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A new theorem shows that information run through an information scrambler such as a black hole will reach a point where any algorithm will be unable to learn the information that has been scrambled. Credit: Los Alamos National Laboratory

A black hole permanently scrambles information that cant be recovered with any quantum machine learning algorithm, shedding new light on the classic Hayden-Preskill thought experiment.

A new theorem from the field of quantum machine learning has poked a major hole in the accepted understanding about information scrambling.

Our theorem implies that we are not going to be able to use quantum machine learning to learn typical random or chaotic processes, such as black holes. In this sense, it places a fundamental limit on the learnability of unknown processes, said Zoe Holmes, a post-doc at Los Alamos National Laboratory and coauthor of the paper describing the work published on May 12, 2021, in Physical Review Letters.

Thankfully, because most physically interesting processes are sufficiently simple or structured so that they do not resemble a random process, the results dont condemn quantum machine learning, but rather highlight the importance of understanding its limits, Holmes said.

In the classic Hayden-Preskill thought experiment, a fictitious Alice tosses information such as a book into a black hole that scrambles the text. Her companion, Bob, can still retrieve it using entanglement, a unique feature of quantum physics. However, the new work proves that fundamental constraints on Bobs ability to learn the particulars of a given black holes physics means that reconstructing the information in the book is going to be very difficult or even impossible.

Any information run through an information scrambler such as a black hole will reach a point where the machine learning algorithm stalls out on a barren plateau and thus becomes untrainable. That means the algorithm cant learn scrambling processes, said Andrew Sornborger a computer scientist at Los Alamos and coauthor of the paper. Sornborger is Director of Quantum Science Center at Los Alamos and leader of the Centers algorithms and simulation thrust. The Center is a multi-institutional collaboration led by Oak Ridge National Laboratory.

Barren plateaus are regions in the mathematical space of optimization algorithms where the ability to solve the problem becomes exponentially harder as the size of the system being studied increases. This phenomenon, which severely limits the trainability of large scale quantum neural networks, was described in a recent paper by a related Los Alamos team.

Recent work has identified the potential for quantum machine learning to be a formidable tool in our attempts to understand complex systems, said Andreas Albrecht, a co-author of the research. Albrecht is Director of the Center for Quantum Mathematics and Physics (QMAP) and Distinguished Professor, Department of Physics and Astronomy, at UC Davis. Our work points out fundamental considerations that limit the capabilities of this tool.

In the Hayden-Preskill thought experiment, Alice attempts to destroy a secret, encoded in a quantum state, by throwing it into natures fastest scrambler, a black hole. Bob and Alice are the fictitious quantum dynamic duo typically used by physicists to represent agents in a thought experiment.

You might think that this would make Alices secret pretty safe, Holmes said, but Hayden and Preskill argued that if Bob knows the unitary dynamics implemented by the black hole, and share a maximally entangled state with the black hole, it is possible to decode Alices secret by collecting a few additional photons emitted from the black hole. But this prompts the question, how could Bob learn the dynamics implemented by the black hole? Well, not by using quantum machine learning, according to our findings.

A key piece of the new theorem developed by Holmes and her coauthors assumes no prior knowledge of the quantum scrambler, a situation unlikely to occur in real-world science.

Our work draws attention to the tremendous leverage even small amounts of prior information may play in our ability to extract information from complex systems and potentially reduce the power of our theorem, Albrecht said. Our ability to do this can vary greatly among different situations (as we scan from theoretical consideration of black holes to concrete situations controlled by humans here on earth). Future research is likely to turn up interesting examples, both of situations where our theorem remains fully in force, and others where it can be evaded.

Reference: Barren Plateaus Preclude Learning Scramblers by Zo Holmes, Andrew Arrasmith, Bin Yan, Patrick J. Coles, Andreas Albrecht and Andrew T. Sornborger, 12 May 2021, Physical Review Letters.DOI: 10.1103/PhysRevLett.126.190501

Funding: U.S. Department of Energy, Office of Science

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Quantum Machine Learning Hits a Limit: A Black Hole Permanently Scrambles Information That Can't Be Recovered - SciTechDaily

Artificial intelligence (AI) is more accessible than ever and is increasingly used to improve business operations and outcomes, not only in transportation and logistics management, but also in diverse fields like finance, healthcare, retail and others. An Oxford Economics and NTT DATA survey of 1,000 business leaders conducted in early 2020 reveals that 96% of companies were at least researching AI solutions, and over 70% had either fully implemented or at least piloted the technology.

Nearly half of survey respondents said failure to implement AI would cause them to lose customers, with 44% reporting their companys bottom line would suffer without it.

Simply put, AI enables companies to parse vast quantities of business data to make well-informed and critical business decisions fast. And, the transportation management industry specifically is using this intelligence and its companion technology, machine learning (ML), to gain greater process efficiency and performance visibility driving impactful changes bolstering the bottom line.

