2. Myth
What is AI?
Often misused word… current progress of AI
stagnates in stage 2, with scientist pushing
hard for the next breakthrough (it may just be
that cloning and DNA mutations will create
faster consciousness beings than algorithms)
3. Myth
Inserting all the human
knowledge into a machine will it
make it intelligent?
Most probably not… maybe confused, if
knowledge capacity would be the issue than
AI problem would have been long time
solved. The tricky part is empathy, cognition
and emotion that put our intelligence to
action.
4. Myth
Can AI develop cognitive/human
like capabilities?
In given time and with not constrained
expectations, yes. Think that a child needs a
time to mature (there is an experience
learning curve), mixing all human knowledge
with a bunch of experiences will not achieve
human like capabilities, as the experiences
will be induced not proprietary.
5. Myth
I am ever going to win Jeopardy
and Go with all of the AI agents
there?
There are just so many possibilities that we
can compute, remember we are also playing
games for fun not to beat the RAM.
6. Myth
What about chatbots?
Let’s acknowledge there are jobs which are
obsolete for people, and in many cases we must
ask ourselves “how much can one learn doing
the same thing every day?”. Furthermore,
sharing your birth experiences with the chatbot
assistant :), people are cognitive beings that
need to speak and interact with each other.
P.S: business of the future, people that are hired
just to have simple conversations.
7. There are multiple ways to build a chatbot:
AIML: You can use Artificial Intelligence Markup Language (AIML) to create
conversational flows for your bot. AIML is very easy to learn and basically an
extension of XML.
NLP/NLU: Natural Language Processing (NLP) and Natural Language
Understanding (NLU) attempt to solve the problem by parsing language into entities,
intents and a few other categories. Different NLP platforms may have different
names however the essence is more so the same.
Machine Learning: The ‘other’ option is to build your own NLP/NLU by using
Machine Learning. One of the first things to consider will be the type of model you
want to build.
https://www.quora.com/What-are-some-open-source-AI-chatbots-that-use-machine-learning
Analyzing the alternatives, not all our conversations are intents or entities,
therefore my strong believe a bot should be more than “an if else intention
mapping in different forms”, there has to be a combination between an NLU
language (maybe AIML maybe not), that closely maps human interactions
together with reinforcement learning, DL and ML to develop a program/bot that
has the ability to learn and achieve human like personality, and in time auto
modify its core behavior as it develops true consciousness.
https://www.smartsheet.com/artificial-intelligence-chatbots
8. Miscellaneous
Myths
1. Everything is cognitive? – NO, but rest in peace as humans we have this
gift from birth
2. What is actually cognitive? - Psychological processes involved in
acquisition and understanding of knowledge, formation of beliefs and
attitudes, and decision making and problem solving. They are distinct from
emotional and volitional processes involved in wanting and intending.
Cognitive capacity is measured generally with intelligence quotient (IQ) tests.
http://www.businessdictionary.com/definition/cognitive.html
3. Is cognitive a fancy term? –YES, studies still show that is a trendy word
4. Cognitive solutions will solve all my issues? – most probably not, however
may improve part of your daily activities and remove some of the useless and
boring one
5. Adding cognitive in all my technical presentation materials will improve
my solution? – not in a sustainable manner and only if the solution makes
sense, not everything needs to be reinvented. Additionally, as we know from
our recent past… a technology, word or trend does not last too long until
Gartner or Forrester decides to change it
6. Do we overate our expectations of AI? - YES, and most scary is that data
scientist in their majority do not make the difference between: ”data, use case,
algorithms & scope”, using an algorithm can easily result in “Garbage-
in/Garbage-out”, if we do not understand our data. Nevertheless, history has
proven many times that is common for people to exacerbate expectations,
sometimes we get lucky and we manage to also push innovation forward
