Artificial Intelligence Ecosystem
Platform for Higher Education.
Access the Next Generation
of Artificial Intelligence Talent.
Vetted talent. Back by Scientific Advisors & Ecosystem
AI GOALS ACHIEVED
ecosystems so that talent at universities match with AI-focused firms to achieve AI Goals
AI Ecosystem Subject Matter Expertise Contributions
Talent must work in teams to solve complex AI projects & struggle to build real-world AI portfolio.
AI-Focused Companies can access the next generation of AI talent to achieve their goals.
Connect your community to
Global AI opportunities
Your students, alumni, faculty, and affiliates have many struggles in their journey to achieve their artificial intelligence goals.
Resources are fragmented, you now have the opportunity to guide them in their journey.
Work on real-world AI projects
Access real-world AI data sets. Get paid for your work. Build your AI brand as a team.
Match with AI teammates to work on complex projects & research. Achieve your AI entrepreneurial goals.
What AI problems would you like to solve?
Post AI projects. Hire Vetted AI Talent. Order AI Gigs that are unique to each university.
Access thousands of AI talent and solve your AI problems.
Every step of
your AI Goals
Journey at University.
Every step of
your AI projects journey.
DAIMLAS is an all-in-one AI ecosystem building platform for academia & AI-focused companies.
DAIMLAS manages, builds, and grows the artificial intelligence ecosystem at your university.
DAIMLAS is the inventor of the proprietary Artificial Intelligence Ecosystem Value Network 8 stakeholder model. We are the only software to build and manage AI ecosystems. Academia and AI-Focused Companies fix their fragmentation with DAIMLAS so they can execute their AI strategies.
An artificial intelligence ecosystem is made up of 8 stakeholders that are upstream resources, downstream channels, and subsidiary resource providers supporting a shared goal within the field to build the next generation of talent to populate ecosystems. In an AI ecosystem there are 8 stakeholders that are made up of AI centers of excellence, scientific advisors, risk capital, institutional capital, AI degree granting universities, other universities, AI students & alumni, other students, practitioners (AI & others), AI entrepreneurs, AI opportunities, AI research labs, AI regulators, AI funding, AI ethicists, AI projects, computation and hardware resources, AI Govt grants, AI data sources, and government representation at the Federal, State, & Local government levels. The common goal is to build the next generation of AI talent, Each resource and stakeholder’s activity adds value to the end goal of this ecosystem. These resources are not compartmentalized or one-to-one, they are relationship nodes that exchange value based on each goal.
1-Seek Real-World AI Experience. Actually work on
AI after graduation
2-Must Work in Teams to Transfer Complex AI Projects from Lab to Industry.
3-Pursue passion area in AI with a Chief Scientific Advisor or Mentor
4-Navigate all the platforms (AWS, Oracle) & navigate latest research
5-Understand PhD Options Related to AI
6-Silicon Valley Perspective (Startups)
7-Brand name internship
8-Too many silos, No AI Collaboration Across Engineering, Law, Medical, Business & More.
Building a pipeline of AI talent and projects requires technical as well as educational merit. For this, AI centres of excellence led by world-renowned scientific advisors are necessary.
Stronger AI ecosystems emerge when government to grassroots initiatives get involved.
AI ecosystems materialize as value networks. They redesign the entry margin for stakeholders to start their journey into AI, offering them a path to participate.
In a government AI value network, there are eight stakeholders that include upstream resources, downstream channels and resource providers supporting a shared goal to build the next generation of talent to populate ecosystems.
To effectively execute government AI strategies, we must solve the AI education problem where those with and without an AI education need a pathway to achieve their goals.
A virtual platform can operationalize government and grassroots AI policies to build talent by creating connections and sharing knowledge that benefits the whole ecosystem.
The US’s new National AI Research Resource aims to provide a shared computing and data infrastructure.
The AI hub is intended to democratize access and help to fuel research and development.
Lessons learned in the US can support global AI innovation across the public and private sectors.