McKinsey research reveals that 61% of executives report decreased costs and 53% report increased revenues as a direct result of introducing AI into their supply chains. For supply chains, lower inventory-carrying costs, inventory reductions and lower transportation and labor costs are some of the biggest areas for savings captured by high volume shippers. Further, AI boost supply chain management revenue in sales, forecasting, spend analytics and logistics network optimization.

For the trucking industry and other freight carriers, AI is being effectively applied to transportation management practices to help reduce the amount of unprofitable empty miles or deadhead trips a carrier makes returning to domicile with an empty trailer after delivering a load. AI also identifies other hidden patterns in historical transportation data to determine the optimal mode selection for freight, most efficient labor resource planning, truck loading and stop sequences, rate rationalization and other process improvement by applying historical usage data to derive better planning and execution outcomes.

The ML portion of this emerging technology helps organizations optimize routing and even plan for weather-driven disruptions. Through pattern recognition, for instance, ML helps transportation management professionals understand how weather patterns affected the time it took to carry loads in the past, then considers current data sets to make predictive recommendations.

The Coronavirus disease (COVID-19) put a tremendous amount of pressure on many industries the transportation industry included but it also presented a silver lining -- the opportunity for change. Since organizations are increasingly pressed to work smarter to fulfill customers expectations and needs, there is increased appetite to retire inefficient legacy tools and invest in new processes and tech tools to work more efficiently.

Applying AI and ML to pandemic-posed challenges can be the critical difference between accelerating or slowing growth for transportation management professionals. When applied correctly, these technologies improve logistics visibility, offer data-driven planning insights and help successfully increase process automation.

Like many emerging technologies promising transformation, AI and ML have, in many cases, been misrepresented or worse, overhyped as panaceas for vexing industry challenges. Transportation logistics organizations should be prudent and perform due diligence when considering when and how to introduce AI and ML to their operations. Panicked hiring of data scientists to implement expensive, complicated tools and overengineered processes can be a costly boondoggle and can sour the perception of the viability of these truly powerful and useful tech tools. Instead, organizations should invest time in learning more about the technology and how it is already driving value for successful adopters in the transportation logistics industry. What are some steps a logistics operation should take as they embark on an AI/ML initiative?

Remember that the quality of your data will drive how fast or slow your AI journey will go. The lifeblood of an effective AI program (or any big data project) is proper data hygiene and management. Unfortunately, compiling, organizing and accessing this data is a major barrier for many. According to a survey conducted by OReilly, 70% of respondents report that poorly labeled data and unlabeled data are a significant challenge. Other common data quality issues respondents cited include poor data quality from third-party sources (~42%), disorganized data stores and lack of metadata (~50%) and unstructured, difficult-to-organize data (~44%).

Historically slow-to-adopt technology, the transportation industry has recently begun realizing the imperative and making up ground with 60% of an MHI and Deloitte poll respondents expecting to embrace AI in the next five years. Gartner predicts that by the end of 2024, 75% of organizations will move from piloting to operationalizing AI, driving a five times increase in streaming data and analytics infrastructures.

For many transportation management companies, accessing, cleansing and integrating the right data to maximize AI will be the first step. AI requires large volumes of detailed data and varied data sources to effectively identify models and develop learned behavior.

Before jumping on the AI bandwagon too quickly, companies should assess the quality of their data and current tech stacks to determine what intelligence capabilities are already embedded.

And, when it comes to investing in newer technologies to pave the path toward digital transformation, choose AI-driven solutions that do not require you to become a data scientist.

If youre unsure how to start, consider partnering with a transportation management system (TMS) partner with a record of experience and expertise in applying AI to transportation logistics operations.

Read more:
Artificial Intelligence and Machine Learning Drive the Future of Supply Chain Logistics - Supply and Demand Chain Executive

"A.I./ML is dominated by just a few companies and elite institutions. Our tech team has held senior positions at companies like Google and have first-hand knowledge of how rapidly this technological gap is growing. A.I. needs to be more decentralized and available to more people."

"99.9% of people can't write A.I./ML code. Many people have great ideas on how A.I. could help their communities in a bias-free manner, but technical hurdles and a lack of capital prevent them from realizing their goals. That's why we built the Multiverse, which lets people start projects without needing to write code or raise large amounts of capital."

"Each project has its own product, tokens, decentralized exchange (DEX), economy, and governance model, initially determined by founders without using code."

"We call these projects 'planets' due to their autonomous, self-contained design and governance. We don't set the rules, worker rights, or economics on a planet project: the project's founders initially do, and these rules are transparent to everyone."

