Artificial intelligence is no longer going to remain the secret sauce of giant technology companies.
Google on Wednesday unveiled ‘Cloud AutoML‘’, which is aimed at helping businesses go beyond limitations of machine-learning expertise and start building their own high-quality custom models using advanced techniques provided by the Internet giant.
The applications range from automating product attributes like patterns and necklines styles for clothing companies to helping various organisations conserve the world’s wildlife by analysing and tagging millions of images of various animal species.
“There are bigger, greater opportunities waiting to be unlocked by AI,” said Fei-Fei Li, chief scientist of AI and machine learning at Google Cloud, during a webcast with reporters.
Google said the new platform would help less-skilled engineers build powerful AI systems they previously only “dreamed of”.
“AI and machine learning is still a field with high barriers to entry and it requires expertise that [only] a few companies can afford on their own,” said Ms. Li, who is also the director of the Artificial Intelligence and Vision Labs at Stanford University.
She said there were perhaps a million data scientists worldwide who might be using AI services. However, it is estimated that there are 21 million-plus developers worldwide and the California-based tech firm “want to make AI accessible to these developers”.
Google’s first ‘Cloud AutoML’ release will be ‘Cloud AutoML Vision’, a service that makes it faster and easier to create custom machine-learning models for image recognition. Its drag-and-drop interface lets enterprises upload images, train and manage models, and then deploy those trained models directly on Google Cloud. “You can create a simple model in minutes,” said Jia Li, head of research and development at Google Cloud AI.
For example, Zoological Society of London (ZSL), is collaborating with Google’s CloudML team to cut costs through automation, and expand the deployment of camera traps in the wild that take pictures of passing animals, such as elephants, lions, and giraffes, when triggered by heat or motion. The millions of images captured by these devices are then analysed and annotated according to the species they exhibit, manually. “This is a labour-intensive and expensive process,” said Sophie Maxwell, conservation technology lead at ZSL. The collaboration aims to automate the tagging of these images. ZSL said it is also gaining a deeper understanding of how to conserve the world’s wildlife effectively.
Asked about his firm’s strategy to implement this technology in India, Rajen Sheth, senior director of product management at Google Cloud AI, said the country was “very strategic” and a “priority market” for the firm. He said a lot of firms here were already using machine learning with really interesting applications. “I think we will find a lot of companies in India that would use it,” said Mr. Sheth.