Mediaboss Marketing

(Precision Vaccinations)

The U.S. Food and Drug Administration issued an emergency use authorizationfor the first machine learning-based,non-diagnostic screening device on March 19, 2021, that identifies certain biomarkers indicative of some types of conditions, such as hypercoagulation, a condition causing blood to clot more easily than normal.

The Tiger Tech COVID Plus Monitor is intended for use by trained personnel to help prevent exposure to and spread of SARS-CoV-2, the virus that causes COVID-19.This device is not a substitute for a COVID-19 diagnostic test and is not intended for use in individuals with symptoms of COVID-19.

The device identifies certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions (such as sepsis or cancer) or hyper-inflammatory states (such as severe allergic reactions) in asymptomatic individuals over the age of 5.

The Tiger Tech COVID Plus Monitor is designed for use following a temperature reading that does not meet the criteria for fever.The device is an armband with embedded light sensors and a small computer processor. The armband is wrapped around a persons bare left arm above the elbow during use. The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes.

Once the measurement is completed, the processor extracts some key features of the pulsatile signals, such as pulse rate, and feeds them into a probabilistic machine learning model trained to make predictions on whether the individual is showing certain signals, such as hypercoagulation in blood. Hypercoagulation is known to be acommon abnormality in COVID-19 patients.

The result is provided in different colored lights used to indicate if an individual is demonstrating certain biomarkers or if the result is inconclusive.

Jeff Shuren, M.D., J.D., director of the FDAs Center for Devices and Radiological Health, stated in a press release:Combining the use of this new screening device, that can indicate the presence of certain biomarkers, with temperature checks could help identify individuals who may be infected with the coronavirus, thus helping to reduce the spread of (SARS-CoV-2)in a wide variety of public settings, including healthcare facilities, schools, workplaces, theme parks, stadiums, and airports.

The Tiger Tech COVID Plus Monitor's clinical performance was studied in hospital and school settings, says the FDA.

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FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate Coronavirus Infection -...

According toBlueWeave Consulting, The globalArtificial Intelligence market&Advanced Machine has reached USD 29.8 Billion in 2019 and projected to reach USD 281.24 Billion by 2026 and anticipated to grow with a CAGR of 37.95% during the forecast period from 2020-2026, owing to increasing overall global investment in Artificial Intelligence Technology.

Artificial Intelligence (AI) is a computer science algorithm and analytics-driven approach to replicate human intelligence in a machine and Machine learning (ML) is an enhanced application of artificial intelligence, which allows software applications to predict the resulted accurately. The development of powerful and affordable cloud computing infrastructure is having a substantial impact on the growth potential of artificial intelligence and the advanced machine learning market. In addition, diversifying application areas of the technology, as well as a growing level of customer satisfaction by users of AI & ML services and products is another factor that is currently driving the Artificial Intelligence & Advanced Machine Learning market. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Proliferation in data generation is another major driving factor for the AI & Advanced ML market. As natural learning develops, artificial intelligence and advanced machine learning technology are paving the way for effective marketing, content creation, and consumer interactions.

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Large enterprises segment in global Artificial Intelligence & Advanced Machine Learning market estimated to have the fastest growth during the forecast period

In the organization size segment, the large enterprises segment is estimated to have the largest market share and the SMEs segment is estimated to grow at the highest CAGR over the forecast period of 2026. The rapidly developing and highly active SMEs have raised the adoption of artificial intelligence and machine learning solutions globally, as a result of the increasing digitization and raised the cyber risks to critical business information and data. Large enterprises have been heavily adopting artificial intelligence and machine learning to extract the required information from large amounts of data and forecast the outcome of various problems.

The rising trend of AI in machine learning and predictive analysis is the key factor for driving global market with a lucrative growth rate in upcoming years.

Predictive analysis and machine learning and are rapidly used in retail, finance, and healthcare. The trend is estimated to continue as major technology companies are investing resources in the development of AI and ML. Due to the large cost-saving, effort-saving, and reliable benefits of AI automation, machine learning is anticipated to drive the global artificial intelligence and advanced machine learning market during the forecast period of 2026.

