AI & Machine Learning
Three People-Centered Design Principles for Deep Learning
To avoid bias, people-centered design principles must be the foundation of deep-learning algorithms.
To avoid bias, people-centered design principles must be the foundation of deep-learning algorithms.
Giving customers what they want quickly is a worthy goal. Businesses can’t always afford to do it.
When employees represent the views of customers, management needs to have their backs.
Small-scale piracy sometimes offers surprising benefits for IP rights holders.
As AI becomes more ubiquitous, we need clear systems for keeping it in check.
The post-digital tech wave — a powerful, integrated stack of technologies — is coming next.
How cities deal with AI-related changes will determine which ones will thrive in the future.
Platform markets are suddenly all the rage with B2B companies. And for good reason.
Why do some business ecosystems dominate their markets over time while others fail?
While hierarchy can impede innovation, handled well it can provide important benefits.
A successful pitch for AI must overcome economic, technical, political, and cultural hurdles.
Developing-world entrepreneurs need to build networks that compensate for weak public institutions.
Consumers’ concerns about data privacy are offset by a desire for personalized service.
Smart machines can help pick crops and reduce traffic — but what’s their impact on privacy?
There’s no oversight on coders who write critical software that runs key systems. That must change.
Unlike the housing bubble, the effects of a bursting AI bubble wouldn’t cause great harm.
The best chief digital officer candidate may not come from outside your company.
The next transformative emerging technology could be 4D printing.
Artificial intelligence may look poised to tackle tricky business decisions — but it only works for certain sorts of problems.
Effective leadership of virtual teams matters more than which collaboration platform you choose.