Interesting stuff of AI, Machine learning, and Deep Learning 2017–09 #3
A List of Chip/IP for Deep Learning (keep updating)
This project aims to build a deep learning pipeline that takes text descriptions and generates unique video depictions of the content described. The crux of the project lies with the Generative Adversarial Network, a deep learning algorithm that pins two neural networks against each other in order to produce media that is unique and realistic.
2. Detecting Malicious Requests with Keras & Tensorflow
So what if you could use the power of Google’s Tensorflow engine to decide on whether a given request is considered malicious? Well that was the question I was looking to answer while participating in Slalom’s recent AI hackathon. The following post outlines the technical details of a PoC for a security monitoring application which was built with the help of a couple other Slalomites.
3. Sketchy Data Visualization in Semiotic
When I open-sourced Semiotic, I expected to get some pushback on its support for hand-drawn “sketchy” rendering in marks. I also expected some questions as to how it and its accompanying “painty” mode are implemented. Instead, except for a couple friendly jibes, mostly of the response to Semiotic has been on its focus on information design. But I wanted to make sure to highlight the sketchy functionality nonetheless.
4. High Time to Regulate Face Recognition A.I.
We’ve reached a tipping point where it is now high time that we start the conversation of regulating Face Recognition Artificial Intelligence (AI). In a previous post, I explored some ideas of how we may regulate AI. I looked at several regulations in other fields and explored how they might apply for AI. The most compelling argument against AI regulation has been that it isn’t clear for many as to precisely what needs to be regulated. However, in recent days, it has come to my attention that a specific kind of AI algorithm needs serious thought for regulation.
5. The inside story of the iPhone X ‘brain,’ the A11 Bionic chip
“We’re clearly on a path now where, with generations of our products, one of the core elements is the chips in them that, to us, they’re intrinsically part of the definition of the product,” said Apple Senior Vice President of Worldwide Marketing Phil Schiller who, along with SVP of Hardware Technologies Johny Srouji, sat down with me 24 hours after the big unveil for an intense chat about silicon, the Apple way.
6. Learning to Optimize with Reinforcement Learning
Since we posted our paper on “Learning to Optimize” last year, the area of optimizer learning has received growing attention. In this article, we provide an introduction to this line of work and share our perspective on the opportunities and challenges in this area.
7. Finding meaning in generative adversarial networks
Artificial intelligence is emerging as a creative force; in the process, it reveals something of itself. If you ask a child to draw a cat, you’ll learn more about the child than you will about cats. In the same way, asking neural networks to generate images helps us see how they reason about the information they’re given. It’s often difficult to interpret neural networks — that is, to relate their functioning to human intuition — and generative algorithms offer a way to make neural nets explain themselves.
8. Why AI Companies Can’t Be Lean Startups
A Conversation with Matt Turck of FirstMark Capital Large companies want to seize the opportunity to strengthen their existing positions, and small companies hope to fuel their rise to market leadership with these new technologies. As an investor at FirstMark, Matt Turck has seen the machine learning-first startups that are poised to change the way we work from up close.
9. Jobs of the future: AI Interaction Designer
Many of the “jobs of the future” are here today, and they didn’t exist as recently as five years ago. One such job is AI Interaction Designer. At x.ai, that position falls to Diane Kim.
10. THE AI CHATBOT WILL HIRE YOU NOW
Eyal Grayevsky has a plan to make Silicon Valley more diverse. Mya Systems, the San Francisco-based artificial intelligence company that he cofounded in 2012, has built its strategy on a single idea: Reduce the influence of humans in recruiting. “We’re taking out bias from the process,” he tells me.
