May 17, 2020

Startup spotlight: Soul Machines putting a human face on AI

William Smith
2 min
Soul Machines offers “autonomously animated” digital characters with realistic faces and what the company describes as a “human-like communication style”
The customer experience is becoming increasingly influenced by artificial intelligence, with the likes of automated chatbots. According to Gartner, by 2...

The customer experience is becoming increasingly influenced by artificial intelligence, with the likes of automated chatbots. According to Gartner, by 2020, 85% of customer interactions will be handled without the involvement of a human agent. Companies are keen, however, to retain and promote a human element. That might involve programming a more conversational tone to text, or using speech synthesis to mimic a human voice, as with digital assistants like Amazon’s Alexa or Apple’s Siri.

Auckland, New Zealand startup Soul Machines is taking things a step further, however. The company offers “autonomously animated” digital characters with realistic faces and what the company describes as a “human-like communication style”. The so-called “Digital Heroes” are said to have uses in customer care, influencing, sales and even healthcare and wellbeing.

The company closed a $40mn Series B funding round led by Singaporean holding company Temasek today. Also participating were the likes of Salesforce Ventures, Swiss venture capital firm Lakestar and Hong Kong’s Horizon Ventures, which also participated in Soul Machines’ $7.5mn Series A in 2016.


“We’re proud to announce Salesforce Ventures’ investment in Soul Machines because it has an obsessive focus on improving customer experience by using artificial intelligence technology in new ways,” said Rob Keith, Head of Australia, Salesforce Ventures in a Soul Machines press release. “We look forward to continuing to work with Soul Machines as it scales and realises its global aspirations.”

Existing customers of the technology include Procter & Gamble, who enlisted Soul Machines to design an artificial brand ambassador for its SK-II skin care products.

Along similar lines are Samsung subsidiary STAR Labs’ realistic digital avatars known as Neon, which debuted at the CES 2020 trade show. While the company was keen to refer to them as ‘artificial humans’, the levels of interaction they are capable of is yet to be seen, a question Soul Machines itself raised in a news post.

(Image: Soul Machines)

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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

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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