CIO and CTO named as highest paid tech jobs
Tech staffing agency Mondo has revealed in its latest Annual tech salary guide that Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) are the highest paid technology professionals for 2018.
Specialising in high-end and niche tech staffing, the guide was compiled using Mondo’s earning statistics across New York City, San Francisco, Washington DC, Philadelphia, Denver, Boston, Chicago, Los Angeles, Atlanta and Dallas.
According to the salary guide, CTOs and CIOs currently earn up to $292,500, followed by Chief Information Security Officers (CISOs) who are paid as much as $275,000, whilst DevOps Lead/ Engineers are on $250,000.
“Salaries for technology professionals continue to increase with more than eight different positions now earning more than $200,000 a year,” said Gianna Scorsone, Senior Vice President of Marketing and Sales Operations for Mondo. “This year, in particular, we have seen a spike in salaries for DevOps Engineers and Ecommerce Developers.”
Whilst VR, IoT and AI specialists do not feature amongst the top eight highest earners, Mondo predicts that these professions will see their salaries rise as these technologies become more sought after and explored throughout 2018.
“Tech professionals with Virtual Reality (VR), IoT, and Artificial Intelligence (AI) expertise can expect to see salary jumps for corresponding titles as demand rises for these skills in 2018,” Scorsone continued.
This prediction is in line with a number of market forecast reports, such as the IDC’s Worldwide Semiannual augmented and virtual reality spending guide, predicting that the market for AR and VR will double to $17.8bn in 2018.
Google AI Designs Next-Gen Chips In Under 6 Hours
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.