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This Machine Learning as a Service report comprises of a deep knowledge and information on what the markets definition, classifications, applications, and engagements and also explains the drivers and restraints of the market which is derived from SWOT analysis. An analytical assessment of the competitors confers clear idea of the most important challenges faced by them in the present market and in upcoming years. Besides, the identity of respondents is also kept undisclosed and no promotional approach is made to them while analyzing the data. Global Machine Learning as a Service market research document covers major manufacturers, suppliers, distributors, traders, customers, investors and major types, major applications.

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Geographically, the globalMachine Learning as a Servicemarket has been fragmented across several regions such asNorth America, Latin America, Asia-Pacific, Africa, and Europe. The study enlists various market key players in order to present a clear idea about different strategies undertaken by top-notch companies. Inclusive of in-depth analysis of market dynamics such as drivers, restraints and global opportunities, the study provides a cogent study about the fluctuating highs and lows of the businesses. Several market parameters are also stated while curating the research report, these include investors, share market and budget of the companies.

Top Key Players in the Global Machine Learning as a Service Market Research Report:

Microsoft (Washington,US), Amazon Web Services (Washington, US), Hewlett Packard Enterprises (California, US), Google, Inc

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In order to understand the competitive business environment, the report studies various market analysis methodologies such as Porters five analysis and SWOT analysis. Several market dynamics have been scrutinized which are responsible for driving or hampering the progress of theMachine Learning as a Servicemarket. Additionally, the study underlines recent technological advancements and tools referred by several industries. Furthermore, it draws attention to several effective sales methodologies which help to increase number of customers rapidly. Insightful case studies from different industry experts also form an inclusive part of the report. The bargaining power of several vendors and buyers also form a salient feature of the report.

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Machine Learning as a Service Market: Indoor Applications Projected to be the Most Attractive Segment during 2020-2027 - Bandera County Courier

When Machines Design: Artificial Intelligence and the Future of Aesthetics

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Are machines capable of design? Though a persistent question, it is one that increasingly accompanies discussions on architecture and the future of artificial intelligence. But what exactly is AI today? As we discover more about machine learning and generative design, we begin to see that these forms of "intelligence" extend beyond repetitive tasks and simulated operations. They've come to encompass cultural production, and in turn, design itself.

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When artificial intelligence was envisioned during thethe 1950s-60s, thegoal was to teach a computer to perform a range of cognitive tasks and operations, similar to a human mind. Fast forward half a century, andAIis shaping our aesthetic choices, with automated algorithms suggesting what we should see, read, and listen to. It helps us make aesthetic decisions when we create media, from movie trailers and music albums to product and web designs. We have already felt some of the cultural effects of AI adoption, even if we aren't aware of it.

As educator and theorist Lev Manovich has explained, computers perform endless intelligent operations. "Your smartphones keyboard gradually adapts to your typing style. Your phone may also monitor your usage of apps and adjust their work in the background to save battery. Your map app automatically calculates the fastest route, taking into account traffic conditions. There are thousands of intelligent, but not very glamorous, operations at work in phones, computers, web servers, and other parts of the IT universe."More broadly, it's useful to turn the discussion towards aesthetics and how these advancements relate to art, beauty and taste.

Usually defined as a set of "principles concerned with the nature and appreciation of beauty, aesthetics depend on who you are talking to. In 2018, Marcus Endicott described how, from the perspective of engineering, the traditional definition of aesthetics in computing could be termed "structural, such as an elegant proof, or beautiful diagram." A broader definition may include more abstract qualities of form and symmetry that "enhance pleasure and creative expression." In turn, as machine learning is gradually becoming more widely adopted, it is leading to what Marcus Endicott termed a neural aesthetic. This can be seen in recent artistic hacks, such as Deepdream, NeuralTalk, and Stylenet.

Beyond these adaptive processes, there are other ways AI shapes cultural creation. Artificial intelligence hasrecently made rapid advances in the computation of art, music, poetry, and lifestyle. Manovich explains that AIhas given us the option to automate our aesthetic choices (via recommendation engines), as well as assist in certain areas of aesthetic production such as consumer photography and automate experiences like the ads we see online. "Its use of helping to design fashion items, logos, music, TV commercials, and works in other areas of culture is already growing." But, as he concludes, human experts usually make the final decisions based on ideas and media generated by AI. And yes, the human vs. robot debate rages on.

According to The Economist, 47% of the work done by humans will have been replaced by robots by 2037, even those traditionally associated with university education. The World Economic Forum estimated that between 2015 and 2020, 7.1 million jobs will be lost around the world, as "artificial intelligence, robotics, nanotechnology and other socio-economic factors replace the need for human employees." Artificial intelligence is already changing the way architecture is practiced, whether or not we believe it may replace us. As AI is augmenting design, architects are working to explore the future of aesthetics and how we can improve the design process.

