“Pachyderm helped found the AIIA because we understand that true innovation will come from a diverse group of incredible companies working together to build the AI/ML infrastructure of tomorrow. Nobody can do it alone and we're excited to work with so many bright minds in one place to create the canonical stack of ML so data scientists can move up the stack to solve more pressing problems like self-driving cars, crushing fraud, better cancer detection and sourcing vaccine candidates in record time.”
“As open source software, we want to promote open standards and interoperability for AI and ML applications in the enterprise. Openness is at the core of everything we do and the AIIA is committed to the same goal. As an Irish startup, it is fantastic to be part of a global network of cutting edge companies defining the future of data-centric analytics and applications.”
“Customers are increasingly asking for the ability to assemble their own AI stack with best of breed components at every layer. For Arthur, joining AIIA is a commitment to being open and interoperable with this rich ecosystem of AI infrastructure innovators."
“ML and data science are only growing in complexity and that's why openness, integration, and community are essential to scaling effective teamwork. It's in that spirit we see the importance of collaborating with other companies that are building the next generation of tools for data science.”
“For YData, being part of AIIA is key for our strategy – the AI landscape is fractured and it's represented by the best tools for each specific niche/task. For that reason, partnering and building technical integrations and interoperability among us is crucial to selling to organizations that need all of those tools (namely, enterprise), and the AIIA is enabling that.”
-Gonçalo Martins Ribeiro, CEO and co-founder, YData.ai
“AI Labs operates in the Aerospace and Defence sector where products and processes need to be certified by external authorities like CAA, FAA. There is a mandatory requirement on any AI system to demonstrate necessary checks and balances before entering the production phase.
The AIIA community is pioneering the consolidation of building blocks and best practices for production-ready AI systems of today and tomorrow. Hence AI Labs is keen to get involved, contribute to and learn from this amazing community.”
-Kiran Krishnamurthy, CEO and Data Science Practitioner, AI-labs.co.uk
“The community is filled with companies like ours, that are helping pave the way for AI to be used in practical and responsible ways. It’s a very exciting space to be in, and one of our biggest missions is to change the way people think about AI in the market. So we have a lot to gain by working together.”
“InfuseAI believes that open-source makes the world better. Joining AIIA is important to us. By fighting together with the community to democratize AI, we aim to eliminate the tech gap and help enterprises form build-up ML environment to deploy AI.”
“The fast adoption of AI is creating new disciplines like MLOps and ModelOps. Technology change creates new topics of discussion and can, at times, lead to confusion. The AIIA provides a forum for having discussions and sharing knowledge around these new AI technology areas and disciplines that benefits both vendors and businesses.”
“UbiOps partner with AIIA to collaborate towards an end-to-end solution for MLOps, that can be tailored for a user's need and prevent major vendors lock-in. There is no one-size-fits-all solution for MLOps. It's an emerging topic that includes a unique combination of challenges (technical, political, social). The current landscape of tools is fragmented and it's a challenge for practitioners to find the right interoperable tools for their needs. The AIIA aims to form a canonical stack of different specific tools and frameworks, give them a stage and help the best they can to solve the multidisciplinary challenge that MLOps currently is, we want to contribute to finding a solution to that problem.”
The AI/ML startup landscape is indeed fragmented, with a wide variety of companies working on different aspects of the technology. While this can make it challenging for startups to stand out and for investors to evaluate them, there are some steps that can be taken to address this fragmentation.
Foster collaboration: One way to address fragmentation is to encourage collaboration between AI/ML startups. This can involve partnerships, knowledge-sharing, and even co-development of technology. By working together, startups can pool their resources and expertise to create more comprehensive solutions.
Establish common standards: Another way to reduce fragmentation is to establish common standards for AI/ML technology. This can help ensure interoperability and ease the integration of different solutions. Common standards can also make it easier for startups to demonstrate the effectiveness and reliability of their technology.
Encourage specialization: While fragmentation can be challenging, it can also be an opportunity for startups to specialize in specific niches within the AI/ML landscape. By focusing on specific applications or technologies, startups can differentiate themselves and become leaders in their particular area of expertise.
Invest in education and training: Finally, education and training can play an important role in reducing fragmentation in the AI/ML startup landscape. By providing resources and support to new startups, entrepreneurs can develop the skills and knowledge needed to succeed in this rapidly evolving field. This can help ensure that startups have access to the latest technology and best practices, regardless of their specific area of focus.
"Despite the innovation delivered by startups, the AI/ML market remains dominated by the tech giants, such as Amazon, Google, Facebook etc. They have access to enormously big data, human resources, money, built tools and infrastructures – all that makes it brutally hard to compete with them. Equalizing the competitive landscape is another goal of the AIIA."
Most of the growth I've noticed is from Web3 and crypto. There are tons of data there along with zero-knowledge proofs that AI would fit into perfectly. Consider talking with some crypto companies. They have the data and your AI companies can regulate such data.
"TheSequence joined the AI Infrastructure Alliance (AIIA) whose mission is to align AI/ML startups and community members to make it more like Lego blocks that can be stacked together. "
Like building blocks in crypto? Would this be done through APIs and 3rd-party integrations or will it be under Web3 Framework?
How would the alliance handle decentralized stacking of AI companies?
I'm curious to see how this goes for the alliance, quite honestly.
“Pachyderm helped found the AIIA because we understand that true innovation will come from a diverse group of incredible companies working together to build the AI/ML infrastructure of tomorrow. Nobody can do it alone and we're excited to work with so many bright minds in one place to create the canonical stack of ML so data scientists can move up the stack to solve more pressing problems like self-driving cars, crushing fraud, better cancer detection and sourcing vaccine candidates in record time.”
