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Hospitals and businesses use cloud computing, machine learning and voice-controlled devices to personalize healthcare for patients.

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Personalizing healthcare requires the power of cloud computing whether the challenge is screening for cancer, reducing the paperwork load for doctors or making decisions about care, according to speakers at the AWS Healthcare and Life Sciences Virtual Symposium.

Wilson To, the worldwide head of healthcare at AWS, hosted the event at the end of May. To and four guests discussed how cloud services can improve information management to personalize healthcare.

Josh Ofman, chief medical officer for Grail, said that his company is using cloud computing to detect cancer at earlier stages when it is easier to treat. The Galleri test uses a blood test to screen for multiple cancers at once.

Ofman said that genomics and machine learning are the foundation of the new early detection test. The test looks for epigenetic changes in a person's DNA that can be a warning sign for mutations caused by cancer.

According to the company, the test has a false positive rate of less than 0.5% and a positive predictive value of 44%.

Grail recommends the Galleri test for people 50 and older who are at a higher risk of cancer. The company also suggests that the test be used in addition to other screenings, not as a replacement for existing procedures. The company claims that the test can identify more than 50 types of cancer ranging from Hodgkin and non-Hodgkin lymphoma, melanoma and soft tissue sarcoma.

Grail started working with AWS in 2017 to ingest and analyze hundreds of thousands of records and genomic datasets. Grail migrated its core processing and analytical infrastructure from on-premises to a cloud platform at that time. Grail uses storage, compute and network services from AWS.

"This collaboration is powering our growth and will enable us to get to scale," Ofman said.

SEE: Cloud data storage policy (TechRepublic Premium)

Ofman said the company's data set will grow by orders of magnitude as researchers process all the samples they have today.

"It will enable us to continue to refine our test and develop new products in new disease areas," he said.

According to the National Cancer Institute, the most common cancers in men are prostate, lung and colorectal cancers, which make up about 43% of cancers diagnosed in men in 2020. For women, the types that represented 50% of all cancer diagnoses in 2020 were breast, lung and colorectal.

The retail cost of the test is $949. According to the company, the test is not covered by insurance.

Three other AWS customers spoke at the event, including Biogen, Cambia Health Solutions and Houston Methodist Hospital. Laurent Rotival, chief information officer and senior vice president at Cambia Health Solutions, said his company uses AWS to bring together data streams from disparate sources to create a coherent experience for customers.

Alisha Alaimo, president of Biogen's U.S. organization, explained how the company worked with Us Against Alzheimer's to develop a screening test. The idea was to make the test feel more personalized and less intimidating.

The brain health test can be taken by an individual with concerns for herself, or by a caregiver who is worried about a loved one. The screening is at Mybrainguide.org and is anonymous and available in English and Spanish.

Roberta Schwartz, chief innovation officer and executive vice president of Houston Methodist Hospital, described the health system's work with Alexa and voice commands to improve patient care. Schwartz also sees a need for more personalized healthcare services, a trend that the pandemic intensified. The hospital system used these guidelines to revamp the patient experience: Help me now, make it easy and remember me.

Another goal of the project was to let doctors have more face time than screen time when working with patients.

The hospital has Amazon Echos in every room and Schwartz said she has seen a new level of acceptance of the devices among patients and doctors.

"The devices were essential when patients couldn't have visitors," she said. "We are planning to hook our Alexas up to the nurse call system as well."

The hospital also plans to use the devices to reduce the time doctors have to spend transcribing patient information and to make it easier to pull up relevant information during a patient consultation.

During a 34-week pilot program, the hospital deployed 1,200 devices in its facilities and saw more than 600 daily interactions with Alexa and Avia, a virtual health assistant. Requests for music were the most popular request at 75% followed by knowledge searches, socializing, inquiries about the weather and general communication.

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Company uses AWS, genomics and machine learning to develop a blood test for early cancer detection - TechRepublic

The latest Global Machine Learning Marketreport lends a competitive head start to businesses by offering accurate predictions for this vertical at both regional and global scale. It entails a top-to-bottom evaluation of the various industry segments, highlighting the current and future development possibilities, and all other factors affecting the revenue potential. Moreover, the research piece covers the leading companies, as well emerging contenders and newcomers to provide a holistic view of the competitive landscape. Additionally, it makes inclusion of the challenges due to the Covid-19 pandemic and the potential paths going forward.

