Category Archives: Artificial Intelligence

I hope everyone is enjoying the latest breakthrough in artificial intelligence (AI) as much as I am.

In one of the latest AI developments, a new computer programme - DALL-E 2 - generates images from a text prompt. Give it the phrase Club Penguin Bin Laden, and it will go off and draw Osama as a cartoon penguin. For some, this was more than a bit of fun: it was further evidence that we shall soon be ruled by machines.

Sam Altman, chief executive of the now for-profit Open AI company which provides the model that underpins DALL-E, suggested that a generalised intelligence (AGI) was close at hand. So too did Elon Musk, who founded Altmans venture. Musk even gave a year for when this would happen: 2029.

Yet when we look more closely, we see that DALL-E really isnt very clever at all. Its a crude collage maker, which only works if the instructions are simple and clear, such as Easter Island Statue giving a TED Talk. It struggles with more subtle prompts, fails to render everyday objects: fingers are drawn as grotesque tubers, for example, and it cant draw a hexagon.

DALL-E is actually a lovely example of what psychologists call priming: because were expecting to see a penguin Bin Laden, thats what we shall see - even if it looks like neither Osama nor a penguin.

Impressive at first glance. Less impressive at second. Often, an utterly pointless exercise at the third, is how Filip Piekowski, a scientist at Accel Robotics, describes such claims, and DALL-E very much conforms to this general rule.

Todays AI hyperbole has gotten completely out of hand, and it would be careless not to contrast the absurdity of the claims with reality, for the two are now seriously diverging. Three years ago Google chief executive Sundai Pinchar told us that AI would be more profound than fire or electricity. However, driverless cars are further away than ever, and AI has yet to replace a single radiologist.

There have been some small improvements to software processes, such as the wonderful way that old movie footage can be brought back to life by being upscaled to 4K resolution and 60 frames per second. Your smartphone camera now takes slightly better photos than it did five years ago. But as the years go by, the confident predictions that vast swathes of white collar jobs in finance, media and law would disappear look like a fantasy.

Any economist who confidently extrapolates profound structural economic changes of the sort of magnitude that affects GDP from AI ventures such as DALL-E should keep those showerthoughts to themselves. This wild extrapolation was given a name by the philosopher Hubert Dreyfus, who brilliantly debunked the first great AI hype of the 1960s. He called it the first step fallacy.

His brother, Stuart, a true AI pioneer, explained it like this: It was like claiming that the first monkey that climbed a tree was making progress towards landing on the moon.

Todays misleadingly-named deep learning is simply a brute force statistical approximation, made possible by computers being able to crunch a lot more data than they could, to find statistical regularities or patterns.

AI has become good at the act of mimicry and pastiche, but it has no idea of what it is drawing or saying. Its brittle and breaks easily. And over the past decade it has got bigger but not much smarter, meaning the fundamental problems remain unsolved.

Earlier this year the neuroscientist, entrepreneur and serial critic of AI, Gary Marcus had enough. Taking Musk up on his 2029 prediction, Marcus challenged the founder of Tesla to a bet. By 2029, he posited, AI models like GPT - which uses deep learning to produce human-like text - should be able to pass five tests. For example, they should be able to read a book and reliably answer questions on its plot, characters and their motivations.

A Foundation agreed to host the wager, and the stake rose to $500,000 (409,000). Musk didnt take up the bet. For his pains, Marcus has found himself labelled as what the Scientologists call a suppressive. This is not a sector that responds to criticism well: when GPT was launched, Marcus and similarly sceptical researchers were promised access to the system. He never got it.

We need much tighter regulation around AI and even claims about AI, Marcus told me last week. But thats only half the picture.

I think the reason were so easily fooled by the output of AI models is because, like Agent Mulder in the X-Files, is because we want to believe. The Google engineer who became convinced his chatbot had developed a soul was one such example, but it is also journalists who seem to want to believe in magic more than anyone.

The Economist devoted an extensive 4,000 word feature last week to the claim that huge foundation models are turbo-charging AI progress, but ensured the magic spell wasnt broken by only quoting the faithful, and not critics like Marcus.