"Each project's decentralized exchange lets the community stake Multiverse coins and obtain a corresponding amount of the project's tokens. As a staker, you're not an investor or shareholder. Project founders can't spend your coins. However, when you stake in, it changes the project's exchange rate automatically. If you change your mind, you can always unstake and get a proportion of your coins back, determined by the current DEX exchange rate. Founders have extremely strict controls on their unstaking, spending, and vesting: everything is transparent and hardcoded into the project's smart contract so you can make informed decisions. We're lowering the barriers for entry and raising the bar for project quality."

"Staking is just one way to participate; you can also offer advice and feedback to founders, volunteer on charitable projects, or start your own project."

Projects are modular, and this allows founders to utilize other Multiverse projects (such as distributed storage, A.I. model training, and data source projects) and build a top-flight AI/ML application with minimal reinvention and waste. Unlike existing platforms, the Multiverse's network effects and open platform incentivize collaboration and accelerate development and project symbiosis.

Multiverse projects have already landed customers from some of the world's most respected companies and with researchers at Stanford University and The Johns Hopkins University. Built on our high-performance and environmentally-efficient Proof-of-Stake parallel-blockchain technology, Multiverse allows people of all stripes to build or support A.I. projects. Multiverse has received support from Matrix Partners, Huobi Ventures, KuCoin, Arrington XRP, and Fenbushi Capital.

About Multiverse

Multiverse decentralized A.I. ecosystem enables the community to easily fund, train, and deploy machine-learning applications (planets) with their own custom tokens and decentralized economic systems.

To find out more about Multiverse, visit https://multiverselabs.com/.

SOURCE The Multiverse Labs

Original post:
Breaking Big Tech's AI monopoly with the Multiverse Developer Ecosystem - PRNewswire

VANCOUVER, BC, May 19, 2021 /PRNewswire/ --In its biggest Clarius Ultrasound App update to date, Clarius Mobile Health is introducing the ability for its wireless ultrasound systems to automatically detect body anatomy being scanned by clinicians. This new feature is now available with the Clarius C3 HD multipurpose and the Clarius PA HD phased array ultrasound systems.

Ideally suited for emergency medicine, EMS, critical care and primary care, these high-definition scanners enable clinicians to quickly examine the abdomen, heart, lungs, bladder, and other superficial structures without additional interaction through the App. Users simply select Auto Preset AI and the Clarius App will automatically adjust settings to optimize imaging for the area being examined.

"Although machine learning and artificial intelligence have been applied to medical imaging over the past several years, this is the first commercially available application that enables an ultrasound system to recognize anatomy on a macro level, allowing the AI to recognize different structures in the human torso," says Kris Dickie, Vice President of Research and Development at Clarius. "We've labelled tens of thousands of ultrasound images within our vast database to achieve this exciting breakthrough, which will help clinicians to get the answers they need more rapidly."

In addition to Auto Preset AI, Version 8.0 of the Clarius Ultrasound App includes dozens of new features and enhancements, most of which are available across the entire Clarius product line. Clinicians across the medical spectrum can choose from ten wireless ultrasound scanners that are operated by the Clarius Ultrasound App, which can be downloaded from the App Store or Google Play store. The App is compatible with most iOS and Android smart devices for high-definition imaging. Always free, the Clarius Ultrasound App 8.0 offers many different capabilities for novice and expert users.

Enabling Ultrasound Mastery

Dr. Oron Frenkel, an emergency physician and Chairman of the Clarius Medical Advisory Board, is dedicated to expanding the use of point-of-care (POCUS) ultrasound. He works closely with Clarius on ultrasound education and developing features that help clinicians master ultrasound imaging.

"Ultrasound is an amazing tool that gives those of us who know how to use it an instant window into the patient's body," says Dr. Frenkel. "I'm excited about the many features in this Clarius Ultrasound App update that will help enhance ultrasound proficiency. Besides the Auto Preset AI, which will set up novice users for success from day one, we now have nearly 100 ultrasound tutorials that can be viewed in-app. Through this integration, users can easily toggle between watching the video and scanning their patient. Clarius Classroom provides an excellent way to learn."

Anatomical Photographs and New Ways to Share

Also new in the latest Clarius Ultrasound App is the ability for clinicians to capture and document photographs, taken with the mobile device camera, alongside the ultrasound images. This is an excellent way to provide context for education, reporting and patient information. Users can also share interesting cases more easily to their social networks for commentary all images and clips remain anonymous to protect patient identity. The new sharing functionality allows users to take advantage of native mobile device integrations such as Apple's AirDrop.

