Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

Follow on

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

Data science is the big draw in business schools
156 days ago

7 Effective Methods for Fitting a Liner
166 days ago

3 Thoughts on Why Deep Learning Works So Well
166 days ago

3 million at risk from the rise of robots
166 days ago

15 Highest Paying Programming Languages Trending
167 days ago

Top 10 Hot Artificial Intelligence (AI) Technologies
181992 views

Here's why so many data scientists are leaving their jobs
74355 views

Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning
67452 views

2018 Data Science Interview Questions for Top Tech Companies
57030 views

Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies
56220 views

Does artificial intelligence have a language problem?

Feb 8, 2018 | 5250 Views

Artificial intelligence is the next big thing. AI is the ability of a machine to perform tasks associated with intelligent beings.

Now, here comes the question 'What is intelligence and how do we understand it?
We tend to anthropomorphize AI based on our own understanding of 'intelligence' and cultural baggage, such as the portraying AI in science fiction. 

In 1983, the American developmental psychologist Howard Gardener described nine types of human intelligence ?? naturalist, musical, logical-mathematical, existential, interpersonal, bodily-kin-aesthetic, and linguistic. 

AI is not as efficient & potential as we believe. They somehow differ than human intelligence. Machines can both recognize and reflect human emotions, the machine processing of emotions is entirely different from the biological process in people, and the interpersonal emotional intelligence categorized by Gardener. 

Intelligence is an undebatable topic to deal with, especially on the grounds on what machines can do or not. However, artificial intelligence adds up to the benefits of businesses today.

Another aspect to deal with is 'Problem with learning'  

Learning is not limited to the education we received. But, here we talk about machines and artificial intelligence. The process of machine learning is different - machines draw conclusions from huge quantities of data according to rules set down in algorithms. They have no inherent context, experience, ethics, or culture to draw upon to.

Learning is different for machines and humans. Organizations ensure that they get best out of machines, and then machine learning comes into play. It is very compulsory to have insights about machine learning.
Machine learning digs deep down to 'Training data'. 

With machine learning, the idea is that you feed in vast amounts of data and the computer uses some pre-defined rules to derive meaning, sometimes even enhancing the original rules. 

For organizations adopting AI, greater emphasis should be given on who is teaching than what is being taught, because a badly taught machine will lead to biasness, discrimination and undesirable outcomes.
AI deals with language, so here aspects arise such as 'Don't Misled by Language'.

AI is very important in decision making for organizations. So, teaching machine learning systems to the highest standards possible is the prime focus of any organization. This will require oversight from the board and strong governance extending right down to the technical level of algorithm design. 

Building machine learning applications should take pride and responsibility for creating "good". Legislation and regulations will be required to hold organizations to account for decisions taken by their algorithms.

In the fog of language and rapid pace of changing technology, we must not lose sight of the fact that humans are the teachers of AI. The intelligence in AI is not human-like and requires its own specific forms of care and nurturing. Where AI goes meaningless, it is the people who are to blame to.

Source: HOB