In a tech report on artificial intelligence, Building Design + Construction explored how Arup had applied a neural network to a light rail design and reduced the number of utility clashes by over 90%, saving nearly 800 hours of engineering. In the same vein, the areas of site and social research that utilize artificial intelligence have been extensively covered, and examples are generated almost daily. We know that machine-driven procedures can dramatically improve the efficiency of construction and operations, like by increasing energy performance and decreasing fabrication time and costs. The neural network application from Arup extends to this design decision-making. But the central question comes back to aesthetics and style.

Designer and Fulbright fellow Stanislas Chaillou recently created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

As Chaillou summarizes, architectural styles carry implicit mechanics of space, and there are spatial consequences to choosing a given style over another. In his words, style is not an ancillary, superficial or decorative addendum; it is at the core of the composition.

Artificial intelligence and machine learningare becomingincreasingly more important as they shape our future. If machines can begin to understand and affect our perceptions of beauty, we should work to find better ways to implement these tools and processes in the design process.

Architect and researcher Valentin Soana once stated that the digital in architectural design enables new systems where architectural processes can emerge through "close collaboration between humans and machines; where technologies are used to extend capabilities and augment design and construction processes." As machines learn to design, we should work with AI to enrich our practices through aesthetic and creative ideation.More than productivity gains, we can rethink the way we live, and in turn, how to shape the built environment.

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When Machines Design: Artificial Intelligence and the Future of Aesthetics - ArchDaily

Awarded to recognize Google Cloud partners who have made a positive impact on the world

Quantiphi, an award-winning applied artificial intelligence and data science software and services company, announced today that it has been named 2019 Social Impact Partner of the Year by Google Cloud. Quantiphi was recognized for its achievements for working with nonprofits, research institutions, and healthcare providers, to leverage AI for Social Good.

We are believers in the power of human acumen and technology to solve the worlds toughest challenges. This award is a recognition of our mission driven culture and our passion to apply AI for social good, said Asif Hasan, Co-founder, Quantiphi. Partnering with Google Cloud has given us the opportunity to work with the worlds leading nonprofit, healthcare and research institutions and we are truly humbled by this recognition.

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Were delighted to recognize Quantiphis commitment to social impact, said Carolee Gearhart, Vice President, Worldwide Channel Sales at Google Cloud. By applying its capabilities in AI and ML to important causes, Quantiphi has demonstrated how Google Cloud partners are contributing to positive change in the world.

A few initiatives that helped Quantiphi earn this recognition:

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Quantiphi previously earned the Google Cloud Machine Learning Partner of the Year twice in a row for 2018 and 2017 and is a premier partner for Google Cloud and holds Specializations in machine learning, data analytics and marketing analytics.

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Quantiphi Wins Google Cloud Social Impact Partner of the Year Award - AiThority

The growth round doubles the telehealth companys total funding after serving over 100 health organizations, thousands of clinicians, and hundreds of thousands of patients in 2019

Tyto Care, the healthcare industrys first all-in-one modular device and telehealth platform for on-demand, remote medical examinations, announced that it has raised $50Min an oversubscribed round co-led by Insight Partners, Olive Tree Ventures,and Qualcomm Ventures LLC with participation from previous investors, bringing the companys total funding to over$105M. The additional funding comes as Tyto Care experiences surging demand with rapid global telehealth adoption, having witnessed 3X growth in sales in 2019 alone.

The funding will allow Tyto Care to continue to expand commercialization throughout the U.S.,EuropeandAsiaas well as to introduce new advanced product capabilities including AI and machine learning-based home diagnostics solutions and other patented technologies.

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Tyto Care has experienced double-digit telehealth utilization, over 10 times higher than standard virtual care programs, which see less than 5% utilization on average. In the wake of COVID-19, hospitals and health organizations around the world are further expanding their use of Tyto Cares telehealth solution to remotely examine quarantined patients in hospitals and isolated patients at home.

Tyto Cares solution enables remote medical exams of the lungs, heart, throat and ears, among other exams and vitals, allowing healthcare organizations to protect providers and avoid exposure during the COVID-19 pandemic. It also enables families and the general population to receive care without entering medical facilities, preventing the spread of the virus and significantly reducing the increased burden on already overworked health organizations. To meet the skyrocketing demand, Tyto Care is currently expediting production to fulfill three times more devices than originally forecasted for the coming quarters.