The rising digitalization boosting growth trend during the forecast period

Digitalization has become a vital driver of artificial intelligence and the advanced machine learning market across the region. Digitalization is increasingly propelling everything from hotel bookings, transport to healthcare in many economies around the globe. Digitalization had led to a rise in the volume of data generated by business processes. Moreover, business developers or crucial executives are opting for solutions that let them act as data modelers and provide them an adaptive semantic model. With the help of artificial intelligence and advanced machine learning, business users are able to modify dashboards and reports as well as help users filter or develop reports based on their key indicators.

North America is expected to dominate the Artificial Intelligence & Advanced Machine Learning market during the anticipated period.

Geographically, the Global Artificial Intelligence & Advanced Machine Learning market is bifurcated into North America, Asia Pacific, Europe, Middle East, Africa & Latin America. North America is dominating the market due to the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. North America has rapidly changed, and the most competitive global market in the world. The Asia-pacific region is estimated to be the fastest-growing region in the global AI & Advanced ML market. The rising awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the Asia-pacific region, has led Asia-pacific to become a highly potential market.

Browse Detailed Table of Contents, Artificial Intelligence & Advanced Machine Learning Market Size, By Function (Manufacturing, Operations, Sales and Marketing, Customer Support, Research and Development, Others), By Organization Size (Small and Medium Enterprise, Large Enterprise), By Industry Vertical (Consumer Goods and Retail, Healthcare, Automotive, IT and Telecom, Banking, Financial Services and Insurance, Government, Others (Education, Media and Entertainment etc.)), and By Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa); Trend Analysis, Competitive Market Share & Forecast, 2016-26

AThttps://www.blueweaveconsulting.com/artificial-intelligence-and-advanced-machine-learning-market-bwc19415

Artificial Intelligence & Advanced Machine Learning Market: Competitive Landscape

The major market players in the Artificial Intelligence & Advanced Machine Learning market are ICarbonX, TIBCO Software Inc., SAP SE, Fractal Analytics Inc., Next IT, Iflexion, Icreon, Prisma Labs, AIBrain, Oracle Corporation, Quadratyx, NVIDIA, Inbenta, Numenta, Intel, Domino Data Lab, Inc., Neoteric, UruIT, Waverley Software, and Other Prominent Players are expanding their presence in the market by implementing various innovations and technology.

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Artificial Intelligence & Advanced Machine learning market is expected to grow at a CAGR of 37.95% from 2020-2026 KSU | The Sentinel Newspaper -...

March 22, 2021 (Reports and Markets) Machine Learning as a Service Market

Reports And Markets newly added a research report on the Machine Learning as a Service market, which represents a study for the period from 2021 to 2027. The research study provides a near look at the market scenario and dynamics impacting its growth. This report highlights the crucial developments along with other events happening in the market which are marking on the growth and opening doors for future growth in the coming years. Additionally, the report is built on the basis of the macro- and micro-economic factors and historical data that can influence the growth.

The report offers valuable insight into the Machine Learning as a Service market progress and approaches related to the Machine Learning as a Service market with an analysis of each region. The report goes on to talk about the dominant aspects of the market and examine each segment.

Key Players: Amazon, Oracle, IBM, Microsoftn, Google, Salesforce, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, and Xeround

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The global Machine Learning as a Service market segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global Machine Learning as a Service market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by region (country), by Type, and by Application for the period 2021-2027.

Market Segment by Regions, regional analysis covers

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, Colombia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Key Points of the Geographical Analysis:

Data and information related to the consumption rate in each region

The estimated increase in the consumption rate

The expected growth rate of the regional markets

Proposed growth of the market share of each region

Geographical contribution to market revenue

Research objectives:

The report lists the major players in the regions and their respective market share on the basis of global revenue. It also explains their strategic moves in the past few years, investments in product innovation, and changes in leadership to stay ahead in the competition. This will give the reader an edge over others as a well-informed decision can be made looking at the holistic picture of the market.

Table of Contents: Machine Learning as a Service Market

Chapter 1: Overview of Machine Learning as a Service Market

Chapter 2: Global Market Status and Forecast by Regions

Chapter 3: Global Market Status and Forecast by Types

Chapter 4: Global Market Status and Forecast by Downstream Industry

Chapter 5: Market Driving Factor Analysis

Chapter 6: Market Competition Status by Major Manufacturers

Chapter 7: Major Manufacturers Introduction and Market Data

Chapter 8: Upstream and Downstream Market Analysis

Chapter 9: Cost and Gross Margin Analysis

Chapter 10: Marketing Status Analysis

Chapter 11: Market Report Conclusion

Chapter 12: Research Methodology and Reference

Key questions answered in this report

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Machine Learning as a Service Market Production, Revenue and Price Forecast by Type 2021 to 2027 Post Impact of Worldwide COVID-19 Spread Analysis|...

