Feb 4, 2021

Evinced raises $17mn to make websites accessible

Evinced
accessibility
AI
computer vision
William Smith
2 min
Evinced offers technology that automatically detects potential web accessibility pitfalls during the development cycle, and suggests changes
Evinced offers technology that automatically detects potential web accessibility pitfalls during the development cycle, and suggests changes...

San Francisco, California-based Evinced offers a digital platform that allows companies to analyse their assets based on accessibility.

Considerations of accessibility are becoming increasingly important and prevalent in the technology industry. One only needs to look at Microsoft’s Xbox Adaptive Controller, a modular system that allows those of varying levels of ability access to video games. On the software side, game developers are also building in options that extend access to as many people as possible.

Achieving a high level of accessibility can be labour intensive, however, as companies try to comply with laws and other regulations on accessibility while at the same time developing software at a much faster pace.

Automating accessibility

Which is where Evinced comes in. The company offers technology that automatically detects potential web accessibility pitfalls during the development cycle, and suggests changes. The technology relies on computer vision and AI algorithms, with customers including financial giant Capital One.

“The root cause of accessibility problems is the fact that large parts of the web are not machine readable; instead, they were designed for visual consumption. Evinced has developed technology that visually analyzes websites and applications, builds a structural semantic model, and then compares it to the actual code to detect potential accessibility issues. This fundamentally new technology approach enables us to significantly outperform legacy approaches,” said founder and CEO Navin Thadani.

A growing industry

Since being founded in 2018, the company has raised almost $20mn. Its latest Series A round, announced yesterday, saw the company receive $17mn from lead investors M12, Capital One Growth Ventures and Benhamou Global Ventures, alongside Engineering Capital.

The company said it would use the funding to deploy products including a site scanner and Debugger.

“With over one billion people globally living with a disability, corporations need to ensure the accessibility of digital properties so that all customers can access their products and services,” said Global Head of M12 Nagraj Kashyap. “Building accessible code is the right thing to do, and it’s also good for business. Evinced has a unique technology approach that will enable enterprise developers to weave accessibility into their software development process, and ultimately, engage more customers.”

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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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