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Monthly Archives: March 2020
AI at the Edge Still Mostly Consumer, not Enterprise, Market
Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs.
Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure.
These kinds of fast-acting activities need lots of data — quickly. So they can’t sustain latency as data travels to and from the cloud. That to-and-fro takes too long. Instead, many of these data-intensive processes must remain localized and processed at the edge and on or near a hardware device. Read More
Google launches TensorFlow Quantum, a machine learning framework for training quantum models
Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The framework can construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models.
Creating quantum models is made possible with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog. Read More
Which Industries Will Be Transformed By Blockchain (and the Ensuing Data Glut That Follows)?
For all the attention that the Bitcoin cryptocurrency has garnered over the last decade, its most exciting contribution may be blockchain — the technological breakthrough that made a viable, decentralized currency even possible.
For those who are not familiar, blockchain is the cryptographic breakthrough that enables decentralized electronic databases where all parties can coordinate and trust each other without the presence of a centralized authority.
While blockchain was invented by Satoshi Nakamoto and revealed as part of his seminal paper unveiling Bitcoin, its application goes far beyond Bitcoin or even cryptocurrency. Its potential to allow coordination among decentralized players could revolutionize the enterprise data space from supply chains to lending to mining.
In this article, we focus on the enterprise applications of blockchain. What are company attitudes toward its potential? What kind of enterprise applications are made possible with blockchain? And how much data are they likely to generate? Read More
Computational predictions of protein structures associated with COVID-19
The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility. Read More
#deep-learningThe intelligence community is developing its own AI ethics
The Pentagon made headlines last month when it adopted its five principles for the use of artificial intelligence, marking the end of a months-long effort with significant public debate over what guidelines the department should employ as it develops new AI tools and AI-enabled technologies.
Less well known is that the intelligence community is developing its own principles governing the use of AI.
“The intelligence community has been doing it’s own work in this space as well. We’ve been doing it for quite a bit of time,” said Ben Huebner, chief of the Office of Director of National Intelligence’s Civil Liberties, Privacy, and Transparency Office, at an Intelligence and National Security Alliance event March 4. Read More
Faster video recognition for the smartphone era
By one estimate, training a video-recognition model can take up to 50 times more data and eight times more processing power than training an image-classification model. That’s a problem as demand for processing power to train deep learning models continues to rise exponentially and concerns about AI’s massive carbon footprint grow. Running large video-recognition models on low-power mobile devices, where many AI applications are heading, also remains a challenge.
Song Han, an assistant professor at MIT’s Department of Electrical Engineering and Computer Science (EECS), is tackling the problem by designing more efficient deep learning models. Read More
AI 100: The Artificial Intelligence Startups Redefining Industries
Algorithmic Warfare: DoD Seeks AI Alliance to Counter China, Russia
Facing growing threats from Russia and China, the Defense Department wants to increase its collaboration with European allies as it pursues new artificial intelligence technology.
Lt. Gen. John N.T. “Jack” Shanahan, director of the Joint Artificial Intelligence Center, said global security challenges and technological innovations are changing the world rapidly. That reality means partner nations must work more closely together in areas such as artificial intelligence. Read More
Researchers Create Neural Network to Predict Quantum System Behavior
Russian researchers from the Moscow Institute of Physics and Technology, Valiev Institute of Physics and Technology, and ITMO University have created a neural network that learned to predict the behavior of a quantum system by “looking” at its network structure. The neural network autonomously finds solutions that are well-adapted toward quantum advantage demonstrations. This will aid researchers in developing new efficient quantum computers. The findings are reported in the New Journal of Physics. Read More
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