Every month I publish a blog post to summarise what I think are the highlights in content on AI & data at large. It’s my personal notebook on interesting content. Previous editions can be found here: March, April, May, June
Personality tests are almost always based on the big five; agreeableness, conscientiousness, extroversion, openness, and neuroticism. Four out of five can now be predicted based on eye movement: agreeableness, conscientiousness, extroversion, and neuroticism (+perceptual curiosity). The study already had a high accuracy, but the researchers suggest to further optimize for even higher accuracy. The implications for this are enormous. Can’t wait to see it in the (mental) healthcare environment.
2. Article: AI in Cardiology
AI and medicine seem to be a good match so far. The huge troves of data that medical institutions possess make it a good candidate. According to this article, AI could greatly speed-up R&D around cardiology.
3. Article: How Amazon Has Reorganized Around Artificial Intelligence And Machine Learning, Forbes
In earlier posts, I already posted links to the reorgs at Google & Microsoft to better facilitate AI within the company, and make one of the most important pillars. This article by Forbes adds the Amazon perspective to the equation. Amazon recently replaced and enhanced a big part of their buyers with AI. While Google has a big focus on research for Amazon a big part of the assignment is saving costs and automating its workforce. Save costs to be even more competitive. I don’t think any of their competitors will come anyway near their capacity any time soon.
4. Article: Carlsberg Research Laboratory behind beer research project based on artificial intelligence
Carlsberg is a good example where I’m pleasantly surprised how far they are with the implementation of AI already.
5. Article: If companies had no employees
“McDonald’s, a fast-food company, has taken things the furthest, outsourcing 100% of its restaurant jobs.”
The gig economy is real. An estimated 42% of workers in the US will be employed via the gig economy by 2020. With this move, it will also be a lot easier to replace humans with AI.
6. Article: AI in Startups, Forbes
AI in Startups (and software development in general) is a double-edged sword. On the one hand, it’s still very labor intensive. To set-up the right data pipelines, integrate everything with the customer’s datasets, is a costly operation currently. This makes it a lot harder to set-up real SaaS like products with the current state. However, there is also another side. AI potentially levels the playing field between startups and bigger corporations because it gives access to market research, easy prototyping. The article mostly explores the ladder, and how AI can be implemented in The Lean Startup methodology.
Successful implementations of computer vision are popping up left right and center recently. And it’s great to see/hear that Wildlife Conservation is another example. As with many of these projects, it starts as a hobby. I really enjoyed listening to Jason Holmberg’s explanation of his projects and how it makes huge contributions to wildlife conservation. Did you know for instance that it takes about 9 hours for a scientist to classify 1 image of dolphin? Imagine the increase in research capacity if you can let AI figure these things out.
“AI is empowerment, and we want to democratize that power for everyone and every business — from retail to agriculture, education to healthcare,” Fei-Fei Li, chief scientist of Google AI, said in a statement. “AI is no longer a niche in the tech world — it’s the differentiator for businesses in every industry. And we’re committed to delivering the tools that will revolutionize them.”
Gillian McCann made the point that conversational AI has made drastic improvements during the last couple of years. According to her, it’s mostly about designing the conversations and improving based on conversational analytics, over simply improving the AI itself.