Insight Softmax Consulting, LLC

Last week, we reached an important milestone in the CA Port Data Interoperability Grant Program. The California Governor's Office of Business and Economic Development (GO-Biz) released the $27M earmarked for data interoperability and technology upgrades for the five containerized ports in California. This grant program is designed to ensure that our containerized ports, which process about 40% of all containerized imports in the U.S., have the technology needed to remain competitive while also driving statewide economic, supply chain, and environmental benefits. ISC and our project partners Momentum (BuildMomentum.io) designed and implemented this first-of-its-kind grant program, and we’re proud to mark this milestone as we move into the next phase of the project. We want to extend our heartfelt congratulations to all five containerized California ports, who have been participating in this program since its inception last summer: Port of Los Angeles, Port of Long Beach, Port of Oakland, Port of Hueneme, and Port of San Diego. And of course, none of this would have been possible without the expert collaboration of Trelynd Bradley and Angela Shepard from GO-Biz. Shout-outs to Tre and Angela for being such awesome partners in this project. The 10 awarded projects, from developing new data standards to reducing emissions via vessel routing tracking, were proposed by the individual ports and analyzed by the project’s Technical Advisory Committee (TAC). The TAC was formed early in the program by ISC to ensure that a well-rounded pool of technical experts reviewed and gave feedback on all proposals. The other members of the TAC aside from ISC are: Cloud303, Data CRT, and Latacora. Another positive outcome from this project is growing cooperative technical relationships between the ports, working together for the benefit of all stakeholders. In the most notable example, ISC is at the forefront of negotiating the adoption of a Universal Trucking Appointment System (UTAS) between Port of Long Beach and Port of LA, serving the San Pedro Bay port complex. The UTAS, once implemented, will improve the scheduling of semi trucks (drayage) loading and unloading containers at both ports, cutting emissions and saving resources while making the lives of their trucking customers much easier. While there is not yet a formal agreement in place, we’re seeing solid momentum as a final solution and implementation roadmap is negotiated. As we enter the implementation phase, ISC will continue to provide technical mentorship for all the approved projects, as well as technical mediation on the UTAS project, for the duration of the program. If you’d like to know more about the RFPs that the individual ports are putting out, or if you have a service you’d like to provide for one or more of these projects, please contact us and we can make an introduction.

No matter the methodology used to calculate its value, it’s certain that Open Source Software is an incredibly valuable commodity. The question is how to take that fact and apply it practically within an organization. In this article, we'll show you some methods developed by our CTO, Alexander Scammon, to effectively measure the value created by the OSS team that he leads for G-Research.

We reached a big milestone in the Port Data Interoperability Grant Program last week: we awarded all the funds to the five participating ports! While we have to wait until the official announcement before we can talk about details here, we used this milestone as an opportunity to meet some of the awesome folks at Port of Oakland in person, and take a tour of their very cool facility. ISC’s project lead Michael Mansour and project assistant Morasha Ahrns were joined by Data CRT's Noah Bruegmann. DataCRT / Noah are part of the grant program's Technical Advisory Committee, which has played such a critical role in this project. Once on-site, our group was joined by Port of Oakland’s Romario James, Pia Franzese, and Carolyn Almquist, who were kind enough to give a tour of the port security ops center and the Evergreen terminal. The security ops center was very cool — modern and impressive — and the Evergreen terminal was just awe-inspiring in its size and scale. After only seeing the port for so long while driving over the Bay Bridge, our group appreciated the opportunity to see it in person. Check out the attached photos to get a sense of the scale! We look forward to announcing more details about the awards within the next couple of weeks, and to working with Port of Oakland and our other stakeholders on the remainder of the project.