A novel machine learning method called DeepBAR could accelerate drug discovery and protein engineering, researchers say.

A new technology combining chemistry and machine learning could aid researchers during the drug discovery and screening process, according to scientists at MIT, US.

The new technique, called DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.

Our method is orders of magnitude faster than before, meaning we can have drug discovery that is both efficient and reliable, said Professor Bin Zhang, co-author of the studys paper.The affinity between a drug molecule and a target protein is measured by a quantity called the binding free energy the smaller the number, the better the bind.A lower binding free energy means the drug can better compete against other molecules, meaning it can more effectively disrupt the proteins normal function.

Calculating the binding free energy of a drug candidate provides an indicator of a drugs potential effectiveness. However, it is a difficult quantity to discover.Methods for computing binding free energy fall into two broad categories:

The researchers devised an approach to get the best of both worlds. DeepBAR computes binding free energy exactly, but requires just a fraction of the calculations demanded by previous methods.

The BAR in DeepBAR stands for Bennett acceptance ratio, a decades-old algorithm used in exact calculations of binding free energy. Using the Bennet acceptance ratio typically requires a knowledge of two endpoint states, eg, a drug molecule bound to a protein and a drug molecule completely dissociated from a protein, plus knowledge of many intermediate states, eg, varying levels of partial binding, all of which slow down calculation speed.

DeepBAR reduces in-between states by deploying the Bennett acceptance ratio in machine learning frameworks called deep generative models.

These models create a reference state for each endpoint, the bound state and the unbound state, said Zhang. These two reference states are similar enough that the Bennett acceptance ratio can be used directly, without all the costly intermediate steps.

It is basically the same model that people use to do computer image synthesis, says Zhang. We are sort of treating each molecular structure as an image, which the model can learn. So, this project is building on the effort of the machine learning community.

These models were originally developed for two-dimensional (2D) images, said lead author of the study Xinqiang Ding. But here we have proteins and molecules it is really a three-dimensional (3D) structure. So, adapting those methods in our case was the biggest technical challenge we had to overcome.

In tests using small protein-like molecules, DeepBAR calculated binding free energy nearly 50 times faster than previous methods. The researchers add that, in addition to drug screening, DeepBAR could aid protein design and engineering, since the method could be used to model interactions between multiple proteins.

In the future, the researchers plan to improve DeepBARs ability to run calculations for large proteins, a task made feasible by recent advances in computer science.

This research is an example of combining traditional computational chemistry methods, developed over decades, with the latest developments in machine learning, said Ding. So, we achieved something that would have been impossible before now.

The research is published in Journal of Physical Chemistry Letters.

The rest is here:
Machine learning calculates affinities of drug candidates and targets - Drug Target Review

Quantum computer, device that employs properties described by quantum mechanics to enhance computations.

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Computers and Technology Quiz

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As early as 1959 the American physicist and Nobel laureate Richard Feynman noted that, as electronic components begin to reach microscopic scales, effects predicted by quantum mechanics occurwhich, he suggested, might be exploited in the design of more powerful computers. In particular, quantum researchers hope to harness a phenomenon known as superposition. In the quantum mechanical world, objects do not necessarily have clearly defined states, as demonstrated by the famous experiment in which a single photon of light passing through a screen with two small slits will produce a wavelike interference pattern, or superposition of all available paths. (See wave-particle duality.) However, when one slit is closedor a detector is used to determine which slit the photon passed throughthe interference pattern disappears. In consequence, a quantum system exists in all possible states before a measurement collapses the system into one state. Harnessing this phenomenon in a computer promises to expand computational power greatly. A traditional digital computer employs binary digits, or bits, that can be in one of two states, represented as 0 and 1; thus, for example, a 4-bit computer register can hold any one of 16 (24) possible numbers. In contrast, a quantum bit (qubit) exists in a wavelike superposition of values from 0 to 1; thus, for example, a 4-qubit computer register can hold 16 different numbers simultaneously. In theory, a quantum computer can therefore operate on a great many values in parallel, so that a 30-qubit quantum computer would be comparable to a digital computer capable of performing 10 trillion floating-point operations per second (TFLOPS)comparable to the speed of the fastest supercomputers.

