AI Chatbots: deep learning and AI reshaping businesses

In the past century, scientists, programmers, and professionals in the artificial intelligence field have sought out the future of how exactly we can use robots and other ways of artificial thinking to help the advancement of humankind. From using advancements in facial recognition for smart devices, playing the world’s hardest game 'Go', to defeating chess champions, artificial intelligence (AI) can be utilized for a multitude of applications across the board.
Futurist Ray Kurzweil coined a term called the “Law of Accelerating Returns” which is defined by every aspect of technology advancing faster and faster as the future progresses. Current evidence states that the amount of information, data, and development that occurred between 2000 and 2014 surpassed the entire 20th century’s information. By using this law, the same will happen before 2021, until the gap incrementally begins to shrink and almost dissipate completely. Some argue against this law, but by understanding the exponential nature of progress and ignoring the human tendency to think in a straight line and believing things will develop at a steady, monotonous rate will give us valuable insight as to how the future will work – and how we may be working with other scientific advances more than we know in the future. [www.alphr.com]
We already utilize many different forms of AI daily already: from Siri and Google on our computers to cars and chatbots – programs that are are meant to be adaptive and give the user a better and more customized experience overtime. So if we want to look at what the future has in store, firstly we must look at the past developments in the field of AI, specifically in regards to the evolution of deep learning in chatbots. 
In 1966, Joseph Weizembaum created ELIZA, a computer-program chatbot psychologist that had the capability of giving patients psychological help by using adaptive algorithms combined with generic fallback responses if the keyed in sentences were not understood or syntactically appropriate. The successor of ELIZA was SHRDLU created by Terry Winograd in 1970. [www.medium.com] SHRDLU was  specifically aimed to observe, manipulate, and understand a tabletop scene with different kinds of blocks and shapes laying on top of it, here is a sample conversation with SHRDLU:

Pick up a big red block.
> Ok. (does it)
Grasp the pyramid.
>  I don't understand which pyramid you mean. 
Find a block which is taller than the one you are holding and put it in the box.
>  By "it", I assumed you mean the block which is taller than the one I am holding. 
What does the box contain?
>  The blue pyramid and the blue block.
What is the pyramid supported by.
>  The box.
How many blocks are not in the box?
> Four of them.
Is at least one of them narrower than the one which I told you to pick up? 
> Yes, the red cube.
Is it supported?
>  Yes, by the red table.
Cane the table pick up blocks?
>  No.
Can a pyramid be supported by a block?
>  Yes.
Can a pyramid support a pyramid?
> I don't know.
Stack up two pyramids.
> I can't.
Will you please stack up both of the red blocks and either a green cube or a pyramid? 
>  Ok. (does it)

The information deduced was SHRDLU could classify objects, individual blocks, keep track of context, name objects, and even discuss the items in terms of past, present, or future… Keep in mind: all of this was over 40 years ago. There is one overarching issue that stems from this level of AI: natural language association. As technology advances, natural language and interaction becomes more natural and less forced and. So, the Law of Accelerating Returns really does hold true if you look at technology today.
As AI has developed over the years since SHRDLU, people are using machine learning to carry out three core processes in a chatbot brain: parsing and understanding the input, modeling human interactions/conversations to predict what to say next, and finally writing the resulting output sentences.
Shifting towards the business world, AI can be utilized in a multitude of ways. A commonly applied method businesses use is the dialogue interface to interact with customers, which has the standard conversation interaction but eliminates using natural language for interactions. Standard user interface components, drop-downs, links and redirections on a company’s website or applications offer an easy and low-error interaction between the user and AI. All the potential options are there, and the user can explore them in any order. 
Another utilized version of AI chatbots in the business field is scripted intents. It feels like a more evolved version of digital interface with more bells and whistles; by allowing natural language processing to “classify intents and extract parameters.” This predictive behavior and association can be used to extrapolate what exactly the person on the other end of the chat will say or do next which offers a better experience for the person interacting with the bot.
There is also the repetitive question right reply interface and the knowledge-based approach method. The right reply interface works well with tech-based companies that receive a lot similar questions and serves as kind of an AI FAQ bot. And the knowledge-based approach method maps “natural language questions to structured queries.”
Machine learning is being utilized by every aspect of our on a daily basis – from people using smart phones to purchase groceries to the use of AI in the business industry - companies that evolve alongside AI have a more robust, user-friendly experience altogether. Digitizing a businesses value chain alongside utilizing AI such as chatbots and other evolving technology creates an intelligent integrated infrastructure by connecting machines, work places, systems  together. Artificial Intelligence is the future, and businesses who stay ahead of the trend will be the trend.