AI and Architect Workplace Design

 Very likely application of this tech.

How A.I. Is Helping Architects Change Workplace Design

By The New York Times,  June 20, 2023

At the headquarters of Zaha Hadid Architects in London, Uli Blum, left, and a colleague analyze a visualization of employees’ locations and interactions in their office.

Credit: Jeremie Souteyrat/The New York Times

"I've been a workplace designer for the last 24 years," said the architect Arjun Kaicker. "I've seen more change in the last 24 months than in the whole of my career."

Mr. Kaicker co-runs Zaha Hadid Analytics + Insights, or ZHAI, a five-person team that uses data and artificial intelligence to design workplaces. The team is part of Zaha Hadid Architects, the firm founded by the influential architect Zaha Hadid in London in 1979.

"The pandemic has really supercharged innovation in the workplace," Mr. Kaicker said in a recent video interview from Atlanta.

Before, "the majority of office buildings had a one-size-fits-all desk for everyone, and the same environment around them, the same everything," he said.

Now that they're back at their desks, "people are requesting more choice, more personalization and more mobility."

From The New York Times

View Full Article     

Quantum Computing Architecture Could Connect Large-Scale Devices

 Architecture from MIT, superconducting quantum chips 

Quantum Computing Architecture Could Connect Large-Scale Devices

MIT News, Adam Zewe, January 5, 2023

A new quantum computing architecture developed by Massachusetts Institute of Technology (MIT) scientists can facilitate extensible, high-fidelity communication between superconducting quantum chips. The architecture can be used to thread multiple processing modules along one waveguide; MIT's Bharath Kannan said the same module can function as both transmitter and receiver. The researchers have demonstrated the deterministic emission of single photons in a user-specified direction with more than 96% fidelity. Said Kannan, "The ability to communicate between smaller subsystems will enable a modular architecture for quantum processors, and this may be a simpler way of scaling to larger system sizes compared to the brute-force approach of using a single large and complicated chip."  .... 

Conversations on Architecture for Digital Twins

Based in part on a conversation with swim.ai   Have yet to look at that service, but plan to.  Seems such architecture would have to be very adaptable.    Linkable to real time and samples of stored operational data.  Useful thoughts here.  

What’s the right computing architecture for digital twins?    by 7wdata     October 17, 2020    

Last week, I found myself having a conversation that covered edge computing, digital twins, and the concept of absolute truth. It started out as a discussion with Simon Croby, the CTO of Swim.ai, about that company’s latest product, which is designed to bring Swim’s edge analytics software to the enterprise and industrial world. But it quickly broadened to a conversation about the way we think about data storage and compute when we want to act on real-time information and insights.

Basically, with IoT we’re trying to get a continuous and current view of machines, traffic, environmental conditions, or whatever else so we can use that information to take some sort of action. That action might be predicting when a machine will fail, or routing traffic more efficiently, but for many use cases, the time between gathering the data, offering an insight, and then taking action will be short.

And by short, I mean the data might need to be analyzed before a traffic light changes or a person walks more than a few feet away from a shelf in a grocery store. Figuring out how to analyze incoming data and then create a model based on it, such as of an intersection or shoppers, so that a computer can act on it is what led to our discussion of truth. Crosby’s point was that truth changes every second, so if we’re trying to build a digital twin that represents the truth of a machine or a model, it needs to constantly change. And that has a lot of implications for how we think about computing architectures for digital twins.

For example, Swim.ai is working with a U.S. telecommunications company to create a digital twin of the carrier’s network in real time and then optimize that network based on the ongoing movements of people and any applications they’re running. The carrier is tracking 150 million cellular devices, which together generate 4 petabytes of data each day. With 5G on the horizon and an increasing number elements to track between devices and base stations, the carrier expects that the amount of data it will need to analyze will reach 20 petabytes.

Prior to Swim, the carrier would take that data and move it to a 400-node Hadoop cluster to analyze it in batches. It took roughly 6 hours and required a lot of servers. After switching to Swim’s software, the carrier can track those 150 million devices and base stations and start taking actions on its network in just 100 milliseconds.  ... " 

Algorithmic Design for Building

Algorithms both generating data and using data for the design and construction of buildings.   Like managing pertinent metadata.

Algorithms are designing better buildings

Silvio Carta in The Conversation
Head of Art and Design, University of Hertfordshire

When giant blobs began appearing on city skylines around the world in the late 1980s and 1990s, it marked not an alien invasion but the impact of computers on the practice of building design.