In addition, a lot of people are doing rather well as things are - waffling about a hypothetical future that may never arrive. Quangos abound, and for example, the UKs research funding body recently threw 3.5m of taxpayers money towards a programme called Enabling a Responsible AI Ecosystem.

It doesnt pay to say the Emperor has no clothes: the Courtiers might be out of a job.

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The artificial intelligence hype is getting out of hand - The Telegraph

For years, physicists have been making major advances and breakthroughs in the field using their minds as their primary tools. But what if artificial intelligence could help with these discoveries?

Last month, researchers at Duke University demonstrated that incorporating known physics into machine learning algorithms could result in new levels of discoveries into material properties, according to a press release by the institution. They undertook a first-of-its-kind project where theyconstructed a machine-learning algorithm to deduce the properties of a class of engineered materials known as metamaterials and to determine how they interact with electromagnetic fields.

The results proved extraordinary. The new algorithm accurately predicted the metamaterials properties more efficiently than previous methods while also providing new insights.

By incorporating known physics directly into the machine learning, the algorithm can find solutions with less training data and in less time, said Willie Padilla, professor of electrical and computer engineering at Duke. While this study was mainly a demonstration showing that the approach could recreate known solutions, it also revealed some insights into the inner workings of non-metallic metamaterials that nobody knew before.

In their new work, the researchers focused on making discoveries that were accurate and made sense.

Neural networks try to find patterns in the data, but sometimes the patterns they find dont obey the laws of physics, making the model it creates unreliable, said Jordan Malof, assistant research professor of electrical and computer engineering at Duke. By forcing the neural network to obey the laws of physics, we prevented it from finding relationships that may fit the data but arent actually true.

They did that by imposing upon the neural network a physics called a Lorentz model. This is a set of equations that describe how the intrinsic properties of a material resonate with an electromagnetic field. This, however, was no easy feat to achieve.

When you make a neural network more interpretable, which is in some sense what weve done here, it can be more challenging to fine tune, said Omar Khatib, a postdoctoral researcher working in Padillas laboratory. We definitely had a difficult time optimizing the training to learn the patterns.

The researchers were pleasantly surprised to find that this model workedmore efficiently than previous neural networks the group had created for the same tasks by dramatically reducing the number of parameters needed for the model to determine the metamaterial properties. The new model could evenmake discoveries all on its own.

Now, the researchers are getting ready to use their approach on unchartered territory.

Now that weve demonstrated that this can be done, we want to apply this approach to systems where the physics is unknown, Padilla said.

Lots of people are using neural networks to predict material properties, but getting enough training data from simulations is a giant pain, Malof added. This work also shows a path toward creating models that dont need as much data, which is useful across the board.

The study is published in the journal Advanced Optical Materials.

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Artificial intelligence has reached a threshold. And physics can help it break new ground - Interesting Engineering

The institution was founded just two years ago

Published: Mon 20 Jun 2022, 6:23 PM

The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) has been ranked 127 globally among institutions that conduct research in computer science including AI, systems, theory, and "interdisciplinary" areas such as robotics, computer graphics, visualization, and more.

This ranking comes in just a short, two-year time span since the university was founded.

The ranking places MBZUAI alongside institutions such as Notre Dame, the University of Liverpool, the Weizmann Institute of Science, Osaka University, Ecole Normale Suprieure and other prestigious schools.

In the areas that MBZUAI currently focuses onartificial intelligence, computer vision, machine learning, and natural language processingMBZUAI ranks 30 globally ahead of several renowned research universities worldwide such as University of Michigan, Georgia Tech, and University of Toronto in North America; Imperial College London, EPFL, Max Planck Society in Europe; University of Tokyo, Seoul National University, and University of Sydney in Asia Pacific. MBZUAI can now claim to be the top ranked CS institution in the Arab World, and in the Middle East and Africa (CSRankings includes Israel as part of Europe).

