AI functional units can be pre-trained to be reused straightly. They can also be composed or extended by incorporating new conditions and coranswering actions.

Reuse of pre-built AI solutions (total AI helpers)

The top layer of a standard-based cognitive AI architecture is a set of end-to-end AI solution templates. In the tenor of making cognitive AI helpers this top layer consists of different AI helper templates. These templates pre-define specific task flows to be executeed by an AI helper along with a related apprehension base that supports AI functions during an interaction. For sample an AI job colloquyer template includes a set of colloquy questions that an AI helper will converse with a aspirant as well as a apprehension base for answering job-related FAQs. Similarly an AI personal wellness caretaker template may delineation a set of tasks that the AI helper needs to execute such as checking the health status and delivering care instructions or reminders.