The ultimate goal of AI research is a system that rivals a human’s thinking power — but it is still far out of sight.
Instead, in the coming years, AI may provide an intelligence boost in a different way: by coordinating still-unsurpassed human brainpower and correcting some of the errors inherent how we think.
By definition, AI processes information very differently from humans and can combat group-think, says Berkeley’s Ken Goldberg, a robotics expert who directs the university’s People and Robots Initiative. Read More
Daily Archives: May 28, 2019
The upside of humans — a lot of them
As work on artificial intelligence plods along, an advanced form of crowdsourcing is emerging as an accelerated way to surpass human thinking.
Why it matters: Specially organized groups of people could “get to superhuman intelligence first,” said Daniel Weld, a computer science professor at the University of Washington.
Collective intelligence — the knowledge of an organized group, which goes beyond that of any individual member — is already present inside every organization, community, company, and government. Read More
An All-Neural On-Device Speech Recognizer
In 2012, speech recognition research showed significant accuracy improvements with deep learning, leading to early adoption in products such as Google’s Voice Search. It was the beginning of a revolution in the field: each year, new architectures were developed that further increased quality, from deep neural networks (DNNs) to recurrent neural networks (RNNs), long short-term memory networks (LSTMs), convolutional networks (CNNs), and more. During this time, latency remained a prime focus — an automated assistant feels a lot more helpful when it responds quickly to requests.
Today, we’re happy to announce the rollout of an end-to-end, all-neural, on-device speech recognizer to power speech input in Gboard. In our recent paper, “Streaming End-to-End Speech Recognition for Mobile Devices“, we present a model trained using RNN transducer (RNN-T) technology that is compact enough to reside on a phone. This means no more network latency or spottiness — the new recognizer is always available, even when you are offline. The model works at the character level, so that as you speak, it outputs words character-by-character, just as if someone was typing out what you say in real-time, and exactly as you’d expect from a keyboard dictation system. Read More
Streaming End-to-End Speech Recognition for Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context(e.g., contact lists); and above all, they must be extremely accurate.In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories. Read More
No cloud required: Why AI’s future is at the edge
For all the promise and peril of artificial intelligence, there’s one big obstacle to its seemingly relentless march: The algorithms for running AI applications have been so big and complex that they’ve required processing on powerful machines in the cloud and data centers, making a wide swath of applications less useful on smartphones and other “edge” devices.
Now, that concern is quickly melting away, thanks to a series of breakthroughs in recent months in software, hardware and energy technologies that are rapidly coming to market.
That’s likely to drive AI-driven products and services even further away from a dependence on powerful cloud-computing services and enable them to move into every part of our lives — even inside our bodies. In turn, that could finally usher in what the consulting firm Deloitte late last year called “pervasive intelligence,” shaking up industries in coming years as AI services become ubiquitous. Read More
Discovering The Power Of The Cloud For Artificial Intelligence
Business loves buzzwords, and there’s been no bigger buzzword recently than artificial intelligence. AI, of course, lets companies optimize their operations, business models and customer experiences around data-driven insights, while developing products and services that align more closely with customer needs.
Now that leading cloud service providers are providing AI-driven machine learning and deep learning training platforms—customized to business user data and accessed as cloud-hosted application programming interfaces—companies of all sizes can seize the benefits of AI.
By offering an alternative to on-premise AI solutions, cloud providers are giving small businesses the same advantages their larger counterparts are looking to exploit. Among the valuable AI tools at their disposal are natural language processing, image recognition, translation, search functions and data analytics. Read More
50 Famous Artists Brought to Life With AI
I’m working on a longer article about democratizing AI for artists, but in the process of writing that article, I started using Runway ML and Jason Antic’s deep learning project DeOldify to colorize old black-and-white photos of artists – I couldn’t stop. So I decided to share an “eye candy” article as a preview of my longer piece.
When I was growing up, artists, and particularly twentieth century artists, were my heroes. There is something about only ever having seen many of them in black and white that makes them feel mythical and distant. Likewise, something magical happens when you add color to the photo. These icons turn into regular people who you might share a pizza or beer with.
That distance begins to collapse a bit and they come to life. Read More
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