Dr. Sultan bin Ahmed Al Jaber, Minister of Industry and Advanced Technology and Chairman of MBZUAI, said: In 2019, Abu Dhabi established the worlds first university dedicated to AI research with the aim of enhancing and benefiting from advanced technology capabilities in line with our leaderships vision for the future and the roadmap set out in the UAEs Strategy for Artificial Intelligence 2031. Today, MBZUAI has achieved a significant milestone as it has been ranked 30 globally by CSRankings in AI, computer vision, machine learning, and natural language processing placing it alongside elite, global research universities. This progress would not have been achieved without the vision, guidance, and support of the leadership, and the sincere efforts of the MBZUAI team. Achieving such recognition is a demonstration of the UAEs commitment to developing a knowledge-based economy through fostering AI-driven research and innovation, as well as empowering youth to become future leaders in this strategic sector.

CSRankings or Computer Science Rankings is "designed to identify institutions and faculty actively engaged in research across a number of areas of computer science, based on the number of publications by faculty that have appeared at the most selective conferences in each area of computer science," according to the organisation's website. CSRankings is considered a trusted source for rankings of institutions which conduct research in these areas of scientific inquiry.

"Attracting this calibre of faculty speaks to our research ambitions and the freedom we offer to innovate. We are a new university, with a strong culture of scientific inquiry that is reflected in the CSRankings we have achieved in just two years. We will continue to create an unparalleled environment here in Abu Dhabi, to attract more talent and to inspire impactful research as we grow at pace," said Professor Eric Xing, President of MBZUAI.

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) attracts world-class researchers in AI, CS, data sciences, and related disciplines such as healthcare, fintech, and engineering and social sciences.

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UAE: MBZUAI ranked 30th globally in universities specialising in Artificial Intelligence - Khaleej Times

Youre at the wheel of your car but youre exhausted. Your shoulders start to sag, your neck begins to droop, your eyelids slide down. As your head pitches forward, you swerve off the road and speed through a field, crashing into a tree.

But what if your cars monitoring system recognised the tell-tale signs of drowsiness and prompted you to pull off the road and park instead? The European Commission has legislated that from this year, new vehicles be fitted with systems to catch distracted and sleepy drivers to help avert accidents. Now a number of startups are training artificial intelligence systems to recognise the giveaways in our facial expressions and body language.

These companies are taking a novel approach for the field of AI. Instead of filming thousands of real-life drivers falling asleep and feeding that information into a deep-learning model to learn the signs of drowsiness, theyre creating millions of fake human avatars to re-enact the sleepy signals.

Big data defines the field of AI for a reason. To train deep learning algorithms accurately, the models need to have a multitude of data points. That creates problems for a task such as recognising a person falling asleep at the wheel, which would be difficult and time-consuming to film happening in thousands of cars. Instead, companies have begun building virtual datasets.

Synthesis AI and Datagen are two companies using full-body 3D scans, including detailed face scans, and motion data captured by sensors placed all over the body, to gather raw data from real people. This data is fed through algorithms that tweak various dimensions many times over to create millions of 3D representations of humans, resembling characters in a video game, engaging in different behaviours across a variety of simulations.

In the case of someone falling asleep at the wheel, they might film a human performer falling asleep and combine it with motion capture, 3D animations and other techniques used to create video games and animated movies, to build the desired simulation. You can map [the target behaviour] across thousands of different body types, different angles, different lighting, and add variability into the movement as well, says Yashar Behzadi, CEO of Synthesis AI.

Using synthetic data cuts out a lot of the messiness of the more traditional way to train deep learning algorithms. Typically, companies would have to amass a vast collection of real-life footage and low-paid workers would painstakingly label each of the clips. These would be fed into the model, which would learn how to recognise the behaviours.

The big sell for the synthetic data approach is that its quicker and cheaper by a wide margin. But these companies also claim it can help tackle the bias that creates a huge headache for AI developers. Its well documented that some AI facial recognition software is poor at recognising and correctly identifying particular demographic groups. This tends to be because these groups are underrepresented in the training data, meaning the software is more likely to misidentify these people.

Niharika Jain, a software engineer and expert in gender and racial bias in generative machine learning, highlights the notorious example of Nikon Coolpixs blink detection feature, which, because the training data included a majority of white faces, disproportionately judged Asian faces to be blinking. A good driver-monitoring system must avoid misidentifying members of a certain demographic as asleep more often than others, she says.

