Month: March 2016

Sidewalk Labs Flow

Sidewalk Labs announced that it is building “Flow,” a digital platform that seeks to address the real-time transit problem and more. Flow will aggregate and analyze mobility data from a great number of sources—including Google Maps, Waze, municipal data, and eventually, remote traffic sensors—to identify what’s causing congestion and which areas need what kind of service. This won’t just be software for transit officials to lord over, though. Flow will also have a public, outward-facing element in the form of digital kiosks that provide real-time transit information and wifi, similar to those currently in beta testing by Link NYC. That way, “citizens without a smartphone or data plan use new dynamic mobility services”. The kiosks will also include remote sensors that anonymously gauge parking availability, traffic flow, and rider demand. Eventually, those sensors could be used to test and regulate autonomous cars.

Evidence-based medicine has been hijacked

RW: You also write that evidence-based medicine “still remains an unmet goal, worthy to be attained.” Can you explain further?

JI: The commentary that I wrote gives a personal confession perspective on whether evidence-based medicine currently fulfills the wonderful definition that David Sackett came up with: “integrating individual clinical expertise with the best external evidence”. This is a goal that is clearly worthy to be attained, but, in my view, I don’t see that this has happened yet. Each of us may ponder whether the goal has been attained. I suspect that many/most will agree that we still have a lot of work to do.

Teaching AI after UBI

3 ways forward once everyone has UBI and is out of a job: Teaching AGIs everything we’ve already learned about the world. This is a herculean task and it has the potential to keep many of us busy doing it for many decades into the future. Collaborating with AGIs to learn things we don’t already know about the world. AGIs can learn how to do things without a formal knowledge of how something works. This is where engineers, scientists, and philosophers live and work. Applying the understanding and capabilities of AGI to do things in the real world better and more easily than ever before. Most of us will be working in this space.

Instant Learning and the Next Economy

all of that earlier innovation is child’s play compared to what is now possible. With limited AGI, it will be possible to exponentially accelerate the gathering, improvement, and sharing of human understanding. Here’s how this is done in its most basic form (currently called cloud robotics): An AGI learns a task or a concept through experience (this is becoming very easy to do with model free deep learning, Big Data and Big Sim as I pointed out yesterday). That understanding is packaged, uploaded, and stored in the cloud. Any other AGI can download that understanding as needed. This is clearly a formula for radically accelerating the growth of human experience. A radical upgrade to the existing process.