During the 1980s and 90s the theory of quantum computers advanced considerably beyond Feynmans early speculations. In 1985 David Deutsch of the University of Oxford described the construction of quantum logic gates for a universal quantum computer, and in 1994 Peter Shor of AT&T devised an algorithm to factor numbers with a quantum computer that would require as few as six qubits (although many more qubits would be necessary for factoring large numbers in a reasonable time). When a practical quantum computer is built, it will break current encryption schemes based on multiplying two large primes; in compensation, quantum mechanical effects offer a new method of secure communication known as quantum encryption. However, actually building a useful quantum computer has proved difficult. Although the potential of quantum computers is enormous, the requirements are equally stringent. A quantum computer must maintain coherence between its qubits (known as quantum entanglement) long enough to perform an algorithm; because of nearly inevitable interactions with the environment (decoherence), practical methods of detecting and correcting errors need to be devised; and, finally, since measuring a quantum system disturbs its state, reliable methods of extracting information must be developed.

Plans for building quantum computers have been proposed; although several demonstrate the fundamental principles, none is beyond the experimental stage. Three of the most promising approaches are presented below: nuclear magnetic resonance (NMR), ion traps, and quantum dots.

In 1998 Isaac Chuang of the Los Alamos National Laboratory, Neil Gershenfeld of the Massachusetts Institute of Technology (MIT), and Mark Kubinec of the University of California at Berkeley created the first quantum computer (2-qubit) that could be loaded with data and output a solution. Although their system was coherent for only a few nanoseconds and trivial from the perspective of solving meaningful problems, it demonstrated the principles of quantum computation. Rather than trying to isolate a few subatomic particles, they dissolved a large number of chloroform molecules (CHCL3) in water at room temperature and applied a magnetic field to orient the spins of the carbon and hydrogen nuclei in the chloroform. (Because ordinary carbon has no magnetic spin, their solution used an isotope, carbon-13.) A spin parallel to the external magnetic field could then be interpreted as a 1 and an antiparallel spin as 0, and the hydrogen nuclei and carbon-13 nuclei could be treated collectively as a 2-qubit system. In addition to the external magnetic field, radio frequency pulses were applied to cause spin states to flip, thereby creating superimposed parallel and antiparallel states. Further pulses were applied to execute a simple algorithm and to examine the systems final state. This type of quantum computer can be extended by using molecules with more individually addressable nuclei. In fact, in March 2000 Emanuel Knill, Raymond Laflamme, and Rudy Martinez of Los Alamos and Ching-Hua Tseng of MIT announced that they had created a 7-qubit quantum computer using trans-crotonic acid. However, many researchers are skeptical about extending magnetic techniques much beyond 10 to 15 qubits because of diminishing coherence among the nuclei.

Just one week before the announcement of a 7-qubit quantum computer, physicist David Wineland and colleagues at the U.S. National Institute for Standards and Technology (NIST) announced that they had created a 4-qubit quantum computer by entangling four ionized beryllium atoms using an electromagnetic trap. After confining the ions in a linear arrangement, a laser cooled the particles almost to absolute zero and synchronized their spin states. Finally, a laser was used to entangle the particles, creating a superposition of both spin-up and spin-down states simultaneously for all four ions. Again, this approach demonstrated basic principles of quantum computing, but scaling up the technique to practical dimensions remains problematic.

Quantum computers based on semiconductor technology are yet another possibility. In a common approach a discrete number of free electrons (qubits) reside within extremely small regions, known as quantum dots, and in one of two spin states, interpreted as 0 and 1. Although prone to decoherence, such quantum computers build on well-established, solid-state techniques and offer the prospect of readily applying integrated circuit scaling technology. In addition, large ensembles of identical quantum dots could potentially be manufactured on a single silicon chip. The chip operates in an external magnetic field that controls electron spin states, while neighbouring electrons are weakly coupled (entangled) through quantum mechanical effects. An array of superimposed wire electrodes allows individual quantum dots to be addressed, algorithms executed, and results deduced. Such a system necessarily must be operated at temperatures near absolute zero to minimize environmental decoherence, but it has the potential to incorporate very large numbers of qubits.

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Quantum computer | computer science | Britannica