-Joey Zwicker, co-founder, Pachyderm.com
“As open source software, we want to promote open standards and interoperability for AI and ML applications in the enterprise. Openness is at the core of everything we do and the AIIA is committed to the same goal. As an Irish startup, it is fantastic to be part of a global network of cutting edge companies defining the future of data-centric analytics and applications.”
-Luke Feeney, Operations Director, TerminusDB.com
“Customers are increasingly asking for the ability to assemble their own AI stack with best of breed components at every layer. For Arthur, joining AIIA is a commitment to being open and interoperable with this rich ecosystem of AI infrastructure innovators."
-Adam Wenchel, CEO and co-founder, Arthur.ai
“ML and data science are only growing in complexity and that's why openness, integration, and community are essential to scaling effective teamwork. It's in that spirit we see the importance of collaborating with other companies that are building the next generation of tools for data science.”
-Dean Pleban, Co-Founder & CEO of DAGsHub.com
“For YData, being part of AIIA is key for our strategy – the AI landscape is fractured and it's represented by the best tools for each specific niche/task. For that reason, partnering and building technical integrations and interoperability among us is crucial to selling to organizations that need all of those tools (namely, enterprise), and the AIIA is enabling that.”
-Gonçalo Martins Ribeiro, CEO and co-founder, YData.ai
“AI Labs operates in the Aerospace and Defence sector where products and processes need to be certified by external authorities like CAA, FAA. There is a mandatory requirement on any AI system to demonstrate necessary checks and balances before entering the production phase.
The AIIA community is pioneering the consolidation of building blocks and best practices for production-ready AI systems of today and tomorrow. Hence AI Labs is keen to get involved, contribute to and learn from this amazing community.”
-Kiran Krishnamurthy, CEO and Data Science Practitioner, AI-labs.co.uk
“The community is filled with companies like ours, that are helping pave the way for AI to be used in practical and responsible ways. It’s a very exciting space to be in, and one of our biggest missions is to change the way people think about AI in the market. So we have a lot to gain by working together.”
-Serkan Piantino, CEO, Spell.ml
“The AIIA wanted to have an independent and vendor-neutral voice in this community.”
-Larysa Visengeriyeva, Chief ml-ops.org Officer, INNOQ
“InfuseAI believes that open-source makes the world better. Joining AIIA is important to us. By fighting together with the community to democratize AI, we aim to eliminate the tech gap and help enterprises form build-up ML environment to deploy AI.”
-InfuseAI.io
“The fast adoption of AI is creating new disciplines like MLOps and ModelOps. Technology change creates new topics of discussion and can, at times, lead to confusion. The AIIA provides a forum for having discussions and sharing knowledge around these new AI technology areas and disciplines that benefits both vendors and businesses.”
-Linda Maggi, VP of Marketing, ModelOp.com
“UbiOps partner with AIIA to collaborate towards an end-to-end solution for MLOps, that can be tailored for a user's need and prevent major vendors lock-in. There is no one-size-fits-all solution for MLOps. It's an emerging topic that includes a unique combination of challenges (technical, political, social). The current landscape of tools is fragmented and it's a challenge for practitioners to find the right interoperable tools for their needs. The AIIA aims to form a canonical stack of different specific tools and frameworks, give them a stage and help the best they can to solve the multidisciplinary challenge that MLOps currently is, we want to contribute to finding a solution to that problem.”
-Wouter Hollander, UbiOps.com
The AI/ML startup landscape is indeed fragmented, with a wide variety of companies working on different aspects of the technology. While this can make it challenging for startups to stand out and for investors to evaluate them, there are some steps that can be taken to address this fragmentation.
Foster collaboration: One way to address fragmentation is to encourage collaboration between AI/ML startups. This can involve partnerships, knowledge-sharing, and even co-development of technology. By working together, startups can pool their resources and expertise to create more comprehensive solutions.
Establish common standards: Another way to reduce fragmentation is to establish common standards for AI/ML technology. This can help ensure interoperability and ease the integration of different solutions. Common standards can also make it easier for startups to demonstrate the effectiveness and reliability of their technology.
Encourage specialization: While fragmentation can be challenging, it can also be an opportunity for startups to specialize in specific niches within the AI/ML landscape. By focusing on specific applications or technologies, startups can differentiate themselves and become leaders in their particular area of expertise.
Invest in education and training: Finally, education and training can play an important role in reducing fragmentation in the AI/ML startup landscape. By providing resources and support to new startups, entrepreneurs can develop the skills and knowledge needed to succeed in this rapidly evolving field. This can help ensure that startups have access to the latest technology and best practices, regardless of their specific area of focus.
"Despite the innovation delivered by startups, the AI/ML market remains dominated by the tech giants, such as Amazon, Google, Facebook etc. They have access to enormously big data, human resources, money, built tools and infrastructures – all that makes it brutally hard to compete with them. Equalizing the competitive landscape is another goal of the AIIA."
Most of the growth I've noticed is from Web3 and crypto. There are tons of data there along with zero-knowledge proofs that AI would fit into perfectly. Consider talking with some crypto companies. They have the data and your AI companies can regulate such data.
"TheSequence joined the AI Infrastructure Alliance (AIIA) whose mission is to align AI/ML startups and community members to make it more like Lego blocks that can be stacked together. "
Like building blocks in crypto? Would this be done through APIs and 3rd-party integrations or will it be under Web3 Framework?
How would the alliance handle decentralized stacking of AI companies?
I'm curious to see how this goes for the alliance, quite honestly.