Thanks to computer-aided design (CAD), architects were able to experiment with new organic forms, free from the restraints of slide rules and protractors. The result was famous curvy buildings such as Frank Gehry’s Guggenheim Museum in Bilbao and Future Systems’ Selfridges Department Store in Birmingham.

Today, computers are poised to change buildings once again, this time with algorithms that can inform, refine and even create new designs. Even weirder shapes are just the start: algorithms can now work out the best ways to lay out rooms, construct the buildings and even change them over time to meet users’ needs. In this way, algorithms are giving architects a whole new toolbox with which to realise and improve their ideas.

At a basic level, algorithms can be a powerful tool for providing exhaustive information for the design, construction and use of a building. Building information modelling uses comprehensive software to standardise and share data from across architecture, engineering and construction that used to be held separately. This means everyone involved in a building’s genesis, from clients to contractors, can work together on the same 3D model seamlessly.

More recently, new tools have begun to combine this kind of information with algorithms to automate and optimise aspects of the building process. This ranges from interpreting regulations and providing calculations for structural evaluations to making procurement more precise. .... "

Blockchain and Cryptocurrency Architectures

For an exploration of cryptocurrencies and supporting blockchain architectures.   Countries like Sweden and China are considering their direct implementation.  See this guide to the top 50 approaches.  Search this list for 'smart contract' for those claiming special relevance for contract use.

Cryptocurrency User Guide for the Top 50 Coins     [Updated to end of year 2019]

Bitcoin Exchange Guide strives to be the leading cryptocurrency content curator for everything bitcoin and blockchain. Along with providing more daily crypto news headlines and bitcoin storylines than anywhere online, our focus has always been giving the inquisitive user the best experience and knowledge in this crazy new world of crypto assets by providing insightful guides to digest.

There are, quite literally, thousands of cryptocurrencies ‘available’ online today. Sure, most have heard of Bitcoin, the godfather coin; and on the fray a few have uttered Ethereum (ETH), Bitcoin Cash (BCH), Litecoin (LTC) and even Ripple (XRP). But how many have heard of other trending coins like Stellar (XLM), Monero (XRM) or Cardano (ADA)? Add in the unofficial blockchain news announcer Justin Sun of Tron (TRX), or the likes of DASH, ETC, NEO, BNB, ZRX (thanks Coinbase) and even the controversial EOS, BSV, IOTA and Tether (USDT) stablecoin tokens – and you are staring down quite a bit of research and understanding before ever even considering investing and getting involved with. ....  " 

Optimal Neural Architecture

Thoughtful and useful piece.    Though I don't seen how this is necessarily universally optimal, which is usually a broad claim.  Link to full and technical paper below.

How to Construct the Optimal Neural Architecture for Your Machine Learning Task

By Adrian de Wynter
Alexa Alexa research Alexa science

The first step in training a neural network to solve a problem is usually the selection of an architecture: a specification of the number of computational nodes in the network and the connections between them. Architectural decisions are generally based on historical precedent, intuition, and plenty of trial and error.

In a theoretical paper I presented last week at the 28th International Conference on Artificial Neural Networks in Munich, I show that the arbitrary selection of a neural architecture is unlikely to provide the best solution to a given machine learning problem, regardless of the learning algorithm used, the architecture selected, or the tuning of training parameters such as batch size or learning rate.

Rather, my paper suggests, we should use computational methods to generate neural architectures tailored to specific problems. Only by considering a vast space of possibilities can we identify an architecture that comes with theoretical guarantees on the accuracy of its computations.

In fact, the paper is more general than that. Its results don’t just apply to neural networks. They apply to any computational model, provided that it’s Turing equivalent, meaning that it can compute any function that the standard computational model — the Turing machine — can.

To be more specific, we must introduce the function approximation problem. This is a common mathematical formulation of what machine learning actually does: given a function (i.e., your model) and a set of samples, you search through the parameters of the function so that it approximates the outputs of a target function (i.e., the distribution of your data). ..... " 

Accelerating AI with Open Source, and More

Update on MLIR, which we had looked at.   See who has joined the consortium.  Architecture always being a key element to doing anything well.   And to do things efficiently it makes lots of sense to share the work.   I would further add there should be better shared ways to manage varying data  'infrastructures' by problem domains, in both the semantics of the data and its metadata.   Lets make that happen too.

