Aristotle begins the Metaphysics with an observation about the link between our desire for knowledge and our delight in sight.

Most readers of this blog already know that training machine learning algorithms requires huge amounts of annotated data. But a recent survey of data scientists we commissioned with Dimensional Research gave us a concrete definition of “huge”. A full 72% of the nearly 300 survey respondents reported that, in their current project, production-level model confidence will require more than 100,000 labeled data items. And 10% indicated they’d need more than 10 million! That’s a lot of bounding boxes and polygons!

The AI industry is picking up steam. IDC predicts that spending on AI systems will grow to nearly $35.8 billion in 2019 worldwide and will more than double to $79.2 billion in 2022.

Alegion sponsored Dimensional Research to conduct a survey of 277 data scientists and other AI professionals in large companies across nearly 20 industries to determine their use and development of AI and ML projects. Their findings suggest that for many organizations it’s still early days for the technology and many of them are struggling specifically with ML training data.

Read all about it at:

In our latest long form piece, we describe five qualities you should look for in a video annotation solution provider:

Disclosure: We are not a Cognilytica client and we are not mentioned in this report.

AI is shaping up to be a large engine for job creation. Automating processes through machine learning and artificial intelligence will change the way we work and will change the work that we do.

...to the second floor. We’re growing. And we’re starting to feel the pain. Recently, we’ve had to take over the second floor of our east side Austin offices. Now we’ve got more conference rooms, seating areas, desk spaces, and breakfast tacos.