A few weeks ago, our team attended one of the largest annual security events in the North America: ISC West in Las Vegas.
In Part 1, we covered the preliminary work that goes into designing a brand-new product. The biggest takeaway here is that we have to sink our teeth into the problem before we set out trying to build a solution. Fostering a deep sense of empathy toward users, customers, and other stakeholders is critical to designing a product that stands apart from the rest.
Disclaimer: This should not be a blog post. It should likely be a trilogy of chapter books complete with glossaries and appendices.
In 2017, security robotics was a pre-mature, yet emerging, trend in the security space. Over the past couple of years, things have started to change.
Critics say it's the end of personal privacy as we know it. Advocates call it the tech advancement that will revolutionize security and means of identification.
"Laaaaaaadies and gennnntlemen, welcome to the event of a lifetime. The showdown between two age-old rivals. A battle for the title. A match like no other.
Artificial intelligence, machine learning, computer vision, and deep learning. They all seem to be used interchangeably. But are they really the same?
According to a recent study, that's how many workers could be displaced by 2030 thanks to automation and artificial intelligence. These stats can be scary, and they tend to flare up heated debates around technology and its role in our work force.
Deep learning and edge computing... they're more than just buzzwords.
Your world is entirely mobile. Everything from checking emails, ordering your morning Starbucks, booking a flight and walking your dog can be done from your mobile device.