Enhanced Workflows and Imaging

Since 2016, Clarius ultrasound scanners have gained a reputation for delivering high-resolution imaging comparable to high performance laptop systems, at a fraction of the cost. Among other enhancements, the new Clarius Ultrasound App offers advanced workflow features that include a TI-RADS reporting module, Lower Extremities Doppler packages, as well as a Labour and Delivery workflow that includes Biophysical Profile reporting. Additional advanced imaging features now include a Dynamic Range control, High Frame Rate Carotid Doppler imaging, and High-Definition Zoom capabilities.

Accurate, easy-to-use and affordable ultrasound imaging is here. Unlike alternatives, Clarius offers advanced innovation in-app, Clarius Cloud storage/management, Clarius Live telemedicine and Clarius Classroom at no additional cost, with zero subscription fees. Clinicians are invited to book a demo with a Clarius sonographer to see the difference high-definition imaging can make in delivering the best patient care.

About Clarius Mobile Health

Clarius is on a mission to make accurate, easy-to-use and affordable ultrasound tools available to all medical professionals in every specialty. With decades of experience in medical imaging, the team knows that great ultrasound imaging improves confidence and patient care. Today, Clarius handheld wireless ultrasound scanners connect to iOS and Android devices, delivering high-resolution ultrasound images traditionally only available with bulkier, high-end systems at a fraction of the cost.

More than one million high-definition scans have been performed using Clarius wireless handheld scanners. Clarius scanners are available in over 90 countries worldwide.

Learn more at http://www.clarius.com.

Media Contact:Gense CastonguayMarketing Vice PresidentPhone: +1 (866) 657-9243 ext. 221 | Direct: +1 (604) 260-7077[emailprotected]

SOURCE Clarius Mobile Health

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Clarius Introduces First Ultrasound System That Uses AI and Machine Learning to Recognize Anatomy for an Instant Window into the Body - PRNewswire

Biomedical information including, scientific literature, regulatory documents, clinical trials, and proprietary research is growing exponentially, and humans are struggling to keep up.

And heres where Causaly helps overcome this informational bottleneck through its sophisticated algorithms that mimic human reading and extract the meanings encoded in language.

Based out of London, Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine. Recently, the company has raised $17 million (approx 12 million) funding in Series A round led by Index Ventures, joined by Marathon, Pentech, and EBRD.

As a part of the funding round, Carlos Gonzalez-Cadenas, partner at Index, joins the board.

With the new funding, Causaly plans to expand its technology team, grow its sales team to allow it to expand into the US, and increase its capacity to work with clients to generate insights from private, proprietary data.

Founded by Artur Saudabayev and Yiannis Kiachopoulos in 2018, Causaly uses machine learning to run deep searches and find answers to complex research questions that would have previously taken weeks months to find with traditional keyword search.

Finding a new drug can take over a decade in research, development, and clinical trials, and requires thousands of experts working together with complex evidence, says Kiachopoulos. Causaly is the first platform to map correlations and relationships within scientific data, allowing researchers and scientists to innovate rather than having to laboriously find the relationships themselves. This means better decisions about which research areas to prioritise and faster learning cycles. Switching from a standard database of documents to Causaly is like going from using a Rolodex of phone numbers to having a smartphone. Its intuitive, interactive, and shows you where you want to go.

Its worth mentioning that Causalys AI reads the entire volume of biomedical literature ever published in seconds.

The companys technology is the fastest way for world-leading researchers to find evidence, explore hidden connections between complex physiological mechanisms, and make new predictions in biomedical science.

At present, the company is working with nine large pharmaceutical companies, including Gilead and Novartis, as well as institutions such as the National Institute of Environmental Health Sciences.

Causalys technology has a wide range of potential applications, including healthcare, cosmetics, consumer goods, and any industry that touches human health.

Causalys platform transforms the biomedical workflow from one of search, read, and synthesise to ask questions and analyse, says Carlos Gonzalez-Cadenas, partner at Index Ventures. Causaly allows researchers to ask extremely complex questions easily, and get results that would have been nearly impossible otherwise. In an era when Covid has reminded us of the significance of biomedical innovation, Causaly is poised to help unleash the potential of new research for the benefit of humanity.

One significant use-case for Causaly is the process of screening for biomarkers that are associated with particular diseases or their relationship to treatment response. This typically takes weeks to months of work and is prone to missing important discoveries due to a large amount of literature to trawl through.

Causalys Clients have shaved off 80% of the time for biomarker screenings and the technology has proposed innovative solutions when identifying possible applications for cancer treatments.

Causaly is the latest example of humans using technology to improve our relationship to knowledge, Kiachopoulos says. After Gutenberg invented the printing press, libraries became a way to categorise all the new information and make it manageable. Then with the digital revolution, libraries moved online, turning into documents and databases. Now we need to move a step beyond static repositories of documents towards much richer, multidimensional and interactive modes of knowledge discovery.

Originally posted here:
UK's machine learning startup Causaly raises 12M to boosts its drug discovery AI - UKTN (UK Technology News