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Artificial IntelligenceData NativesData Science 101EducationEventsFeaturedInterviewsMachine LearningTech TrendsTechnology & IT
Why we need to open up AI black boxes right now
AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate
Three Critical Aspects of Design Thinking for Big Data Solutions
Design thinking continues to be all the rage amongst organizations of all kinds – from academia to startups, to agencies and consultancies, or large enterprises. The concept is popular today not because it’s new per se, but its approach to problem-solving fits well with the digital transformation that companies are
What is the real difference between Data Science and Software Engineering Teams?
Although there are lots of similarities across Software Development and Data Science , they also have three main differences: processes, tooling and behavior. Find out. In my previous article, I talked about model governance and holistic model management. I received great response, along with some questions about the differences between
Not Accounting for Bias in AI Is Reckless
I’ll never forget my “aha” moment with bias in AI. I was working at IBM as the product owner for Watson Visual Recognition. We knew that the API wasn’t the best in class at returning “accurate” tags for images, and we needed to improve it. I was nervous about the
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Artificial IntelligenceData NativesData Science 101EducationEventsFeaturedInterviewsMachine LearningTech TrendsTechnology & IT
Why we need to open up AI black boxes right now
AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate
Three Critical Aspects of Design Thinking for Big Data Solutions
Design thinking continues to be all the rage amongst organizations of all kinds – from academia to startups, to agencies and consultancies, or large enterprises. The concept is popular today not because it’s new per se, but its approach to problem-solving fits well with the digital transformation that companies are
What is the real difference between Data Science and Software Engineering Teams?
Although there are lots of similarities across Software Development and Data Science , they also have three main differences: processes, tooling and behavior. Find out. In my previous article, I talked about model governance and holistic model management. I received great response, along with some questions about the differences between
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Artificial IntelligenceData NativesData Science 101EducationEventsFeaturedInterviewsMachine LearningTech TrendsTechnology & IT
Why we need to open up AI black boxes right now
AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate
Three Critical Aspects of Design Thinking for Big Data Solutions
Design thinking continues to be all the rage amongst organizations of all kinds – from academia to startups, to agencies and consultancies, or large enterprises. The concept is popular today not because it’s new per se, but its approach to problem-solving fits well with the digital transformation that companies are
What is the real difference between Data Science and Software Engineering Teams?
Although there are lots of similarities across Software Development and Data Science , they also have three main differences: processes, tooling and behavior. Find out. In my previous article, I talked about model governance and holistic model management. I received great response, along with some questions about the differences between
Not Accounting for Bias in AI Is Reckless
I’ll never forget my “aha” moment with bias in AI. I was working at IBM as the product owner for Watson Visual Recognition. We knew that the API wasn’t the best in class at returning “accurate” tags for images, and we needed to improve it. I was nervous about the
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