The autonomous future of cars is seemingly very nearly here.

With machine learning making disruptive innovation easier than ever before,
it’s up to new entrepreneurs to show established players how it’s done.

Brian Korzynski, senior application developer at United Shore, explains why
it’s essential that businesses understand the pros and cons of machine
learning. 

A great video of Tesla’s updated “autopilot” software which gives Tesla
electric cars quite impressive self-driving capabilities.

Amazon revealed its SageMaker AI service today, unveiling how its customers
are able to train machine learning models at massive scale while keeping
costs down. The company uses novel techniques to keep the needed amount of
computing power locked down while providing similar performance. 

Many of Google’s machine learning efforts are open sourced so that
developers can take advantage of the latest advancements. The latest
release is for semantic image segmentation, or the technology behind the
Pixel 2’s single lens portrait mode. 

How to use Mechanical Turk in combination with machine learning for dataset
labelling. Whether you build your own machine learning models in the cloud
or using complex mathematical tools, one of the most expensive and
time-consuming part of building your model is likely to be generating a
high-quality dataset. 

AI assistants will have to become more harmonious with people and
intelligent about emotion, Google Empathy Lab founder and principal
Danielle Krettek said.

No one could argue that life hasn’t been more convenient since the initial
breakthrough of artificial intelligence (AI) in 2012. The world is moving
faster and faster, driven by technology and innovation. However, with the
hype and noise around AI, it’s easy for companies to take missteps along
the way when trying to take advantage of the technology.