MLIR: accelerating AI with open-source infrastructure

Chris Lattner, Distinguished Engineer, TensorFlow

Tim Davis,  Product Manager, TensorFlow

Machine learning now runs on everything from cloud infrastructure containing GPUs and TPUs, to mobile phones, to even the smallest hardware like microcontrollers that power smart devices. The combination of advancements in hardware and open-source software frameworks like TensorFlow is making all of the incredible AI applications we’re seeing today possible--whether it’s predicting extreme weather, helping people with speech impairments communicate better, or assisting farmers to detect plant diseases. 

But with all this progress happening so quickly, the industry is struggling to keep up with making different machine learning software frameworks work with a diverse and growing set of hardware. The machine learning ecosystem is dependent on many different technologies with varying levels of complexity that often don't work well together. The burden of managing this complexity falls on researchers, enterprises and developers. By slowing the pace at which new machine learning-driven products can go from research to reality, this complexity ultimately affects our ability to solve challenging, real-world problems. 

Earlier this year we announced MLIR, open source machine learning compiler infrastructure that addresses the complexity caused by growing software and hardware fragmentation and makes it easier to build AI applications. It offers new infrastructure and a design philosophy that enables machine learning models to be consistently represented and executed on any type of hardware. And today we’re announcing that we’re contributing MLIR to the nonprofit LLVM Foundation. This will enable even faster adoption of MLIR by the industry as a whole.   .... " 

A History and Future of Computer Hardware Capabilities

 Quite interesting talk I attended that presented how hardware, software methods and algorithms have influenced changes in architecture and speed.   Somewhat technical but instructive for anyone with an interest in the future forecasting of solving complex computational problems.

" ... Following his talk, "A New Golden Age for Computer Architecture," David Patterson was kind enough to answer some additional questions we were not able to get to during the live event. You'll find the questions and answers (including some interesting pointers) on our Discourse forum page.  

For those of you who were not able to attend live, this webcast can now be viewed on-demand.
Use the link below to enter the webcast at any time: A New Golden Age for Computer Architecture

View the most recent ACM TechTalk, "A New Golden Age for Computer Architecture," on demand. The talk was presented by David Patterson, Distinguished Engineer at Google, Professor Emeritus of Computer Science at UC Berkeley, and 2018 ACM A.M. Turing Award Laureate.  Cliff Young, Software Engineer at Google Brain, moderated the Q&A. Leave comments, questions, and check out further resources on ACM's Discourse page. ... '

Risk Aware Traffic Engineering

Analysis and use risk measures a favorite approach of mine.  Risk-aware always a good idea.  Especially considering architectures.

Using Wall Street secrets to reduce the cost of cloud infrastructure
“Risk-aware” traffic engineering could help service providers such as Microsoft, Amazon, and Google better utilize network infrastructure.

By Rob Matheson | MIT News Office 

Stock market investors often rely on financial risk theories that help them maximize returns while minimizing financial loss due to market fluctuations. These theories help investors maintain a balanced portfolio to ensure they’ll never lose more money than they’re willing to part with at any given time.

Inspired by those theories, MIT researchers in collaboration with Microsoft have developed a “risk-aware” mathematical model that could improve the performance of cloud-computing networks across the globe. Notably, cloud infrastructure is extremely expensive and consumes a lot of the world’s energy.

Their model takes into account failure probabilities of links between data centers worldwide — akin to predicting the volatility of stocks. Then, it runs an optimization engine to allocate traffic through optimal paths to minimize loss, while maximizing overall usage of the network.

The model could help major cloud-service providers — such as Microsoft, Amazon, and Google — better utilize their infrastructure. The conventional approach is to keep links idle to handle unexpected traffic shifts resulting from link failures, which is a waste of energy, bandwidth, and other resources. The new model, called TeaVar, on the other hand, guarantees that for a target percentage of time — say, 99.9 percent — the network can handle all data traffic, so there is no need to keep any links idle. During that 0.01 percent of time, the model also keeps the data dropped as low as possible.

In experiments based on real-world data, the model supported three times the traffic throughput as traditional traffic-engineering methods, while maintaining the same high level of network availability. A paper describing the model and results will be presented at the ACM SIGCOMM conference this week. ..... " 

AI in Experimental Architecture

Fascinating approach,  here we are talking physical architecture, but could the same approach for business model and process?   Design in general?   Whole thing is worth reading, with lots of links to related papers.

AI & Architecture
An Experimental Perspective

Stanislas Chaillou, Harvard Graduate School of Design | Feb. 24th, 2019
GAN-Generated Masterplan | Source: Author

In this article, we release a part of our thesis, developed at Harvard, and submitted in May 2019. This piece is one building block of a larger body of work, investigating AI’s inception in Architecture, its historical background, and its potential for space organization & style.