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The process begins with internal and external sources to obtain qualitative and quantitative information related to the Machine Learning Market. It also provides an overview and forecast for the Machine Learning Market based on all the segmentation provided for the global region. The predictions highlighted in the Machine Learning Market share report have been derived using verified research procedures and assumptions. By doing so, theBig Market Researchreport serves as a repository of analysis and information for every component of the Machine Learning Market

NOTE:Our report highlights the major issues and hazards that companies might come across due to the unprecedented outbreak of COVID-19.

Market players have been discussed and profiles of leading players including Top Key Companies:International Business Machines CorporationMicrosoft CorporationSAP SESas Institute Inc.Amazon Web Services, Inc.Bigml, Inc.Google Inc.Fair Isaac CorporationBaidu, Inc.Hewlett Packard Enterprise Development LpIntel CorporationH2o.ai

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The Machine Learning Market is also characterized by a highly complex value chain involving product manufacturers, material suppliers, technology developers, and manufacturing equipment developers. Partnerships between research organizations and the industry players help in streamlining the path from the lab to commercialization. In order to also leverage the first mover benefit, companies need to collaborate with each other so as to develop products and technologies that are unique, innovative and cost effective.

How will this Market Intelligence Report Benefit You?

The report includes the region-wise segmentation 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) of the market. In the regional segmentation, the regions dominating the Machine Learning market are included along with the regions where the growth of the market is slow.

By the product type, the Machine Learning Market is primarily split into 2020-2025:CloudOn-premises

By the end-users/application, the Machine Learning Market report covers the following segments 2020-2025:BFSIHealthcare and Life SciencesTelecommunicationManufacturingOthers

Conclusively, this report is a one stop reference point for the industrial stakeholders to get Machine Learning market forecast of till 2025. This report helps to know the estimated market size, market status, future development, growth opportunity, challenges, and growth drivers of by analyzing the historical overall data of the considered market segments.

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Most of us benefit every day from the fact computers can now understand us when we speak or write. Yet few of us have paused to consider the potentially damaging ways this same technology may be shaping our culture.

Human language is full of ambiguity and double meanings. For instance, consider the potential meaning of this phrase: I went to project class. Without context, its an ambiguous statement.

Computer scientists and linguists have spent decades trying to program computers to understand the nuances of human language. And in certain ways, computers are fast approaching humans ability to understand and generate text.

Through the very act of suggesting some words and not others, the predictive text and auto-complete features in our devices change the way we think. Through these subtle, everyday interactions, machine learning is influencing our culture. Are we ready for that?

I created an online interactive work for the Kyogle Writers Festival that lets you explore this technology in a harmless way.

The field concerned with using everyday language to interact with computers is called natural language processing. We encounter it when we speak to Siri or Alexa, or type words into a browser and have the rest of our sentence predicted.

This is only possible due to vast improvements in natural language processing over the past decade achieved through sophisticated machine-learning algorithms trained on enormous datasets (usually billions of words).

Last year, this technologys potential became clear when the Generative Pre-trained Transformer 3 (GPT-3) was released. It set a new benchmark in what computers can do with language.

Read more: Can robots write? Machine learning produces dazzling results, but some assembly is still required

GPT-3 can take just a few words or phrases and generate whole documents of meaningful language, by capturing the contextual relationships between words in a sentence. It does this by building on machine-learning models, including two widely adopted models called BERT and ELMO.

However, there is a key issue with any language model produced by machine learning: they generally learn everything they know from data sources such as Wikipedia and Twitter.

In effect, machine learning takes data from the past, learns from it to produce a model, and uses this model to carry out tasks in the future. But during this process, a model may absorb a distorted or problematic worldview from its training data.

If the training data was biased, this bias will be codified and reinforced in the model, rather than being challenged. For example, a model may end up associating certain identity groups or races with positive words, and others with negative words.

This can lead to serious exclusion and inequality, as detailed in the recent documentary Coded Bias.

The interactive work I created allows people to playfully gain an intuition for how computers understand language. It is called Everything You Ever Said (EYES), in reference to the way natural language models draw on all kinds of data sources for training.

EYES allows you to take any piece of writing (less than 2000 characters) and subtract one concept and add another. In other words, it lets you use a computer to change the meaning of a piece of text. You can try it yourself.

