🤜🤛 AI/ML startups align to build a canonical stack and compete with the incumbents
📝 Editorial
In December 2020 we wrote about the frenzy of AI acquisitions by the large tech firms that made it a bit difficult for AI startups to achieve maturity. That situation remains a long-term challenge for the evolution of the AI/ML market. But not only that. The current AI/ML startup landscape is really fragmented, looking more like a set of scattered puzzles that are not necessarily combinable. Recently, 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. The Lego metaphor is the same analogy used by Dan Jeffries, the managing director of the AIIA and Chief Technical Evangelist at Pachyderm. One of the missions of the AIIA is to define and frame the key components of the AI/ML canonical stack (like the LAMP stack for software development if you know what we mean ;).
There are almost 50 members in the AIIA now. Some of them are direct competitors. Wait, isn’t it controversial to unite with the competitors? 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. As H.O. Maycotte, Molecula’s CEO puts it: “AI and ML have held so much promise for the last several decades, but successful AI has remained exclusive to the tech giants and therefore has remained impractical.” Joining the AIIA, Mr. Maycotte hopes to work with other members to create practical, operational end-to-end AI/ML stacks that can bring the promise of AI to life and create step-function improvements for the world.
For Mariya Davydova, Head of Product Neu.ro, the AIIA also means building a canonical ML stack, and through “collaboration and competition, creating a clear path to interoperability for the universe of open-source and proprietary ML tools.” Diego Oppenheimer, Algorithmia’s CEO also mentions that it is important for them to “partner with other vendors to ensure an interoperable AI stack.” For David Aronchick, co-founder of Kubeflow and the SAME Project, the formalization of the stacks of ML tools “enables both reproducibility and avoidance of vendor lock-in.” Alex Shee, BD at Sama, pointed to the importance of a shared vision for an ethical AI infrastructure, “ensuring that we are building fair and trustworthy algorithmic tools that represent society at large and empower people through AI.” Frans van Dunné, Chief Data Officer at ixpantia, was the most poetical: “Being part of the AIIA allows us to be close to the fire where MLOps best practices are forged.”
Forging the best practices together, the members create the important standards, help democratize the AI/ML industry and move it forward.
*All mentioned companies are members of the AIIA. This is just a small part of the quotes we’ve received. We put the rest of them in the comment section. Please go and check these quotes, these are the voices of the MLOps community united by AIIA.
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🗓 Next week in TheSequence Edge:
Edge#87: the concept of model-based reinforcement learning; how Google Dreamer uses model-based reinforcement learning to learn long-horizon tasks; Uber Fiber, a distributed computing framework optimized for RL agents.
Edge#88: deep dive into IBM’s use case with Snorkel Flow.
Now, let’s review the most important developments in the AI industry this week
🔎 ML Research
Deep RLSP – Better Reinforcement Learning Policies by Simulating the Past
Berkeley AI Research (BAIR) published the evaluation of Deep RLSP, a reinforcement learning model that can learn a policy without human supervision, by simulating past states in a given environment ->read more on BAIR blog
SCoRe – task-oriented conversational system
Built upon Microsoft Research’s previous work in RAT-SQL and StruG, SCoRe’s pretraining methodology learns language representations to achieve state-of-the-art performance in different task-oriented language benchmarks ->read the overview of RAT, StruG and SCorE on Microsoft blog
FELIX – Flexible Text Editing Through Tagging and Insertion)
In their new paper, Google AI introduces a text-editing system that is both fast and flexible compared to seq2seq approaches (a 90x speed-up) and previous text-editing systems ->read more on Google AI blog
🤖 Cool AI Tech Releases
IBM Quantum Experience
A fascinating reminiscence by IBM about how, five years ago, they put their first 5-qubit quantum system on the cloud, so that anyone could run their own quantum computing experiments, and how it changed the whole field ->read more on IBM blog
Google Chat Assistant
Google Cloud released for public preview an improvement to their Contact Center AI (CCAI). The new Agent Assist for Chat provides two key features to make the conversations better: Smart Reply provides response suggestions, and Knowledge Assist makes suggestions about useful articles and FAQs from your knowledge base ->read the details on Google blog
Counterfeit
Microsoft open-sourced Counterfeit, an automation tool to conduct security risk assessments of AI systems and help prevent hacking ->read more on Microsoft blog
💬 Useful Tweet
It means more options to infuse core-level innovations to improve capabilities for leading-edge workloads like AI and cloud computing.
💸 Money in AI
Cyber protection startup Acronis raised a humongous $250 million funding round led by CVC Capital Partners VII. With AI-based antimalware and blockchain-based data authentication technologies, Acronis unifies data protection and cybersecurity to deliver integrated support, solving the safety, accessibility, privacy, authenticity, and security challenges of the modern digital world.
AI-based insurance SaaS provider Shift Technology raised $220 million in a Series D investment round led by Advent International. The company creates a wide range of products that apply AI and advanced data science to key insurance processes.
Photonic computing startup Lightmatter raised $80 million in a Series B round led by Viking Global Investors. The company created ultra-fast photonic chips specialized for AI work. It promises higher performance with lower environmental impact of AI compute solutions.
Time-series database maker Timescale raised a $40 million Series B led by Redpoint Ventures. Its core product is TimescaleDB, an open-source and free relational database for time‑series, optimized for advanced analytics and built for production use cases.
Predictive analytics startup Pecan.ai raised $35 million in a Series B round led by GGV Capital. First, their AutoML platform automates data preparation, feature engineering and selection, and then uses AI-based predictive analytics algorithms to transform the results into actionable predictions.
Cybersecurity management automation startup JupiterOne raised a $30 million Series B round led by Sapphire Ventures. JupiterOne makes security teams more efficient by centralizing the data from dozens of cloud services into a single hub for management, analysis, and alerts.
AI-powered market intelligence company Crayon raised $22 million in a Series B round led by Baird Capital. It enables businesses to capture, analyze, and act on market movements from their competitors.
“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.