Artificial Intelligence, as a discipline, has already been permeating countless fields, bringing means and methods to previously unresolved challenges, across industries. The advent of AI in Architecture, described in a previous article, is still in its early days but offers promising results. More than a mere opportunity, such potential represents for us a major step ahead, about to reshape the architectural discipline.  ..... " 

Secure Cloud Architecture for Smart Cities

Having seen how municipalities are being attacked by malware, this is becoming essential.

A Secure Cloud Architecture for Smart Cities
Government Computer News
Stephanie Kanowitz   Syracuse University
July 11, 2019

Syracuse University researchers have issued a new blueprint designed to help smart cities and communities create a hybrid cloud architecture that upholds confidentiality, access control, least privileges, and security of personally identifiable information. The Smart City and Community Challenge cloud privacy security rights inclusive architecture action cluster developed the framework, which is designed to back up critical systems in the event of attacks. The architecture employs a three-tiered data/risk classification scheme, with workflows applied to data depending on its classification. Officials then assign probability, impact, and overall ratings to each risk, and install mitigation controls. The researchers first tested the architecture by applying it to a network of city-owned smart streetlights in Syracuse, NY; other projects under consideration for the architecture include catch-basin monitoring and water-metering projects, in addition to others involving the ethics of artificial intelligence, facial recognition, and machine learning.  ... " 

Amazon Maps its Global Architecture

An interactive map of Amazon's  AWS general and global location architecture.   No exact locations,  but an indication of its breadth.   More in TheVerge.

Blockchains From a Distributed Computing Perspective

The underlying idea of blockchains is not new, and this paper makes the point.   An introduction to those who want to understand  its relationship to other  forms of computational distributed architecture.   Thoughtful, not overly technical.

Introductory video:  https://vimeo.com/310203954  Good point within that blockchains are finally a application of elegant distributed computing algorithms.

Blockchains From a Distributed Computing Perspective   By Maurice Herlihy 

Communications of the ACM, February 2019, Vol. 62 No. 2, Pages 78-85
10.1145/3209623 

Bitcoin first appeared in a 2008 white paper authored by someone called Satoshi Nakamoto,  the mysterious deus absconditus of the blockchain world. Today, cryptocurrencies and blockchains are very much in the news. Much of this coverage is lurid, sensationalistic, and irresistible: roller-coaster prices and instant riches, vast sums of money stolen or inexplicably lost, underground markets for drugs and weapons, and promises of libertarian utopias just around the corner.

This article is a tutorial on the basic notions and mechanisms underlying blockchains, colored by the perspective that much of the blockchain world is a disguised, sometimes distorted, mirror image of the distributed computing world.

This article is not a technical manual, nor is it a broad survey of the literature (both widely available elsewhere). Instead, it attempts to explain blockchain research in terms of the many similarities, parallels, semi-reinventions, and lessons not learned from distributed computing.

This article is intended mostly to appeal to blockchain novices, but perhaps it will provide some insights to those familiar with blockchain research but less familiar with its precursors.  ... " 

Full PDF:    https://cs.brown.edu/courses/csci2952-a/papers/perspective.pdf 

VDML and Business Architecture Modeling

Comment below upgraded here, plan is to trial this for upcoming applications.  Really like the idea of making the interaction between architecture, process and value delivery very clear.

" .... Fred A. Cummins has left a new comment on your post "Value Delivery Modeling Language (VDML)": 

We are currently competing in response to an RFP from the Object Management Group for a specification to support business architecture modeling. Our proposal is to show how VDML supports the requirement with some extensions--some already implemented by VDMbee. In support of that proposal, I have posted a series of blogs on Key Features of VDML that provide powerful capabilities for business architecture and business design. See https://fredacummins.blogspot.com/2018/09/vdml-for-business-architects-part-1-of.html 

Talk on Advances in Image Recognition

I note this is an advanced technical talk on elements of image recognition ...

Invitation to the ISSIP Cognitive Systems Institute Group Webinar

Full series list, past and present  recordings are here:  http://cognitive-science.info/community/weekly-update/

Date and Time: September 06, 2018 - 10:30am US Eastern
Talk Title: Learning to Find Good Correspondences

Speaker: Eduard Trulls, EPFL

Talk Description:  

In this talk, Eduart will present a novel deep architecture to learn to find good correspondences for wide-baseline stereo. Our solution is based on putative keypointmatches, which we learn to label as inliers or outliers while simultaneously using them to recover the camera pose, a fundamental Computer Vision problem. Our solution is simple (no convolutional or fully-connected layers), small (4 Mb), easy to train (state of the art matching outdoors scenes with only 59 images) and generalizes well, particularly in contrast to dense networks which require the entire image.