The typical response to this problem is to gather more data from the underrepresented groups in real-life settings. But companies such as Datagen say this is no longer necessary. The company can simply create more faces from the underrepresented groups, meaning theyll make up a bigger proportion of the final dataset. Real 3D face scan data from thousands of people is whipped up into millions of AI composites. Theres no bias baked into the data; you have full control of the age, gender and ethnicity of the people that youre generating, says Gil Elbaz, co-founder of Datagen. The creepy faces that emerge dont look like real people, but the company claims that theyre similar enough to teach AI systems how to respond to real people in similar scenarios.

There is, however, some debate over whether synthetic data can really eliminate bias. Bernease Herman, a data scientist at the University of Washington eScience Institute, says that although synthetic data can improve the robustness of facial recognition models on underrepresented groups, she does not believe that synthetic data alone can close the gap between the performance on those groups and others. Although the companies sometimes publish academic papers showcasing how their algorithms work, the algorithms themselves are proprietary, so researchers cannot independently evaluate them.

In areas such as virtual reality, as well as robotics, where 3D mapping is important, synthetic data companies argue it could actually be preferable to train AI on simulations, especially as 3D modelling, visual effects and gaming technologies improve. Its only a matter of time until you can create these virtual worlds and train your systems completely in a simulation, says Behzadi.

This kind of thinking is gaining ground in the autonomous vehicle industry, where synthetic data is becoming instrumental in teaching self-driving vehicles AI how to navigate the road. The traditional approach filming hours of driving footage and feeding this into a deep learning model was enough to get cars relatively good at navigating roads. But the issue vexing the industry is how to get cars to reliably handle what are known as edge cases events that are rare enough that they dont appear much in millions of hours of training data. For example, a child or dog running into the road, complicated roadworks or even some traffic cones placed in an unexpected position, which was enough to stump a driverless Waymo vehicle in Arizona in 2021.

With synthetic data, companies can create endless variations of scenarios in virtual worlds that rarely happen in the real world. Instead of waiting millions more miles to accumulate more examples, they can artificially generate as many examples as they need of the edge case for training and testing, says Phil Koopman, associate professor in electrical and computer engineering at Carnegie Mellon University.

AV companies such as Waymo, Cruise and Wayve are increasingly relying on real-life data combined with simulated driving in virtual worlds. Waymo has created a simulated world using AI and sensor data collected from its self-driving vehicles, complete with artificial raindrops and solar glare. It uses this to train vehicles on normal driving situations, as well as the trickier edge cases. In 2021, Waymo told the Verge that it had simulated 15bn miles of driving, versus a mere 20m miles of real driving.

An added benefit to testing autonomous vehicles out in virtual worlds first is minimising the chance of very real accidents. A large reason self-driving is at the forefront of a lot of the synthetic data stuff is fault tolerance, says Herman. A self-driving car making a mistake 1% of the time, or even 0.01% of the time, is probably too much.

In 2017, Volvos self-driving technology, which had been taught how to respond to large North American animals such as deer, was baffled when encountering kangaroos for the first time in Australia. If a simulator doesnt know about kangaroos, no amount of simulation will create one until it is seen in testing and designers figure out how to add it, says Koopman. For Aaron Roth, professor of computer and cognitive science at the University of Pennsylvania, the challenge will be to create synthetic data that is indistinguishable from real data. He thinks it is plausible that were at that point for face data, as computers can now generate photorealistic images of faces. But for a lot of other things, which may or may not include kangaroos I dont think that were there yet.

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Is fake data the real deal when training algorithms? - The Guardian

Artificial intelligence (AI) is everywhere, and it has an impact on all our lives.

However, years of bold proclamations have resulted in AI becoming overhyped, with reality often falling short of the world-altering promises.

The coming years will be more about practical uses of AI, as businesses ensure return on investment by using AI to address specific cases.

Power Technologys artificial intelligence in power dashboard covers all you need to know about this emerging technology and its impact on the sector.

The power sector, especially in Europe is expected to be impacted due to gas availability and price issues. Utilities will have to look for alternate sources of gas or shift to other sources of generation. AI in power industry usage is likely to be impacted alongside many other corporate tools.

