May 17, 2020

Startup spotlight: H2O.ai - democratising AI

H2O.ai
Startup
Goldman Sachs
Ping An
Harry Menear
4 min
Meet H2O.ai, the Silicon Valley startup that just raised $72.5mn from Goldman Sachs and Ping An to democratize AI
Artificial intelligence (AI) is taking the world by storm. Earlier in 2019, Gartner reported that the number of enterprises implementing AI grew by 270...

Artificial intelligence (AI) is taking the world by storm. Earlier in 2019, Gartner reported that the number of enterprises implementing AI grew by 270% in the past four years. Chris Howard, Research VP at the company warned executives: “If you are a CIO and your organisation doesn’t use AI, chances are high that your competitors do and this should be a concern.”

However, while the demand for AI use cases and adoption strategies is higher than ever, Gartner’s survey also uncovered the fact that the industry is experiencing acute talent shortages, and this is only expected to become more of a pain point for companies looking to incorporate AI into their business strategies as adoption spreads. “In order to stay ahead, CIOs need to be creative,” said Howard. 

One company looking to provide a solution to the AI skills shortage problem is, like many companies set on changing the world, based in Silicon Valley. This Mountain View, California-headquartered firm is gunning to do to AI what AirBnB did to the hotel industry and Ender to 3D printing: democratise it. 

“Empowering Every Company to be an AI Company”

Meet H2O.ai. Co-founded in 2012 by CEO Sri Ambati, the startup has spent the last seven years working to make AI accessible to all. "H2O.ai is democratising AI and powering the imagination of every entrepreneur and business globally - we are making them the true AI superpowers," said Ambati. "Our customers are unlocking discovery in every sphere and walk of life and challenging the dominance of technology giants. This will be fun."

Today, the company’s open source data science and machine learning platform is used by nearly half of the Fortune 500 and trusted by over 18,000 organizations and hundreds of thousands of data scientists around the world.

H2O.ai’s Driverless AI platform was enhanced again recently, with the creation of over 100 “open source recipes” - curated design patterns for AIs developed by the company’s Grandmasters, Data Scientists and Domain Experts. H2O.ai aims to let its customers use these recipes to enhance and nurture their AI capabilities in the face of a global talent and resource scarcity. Since launch, Driverless AI has tripled the company’s customer base. 

"Our makers have been innovating relentlessly to simplify the AI to help with the scarcity of talent and time, and to bring trust, and explainability. This is a quantum leap in the fast-moving AI and autoML space,” Ambati said on Tuesday. 

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"We are expecting a rapid adoption of AI in capital markets, as AI models started demonstrating ROI," commented Ediz Ozkaya, Head of Machine Learning Stats at Goldman Sachs. "H2O.ai is at the forefront of the space with Driverless AI, which enables us to inject our domain-specific AI capability into the platform in a consistent manner while protecting in-house IP and staying compliant."

This Tuesday, H2O.ai also took its relationship with Goldman Sachs to a new level, as the finance giant led a $72.5mn Series D funding round, alongside Ping An, Wells Fargo and Nexus Venture Partners. 

"We're thrilled to partner with the H2O.ai team on their mission to democratise artificial intelligence," said Jade Mandel, Vice President, Principal Strategic Investments Group at Goldman Sachs. "Their deep technical bench and customer centricity make them well positioned to bring transparency and efficiency to the world of prediction."

"We are keen on how the power of artificial intelligence enables insightful and personalised client experiences, and enhances our work with our clients," said Basil Darwish, Managing Director, Strategic Investments at Wells Fargo. "H2O.ai's focus on machine learning transparency and model interpretability facilitates adoption across our industry, and we are delighted to continue to invest in H2O.ai and to further deepen our partnership."

The future looks exciting, both for H2O.ai and AI itself. The startup will use the $72.5mn investment to accelerate innovation, develop new simplified AI product for business users, and expand sales and marketing globally. 

 "We have been a big believer in H2O.ai since day one. We are ecstatic to see their success across the world with so many companies, in so many industries," said Jishnu Bhattacharjee, Managing Director at Nexus Venture Partners. "AI in the enterprise is a reality that H2O.ai is driving. We are thrilled to continue backing Sri and team as they accelerate their growth trajectory." 

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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

Google
AI
Manufacturing
semiconductor
3 min
Google AI’s deep reinforcement learning algorithms can optimise chip floor plans exponentially faster than their human counterparts

In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product. 

 

“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates

 

Training the Algorithm 

In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own. 

 

Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could. 

 

As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks. 

 

Major Implications for the Semiconductor Sector 

Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.

 

Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”. 

 

All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society. 

 

 

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