AI but do not seek to perfectly

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Copeland defines artificial intelligence as “the science of making
computers do things that require intelligence when done by humans.” The term
was coined by John
McCarthy in 1956 during the Dartmouth Artificial
Intelligence Conference.

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There are three camps of AI—strong AI, weak AI, and a middle
camp. Proponents of strong AI aim to genuinely simulate human reasoning and
build systems that not only mimic human thoughts but also explain how humans
think. This is a particularly difficult ask, and we are yet to see a real model
of strong AI.

Those who believe in weak AI want to build systems that
behave like humans but cannot tell us anything about how humans think. E.g.
IBM’s Deep
Blue. The system was an expert chess player, but it did not play chess the
way humans do.

The middle camp consists of people who want to build systems
that are inspired or informed by human reasoning, but do not seek to perfectly
mimic it. Most research work in AI is done in this area. E.g IBM Watson. This system has the ability
to pick up patterns in vast amounts of text that make up the evidence for the
answer it is seeking, and then add up the evidence to arrive at the answer.

Google’s research in Deep Learning is inspired by the
structure of the brain. Learning from the behavior of neurons, Deep Learning
systems learn layers of representations for tasks such as image and speech

Is the hype around
artificial intelligence justified?

According to Phil
Fersht, CEO and Chief Analyst at HfS Research, “AI refers to the simulation
of human thought processes across enterprise operations, where the system makes
autonomous decisions, using high-level policies, constantly monitoring and
optimizing its performance and automatically adapting itself to changing
conditions and evolving business rules and dynamics. It involves self-learning
systems that use data mining, pattern recognition, machine learning. virtual
agents, computer vision and natural language processing to mimic the way the
human brain works, without continuous manual intervention.”

HfS has released some data that reveals the projected
spending patterns of businesses on automation and AI between 2016 and 2021. It
is clear that AI is already emerging as a billion-dollar market for enterprise
operations and spending on AI is expected to increase by as much as three times
in the next four years.

Source: HfS Research Ltd.

HfS also discovered, in its State
of Automation 2017 report which surveyed 400 enterprise automation and AI
decision makers across the Global
2000, that AI and machine learning are now one of the most critical
strategic C-suite directives for operations strategy. Eighty-one percent of the
respondents feel that reliance on mid- or high-skilled labor should be reduced,
and investment in AI technologies and machine learning will help achieve this
goal. Although reducing operating costs is still top priority, it is
increasingly being understood that costs cannot be driven down any further
without embracing digital technologies.



Source: Hfs Research Ltd.


Not surprisingly, AI tools are already being piloted and
evaluated extensively in anticipation of the digital transformation that is
beginning to change the way business is done.

Source: HfS Research Ltd.


Human Resources
Management and Chatbots

Chatbots, or intelligent assistants, are computer algorithms
that can mimic human conversation. They are being increasingly used in HR to
recruit people, answer employee questions to HR, and personalize training and
development. Chatbots digitize HR processes and enable employees to get HR
solutions, no matter where they are.

IBM Institute for Business Value conducted a survey
with nearly 400 chief HR officers, wherein it was found that over half the
respondents recognized the importance of cognitive computing in key HR areas,
such as Talent Acquisition, HR Operations, and Talent Development. It is clear
that HR leaders are beginning to harness chatbots to transform the employee experience.

ServiceNow conducted a survey
in the HR Tech Conference and Expo 2017, covering 350 HR leaders. Ninety-two
percent of the HR leaders felt that chatbots will be required to provide an
enhanced level of employee service. The survey also revealed that over
two-thirds of the HR leaders felt that their employees were comfortable using
chatbots to access information when and where they want to.

Chatbots are asked both personal as well as mundane questions:

How many paid leaves do I have left?

How do I report sexual harassment in office?