Over the past two years, Tyto Care has increased momentum faster than ever before and is playing a leading role in changing how people receive healthcare. Telehealth is heeding the call of the COVID-19 pandemic and we are proud that our unique solution is aiding health systems and consumers around the world in the fight against the virus, saidDedi Gilad, Co-Founder and CEO of Tyto Care. This new funding comes at a pivotal moment in the evolution of telehealth andwill enable us to continue to transform the global healthcare industry with the best virtual care solutions. We look forward to further expanding the reach of telehealth and introducing new solutions as demand for remote care continues to soar.

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The on-demand era has finally reached healthcare, saidJeff Horing, co-founder and Managing Director at Insight Partners. Adoption of telehealth is at an all-time high and as the only solution on the market with diagnostic capabilities that can deliver clinic-quality remote care, Tyto Care is significantly disrupting the health ecosystem. As a partner that empowers fast-growing ScaleUp software companies transforming daily life, we are excited to work with Tyto Care to help usher in the next generation of healthcare.

Olive Tree Ventures, a digital healthfund, is ecstatic to partner with Tyto Care to accelerate the adoption of global telehealth solutions. We experienced first-hand the value of Tyto Cares solution in the Israeli market. As digital health investors, we strongly believe in the promise of telehealth and are excited to collaborate with the visionary management at Tyto Care, saidAmir Lahat, General Partner, Olive Tree Ventures.

Tyto Care witnessed threefold growth in 2019 and is working with hundreds of hospitals and over 100 health organizations including health systems, payers and strategic partners, primarily inNorth America,EuropeandIsrael. The company served hundreds of thousands of patients and performed over 200,000 telehealth exams in 2019 alone, a testament to the growing adoption of its virtual medical exam solution as an efficient, convenient and high-quality means of delivering primary care.

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Tyto Care Raises $50M to Expand Its Telehealth Exam Offering Globally - AiThority

TheGlobal Machine Learning in Finance MarketResearch report provided by Market Expertz is a detailed study report of theGlobal Machine Learning in Finance Market, which covers all the necessary information required by a new market entrant as well as the existing players to gain a deeper understanding of the market. The Global Machine Learning in Finance Marketreport is segmented in terms of regions, product type, applications, key players, and several other essential factors. The report also covers the global market scenario, providing deep insights into the cost structure of the product, production, and manufacturing processes, and other essential factors.

The report also covers the global market scenario, highlighting the pricing of the product, production and consumption volume, cost analysis, industry value, barriers and growth drivers, dominant market players, demand and supply ratio of the market, the growth rate of the market and forecast till 2026.

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The report includes accurately drawn facts and figures, along with graphical representations of vital market data. The research report sheds light on the emerging market segments and significant factors influencing the growth of the industry to help investors capitalize on the existing growth opportunities.

In market segmentation by manufacturers, the report covers the following companies-

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinanceOthers

Get to know the business better:The global Machine Learning in Finance market research is carried out at the different stages of the business lifecycle from the production of a product, cost, launch, application, consumption volume and sale. The research offers valuable insights into the marketplace from the beginning including some sound business plans chalked out by prominent market leaders to establish a strong foothold and expand their products into one thats better than others.

In market segmentation by types of Machine Learning in Finance, the report covers-

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced LeaningOthers

In market segmentation by applications of the Machine Learning in Finance, the report covers the following uses-

BanksSecurities CompanyOthers

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A conscious effort is made by the subject matter experts to analyze how some business owners succeed in maintaining a competitive edge while the others fail to do so makes the research interesting. A quick review of the realistic competitors makes the overall study a lot more interesting. Opportunities that are helping product owners size up their business further add value to the overall study.

With this global Machine Learning in Finance market research report, all the manufacturers and vendors will be aware of the growth factors, shortcomings, opportunities, and threats that the market has to offer in the forecast period. The report also highlights the revenue, industry size, types, applications, players share, production volume, and consumption to gain a proper understanding of the demand and supply chain of the market.

Years that have been considered for the study of this report are as follows:

Major Geographies mentioned in this report are as follows:

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The complete downstream and upstream essentials and value chains are carefully studied in this report. Current trends that are impacting and controlling the global Machine Learning in Finance market growth like globalization, industrialization, regulations, and ecological concerns are mentioned extensively. The Global Machine Learning in Finance market research report also contains technical data, raw materials, volumes, and manufacturing analysis of Machine Learning in Finance. It explains which product has the highest penetration in which market, their profit margins, break-even analysis, and R&D status. The report makes future projections for the key opportunities based on the analysis of the segment of the market.

Key features of the report:

What does the report offer?

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Well-versed in economics and mergers and acquisitions, Jashi writes about companies and their corporate stratagem. She has been recognized for her near-accurate predictions by the business world, garnering trust in her written word.

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Machine Learning in Finance Market Provides in-depth analysis of the Industry, with Current Trends and Future Estimations to Elucidate the Investment...