Daily Archives: March 21, 2019
This ‘Smart City’ in China Is Controlled By An Artificial Intelligence
The idea of smart cities – infrastructure interlinked by software – isn’t new, but it’s undeniably cool. Who wouldn’t want to live somewhere where programs use data and evidence, not intuition, to actively improve their day-to-day lives?
Now imagine that an entire smart city actually exists, but it’s even more advanced than you could possibly imagine, where infrastructural systems are altered on the fly by an artificial intelligence (AI). This may sound futuristic, but one such place can already be found in China. Read More
Five Chinese smart cities leading the way
Across the world, over a thousand smart city pilots have been launched. China is home to half of these cities, amounting to a staggering 500 pilots.
The country’s smart city ambitions have been predominantly powered by private-sector giants, which has enabled cities across China to rapidly enhance their tech and innovation capabilities to meet citizen needs.
GovInsider shares five Chinese cities leading the way. Read More
Google’s “Smart City of Surveillance” faces new resistance in Toronto
THE WORLD’S MOST ambitious “smart city,” known as Quayside, in Toronto, has faced fierce public criticism since last fall, when the plans to build a neighborhood “from the internet up” were first revealed. Quaysiderepresents a joint effort by the Canadian government agency Waterfront Toronto and Sidewalk Labs, which is owned by Google’s parent company Alphabet Inc., to develop 12 acres of the valuable waterfront just southeast of downtown Toronto.
In keeping with the utopian rhetoric that fuels the development of so much digital infrastructure, Sidewalk Labs has pitched Quayside as the solution to everything from traffic congestion and rising housing prices to environmental pollution. The proposal for Quayside includes a centralized identity management system, through which “each resident accesses public services” such as library cards and health care. An applicant for a position at Sidewalk Labs in Toronto was shocked when he was asked in an interview to imagine how, in a smart city, “voting might be different in the future.” Read More
Google Is Building a City of the Future in Toronto. Would Anyone Want to Live There?
TORONTO—Even with a chilly mid-May breeze blowing off Lake Ontario, this city’s western waterfront approaches idyllic. The lake laps up against the boardwalk, people sit in colorful Adirondack chairs and footfalls of pedestrians compete with the cry of gulls. But walk east, and the scene quickly changes. Cut off from gleaming downtown Toronto by the Gardiner Expressway, the city trails off into a dusty landscape of rock-strewn parking lots and heaps of construction materials. Toronto’s eastern waterfront is bleak enough that Guillermo del Toro’s gothic film The Shape of Water used it as a plausible stand-in for Baltimore circa 1962. Says Adam Vaughan, a former journalist who represents this district in Canada’s Parliament, “It’s this weird industrial land that’s just been sitting there—acres and acres of it. And no one’s really known what to do with it.” Read More
Can we fix Smart City deployments using AI, Cloud and Video?
Currently, most Smart city deployments are seen as ‘City planning gone digital using some sensors(IoT)’. While this approach may sound logical – it has many issues since deployment of sensors is expensive. It leads to siloed applications with only incremental gains. Tech companies have been championing this approach and at best, it leads to specific applications which we label as ‘smart’ because they have some sensing capabilities. These applications include smart parking, smart street lighting etc. But such applications do not talk to each other – and so remain niche. City planners love them because they can point to some ROI. Everyone is happy – but there are only incremental gains in specific applications. Read More
A Beginner’s Guide to Deep Reinforcement Learning
While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo.
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child incentivized by spankings and candy, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement. Read More
Federated Machine Learning: Concept and Applications
Today’s AI still faces two major challenges. One is that in most industries, data exists in the form of isolatedislands. The other is the strengthening of data privacy and security. We propose a possible solution to thesechallenges: secure federated learning. Beyond the federated learning framework first proposed by Google in2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federatedlearning, vertical federated learning and federated transfer learning. We provide definitions, architectures andapplications for the federated learning framework, and provide a comprehensive survey of existing workson this subject. In addition, we propose building data networks among organizations based on federatedmechanisms as an effective solution to allow knowledge to be shared without compromising user privacy. Read More
Federated Learning with Non-IID Data
Federated learning enables resource-constrained edge compute devices, such asmobile phones and IoT devices, to learn a shared model for prediction, while keep-ing the training data local. This decentralized approach to train models providesprivacy, security, regulatory and economic benefits. In this work, we focus on thestatistical challenge of federated learning when local data is non-IID. We first showthat the accuracy of federated learning reduces significantly, by up to ~55% forneural networks trained for highly skewed non-IID data, where each client devicetrains only on a single class of data. We further show that this accuracy reductioncan be explained by the weight divergence, which can be quantified by the earthmover’s distance (EMD) between the distribution over classes on each device andthe population distribution. As a solution, we propose a strategy to improve trainingon non-IID data by creating a small subset of data which is globally shared betweenall the edge devices. Experiments show that accuracy can be increased by ~30%for the CIFAR-10 dataset with only 5% globally shared data. Read More
Federated Learning via Over-the-Air Computation
The stringent requirements for low-latency andprivacy of the emerging high-stake applications with intelligentdevices such as drones and smart vehicles make the cloudcomputing inapplicable in these scenarios. Instead,edge machinelearningbecomes increasingly attractive for performing trainingand inference directly at network edges without sending data to acentralized data center. This stimulates a nascent field termed asfederated learningfor training a machine learning model on com-putation, storage, energy and bandwidth limited mobile devicesin a distributed manner. To preserve data privacy and addressthe issues of unbalanced and non-IID data points across differentdevices, the federated averaging algorithm has been proposed forglobal model aggregation by computing the weighted averageof locally updated model at each selected device. However, thelimited communication bandwidth becomes the main bottleneckfor aggregating the locally computed updates. We thus proposea novelover-the-air computationbased approach for fast globalmodel aggregation via exploring the superposition property ofa wireless multiple-access channel. This is achieved by jointdevice selection and beamforming design, which is modeled asa sparse and low-rank optimization problem to support efficientalgorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect thefixed-rank constraint in the procedure of device selection.A DCalgorithm is further developed to solve the resulting DC programwith global convergence guarantees. The algorithmic advantagesand admirable performance of the proposed methodologies aredemonstrated through extensive numerical results. Read More
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