LAS VEGAS — Machine learning and deep learning — two subsets of artificial intelligence — hogged the limelight at the recent Amazon Web Services (AWS) re:Invent, as the cloud giant showcased a broad depth of advanced AI-powered tools to assert its dominance.
AWS, which leads the public cloud market with 44.2% market share according to Gartner, announced a total of 22 new services during its five-day conference held in Las Vegas, Nevada. Majority of these cloud solutions featured capabilities for machine learning and deep learning — two subsets of AI that have the potential to automate industries and vastly improve productivity.
“It’s traditionally what AWS has done, as in mass announce a wide variety of products to the market,” said Jens Butler, an analyst at Tech Research Asia in an interview with BusinessWorld. “Some say it is a little bit overwhelming and that can be the case, but let’s be honest, it’s working. What [AWS] is doing is it’s tapping into multiple frameworks, allowing customers and partners flexibility to step across what would traditionally be silos.”
AWS has been rolling out AI-based products every year at its AWS re:Invent cloud bonanza. What’s different this year is that most of the company’s latest offerings are designed to make machine learning and deep learning more accessible to “everyday developers”, something that AWS believes will pave the way to robotics revolution.
Machine learning is defined as a subset of AI that focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing. Deep learning, on the other hand, is a form of machine learning that attempts to simulate the way human brains learn and process information by creating artificial “neural networks” that can extract complicated concepts and relationships from data.
“[Machine learning] is absolutely the buzzword du jour today. The hype and hope here is tremendous,” said AWS Chief Operating Officer Andy Jassy in his keynote address on Nov. 30. “And still there are a lot of constraints for builders and we know this because we’ve been doing machine learning in a really serious way at Amazon for 20 years.”
Machine learning algorithms, Mr. Jassy said, drive many of Amazon’s internal systems, particularly in areas involving customer experience – from Amazon.com’s recommendations engine to Echo powered by Alexa, from its drone initiative Prime Air to its new cashier-less physical store, Amazon Go. The company’s new mission is to share its learnings on machine learning and make developers and data scientists become experts in this field. Doing so will lead to more innovations in artificial intelligence.
“There aren’t many expert [machine learning] practitioners in the world because it is still too complicated for developers to learn. If you want to enable most enterprises to use machine learning in an expansive way, we have to solve the problem by making it accessible for everyday developers,” he said.
One of AWS’ latest offerings that serve this purpose is Amazon SageMaker, a fully managed end-to-end machine learning service that removes the guesswork from each step of the machine learning process by providing developers and data scientists pre-built development kits, popular machine learning algorithms, and automatic model tuning.
“Our original vision for AWS was to enable any individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world. Our vision for machine learning is no different,” Swami Sivasubramanian, AWS vice president of Machine Learning, said in a press brief. “We want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level.”
A new tool that goes hand-in-hand with SageMaker is Amazon DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning.
“You can program this thing to do almost anything you can imagine, for instance, imagine programming the camera with computer vision models where it will recognize a license plate coming into your driveway and it’ll open the garage door, or you could program it to send you an alert when your dog gets on the couch,” Mr. Jassy explained as he introduced DeepLens during his keynote speech.
AWS also unveiled four new machine-learning tools that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time.
ROBOTS KILLING JOBS?
These powerful new services underscore Amazon’s push to transform industries through artificial intelligence, which some experts see as laying the groundwork for a robot revolution. This raises the question: is Amazon killing human jobs in favor of robots?
Data collated by Quartz, for instance, suggest that as Amazon integrates artificial intelligence in its retail operations by introducing robot workers, it will eventually reduce its human workforce in the years to come.
“Amazon’s growing army of robots may seem helpful and benign but they are also highly effective at terminating human retail employees,” Quartz authors Dave and Helen Edwards wrote.
On a global scale, a December study by McKinsey Global Institute estimated that between 400 million and 800 million individuals will need to find new jobs by 2030 as their work will be displaced by automation and artificial intelligence.
Aside from job disruption, some skeptics the likes of Stephen Hawking and Elon Musk warn that breakthroughs in artificial intelligence could do more harm than good. Tales of chatbots creating their own language to communicate with each other and voice-activated digital assistants ordering from TV shopping channels on their own stoke fears about the rise of robots.
Tech Research Asia’s Mr. Butler argued that at this stage, artificial intelligence should not be seen as a threat, and suggested a wait-and-see attitude.
“The examples [of destructive impact of AI or robots] are a small proportion of the real value AI provides. Remember: AI is a learning protocol and it’s going to get smarter the more data it gets access to. Until we allow technology to evolve we’re not going to be able to understand which works and which doesn’t,” he said. — Mira B. Gloria