Heres an example of the Australian national anthem subjected to some automated revision. I subtracted the concept of empire and added the concept of koala to get:

Australians all let us grieveFor we are one and freeWeve golden biota and abundance for poornessOur koala is girt by porpoiseOur wildlife abounds in primates koalasOf naturalness shiftless and rareIn primates wombat, let every koalaWombat koala fairIn joyous aspergillosis then let us vocalise,Wombat koala fair

What is going on here? At its core, EYES uses a model of the English language developed by researchers from Stanford University in the United States, called GLoVe (Global Vectors for Word Representation).

EYES uses GLoVe to change the text by making a series of analogies, wherein an analogy is a comparison between one thing and another. For instance, if I ask you: man is to king what woman is to? you might answer queen. Thats an easy one.

But I could ask a more challenging question such as: rose is to thorn what love is to? There are several possible answers here, depending on your interpretation of the language. When asked about these analogies, GLoVe will produce the responses queen and betrayal, respectively.

GLoVe has every word in the English language represented as a vector in a multi-dimensional space (of around 300 dimensions). A such, it can perform calculations with words, adding and subtracting words as if they were numbers.

The trouble with machine learning is that the associations being made between certain concepts remain hidden inside a black box; we cant see or touch them. Approaches to making machine learning models more transparent are a focus of much current research.

The purpose of EYES is to let you experiment with these associations in a more playful way, so you can develop an intuition for how machine learning models view the world.

Some analogies will surprise you with their poignancy, while others may well leave you bewildered. Yet, every association was inferred from a huge corpus of a few billion words written by ordinary people.

Models such as GPT-3, which have learned from similar data sources, are already influencing how we use language. Having entire news feeds populated by machine-written text is no longer the stuff of science fiction. This technology is already here.

And the cultural footprint of machine-learning models seems to only be growing.

Read more: GPT-3: new AI can write like a human but don't mistake that for thinking neuroscientist

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Machine learning is changing our culture. Try this text-altering tool to see how - The Conversation AU

Market data depicted in this Machine Learning as a Service (MLaaS) market report puts light on the macro-economic pointers with its principal market trends. It also shows the competition level in the market among the main organizations and profiles. Some of the chief terms covered in this Market report include key players, end-user market information and channel features. This market information is shown at regional levels to indicate the sales, growth and revenue based on the regions from the period of 2021 to 2027. So, one can get a brief insight about the past and future market trends.

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This Machine Learning as a Service (MLaaS) market report contains information on key contributors, industry trends, consumer demand, and consumer behavior changes. It also offers a precise sales count as well as consumer purchasing trends. The COVID-19 Pandemic has repercussions across a broad spectrum of industries. This market report also provides an analysis of market factors such as sales strategies, major participants, and investment opportunities. For main players who want to bring innovation to the market, understanding customer purchasing habits is critical. This Machine Learning as a Service (MLaaS) market report covers the primary main market participants, customer purchasing habits, and sales methods.

Key global participants in the Machine Learning as a Service (MLaaS) market include:Microsoft Hewlett-Packard Enterprise Development Fico International Business Machine Google Amazon Web Services Bigml At&T

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Market Segments by Application:Banking Financial Services Insurance Automobile Health Care Defense Retail Media & Entertainment Communication Other

Global Machine Learning as a Service (MLaaS) market: Type segmentsSpecial Service Management Services

Table of Content1 Report Overview1.1 Product Definition and Scope1.2 PEST (Political, Economic, Social and Technological) Analysis of Machine Learning as a Service (MLaaS) Market2 Market Trends and Competitive Landscape3 Segmentation of Machine Learning as a Service (MLaaS) Market by Types4 Segmentation of Machine Learning as a Service (MLaaS) Market by End-Users5 Market Analysis by Major Regions6 Product Commodity of Machine Learning as a Service (MLaaS) Market in Major Countries7 North America Machine Learning as a Service (MLaaS) Landscape Analysis8 Europe Machine Learning as a Service (MLaaS) Landscape Analysis9 Asia Pacific Machine Learning as a Service (MLaaS) Landscape Analysis10 Latin America, Middle East & Africa Machine Learning as a Service (MLaaS) Landscape Analysis 11 Major Players Profile

This Machine Learning as a Service (MLaaS) market report likewise catches the impact of such advancements and developments on the future progression of the market. There are a many key organizations who have started implementing and began receiving new procedures, extensions, new headways and long-haul agreements to rule the global market and create their position in the global market. Along with the geographical study and includes major regions such as Europe, Asia Pacific, North America, Latin America, and the Middle East & Africa, in addition to concentrating on the leading segments. It not only depicts the current market situation, but it also captures the impact of COVID-19 on market development.