Employee comfort level with respect to using AI to answer
employee questions is shown in the graph below:

Image source:


Many technology firms are now considering HR solutions armed
with AI for activities such as:

Sourcing (Textio)

Interviewing (MontageTalent)

On-boarding (Talla)

Coaching (mobile

Social recognition (growBot)

Employee service centers (ServiceNow)

Chatbots to Answer HR
Queries in Real Time

Loka created Jane, the
chatbot, in 2014 to answer HR questions. It provides answers to any question
that can be stored in its database. It also promotes policies and benefits to
employees who may not know about them yet.

Jane can also track employee issues in real time and then
use sentiment analysis to resolve the issue before it blows up. For instance,
if a majority of employees are asking questions about delayed reimbursements,
Jane will indicate that there is a problem. HR leaders can work to correct it and
communicate a solution before it becomes a huge issue.

Jane may not be able to answer all HR questions yet, but it
represents an opportunity to integrate AI into HR-related questions.


Thus, the presence of AI in HR makes the employee experience
more user-driven, quick, and seamless.

Chatbots to Improve
Talent Acquisition

In the area of talent acquisition and on-boarding of new
hires, chatbots can play an important role by drawing on multiple sources of
data to create candidate profiles, schedule candidate interviews, and select
potential hires.

Since AI arrives at conclusions using data, a benefit of
using the technology is that “ingrained bias” is removed. Ingrained bias is a
human tendency that causes people to be biased toward others based on their characteristics.

Talla is a chatbot created
to supplement HR processes that source candidates. It can pull up a set of
questions for a particular role and can conduct a Net Promoter Score survey
after the recruiting process is over. Thus, it allows HR professionals to
devote their time to tackling strategic issues. Eventually, Talla may grow to
be a real-time advisor to HR professionals in sourcing and on-boarding new

Reference checking is traditionally an admin-heavy, slow,
and labor-intensive process. Using AI, it can be made faster and more
efficient. Xref, a tool used by recruiters for reference-checking, has a
greater than 90 percent completion rate on references. Xref research shows that,
in the UK:

36 percent of job seekers have exaggerated
claims in their resume

29 percent of job seekers have deliberately lied
to an employer

28 percent of job seekers have taken advantage
of the flaws in the reference checking process to improve their chances of
getting a job.

Xref has a “Sentiment Engine,” which uses an algorithm to
judge the tone of voice in which feedback is written, and categorizes it as
negative, positive, or neutral. Currently, the AI-driven tool has an accuracy
of 92 percent-98 percent—a statistic which humans cannot replicate.

AI in the Area of
Learning and Mentoring

Intelligent assistants have become indispensable for
professors who teach online courses on Massive Open Online Courses (MOOC) platforms.
They augment the role of teaching assistants by doing tasks like answering
students’ questions faster, sending reminders for due dates, providing
feedback, and posting questions to encourage students to think more about their

According to Ed Miller, CEO of NovoEd, “AI will make it
easier to scale learning experiences that are personalized and adaptive to the
learner.” All aspects of HR will be impacted, including corporate learning,
talent acquisition, and HR service centers.

Barriers to Embracing
AI in HR Processes

Senior HR leaders have identified the following roadblocks
to adopting AI in HR processes:

Fear of job loss

Lack of AI training

Lack of change management

HR leaders will have to work on a strategy and a roadmap to embracing
AI in their companies. A few suggestions to go about it are:

Implement a range of chatbots to augment HR
processes that will help HR professionals automatically generate documents,
schedule meetings, and provide personalized health data.

Develop a shared vision of a compelling employee
experience involving C-level executives from IT, HR, Digital Transformation,
Corporate Communications, and Real Estate.

Jointly (IT, HR, Digital Transformation) define
the technology roadmap as a consequence of AI adoption.

Identify and/or create new job roles to support
AI implementation in HR.

Upgrade the skills of HR professionals to enable
them to recognize the importance of AI in HR.

At present, the journey of integrating AI in HR has only
just begun with chatbots and AI-driven algorithms transforming HR processes. AI
has the potential to empower HR professionals with predictive intelligence that
will help them avoid potential dangers and exploit opportunities.






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