How Google Cloud is making AI more accessible
ARTIFICIAL INTELLIGENCE (AI) isn’t just a buzzword anymore. Real companies are creating real solutions with the technology, gaining an edge over their competitors, and disrupting marketplaces.
However, it’s not the easiest technology to understand, and some companies are falling behind simply because they feel AI is beyond their reach. Google aims to change that through Google Cloud.
“AI is empowerment, and we want to democratize that power for everyone and every business—from retail to agriculture, education to healthcare. AI is no longer a niche in the tech world—it’s the differentiator for businesses in every industry. And we’re committed to delivering the tools that will revolutionize them,” said Google AI Chief Scientist Fei-Fei Li.
Unfortunately, a significant gap exists between the extremes of what’s currently possible with machine learning.
At one end, experienced practitioners such as data scientists use tools like TensorFlow and Cloud ML Engine to build custom solutions from the ground up.
On the other end, pre-trained machine learning models like Cloud Vision API deliver immediate results with minimal investment and technical proficiency.
But what about the countless customers that fall in between? Many have needs beyond what’s available with pre-trained models, but don’t have the skills or resources to build their own custom solutions.
To address this middle ground, Google created Cloud AutoML. The product makes it possible for anyone to extend powerful ML models to suit the specific needs of their domain, without requiring any specialized knowledge in machine learning or coding.
Google’s first release, AutoML Vision, extends the Cloud Vision API to recognize entirely new categories of images.
After refining the experience with their alpha users, the company has finally brought AutoML Vision to public beta.
However, image classification is just one of the countless applications of machine learning. Hence, the company has also created two new AutoML offerings: AutoML Natural Language and AutoML Translation.
The former helps users automatically predict custom text categories specific to domains our customers desire, while the latter helps users upload translated language pairs to train their own custom translation model.
Last year, Google also introduced Cloud TPUs, a custom processor designed to dramatically accelerate machine learning tasks. Now, the company has launched the third generation of Cloud TPUs, making support for larger amounts of machine learning computation possible for more businesses.
Finally, the company also launched several important updates to its core machine learning APIs.
Cloud Vision API, for example, now recognizes handwriting, supports additional file types (PDF and TIFF) and product search, and can identify where an object is located within an image.
They also launched improvements to Cloud Text-to-Speech, such as multilingual access to voices generated by DeepMind WaveNet technology and the ability to optimize for the type of speaker from which a user’s speech is intended to play.
Cloud Speech-to-Text now also has the ability to identify what language is spoken as well as different speakers in a conversation, word-level confidence scores, and multi-channel recognition so you can record each participant separately in multi-participant recordings.
All of these advancements make AI more accessible for businesses, and it’s exciting to see what companies will build in the future.