Category Archives: Deep Mind
Alma Allens biomorphic sculptures have minds of their own – Wallpaper*
Alma Allens biomorphic sculptures have minds of their own
In a bold takeover of Kasmins gallery and sculpture garden in New York, American artist Alma Allen introduces his latest series of curious creatures in bronze
If you didnt know they were rendered in static bronze, you might be mistaken for thinking that Alma Allens sculptures were alive. Imbued with organic, surreal and creature-like characteristics, they appear to be growing or evolving;blink and you might find them somewhere else.
Allens biomorphic sculptures can currently be found both indoors and outdoors at Kasmin, New York. In the gallerys 514 West 28th Street location, Allen is presenting more than 20 small-scale bronzes. Hyper-polished almost to the point of liquidity, these works are both lifeforms in their own right and proposals for future large-scale works. Atop Kasmins elevated and newly rewilded urban garden, the artists monumental outdoor sculptures can be experienced by all those who walk the adjacent New York High Line.
Allens deep affinity with the natural world stems from a childhood spent in Utah, where proximity to the desert allowed him the chance to roam, whittle wood, and hand-carve stones that he stumbled upon. The sculptures are often in the act of doing something: they are going away, or leaving, or interacting with something invisible, Allen has previously said. Even though they seem static as objects, they are not static in my mind. In my mind, they are part of a much larger universe. They are interacting with each other as well, with works I made 20 years ago.
Allen begins his process by instinctively hand-sculpting intimately scaled model clay or wax forms. Its a gradual emergence as the artist works and reworks until each has a life of its own. The artist casts and finishes the sculptures at his own foundry, on site at his studio in the hills of Tepoztln, Mexico.
Installation view of Alma Allens exhibition at Kasmin gallerys514 West 28th Street location. Photography:Diego Flores
The forms and shapes of Allens work are only half the story; a great deal exists on the surface. The artists expressive and tactile finishes involve welding smaller pieces together, brazing, polishing, and developing chemical patinas until surfaces almost resemble paintings, or even rippling landscapes.
Kasmin is in the process ofimagining new ways to bring Allens sculpture to the public. The gallery ispartnering with Membit, an augmented reality platform to create anart anywhereexperience.Through this, users are able to introduce a 3D image of one of Allens sculptures into their own environments, whether athome, a local wilderness, or in a public space.
Here, small-scale meets monumental, exhibiting the versatility and ambition of Allens work. Whether occupying the clean white walls of the gallery or reaching for the skyline in Kasmins urban garden, Allens sculptures feel very much at home.
Photography:Diego Flores
Photography:Diego Flores
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Alma Allens biomorphic sculptures have minds of their own - Wallpaper*
Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed…
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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Machine Learning Artificial intelligence Market Global Industry Analysis, Size, Share, Growth, Trends And Forecast To 2027 | AIBrain, Amazon, Anki,…
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Mind the gap: why training is vital to pursuing transgender inclusion – TrainingZone.co.uk
Transgender inclusion at work is a topic that has received more attention in recent years, but organisations still have a long way to go to achieve a long-term vision of change.
A proactive approach to inclusion is critical to surmount the serious barriers that stifle transgender equality at work. There is a deep and broad knowledge gap regarding transgender issues even within the HR and L&D ranks.
Indeed, transgender is one of the most misunderstood identity aspects in the social realm, especially the workplace. Transgender individuals present a gender identity that is different from their gender assigned at birth. Yet there are a multitude of articulations in which transgender identity can take shape. As such, transgender is often referred to as an umbrella term, which encompasses a vast spectrum of minority gender identity expressions.
For example, a transgender individual may be someone who undertakes hormonal and/or surgical interventions to achieve a destination gender identity. Equally, some transgender people adopt a new gender identity by means of non-medical steps, such as changes in everyday dress, speech or manners.
In addition, there are transgender individuals who express a non-binary gender identity by altogether eschewing the conventional categories of male and female. Whats more, transgender status does not connote a particular sexual orientation, so transgender people can be gay, lesbian, bisexual, straight or pansexual.
The complexity of transgender is often glossed over by the uncritical use of the label LGBT in both HR practice and academic research, which sometimes conflates not only sexual orientation and gender identity, but the distinct needs of different groups.
While it is true that transgender people may have overlapping concerns with LGB cis-individuals, they also have unique needs that must be addressed in their own right. For example, many transgender people undergo transition the complex process of re-configuring an existing gender identity in ways that more closely match a desired gender identity.
