(2/2) With data in any form—incl. Excel—there is now opp. to exch. data btwn contexts w/guaranteed semantic integrity.
With applications in most every industry, the example in the technical paper shows the manifestly large savings for the energy sector.
(1/2) Collaboration among engineers often requires manual consensus. This is time-consuming, error-prone, and still requires over-engineering to account for uncertainty.
With data in any form—including Excel—there is now opportunity to exchange data between contexts.
(4/4) Data relationships are expanding at an unfathomably large rate.
Bringing that knowledge together, which is represented in those data relationships, and capturing it throughout an enterprise that guarantees the integrity of that meaning is what Conexus uniquely provides.
(3/4) What is less known than the quadratic explosion of data is that the data sources are also increasing quadratically.
The tough part is in the data relationships. That is expanding unfathomably large rate. #categorytheory#composability#semanticintegrity
(2/4) Everybody's kinda gotten the memo we'd say about data being the new oil and all that.
Data is growing quadratically.
What is less known is that the data sources are also increasing quadratically. #categorytheory#composability#semanticintegrity
Global success needs global relations. @VisitOSLO has the promise to be one of Europe’s technology capitals. @OsloIW
This is a room you want to be in! #oiw2022
How my name wants to spelled while in Oslo.
(4/5) There are some things in school are legitimately hard. That was very hard to do two PhD programs at the same time. Even though they're somewhat complimentary, somewhat overlapping, that was tough.
(3/5) I did the really silly thing of enrolling at Stanford and then looking at Carnegie Mellon and say well, I think I'm going to do that, but I don't want to drop the Stanford program. So for awhile, I was actually enrolled in both. It was complete craziness.
(1/5) Friday Career Reflections (Part 2 of 2): PhD programs are driven by research funding. I am grateful to have enjoyed the career success allowing me to find my own research funding. At Carnegie Mellon, that allowed a lot of freedom. At Stanford, not so much.
(5/5) For those looking to build skills in Public IT, the program will bridge the divide between areas like policy and tech, and provide additional instruction and training for those in gov't , or interested in working in related digital service areas.
(3/5) Each of the three component certificates, scheduled over a two-month period, will cover key topical areas tailored for public sector leaders, and be delivered via live distance learning by CMU faculty, government experts, and select industry practitioners.
(3/5) Can a database represent everyone's viewpoint in one world view without requiring consensus? This is just a complete mind shift from how most people have to operate in requiring consensus. Blockchains work that way.
(2/5) A deductive database allows for a database of viewpoints. In a relational database, this process is manual and prone to error. A deductive database is one of perspectives. Instead of a database of just data in a table, the data relationships become more important.
(9/9) With a lot of luck, I found myself in demand for taking some academic spin-outs and finding commercial opportunities for them. With a series of fortunate events that include wonderful collaborators, I am grateful for that all having played out pretty well so far.
(8/x) Spending a lot of years working with AI from different perspectives gave me a lot of useful exposure to provide value at the Board and Policy level. I had been a researcher, a venture capitalist, and an entrepreneur.
(5/x) Fortunate events: Somehow I was at the right age, at the right stage of where AI was. It was hot. I thought: "Maybe I can contribute something here." From a young age I was interested in AI and Robotics.
(1/x) Friday afternoon career observations: At a young age, it became clear that Artificial Intelligence was a future that interested me. It was a future on which I could stake my career and it would be of interest....
I think of math & data as both existing ‘within logic’- data is ground facts (no quantifiers), math is formulae w/quantifiers. We trade different kinds of formula for each other & ask if large theory of small formula complexity is better than small theory of large complexity, etc twitter.com/pmddomingos/st…
Those sort of every day, day-to-day business questions were really difficult for them to execute on when they then had 300,000 databases. This is a very rich company with very smart people with a Greenfield opportunity but still had a non-optimal IT infrastructure.