Steve Jobs: Overcoming Adversity

Steve Jobs: Overcoming Adversity

What an inspiring story! Steve Jobs has endured through great
diversity and tragedy in his life. The fact that he could become
so successful without a college degree is amazing in itself.
He is truly a testament to the fact that people can achieve the
“American Dream” through hard work and dedication. His
determination not to give up on his ideals and to never settle
on someone else’s shows great character. I would say his
“never give up” attitude was what was most inspiring for me.

Design Thinking vs. Innovation

Design Thinking & Innovation Go Hand in Hand

There is nothing unique about the design thinking approach
versus the innovation approach. One does lead to the other.
Designing is innovating. Brown’s view of “innovation” follows
Edison’s by which understanding through observation of what
people want and need in their lives and what they like or
dislike about a product or service can be improved.
Inspiration, Ideation and Implementation are the three I’s of
design thinking. One must imagine the world from multiple
cultures and perspectives (Empathy), not rely only on analytical
processes (Integrative thinking), have a positive attitude
(Optimism), believe in new ideas (Experimentation), and
seek out other people’s perspectives to broaden their view
(Collaboration). Brainstorming, sandboxing, (having a beer)
or whatever you wish to call it, leads to design thinking and
innovation since computers only do what they are told and
humans are the major source of creativity.

Greg Burrow

Neural Networks in Business and Banking

Neural Networking in Business and Banking


Neural network applications are being used to predict bankruptcy,
costs, revenue forecasts, document processing and more. It detects
common characteristics and classifies large amounts of information.
This data can then be used to determine relationships between
business factors and forecast changes and effects. This approach
can potentially help predict bankruptcy for credit risk or sales
forecasting. Using previous data can help establish trends that
banks can use to predict high risk loans. Data from studies concerning
credit risks can be evaluated by extracting different rules for
determining credit risk. Neural network decisions can clarify
by explanatory rules that capture learned knowledge embedded
in the network as to an applicants level of risk for default on loans.
In a nutshell, neural networks are used to predict certain outcomes
or probabilities as to whether an event constitutes a high or low
amount of risk. This enables businesses to make better business
decisions.

Greg Burrow