In addition, transgender individuals are much more profoundly stigmatised than LGB cis men and cis women. Transgender is viewed in society as a threat to gender, which operates as the most fundamental organising principle of social and organisation life.
Through conducting research on gender identity inclusion in the workplace, in collaboration with Professor Ahu Tatli from Queen Mary University of London, three key challenges faced by transgender employees came to light.
Our qualitative research based on in-depth interviews with transgender employees in the UK shows disturbing trends that must be addressed swiftly and decisively (Ozturk and Tatli, 2016).
First, transgender employees have a severe under-representation problem in UK workplaces. Many organisations view gender identity diversity as too far a goal to advance. Transgender workers are implicitly understood as incongruent with professional work contexts as well as stereotypically gendered lines of work.
As a result, transgender workers are severely discriminated against at the recruitment and selection stage. The continuing lack of organisational awareness or expertise regarding transgender people exacerbates the under-representation problem.
Disclosure was another key issue highlighted by our research (Ozturk and Tatli, 2016). The ability of a transgender worker to be upfront about their gender identity is often severely compromised due to gender-typical industry norms, as well as unsupportive organisational contexts.
Transgender employees often have to remain silent about their gender identity journeys as well as their pressing needs and requirements as transgender workers, because they fear reprisals from employers, peers and customers. As many organisations are not furnished with adequate know-how to create a trans-supportive environment, disclosure concerns continue to loom large in transgender work lives.
Another significant issue highlighted by our research is transition (Ozturk and Tatli, 2016). Transgender workers, who undertake gender identity transition while employed in the same organisation, are often subjected to invasive personal questions, treated as objects of curiosity and represent a problem that must be managed.
Organisations often worry too much about coordinating potential absences during transition, or how schedules can be managed so as to minimise any performance shortfalls.
Conversely, they worry too little about providing transgender employees with genuine support during this challenging period. As a result, transition often involves forced career breaks and the necessity of job change for transgender employees.
A proactive approach to inclusion is critical to surmount the serious barriers that stifle transgender equality at work. There is a deep and broad knowledge gap regarding transgender issues even within the HR and L&D ranks. As such, HR and L&D professionals, as well as equality and diversity officials need training on how to develop a sensitive and precise understanding of transgender issues.
Without this, they are not fully armed with the requisite knowledge to tackle deep-seated prejudices. In addition, the training for these groups must be followed by broader transgender training provided across the organisation, from the management ranks to entry-level employees. This encompassing roll out of training across different levels and functions is a pre-requisite for securing in-depth organisational awareness and appreciation of transgender issues.
Training is only truly effective, however, when it is deployed to engage people meaningfully by enlisting their active participation and imagination. Therefore, it is important to make training more than a single event, which requires one-time participation in a limited manner. Transgender inclusion training needs to be a series of developmental activities that align the organisational community with a strong appreciation of differences.
Training that cuts ice requires serious resource investment, which involves top management support. Top management support is also necessary to make transgender inclusion a strategic aspect of organisational culture and development. This may come in the form of concrete efforts such as targeted recruitment programmes, positive action at the selection stage, and mentoring and leadership programmes aimed at transgender workers. Such acts of ongoing support must be complemented by discursive steps such as amendment of strategic plans, policy documents and communications to ensure that transgender identities are a visible component of organisational activities.
Deep and meaningful progress to achieve transgender inclusion in work organisations requires significant action based on a long-term vision of change, which should be pursued by L&D, HR and top management.
Interested in this topic? ReadDiversity and inclusion: getting it right on the road to recovery.
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Mind the gap: why training is vital to pursuing transgender inclusion - TrainingZone.co.uk
Phaidra raises more cash from Mark Cuban and others to build the future of industrial automation – GeekWire
Phaidra co-founders Katherine Hoffman (left), Jim Gao (top right), and Vedavyas Panneershelvam. (Phaidra Photos)
New funding: Phaidra, a startup that launched in 2019 and is building AI tech for industrial customers, raised $4 million. The round was led by Seattle-based Flying Fish, which also led the companys pre-seed round in May 2020. Section 32, Character, Starshot Capital, and Dallas Mavericks owner Mark Cuban also invested.
