Frig Controller for Quantum Computing

Fancy Refrigeration to Control Quantum Chips

 In-Fridge Controller Could Scale Up Quantum Computers

University of Chicago Department of Computer Science, January 10, 2022

Scientists at the University of Chicago were able to control quantum chips by routing signals within a dilution refrigerator using a classical controller operating at room temperature. The researchers confirmed the execution of low-error two-quantum bit (qubit) operations using Superconducting Single Flux Quantum (SFQ) pulses. SFQ is a classical logic technology that can function within the quantum fridge with low power consumption, to enable an in-fridge controller with maximized scalability. This achievement is a critical step toward realizing universal quantum computing at large scales.  ...

Scaling up Chatbots

 Below just the introduction, much more at the link.

Scaling Up Chatbots for Corporate Service Delivery Systems  By Alistair Barros, Renuka Sindhgatta, Alireza Nili   Communications of the ACM, August 2021, Vol. 64 No. 8, Pages 88-97 10.1145/3446912

Conversational agents, or chatbots, providing question-answer assistance on smart devices, have proliferated in recent years and are poised to transform online customer services of corporate sectors.1,6 Implemented through dialogue management systems, chatbots converse through voice-based and textual dialogue, and harness natural language processing and artificial intelligence to recognize requests, provide responses, and predict user behavior.5,28 Market analysts concur on current adoption trends and the magnitude of growth and impact of chatbots anticipated in the next five years. According to a report by Grand View Research, for instance, already 45% of users prefer chatbots as the primary point of communications for customer service enquiries, translating into a global 'chatbot' market of $1.23 billion by 2025, at a compounded annual growth rate (CAGR) of 24.3%.9   .... '

Evolution of Distributed Systems on Kubernetes

Ultimately in delivery,  workflow design is key, here a presentation on the topic.

Kubernetes  is an open-source container-orchestration system for automating application deployment, scaling, and management ... 

The Evolution of Distributed Systems on Kubernetes

Bilgin Ibryam takes us on a journey exploring Kubernetes primitives, design patterns and new workload types.

Bio
Bilgin Ibryam is a product manager and a former architect at Red Hat. In his day-to-day job, he works with customers from all sizes and locations, helping them to be successful with adoption of emerging technologies through proven and repeatable patterns and practises. His current interests include enterprise blockchains, cloud-native data and serverless.

About the conference
Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in the  ... " 

New Scaling Approach for Deep Learning

Fast and effective training is important, especially for many IOT and edge devices.  So a potential advance here.

Deep Learning breakthrough made by Rice University scientists
Rice University's MACH training system scales further than previous approaches.    Jim Salter in ArsTechnica

In an earlier deep learning article, we talked about how inference workloads—the use of already-trained neural networks to analyze data—can run on fairly cheap hardware, but running the training workload that the neural network "learns" on is orders of magnitude more expensive.

In particular, the more potential inputs you have to an algorithm, the more out of control your scaling problem gets when analyzing its problem space. This is where MACH, a research project authored by Rice University's Tharun Medini and Anshumali Shrivastava, comes in. MACH is an acronym for Merged Average Classifiers via Hashing, and according to lead researcher Shrivastava, "[its] training times are about 7-10 times faster, and... memory footprints are 2-4 times smaller" than those of previous large-scale deep learning techniques.  .... "

Blockchains plus Clouds Scale

Technical.

In the current ACMQueue:

There is a growing expectation, or at least a hope, that blockchains possess a disruptive potential in numerous domains because of their decentralized nature (i.e., no single entity controls their operations). Decentralization comes with a price, however: blockchains do not scale. Provably neutral clouds are undoubtedly a viable solution to blockchain scaling.

“Net Neutrality: Unexpected Solution to Blockchain Scaling,” by Aleksandar Kuzmanovic in the latest issue of ACM Queue, explores how by optimizing the transport layer, not only can the throughput be fundamentally scaled up, but the latency could be dramatically reduced. Kuzmanovic argues that the key to this vision lies in establishing trust by the blockchain ecosystem into the underlying networking infrastructure. This, in turn, is achieved by decoupling authority from infrastructure via a provably neutral network design.  ... "

Scalable Intelligent Systems

Quite valuable and a considerable challenge, see the full article.

Scalable Intelligent Systems by 2025  in the ACM     By Carl Hewitt, who is an emeritus professor of the Massachusetts Institute of Technology.  He is board chair of iRobust, an international scientific society for the promotion of the field of Inconsistency Robustness, and board chair of Standard IoT, an international standards organization for the Internet of Things, which is using the Actor Model to unify and generalize emerging standards for IoT.

