Aug 24, 2020

What is machine learning?

Machine Learning
Kayleigh Shooter
2 min
machine learning - robot hand on computer
Machine learning technology is growing everyday and is being deployed throughout all industries, but what exactly is it...

 Machine learning (also known as ML) is the study of computer algorithms that improve automatically through more and more experience. Machine learning tends to be seen as a subset of artificial intelligence (AI) as they work hand in hand together. Machine learning algorithms build an intelligent mathematical model based on sample data, also commonly known as "training data", which allows it to create predictions or decisions without being specifically programmed to do so. 

Machine learning, whether you know or not, is integrated into your day to day life. For example, when you are scrolling through Instagram, machine learning is working hard to personalise your feed to your interests and personal needs. Similarly, once you view an item on your browser, similar items will be advertised to you through multiple channels, this refines your shopping experience and recommends products that may be of interest to you. 

A more in-depth example of a use of machine learning when Netflix Netflix held its first "Netflix Prize" back in 2006, the competition was to find a program to better predict user preferences and improve the accuracy on its already existing movie recommendation algorithm and service by at least 10%. This was used to enhance the user experience and ultimately get users to stay on the site/application for longer if their movie recommendations were correct and specific enough.

Machine learning is able to easily identify trends and patterns by reviewing large volumes of data and discovering specific trends and patterns that would not be apparent to humans. The technology is continuously improving and advancing which allows for continuous improvement and therefore improved accuracy and efficiency.

However, machine learning requires lots of effort and time, this is because you will need to allow the algorithms to learn and develop enough to fulfil its purpose with a high level of accuracy, this will take a lot of time and effort to set up and develop. It will also require a large number of resources, which could be costly and also time-consuming. 

Share article

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. 



Share article