Bio: 
Eduard Trulls is currently a post-doc at the Computer Vision Lab at EPFL in Lausanne, Switzerland. He obtained his PhD from the Institute of Robotics in Barcelona, Spain, in 2015. His thesis explored novel strategies to enhance local, low-level features (e.g. SIFT, HOG) with global, mid-level data such as motion or segmentation cues. His current work focuses on designing novel approaches to apply deep learning techniques to classical, low-level computer vision problems such as local feature extraction and matching for 3D reconstruction.  ... 

Date and Time : September 06 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
(Check the website in case the date or time changes: http://cognitive-science.info/community/weekly-update/ )

Please retweet  - https://twitter.com/sumalaika/status/1036933287640489984

Join LinkedIn Group https://www.linkedin.com/groups/6729452

Microsoft Releases all US Building Footprints

Reported in Flowingdata, fascinating dataset.   Architectural and building industry studies?  An Exmple of open data

Details in Microsoft Github.

" ... This dataset contains 124,885,597 computer generated building footprints in all 50 US states. This data is freely available for download and use.

License
This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL)

FAQ

What the data include:
Approximately 125 million building footprint polygon geometries in all 50 US States in GeoJSON format.  .... " 

Open Fog Consortium

The Open Fog Consortium

Welcome to the Fog Computing Era

The growth in IoT is explosive, impressive – and unsustainable under current architectural approaches. Many IoT deployments face challenges related to latency, network bandwidth, reliability and security, which cannot be addressed in cloud-only models.  Fog computing adds a hierarchy of elements between the cloud and endpoint devices, and between devices and gateways, to meet these challenges in a high performance, open and interoperable way

We're defining an Open, Interoperable Fog Architecture 

Our work is centered around creating a framework for efficient & reliable networks and intelligent endpoints combined with identifiable, secure, and privacy-friendly information flows between clouds, endpoints and services based on open standard technologies.  .... "

Members include:  ARM, Cisco, Dell, Intel, Microsoft, Princeton, GE, Foxconn, Hitachi, Sakura

Upcoming CSIG Talk: Predictor for Architecture Searches

June 7, 2018 10:30 AM EDT
     TAPAS: Train-less accuracy predictor for architecture searches

Zoom meeting Link: https://zoom.us/j/7371462221

Slides and Recording will be placed here:   http://cognitive-science.info/community/weekly-update/ 

Speaker: Roxana Istrate, IBM & Queen's University Belfast

Talk Description:
In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.

Bio: Roxana Istrate graduated from the Polytechnic University of Bucharest Faculty of Computer Science in 2015 and joined IBM Research the same year as a Great Minds intern. During the internship she worked on distributed scaling of sparse matrix operations, and contribute to win the 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)best paper award. After completing her internship, Roxana started her PhD with IBM Research in collaboration with the Q

Google Federated Learning

New way to share data for training prediction models. 

Google's Federated Learning Architecture Can Enhance Privacy While Ending the Centralized Dataset     Analytics India     By Abhijeet Katte

Google engineers recently announced Federated Learning, a new cloud architecture for processing machine learning data for models that are trained from user interactions on mobile devices. In the past, machine learning has required that data be in the data center or on the machine on which the model is being trained. Federated Learning uses mobile phones to collaborate and learn a shared prediction model, with training data remaining on the device and not transmitted to the cloud. With the new architecture, the mobile device downloads the current model and improves it by learning from data on the phone. The phone then generates a summary of what it has learned, which is sent to the cloud and aggregated with other user summaries to refine the shared model. This type of architecture enables smarter models, reduced latency, and lower power consumption while ensuring data privacy through encryption. The researchers say Federated Learning "has only scratched the surface of what is possible" with this type of distributed prediction model. ... " 

Bird Calls from Audubon

A bit of a bird enthusiast myself, found this interesting.  It is quite simple. You recite the name of a bird, and you get back something like:  'There are 6 calls for this bird', and you choose one and it is played for you.   The audio is very good.  So the architecture is a simple look up, and in general the voice query gets it right.  But there is not much background to support the lookup,  and in a perhaps typical use scenario, you have heard a bird,  or seen an unknown bird, and want to look it up,  its hard to apply. Now if I could record a bird or take a picture of one and use that to search?

Amazon’s Alexa Is Ready to Help You Learn Bird Calls
The virtual assistant can now access more than 2,000 birds sounds from the Audubon library—as long as you say the magic words.  ... "