Scalable Intelligent Systems can be brought to fruition by 2025 with the following characteristics:

Interactively acquire and present information from video, web pages, hologlasses, online data bases, sensors, articles, human speech and gestures, etc.
Real-time integration of massive, pervasively inconsistent information
Close human collaboration using hologlasses for secure mobile interaction.
Organizations of people and IoT devices (Citadels) for trustworthiness, resilience, and performance with no single point of failure
Scalability in all important dimensions including no hard barriers to continual improvement in the above areas.

There is no computer-only solution that can implement the above, i.e., people are fundamental to a Scalable Intelligent System.

Scalable Intelligent Systems are the most complex software that has ever been created by a long shot. Numerous enormous government programs are under way. [Canada 2017, China 2017, European Union 2018, France 2018, Germany 2018, Japan 2017, Nordic-Baltic Region 2018, Taiwan 2018, United Kingdom 2018, United States 201]. The development of Scalable Intelligent Systems will create enormous social and policy challenges. [Hewitt 2018]

Scalable Intelligent Systems can be of enormous value in Pain Management

According to Eric Rodgers, PhD., director of the VA's Office of Evidence Based Practice, "The use of opioids has changed tremendously since the 1990s, when we first started formulating a plan for guidelines. The concept then was that opioid therapy was an underused strategy for helping our patients and we were trying to get our providers to use this type of therapy more. But as time went on, we became more aware of the harms of opioid therapy and the development of pill mills. The problems got worse.

"It's now become routine for providers to check the state databases to see if there's multi-sourcing — getting prescriptions from other providers. Providers are also now supposed to use urine drug screenings and, if there are unusual results, to do a confirmation. [For every death from an opioid overdose] there are 10 people who have a problem with opioid use disorder or addiction. And for every addicted person, we have another 10 who are misusing their medication."  .... " 

Scaling Knowledge

Interesting question.   Ultimately we must scale to operation.  What are the challenges?  In part the knowledge within the org charts, and what are the implications for decision makers.

Scaling Knowledge
The hardest thing about scaling a company is communication.

The more people that work at a company, the more nodes in the network, the more links between them. In a completely decentralized network, the number of connections is proportional to the square of the number of nodes. In a hierarchical network, each node is only connected to X peers, which limits complexity but also significantly decreases the flow of information. The reason why messaging platforms like Slack are so important today is that they reduce communication friction, allowing companies greater flexibility and/or greater throughput in how they design their communications network architectures. Or, in people-speak: their org charts.

If you’ve ever been at a company that has grown from 20 to 500 employees, you will have felt this process in your lived experience. What were once impromptu conversations turn into scheduled meetings. Your calendar starts to come under pressure. You simply spend a greater percentage of your time saying or typing words to other people.

There are three types of knowledge that people within a company need to communicate to one another.  ... "

Examples of Successful Scaling of Analytics

Ultimately we did this in another era, now the tools and computing are different.   Scaling is the most important thing.  Scaling means both size and connecting to new contexts.  And now scaling also means embedding in systems, human and otherwise.  From after-the-fact and augmented experimentation, to real time. Analytics also now includes much more than just statistical analysis, consider the integration of real-time assistance.    Real time sensory analysis in an internet of things.

Breaking away: The secrets to scaling analytics
By Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai  by Mckinsey

A handful of the world’s companies have cracked the code on embedding analytics into every layer of their organizations.

The time for simple experimentation with analytics is over—and most companies know it. Across industries, we see organizations investing heavily to integrate analytics throughout their entire business in an effort to capture a portion of the $9.5 trillion to $15.4 trillion of value that the McKinsey Global Institute estimates advanced analytics can enable across industries globally.

Despite this investment, senior executives tell us that their companies are struggling to capture real value. The reason: while they’re eking out small gains from a few use cases, they’re failing to embed analytics into all areas of the organization.

However, in a recent McKinsey Analytics survey of 1,000 companies with more than $1 billion in revenue and spanning 13 sectors and 12 geographies, we identified an elite group of companies that is achieving the elusive goal of analytics at scale.

What does analytics at scale look like? One major US retailer responded to fast-changing consumer behaviors and fierce online competition by reshaping its entire business around analytics. A state-of-the-art analytics capability would span all eight of its business units, all six of its major operational functions, and all 60 million of its customers.

The results have been impressive. The new capability aggregates all customer interactions and extensive customer information across brands and channels, enabling the company’s analytics teams to target offers to customers at a microsegment level. And the impact truly spans the organization. In marketing, for example, the company can deliver personalized content through its website, emails, and digital ads. In strategic planning, the capability pinpoints neighborhoods where people make the most online and catalog purchases in order to identify the most promising future store locations. ... "