Aug 24, 2020

What is 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. 

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Jun 10, 2021

Google is using AI to design faster and improved processors

2 min
Google scientists claim their new method of designing Google’s AI accelerators has the potential to save thousands of hours of human effort

Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.

Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.

Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption. 

Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.

Fast chip design

In a new paper, Googlers Azalia Mirhoseini and Anna Goldie, and their colleagues, describe a deep reinforcement-learning system that can create floorplans in under six hours. 

They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists. 

In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."

Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.

"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.

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