Browsing Tag
ai
8 posts
It’s Complicated: On Imagination, Marvin Minsky & Shawn Carter
Computers remain dependent on humans to be able to imagine & architect what the computer should & can do. And perhaps we’re not building machines to be able to do anything beyond specialized intelligence because we don’t yet have reverence for the power of imagination and all that is required to discover and execute better ideas.
Unstructured data – Opportunities, Challenges, Dangers + Homework
It is estimated that 80% of new data generated is unstructured, largely driven by audio, video and rich media content. Here are a few opportunities and challenges presented with increased availability and use of unstructured data.
Artificial Intelligence : 4 Key Challenges Facing Engineers
The internet is filled with stories of AI that resulted in negative human impact and unintended consequences. If you are developing or plan to develop artificial intelligence solutions, here are some questions for your consideration:
Characteristics of Machine Learning solutions
It is estimated that 87% of data science projects never reach production. One of the pitfalls to developing a production-ready machine learning solution is the ability to define if it's an appropriate tool for solving the problem. Not every problem should be solved with machine learning.
Agile Machine Learning
The future of software is being driven by intelligent applications. By the year 2020, more than 85% of…
AI, Gatekeepers & New Rules for Solving the World’s Biggest Problems
Did you know that globally only 22% of AI professionals are women? Most business leaders agree that artificial intelligence (AI) will play a key role in shaping the future of work...
Enterprise architecture for artificial intelligence
Enterprise architecture for artificial intelligence The road to enterprise intelligence starts with the humans behind the curtain. This…
Data Iceberg Model for Machine Learning
One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset. The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.