AI is out there ready to be consumed by startups and corporations alike to solve almost any problem from commuting to visualizing, replacing many mundane human tasks with efficient machines and leaving us humans to make more complex decisions. When Turing proposed the concept of the thinking machine, this ability of a machine to think for itself was too farfetched and crazy. As a result, the project titled 'Artificial Intelligence' (AI) kept getting shelved. But if we were to learn from history machines would also become smarter than humans once they get the drift. So, we should ask ourselves, 'How close will we be to that stage in 2019?' Only that can summarize any projections for 2019 because 'projections' are towards an inevitable future, otherwise they're merely wishful thoughts or prophesies. AI could impact every aspect of our lives but due to the limitations of space and time I will restrict myself to AI in text processing which we've been working on for the last five years.

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THE PATH OF ARTIFICIAL INTELLIGENCE IN 2019
VARUN KESAVAN, RESEARCH SCHOLAR, E – MAIL ID – varunkesavan@yahoo.com
AI is out there ready to be consumed by startups and corporations alike to solve almost
any problem from commuting to visualizing, replacing many mundane human tasks with
efficient machines and leaving us humans to make more complex decisions.
When Turing proposed the concept of the thinking machine, this ability of a machine to
think for itself was too farfetched and crazy. As a result, the project titled 'Artificial
Intelligence' (AI) kept getting shelved. But if we were to learn from history machines would
also become smarter than humans once they get the drift. So, we should ask ourselves,
'How close will we be to that stage in 2019?' Only that can summarize any projections for
2019 because 'projections' are towards an inevitable future, otherwise they're merely
wishful thoughts or prophesies.
AI could impact every aspect of our lives but due to the limitations of space and time I will
restrict myself to AI in text processing which we've been working on for the last five years.
Data Integration: Natural Language Processing (NLP) has played an important role in
analyzing textual information and this continues to be the case. Up until late 80s there
wasn't much digital data which meant machines didn't have enough training data or it had
to be entered manually. In fact, IBM's Watson had engineers feeding data like 'water
makes you wet', 'milk is nourishing', etc. In 2019, the availability of data is pretty decent
and structured. Almost every organization has its data sources; CRM, HRMS, ERP, LMS,
etc. Coming to unstructured data, which has a lot of intelligence still untapped and growing

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at a phenomenal rate, thanks to e-mail and collaborative platforms like MS Teams, Slack,
Chat assistance, WhatsApp for business, Skype for business, etc.
There is now a huge possibility of combining these databases to derive meaning from
them, automate conversations, processes, and bring enterprise conversations a lot closer
to human parity.
Omnipresence of Chatbots and Virtual Assistants: By 2019, at least 25 percent of
employees at all large corporations will communicate with a bot for information. More than
half of organizations have invested in VCAs for customer service, as they realize the
advantages of automated self-service and the ability to escalate to a human in complex
situations. Across industry verticals, business functions that are seeing most demand with
customers span across sales, marketing and HR.
60 percent of all hires would have been either screened or shortlisted or interviewed by
some NLP AI engine. Any bot can be deployed on any integrated channel in a few clicks,
so there is only one bot overall, saving time and effort. An AI assistant semantically
understands job descriptions you feed in and finds relevant matches for the requirement
from available job portals and databases. A hiring assistant can also reach out to identified
candidates and engage in a chat to pre-qualify them as per company requirements.

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Reinforcement learning: When learned about structured and unstructured data, the AI
learns from categorised and uncategorised information and forms an output. But what
happens when the AI must make an unbiased decision? This is where reinforcement
learning comes into place. The framework does not use data recognition as above but
takes into consideration its previous experience and outcomes that resulted into rewards.
Reinforcement learning is mostly used in computer games. The actions taken by the
computer and the player to decide the winner of the game. This type of learning is still
unchartered territory and can be useful in several ways like determining treatment
methods for chronic illnesses like Alzheimer's or schizophrenia. It can also help in higher
education or career choices.
AI & DevOps: Modern day applications constantly collect information on how a user
interacts with an application, as well as on how the application is being delivered. There
is a large amount of data that can be used for indexing and analytical purposes. Add to
this machine learning and this data can be processed at a remarkable pace. By integrating
machine learning into the delivery system of the application, organizations would be able
to generate insights into all bottlenecks and relevant patterns that make the user
experience and app delivery seamless and avoid similar blockages in the future.

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AI is out there ready to be consumed by startups and corporations alike to solve almost
any problem from commuting to visualizing, replacing many mundane human tasks with
efficient machines and leaving us humans to make more complex decisions. According
to O'Reilly data, 51 percent of surveyed organizations already use data science teams to
develop AI solutions for internal purposes. There is no doubt that adoption of AI tools
would be one the most important AI trends in 2019