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The Technological Singularity is a hypothetical scenario that is assumed and believed to follow the invention of a true Artificial Intelligence.

Artificial Intelligence is a branch of Computer Science and it was pursued because the computers, programs and machines were made intelligent or to think like humans.

Over time, AI or Artificial Intelligence practices, intend to design software systems that mimic or display some form of human intelligence. These techniques have been engaged to aid or computerize many different actions and activities in software engineering.

Hence the attempt to make or create a computer, a software or a robot to think and act like human beings or to behave intelligent. As just as the brain, thinks, observes, perceives, and decides, the software too is created to represent this.

The world of AI Chatbots aided by Katpro

The father of Artificial Intelligence, John McCarthy, defined AI as, "The science and engineering of making intelligent machines, especially intelligent computer programs."

The Technological Singularity is a hypothetical scenario that is assumed and believed to follow the invention of true Artificial Intelligence.

Katpro is aiming to seamlessly integrate and incorporate AI into everyday realms, genre, and domains. And this in a way is achieved by way of innovation and technological advancements. The end result is meant to trigger ease of use, an exponential change, and growth for the user.

Our AI interfaces are a series of “Self-Improvement” cycles that will create the first “Intelligence Explosion,” an analogy to today’s “Data Explosion” theory.

Our BOTs and AI-driven interfaces are meant to supersede even human intelligence and one such aspect is evident in the Sentient Chatbot.

Sentient Chatbot

It is a chat interface that has its intelligence linked to an Artificial Intelligence service with a deep learning algorithm built into it. When connected to a language repository, the BOT is able to interact with real humans in a seamless way. This in a way offers businesses and enterprises an opportunity to fully automate many of their current processes, and they can in turn cut down on:

  • Operational Costs (overall reduction in operational overheads spends and outlays)
  • Recurring Costs (any untoward, avoidable recurring costs can be cut down or eliminated)
  • Personnel Costs (If automation is in place, and machines can handle, manage and operate, manpower too can be reduced)

The Sentient BOT finds its usage in:

  • Reporting
  • Virtual Assistant (FAQs BOT)
  • Travel
  • Recruitment
  • Customer service
  • Ecommerce
  • News, Media & Events
  • Banking

To help users’ and customers’ to streamline and rationalize costs and overheads, and for ease of use and to better interactions, is one of the principal aims of Katpro’s Artificial Intelligence methodologies.

“You may not realize it, but artificial intelligence is all around us”- Judy Woodruff

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“Artificial Intelligence is whatever hasn't been done yet.”

-Larry Tesler

Some sample use case business verticals for the Sentient BOT are as follows:

We build BOTs to interact with the end user on Multiple Platforms. Some of our most successful platforms are

BOTs and the SDLC phase

As per Rech and Altoff (2008), ""The disciplines of artificial intelligences and software engineering have many commonalities. Both deal with modeling real world objects from the real world like business process, expert knowledge, or process models."

Before embarking on any work or project, the outline is first created with the help of the mock-up lifecycle, and stage by stage we move closer to the prototype or finalizing the model.

BOTs or Chatbots are built for the benefit of the end-user for use in multiple platforms and settings.

Gather Requirements

Analyzing and gathering requirements kick starts the project. This phase typically involves interactions with all relevant stakeholders both on the client’s side as well as ours, and also understanding what end users’ seek, product relevance and its impact. Once the documentation is ready and good to go, with the outline firmly etched, we move into the subsequent stages of the lifecycle accordingly.

And when weaving in AI into the requirements phase would ensure:

  • Understanding the natural language requisites and altering them in a way that they are not so ambiguous or indistinct.
  • Build knowledge-based systems that aid in this phase and also has solutions for problems encountered.
  • For a more prescribed spec gathering, the semantics should be as detailed and descriptive- essentially it should be very expressive.
  • In order to better understand and validate, readability is of utmost importance and
  • The flow, composition and other aspects should be neatly structured and arranged.

Design

For design architecture as well as design detailing, designers need to rely on their experience, awareness, and technical knowhow in order to arrive at the design solution. Yes. There is a lot of back and forth before a definite design detail is arrived at. And as such, this phase is a protracted, intricate phase and one of the best ways around this is to use tools that are aided by certain AI mediations.

With an outline conceived, the characterization has to evolve with a complementing connotation and the design perspective should put this all in place. The design determines the how, the what, the where, and the which. As in, what needs to be part of the package, the requisite software and/or hardware that would add wings to the design architecture and prop it up so it is good to move to the next phases leading up to completion and deployment.

At this stage, an “Intelligence Requirements Specifications” document is shared so we both are in the know and are on the same changes and any iterative changes can be made, if need be.

Coding

Once the design solution has been arrived at and finalized, then the work moves to the next phase of coding and the outline is further divided into sections or segments and coding is carried out. As part of this, the list of platforms and integrations are selected and tailored coding work is carried out by a developer. The coding stage is said to be one of the longest stages in this lifecycle.

In the world of AI and BOTs it is felt that to fast track the coding process, there is a requirement for design by testing and experimenting. This feasibility can be explored and bettered with automated programming. By way of automated tools, coding is far more efficient and it can be adapted and changed easily.

Training

Once the design solution has been arrived at and finalized, then the work moves to the next phase of coding and the outline is further divided into sections or segments and coding is carried out. As part of this, the list of platforms and integrations are selected and tailored coding work is carried out by a developer. The coding stage is said to be one of the longest stages in this lifecycle.

Deployment Phase

Once the BOT is designed and completed, it is ready to be deployed, delivered for testing out and trying at the client’s end. This is the acid test phase where it is no more tried, and tested in a controlled environment; instead users’ interact, and try it in an unrestricted environment. But even at this stage, the BOT’s machinations are observed and monitored to rule out any bugs or issues.

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