The software: Phaidra analyzes massive amounts of sensor data and creates intelligent AI agents that automatically control and optimize complex industrial facilities. Customers use Phaidra to improve energy efficiency and sustainability; increase plant safety and stability; and maximize yield.
It will use the fresh cash to accelerate growth in industries such as process heating and cooling, chemical manufacturing, and paper and pulp.
Leadership: The 15-person company is led by Jim GaoandVedavyas Panneershelvam, two former employees at DeepMind, the Alphabet-owned AI research hub, as well asKatherine Hoffman, a defense and HVAC industry veteran.
Our goal is to become the future of industrial automation, Gao said.
Gao led a 14-person team at DeepMind focused on building AI tech for the energy sector. It helped reduceGoogles data center cooling energy consumption by 40%. DeepMind, acquired by Alphabet-owned Google in 2014, is famous for developing the AlphaGo program that beat a human world champion.
The companys leadership is in Seattle but it describes itself as an all-remote team.
Pandemic impact: Gao said the pandemic accelerated digital transformation plans for many industrial companies. The companies we work with view Phaidras intelligence services (i.e. converting sensor data into optimal actions) as a critical competitive edge, he said.
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Phaidra raises more cash from Mark Cuban and others to build the future of industrial automation - GeekWire
DeepMind reportedly lost a yearslong bid to win more independence from Google – The Verge
Tensions between Google and its AI brain trust DeepMind have always been fascinating. To put the relationship in crude terms: DeepMind, founded in 2010, is home to some the best AI researchers in the world who output a steady stream of insightful academic papers and Nature front covers. Google, meanwhile, bought DeepMind in 2014 and bankrolls its large losses, and it really, really wants to squeeze some money out of all those juicy brains.
Thats why a recent story on the two companies from The Wall Street Journal is so interesting. In it, Parmy Olson reports that Google has ended yearslong negotiations between the two firms, ultimately rejecting a plea from DeepMind for more independence.
According to Olson, DeepMind told staff the talks were over late last month. One suggestion from DeepMinds founders was apparently for the company to have the same legal structure as a nonprofit, reasoning that the powerful artificial intelligence they were researching shouldnt be controlled by a single corporate entity, according to people familiar with those plans. But Google wasnt on board with this, telling DeepMind it didnt make sense considering how much money the company has poured into DeepMind.
This conflict isnt surprising. Google execs have said repeatedly the companys future lies in AI, and numerous news stories suggest the mothership has been pressuring DeepMind into commercializing its work. This has led to projects from DeepMind using its research to improve battery life on Android and reduce energy costs in its data centers, but the financial benefits of these efforts are unclear. Meanwhile, the UK firms losses keep rising hitting a high of 477 million (around $660 million) in its most recent public filings for 2019. If Google wants its moneys worth, it cant give DeepMind anything like nonprofit status.
Alongside financial pressures, another bone of contention between the two companies seems to be ethical oversight. A much-trumpeted element in Googles acquisition of DeepMind was a promise that Google would set up an ethics board to ensure its technology was always deployed fairly. The exact nature and scope of this board, though, including who sits on it, has always been unclear. A 2019 report from The Economist said the board even held ownership over any artificial general intelligence created by DeepMind a term that refers to AI that meets or exceeds human capacity across a broad range of tasks.
The status of this board is not mentioned in the WSJs report, but Olson notes that DeepMinds future work will now be overseen by a separate ethics board staffed mostly by senior Google executives. Olson noted in a tweet that this is the Advanced Technology Review Council, or ATRC. This is reportedly Googles highest review board.
In a statement given to The Verge, a spokesperson for DeepMind said: Over the years, of course weve discussed and explored different structures within the Alphabet group to find the optimal way to support our long term research mission to solve intelligence. We could not be prouder to be delivering on this incredible mission, while continuing to have both operational autonomy and Alphabets full support.
Update, May 21st, 11:04AM ET: Story has been updated to note that the ethics board mentioned in the WSJs report is the ATRC.
Update, May 24th, 04:55AM ET: Story has been updated with statement from DeepMind.
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DeepMind reportedly lost a yearslong bid to win more independence from Google - The Verge
Aging With Honor and Dignity: An Intuitive Approach for Men – The Good Men Project
How can we men negotiate our elder years with honor and dignity? How can we continue to stand tall in spirit and remain strong in sensibility when the accumulating losses of aging keep taking their toll?
For the last several years I have been thinking, studying, and writing about this topic in articles, videos, and books. I have come to believe that aging is not all a downhill slope. Yes, there is loss and decline as we age, but there are gifts too, and rewards. There is a path for us men to take our places of honor within ourselves and the wider community, as leaders and eldersindeed to age with honor and dignity. As I say in my latest book, Every Breath, New Chances:
Aging is a two-faced coin. On one side is lossloss of virility, strength, health, power and possibility. On the other side is transformation and growth. This transformation is not something that can be grasped intellectually. The aging journey is emotional; it happens in the realm of feeling.
In doing research for this book, I have talked to many men about their aging, both individually and in groups. Its clear that menof my generation particularlyface unique challenges around growing older. We were raised in the crucible of traditional masculinity. We were encouraged to be strong. We were told when tears came that boys dont cry. We were taught to mask and hide our emotions, particularly feelings of vulnerability, uncertainty, and fear. We were urged to compete, to work hard, push through adversity and avoid showing weakness, so that we could stand tall as men worthy of respect.
Men vary in how much they absorb and internalize this traditional male view, but we have all been exposed to it and influenced by it to some degree.
We can debate how well these values prepared us to be men of goodness and integrity during our earlier adulthood, but whatever their worth, these principles are less valuable in the last phase of lifewhen men experience decline and loss of physical, sexual and mental capacity. In the face of these new challenges, the old masculine values dont hold up so well. You cant fight aging by powering through, or by putting your dukes up and trying to be a tough guy. Aging just laughs at you when you try to do that. If you frame the battle that way, aging always wins.
In order to prevail and prosper as we age, we need to come up with a different strategy, one that invokes the very aspects of ourselves that we set aside early on in our effort to be strong men. We need to go back and refamiliarize ourselves with vulnerability, emotionality, fear, andmost of allintuition. Acknowledging these aspects of ourselves, finding ways to honor them and learn from them, is the path to transforming aging from a framework of loss and weakness to a newfound nexus of elderhood and inner strength. Aging is not just another of lifes battles to be fought and won, it is an inner flame that needs to be ignited from within, cultivated and grown until it is a heartwarming fire.
In this effort, I teach that intuition is key. At first, hearing that may seem like an odd notion. How can intuition help? What is intuition, anyway?
The phrase womens intuition comes to mind, and most men want nothing to do with that. The good news is that this old hackneyed expression is flat wrong; there is no male or female in intuition. The latest research confirms it; men and women are equally intuitive, although men may not always recognize intuition when they are using it, even though we use it all the time. Any time you size someone up whether a colleague at work, a contractor to hire, or a person youre talking to at a partyyou are using intuition. In business we refer to a gut player, someone who plays his hunches.
Sports is another realm where intuition rulesthink of a running back weaving downfield or a point guard driving to the basket. These players arent thinking about what they are doing, they are just doing it faster than they can think. When reporters ask them later how they made the touchdown or the basket, often they cant say. Thats because intuition is not a verbal thing. It happens too fast. The research confirms this; intuition is three times quicker than thinking, and is mostly unconscious.
Aging is another realm where intuition can guide you where thinking cannot go. You use intuition to size other people up; when it comes to aging, you can use intuition to, in a sense, size yourself up. This is different than thinking about aging, though thats something you probably do a lot. You may worry about your health, your finances, your sexual prowess, your painful joints, your weight, your weakening eyesightthe list goes on. These thoughts spin inside the privacy of your mind like a clothes dryer that wont turn off. This spinning, however, is not really sizing yourself up in an intuitive sense. Thoughts about aging are like cotton candysatisfying for a while, but not deeply nourishing in the long run. Besides, much of this thinking is speculation; it may or may not be true. If you want to know the deep truth about yourself, you need intuition. Intuition is subtle, yet powerful. Unlike thinking, it knows the truth of things; it sees where the real opportunities are, as well as the hazards.
So how do you access this inner sizing up? It would be nice if you could just talk to intuition like a buddy, and say, Hey, tell me what you know. How am I doing?
Intuition doesnt work that way, though. Instead, it is an ancient faculty, far older than thinking, and it communicates not through words, but pictures and images. Thinking is about knowledgefacts and figures, whether true or falsebut intuition is wise, and that difference is telling. Intuition remembers everything you did, and it is clear-eyed and courageous in knowing that one day you may become seriously ill and, eventually, you will die. Intuition knows not just about your aging; since aging is universal it knows about everyones aging.
Intuition is like a kindly old grandfather to whom you can goas perhaps you once did as a boyfor solace and advice. In fact, in every traditional community, there were grandfathers and uncles who could offer models and guidance to men as they aged. In the modern world, we have few such models, which is one reason it is so hard to be a good man and to grow old as one.
To help you connect with this inner grandfather I have developed simple exercises I call deep mind reflections. These exercises use key words and images in a free-associative manner to connect with your intuition, your inner grandfatherto ask him questions, to solicit his advice, and to let him tell you which way to go. Deep mind reflections can be used to inquire into many topics of keen interest to aging mensuch as virility, power, retirement, loneliness, health, illness, and death.In future articles I will go into more depth about these methods, but here is a simple example to get you started:
Bring up the word aging in your mind. Repeat it silently to yourself a few times. Now look into your minds eye and notice the first image or picture that comes to mind. It could be anythingyour greying hair, your bad knee, the recent death of a friend. The main thing is not to censor or select. Notice the very first image that comes. When you have it in mind, keep watching it and see what happens. That is one form of deep mind reflection.
There are many deep mind reflections, but the deepest one asks yourself, Who am I really? Are you really your body, your thoughts, your sexuality, your attractiveness, your net worth, your status and reputation, or any number of myriad ways you define who you are? Or are you something separate from all that, something deeper and more fundamental? Your intuition knows the answer to that question, and that answer has timeless value in upholding the honor and dignity of elder men of every generation.
The Good Men Project gives people the insights, tools, and skills to survive, prosper and thrive in todays changing world. A world that is changing faster than most people can keep up with that change. A world where jobs are changing, gender roles are changing, and stereotypes are being upended. A world that is growing more diverse and inclusive. A world where working towards equality will become a core competence. Weve built a community of millions of people from around the globe who believe in this path forward. Thanks for joining The Good Men Project.
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Aging With Honor and Dignity: An Intuitive Approach for Men - The Good Men Project
Damien Harris clearly wouldn’t mind a trade that lands Julio Jones with Patriots – Patriots Wire
The possibility of Julio Jones joining the New England Patriots appears to be growing every day.
There was speculation about it being a good match when reports came out about the Atlanta Falcons looking to trade the 32-year-old, but nothing substantial linked him to New England. On Friday, former Patriots executive Michael Lombardi said he doesnt doubt the team is looking into a potential trade for Jones.
NFL Networks Mike Giardi took it a step further on Saturday with a report saying the Patriots are having internal conversations about a trade. Sports Illustrateds Albert Breer said on Monday the potential asking price for Jones would just be a second-round pick.
Patriots running back Damien Harris is seeing the rumors and clearly is a fan of the potential move.
New Englands offensive line is one of the best in the league, the running back corps is deep and the tight end group is now among the best. The quarterback situation with Cam Newton and Mac Jones on the roster is much better than the scenario in 2020. Now, the Patriots need an elite receiver to cap off the much-improved offensive unit.
Harris likely isnt the only Patriot who approves of this move.
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Damien Harris clearly wouldn't mind a trade that lands Julio Jones with Patriots - Patriots Wire
AI in Healthcare Market Rugged Expansion Foreseen by 2031 | Nuance Communications, Inc., DeepMind Technologies Limited, IBM Corporation The Courier -…
Global AI in Healthcare Market 2021by Manufacturers, Regions, Type, and Application, Forecast to 2031 holds key importance for professionals entailing data and market analytics. The main purpose of this report is to offer independent and consensus-based information highlighting and addressing critical data and necessary information regarding the market. The report helps our clients make superior data-driven decisions, understand market forecasts, capitalize on future opportunities and optimize accurate and valuable information.
The report categorizes the global AI in Healthcare market by segment by the key player, type, application, marketing channel, and region. This research highlights the current drivers which can help increasing its demand from consumers, knowledge about industry leaders, industry developments and changes, market share, and market analysis.
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NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.
The report estimates market size (value and volume), market share, growth rate by types, applications. It AI in Healthcare on the competitive scenario of the global AI in Healthcare market to know the competition at both the domestic and global levels. Every leading player of the global market is outlined considering the key aspects such as areas of operation, production, and product portfolio. Additionally, companies in the report are studied based on key factors such as company size, market share, market growth, revenue, production volume, and profits. It then investigates detailed trends and outlooks of the global market, along with market drivers and market hurdles.
Major leading companies are covered in this market report are Nuance Communications, Inc., DeepMind Technologies Limited, IBM Corporation, Intel Corporation and Microsoft and NVIDIA Corporation.
Analysis of Drivers, Restraints, Opportunities, and Trends of AI in Healthcare Market:
Drivers:
The AI in Healthcare market report in this section identifies dominant market drivers and favorable trends that leverage high-end growth, peculiar to the usual growth trajectory. The report in this section also unearths eminent demand possibility and customer inclination towards product and service consumption, thus effectively deciding growth prognosis across the timeline.
Restraints:
The AI in Healthcare market report also carefully identifies various restraining factors operational in the market and their limitations which directly interfere with the usual growth spurt.
Opportunities:
The following sections of the report evaluate the potential of existing AI in Healthcare market opportunities in growth diversification, besides also unraveling new avenues that further enhance growth likelihood.
Trend Estimation:
Relentless market developments and novelties also augment the growth route in several desirable ways that also reflect growth stability and sustainability in the forthcoming years.
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In the last section of the report, the companies responsible for increasing sales in the AI in Healthcare market have been presented. These companies have been analyzed in terms of their manufacturing base, basic information, and competitors. In addition, the application and product type introduced by each of these companies also form a key part of this section of the report. The recent enhancements that took place in the global AI in Healthcare market and their influence on the future growth of the market have also been presented through this study.
AI in Healthcare Market Segmentation by Region:
North America [United States, Canada, Mexico]South America [Brazil, Argentina, Columbia, Chile, Peru]Europe [Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland]The Middle East & Africa [GCC, North Africa, South Africa]Asia-Pacific [China, Southeast Asia, India, Japan, Korea, Western Asia]
Key Answers Captured in AI in Healthcare Study are
1.Which geography would have better demand for products/services?2.What strategies of big players help them acquire shares in the regional market?3.Countries that may see the steep rise in CAGR & year-on-year (Y-O-Y) growth?4.How feasible is the AI in Healthcare market for long-term investment?5.What opportunity the country would offer for existing and new players in the AI in Healthcare market?6.What are influencing factors driving the demand for AI in Healthcare near future?7.What is the impact analysis of various factors in the Global AI in Healthcare market growth?8.What are the recent trends in the regional market and how successful they are?
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AI in Healthcare Market Rugged Expansion Foreseen by 2031 | Nuance Communications, Inc., DeepMind Technologies Limited, IBM Corporation The Courier -...
AI in Healthcare Market in deep Research about Growth & Competitive Analysis by 2021-2031 | Nuance Communications, Inc., DeepMind Technologies…
A new business intelligence report released by insightSLICE with title AI in Healthcare Market Insights, forecast to 2031 has abilities to raise as the most significant market worldwide as it has remained playing a remarkable role in establishing progressive impacts on the universal economy. The AI in Healthcare Market Report offers energetic visions to conclude and study market size, market hopes, and competitive surroundings. The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing.
Some of the key players profiled in the study are: Nuance Communications, Inc., DeepMind Technologies Limited, IBM Corporation, Intel Corporation and Microsoft and NVIDIA Corporation.
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Important Features that are under offering & key highlights of the report:
Market Data Segmentation with production, consumption, revenue (million USD), and Price Analysis Detailed overview of AI in Healthcare market Changing market dynamics of the industry In-depth market segmentation by Type, Application etc Historical, current and projected market size in terms of volume and value Recent industry trends and developments Competitive landscape of AI in Healthcare market Strategies of key players and product offerings Potential and niche segments/regions exhibiting promising growth A neutral perspective towards AI in Healthcare market performance Must have information for market players to sustain and enhance their market footprint
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Strategic Points Covered in Table of Content of AI in Healthcare Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the AI in Healthcare marketChapter 2: Exclusive Summary the basic information of the AI in Healthcare Market.Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the AI in HealthcareChapter 4: Presenting the AI in Healthcare Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.Chapter 5: Displaying the by Type, End User and Region 2021-2031Chapter 6: Evaluating the leading manufacturers of the AI in Healthcare market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company ProfileChapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions.Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source
Key questions answered Who are the Leading key players and what are their Key Business plans in the AI in Healthcare market? What are the key concerns of the five forces analysis of the AI in Healthcare market? What are different prospects and threats faced by the dealers in the AI in Healthcare market? What are the strengths and weaknesses of the key vendors?
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