Will you be at KubeCon Paris this week? Come talk Armada, #Kubernetes, FastTrackML, and more at the G-Research OSS kiosk on Friday from 10:30-14:30. #KubeCon #KubeCon2024 Kubernetes Cloud Native Computing Foundation (CNCF)

Coming to #KubeCon2024? Calling all *passionate* cloud native batch system maintainers to join Alexander Scammon + Klaus Ma at #KubeCon #CNCF Batch System Initiative Working Group! Thurs, March 21 • 14:30 - 15:05 Pavilion 7 | Level 7.3 | E05 - E06 https://lnkd.in/dkcTzcy5 Cloud Native Computing Foundation (CNCF) Kubernetes

Our CTO Alexander Scammon will be at #kubecon2024 this week with some of his G-Research Open Source Software compatriots. Come get some fun swag at their #KubeCon ContribFest Armada session! ➡ Add it to your schedule! 📅 Wed 11:15am (CET) 📍 Pavilion 7 | Level 7.3 | W01 https://sched.co/1YheS All experience levels welcome! #kubecon #kubecon2024 #opensource Cloud Native Computing Foundation (CNCF)

Is your project using large-scale Kubernetes clusters? Will you be at #kubecon2024 Paris? Come hear G-Research Open Source Software's Dejan Pejčev discuss how to use KWOK for simulations at K8s Contributor Summit #kubecon2024 Paris. Tues, March 19 • 13:45 - 14:15 (CET) https://sched.co/1aOpx Cloud Native Computing Foundation (CNCF) Kubernetes

Every company needs data science and AI — or so they think — while at the same time most data science projects fail to deliver any lasting value. While there is no exact measurement on the percentage of data science projects that fail, there is consensus that the failure rate is quite high. Read on to see some of the most common causes of data science project failure.

What happens when you bring together a diverse group of researchers from underserved regions to discuss the challenges of running large-scale HPC clusters with limited resources? Our intrepid CTO, Alexander Scammon, was at NRG@SC23 and has some thoughts. Most notable is a nascent project aiming to package HPC gear in a form factor that can be delivered to developing nations relatively cheaply.

Ethics, bias, and fairness in AI It’s impossible to watch the news or scroll social media lately without hearing about AI, often citing its dangers. Like any powerful tool, it has the potential to do harm. You could end up harming people (and your own business) if you don’t design your AI solution with this in mind. Here I’ll give you a high-level overview of three essential focus areas to help you utilize the power of AI in a safe way. Principles for human-centric design for AI (AI ethics): 1. Understand people’s pain points and needs in order to better define the problem 2. Ask if AI adds value to any potential solution 3. Consider the potential harms that the AI system could cause 4. When prototyping, start with non-AI solutions and make sure that people from diverse backgrounds are included in the process 5. Provide ways for people to challenge the system 6. Build in safety measures Bias in AI (six examples): • Historical bias (occurs when the state of the world in which the data was generated is flawed) • Representation bias (occurs when building datasets for training a model, if those datasets poorly represent the people that the model will serve) • Measurement bias (occurs when the accuracy of the data varies across groups. This can happen when working with proxy variables) • Aggregation bias (occurs when groups are inappropriately combined) • Evaluation bias (occurs when evaluating a model, if the benchmark data does not represent the population that the model will serve) • Deployment bias (occurs when the problem the model is intended to solve is different from the way it is actually used) AI Fairness (four examples): • Demographic parity / statistical parity (the composition of people who are selected by the model matches the group membership percentages of the applicants) • Equal opportunity (the proportion of people in each group who should be selected by the model are actually selected by the model) • Equal accuracy (the percentage of correct classifications should be the same for each group) • Group unaware / "Fairness through unawareness” (removes all group membership information from the dataset) We hope this list gives you a better understanding of how you can design these powerful AI tools with ethics, bias, and fairness in mind. We want to thank Hult International Business School for inviting our COO Marcus to teach another installment of his course, Computational Analytics with Python, at their Boston campus over January and February. Shoutouts to some of Marcus’s awesome students in the course: Nigel Nkomo Pablo Leandro Guala Suraj Udasi Daniela Mayorga Lucero Vergara De La Torre Komal Ghazanfar Sebastian Prieto Omonefe Edewor