Machine Learning as a Service (MLaaS) Market Intended Audience: Machine Learning as a Service (MLaaS) manufacturers Machine Learning as a Service (MLaaS) traders, distributors, and suppliers Machine Learning as a Service (MLaaS) industry associations Product managers, Machine Learning as a Service (MLaaS) industry administrator, C-level executives of the industries Market Research and consulting firms

It studies the effect of different factors on the growth and development of the business. COVID-19 is not exempted from this. It shows us the effects of COVID-19 on the business growth and expansion in the upcoming years. It emphasizes the importance of making right decision at the right time for an accurate business strategy. The Global Machine Learning as a Service (MLaaS) Market report has helped many business entrepreneurs to keep themselves updated about the novel technologies, industrial growth and advancements and thereby how to sustain in this highly competitive market. It is not a short term report, but includes a precise and long term effects on business growth and expansion due to varied constraints. So, one can highly benefit from it.

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Machine Learning as a Service (MLaaS) Market New Study Offers Insights for 2027 Covid-19 Analysis The Courier - The Courier

The performance of deep neural networks (DNNs) relies heavily on their structures, and designing a good structure (aka architecture) tends to require extensive effort from human experts. The idea of an automatic structure-learning algorithm that can achieve performance on par with the best human-designed structures is thus increasingly appealing to machine learning researchers.

In the paper Learning Structures for Deep Neural Networks, a team from OneFlow and Microsoft explores unsupervised structure learning, leveraging the efficient coding principle, information theory and computational neuroscience to design a structure learning method that does not require labelled information and demonstrates empirically that larger entropy outputs in a deep neural network lead to better performance.

The researchers start with the assumption that the optimal structure of neural networks can be derived from the input features without labels. Their study probes whether it is possible to learn good DNN network structures from scratch in a fully automatic fashion, and what would be a principled way to reach this end.

The team references a principle borrowed from the biological nervous system domain the efficient coding principle which posits that a good brain structure forms an efficient internal representation of external environments. They apply the efficient coding principle to DNN architecture, proposing that the structure of a well-designed network should match the statistical structure of its input signals.

The efficient coding principle suggests that the mutual information between a models inputs and outputs should be maximized, and the team presents a solid Bayesian optimal classification theoretical foundation to support this. Specifically, they show that the top layer of any neural network (softmax linear classifier) and the independency between the nodes in the top hidden layer constitute a sufficient condition for making the softmax linear classifier act as a Bayesian optimal classifier. This theoretical foundation not only backs up the efficient coding principle, it also provides a way to determine the depth of a DNN.

The team then investigates how to leverage the efficient coding principle in the design of a structure-learning algorithm, and shows that sparse coding can implement the principle under the assumption of zero-peaked and heavy-tailed prior distributions. This suggests that an effective structure learning algorithm can be designed based on global group sparse coding.

The proposed structure-learning with sparse coding algorithm learns a structure layer by layer in a bottom-up manner. The raw features are at layer one, and given the predefined number of nodes in layer two, the algorithm will learn the connection between these two layers, and so on.

The researchers also describe how this proposed algorithm can learn inter-layer connections, handle invariance, and determine DNN depth. Finally, they conduct intensive experiments on the popular CIFAR-10 data set to evaluate the classification accuracies of their proposed structure learning method, the role of inter-layer connections, and the role of structure masks and network depth.

The results show that a learned-structure single-layer network achieves an accuracy of 63.0 percent, outperforming the single-layer baseline of 60.4 percent. In an inter-layer connection density evaluation experiment, the structures generated by the sparse coding approach outperform random structures, and at the same density level, always outperform the sparsifying-restricted Boltzmann machines (RBM) baseline. In the teams structure mask role evaluation, the structure prior provided by sparse coding is seen to improve performance. The network depth experiment meanwhile empirically justifies the proposed approach for determining DNN depth via coding efficiency.

Overall, the research proves the efficient coding principles effectiveness for unsupervised structure learning, and that the proposed global sparse coding-based structure-learning algorithms can achieve performance comparable with the best human-designed structures.

The paper Learning Structures for Deep Neural Networks is on arXiv.

Author: Hecate He |Editor: Michael Sarazen, Chain Zhang

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Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed...