Building Trustworthy Behaviomedics with Blueskeye CEO Michel Valstar

11/11/21 • 22 min

2 Listeners

Academic turned entrepreneur Michel Valstar joins How AI Happens to explain how his behaviomedics company, Blueskeye AI, prioritizes building trust with their users. Much of the approach features data opt-ins and on-device processing, which necessarily results in less data collection. Michel explains how his team is able to continue gleaning meaningful insight from smaller portions of data than your average AI practitioner is used to.

Michel Valstar on LinkedIn

Blueskeye AI

Michel Valstar on LinkedIn

Blueskeye AI

Previous Episode

Egocentric Perception with Facebook's Manohar Paluri

Joining us today is Senior Director at Facebook AI, Manohar Paluri. Mano discusses the biggest challenges facing the field of computer vision, and the commonalities and differences between first and third-person perception. Manohar dives into the complexity of detecting first-person perception, and how to overcome the privacy and ethical issues of egocentric technology. Manohar breaks down the mechanism underlying AI based on decision trees compared to those based on real-world data, and how they result in two different ideals: transparency or accuracy.

Key Points From This Episode:

Talking to Manohar Paluri, his background in IT, and how he wound up at Facebook AI.
Manohar's advice on the pros and cons of doing a Ph.D.
Why computer vision is so complex for machines but so simple for humans.
Why the term “computer vision” is not a limiting definition in terms of the sensors used.
How computer vision and perception differ.
The two problems facing computer vision: recognizing entities and augmenting perception.
Personalized data; generalized learning ability; and adaptability: the three problems that are responsible for the low number of entities that computer vision recognizes.
Managing the direction Manohar's organization is going: egocentric vision, predicting the impact of modeling, and finding the balance between transparency and accuracy.
Find out what the differences are between first- and third-person perception: intention, positioning, and long-form reasoning.
The similarity between first- and third-person perception: both are trying to understand the world.
Which sensors are required to predict intention: gaze and hand-object-interaction.
What the privacy and ethical issues are with regard to egocentric technologies.
Why Manohar believes striking a balance between accuracy and transparency will set the standard.
The three prospects in AI that excite Manohar the most: the next computing platform, bringing different modalities together, and improved access to technology.

Tweetables:

“What I tell many of the new graduates when they come and ask me about ‘Should I do my Ph.D. or not?’ I tell them that ‘You’re asking the wrong question’. Because it doesn’t matter whether you do a Ph.D. or you don’t do a Ph.D., the path and the journey is going to be as long for anybody to take you seriously on the research side.” — Manohar Paluri [0:02:40]

“Just to give you a sense, there are billions of entities in the world. The best of the computer vision systems today can recognize in the order of tens of thousands or hundreds of thousands, not even a million. So abandoning the problem of core computer vision and jumping into perception would be a mistake in my opinion. There is a lot of work we still need to do in making machines understand this billion entity taxonomy.” — Manohar Paluri [0:11:33]

“We are in the research part of the organization, so whatever we are doing, it’s not like we are building something to launch over the next few months or a year, we are trying to ask ourselves how does the world look like three, five, ten years from now and what are the technological problems?” — Manohar Paluri [0:20:00]

“So my hope is, once you set a standard on transparency while maintaining the accuracy, it will be very hard for anybody to justify why they would not use such a model compared to a more black-box model for a little bit more gain in accuracy.” — Manohar Paluri [0:32:55]

Links Mentioned in Today’s Episode:

Manohar Paluri on LinkedIn

Facebook AI Research Website

Facebook AI Website: Ego4D

Next Episode

Developing Solid State LiDAR with Baraja CTO Cibby Pulikkaseril

Traditional LiDAR systems require moving parts to operate, making them less cost-effective, robust, and safe. Cibby Pulikkaseril is the Founder and CTO of Baraja, a company that has reinvented LiDAR for self-driving vehicles by using a color-changing laser routed by a prism. After his Ph.D. in lasers and fiber optic communications, Cibby got a job at a telecom equipment company, and that is when he discovered that a laser used in DWDM networks could be used to reinvent LiDAR. By joining this conversation, you’ll hear exactly how Baraja’s LiDAR technology works and what this means for the future of autonomous vehicles. Cibby also talks about some of the upcoming challenges we will face in the world of self-driving cars and the solutions his innovation offers. Furthermore, Cibby explains what spectrum scan LiDAR can offer the field of robotics more broadly.

Key Points From This Episode:

Cibby’s background in fiber optic communications and what led him to found Baraja.
Realizing that a laser used in DWDM networks could be applied to LiDAR.
Why Cibby decided that autonomous vehicles (AVs) were a good application for the laser.
How the laser used by Baraja can steer a LiDAR beam without any moving parts thus making the system cheaper.
Velodyne’s contributions and other innovations in the LiDAR space.
A description of how the spectrum scan LiDAR works using a color-changing laser routed by a prism.
The infinite resolution made possible by colored light and how AI will make use of it.
Hazards around the over-proliferation of conventional LiDAR laser and how Baraja’s tech gets past this.
Other challenges Cibby predicts will exist once AVs start to proliferate.
How Baraja’s solid-state LiDAR technology will advance other fields of robotics.
Cibby’s level of involvement in the coding and R&D at Baraja as the CTO.
Technical areas that the Baraja team is researching and developing such as homodyne detection.
Advice from Cibby for how to innovate in the already cutting-edge space of computer vision.

Tweetables:

“We started to think, what else could we do with it. The insight was that if we could get the laser light out of the fiber and into free space, then we could start doing LiDAR.” — Cibby Pulikkaseril [0:01:23]

“We were excited by this idea that there was going to be a change in the future of mobility and we can be a part of that wave.” — Cibby Pulikkaseril [0:02:13]

“We are the inventors of what we call spectrum scan LiDAR that is harnessing the natural phenomenon of the color of light to be able to steer a beam without any moving parts.” — Cibby Pulikkaseril [0:03:37]

“We had this insight which is that if you can change the color of light very rapidly, by coupling that into prism-like optics, this can route the wavelengths based on the color and so you can steer a beam without any moving parts.” — Cibby Pulikkaseril [0:03:57]

Links Mentioned in Today’s Episode:

Cibby Pulikkaseril on LinkedIn

Baraja