The recent ban on Russian oil and gas supplies will have a varied impact on both the buying and selling nations. Russia is an important source of energy supply for the US and European countries.

Pressure on the Western Bloc to impose more sanctions on Russian energy imports due to civilian killings in Ukraine by Russian Army. Fuel prices (such as for oil) have increased due to talks of a boycott of Russian oil.

Energy companies continue to exit or halt operations in Russia due to increasing pressure to cut ties amid civilian killings in Ukraine.

The electric vehicle and energy storage market will be impacted due to a shortage of nickel and an increase in commodity prices.

The International Energy Agency (IEA) recently published A 10-Point Plan to Reduce the European Unions Reliance on Russian Natural Gas, providing short-term measures and claiming that the EU could cut Russian gas imports by more than 33%.

It also advocates for gas-to-coal switching that could account for the majority of the potential reduction in gas demand.

GlobalData estimates that the global AI platform market will be worth $52bn in 2024, up from $28bn in 2019.

Total spending on AI technology is certainly higher, but it is difficult to estimate. There are two main reasons for this.

Firstly, AI is an intrinsic part of many applications and functions, making it almost impossible to identify revenue explicitly generated by AI.

Secondly, the range of sub-sets and technologies that make up AI can be challenging to locate and track. In general, valuations of the overall AI market range from a few billion dollars to several trillion, depending on the source.

Rather than attempting to size the market, some companies have tried to forecast its economic impact. A PwC report in 2017 estimated that AI would add $15.7 trillion to the global economy by 2030 and boost global GDP by up to 14%.

Global AI platform revenue will reach $52bn by 2024, up from $28bn in 2019.

The competitive landscape for AI is highly fragmented. Companies are investing considerable sums, and there is a swath of innovative AI start-ups that possess innovative expertise. When it comes to the use of artificial intelligence in power industry terms, the competition is constant.

Yet, there is no denying that companies with access to large repositories of data to power AI models are leading the development of AI.

Big Tech excels in this regard, and several tech giants set the overall tone in AI. GAFAM (Google, Apple, Facebook, Amazon, and Microsoft), BAT (Baidu, Alibaba, and Tencent), early-mover IBM, and the two hardware giants Intel and Nvidia are key players within the field.

All industries are feeling the impact of AI, with established incumbents coming up against game-changing disruption from AI platforms developed either by technology giants such as Amazon, Google, and Microsoft; or AI-focused start-ups, such as Lemonade, Trax, and Butterfly Network.

It is not only companies that are making AI investment a priority, but countries as well.

China is the most obvious example, having pledged to become the world leader in AI by 2030, but governments in several nations are backing large spending projects to make sure they do not miss out on AIs positive effects.

The US remains the dominant player in the development of AI technologies, accounting for almost one-third of AI platform revenues in 2019, according to GlobalData estimates.

In a 2019 report from the Center for Data Innovation that compared China, the European Union (EU), and the US in terms of their relative standing in the AI economy, the US came out on top in four out of the six categories of metrics that were examined, including talent, research, development, and hardware.

China led in data and adoption, but its advantage in AI adoption was due to a strong position in a limited number of AI technologies such as facial recognition and smart surveillance.

These are related to the governments extensive use of surveillance and are unlikely to create benefits across the economy.

The US and Europe have a sizable lead in terms of access to high-quality talent and research, and the US has the most AI start-ups and a more developed private equity and venture capital ecosystem.

Therefore, while China is making considerable investments, the USs structural advantages may even enable it to extend its lead.

Discussions about the race for AI dominance tend to focus on the US and China, but other countries are also in the race. Japan has long been at the forefront of AI when it comes to robotics.

The Japanese government released its AI strategy in 2017, and the country boasts a major AI investor in the form of Softbank, which, in 2019, created a $108bn fund to invest in AI companies and opened the Beyond AI institute in Tokyo, a $184m initiative to accelerate AI research in Japan.

In the UK, AI companies secured a record 1.3bn ($1.7bn) of investments in 2019, according to a study by Crunchbase and Tech Nation.

The UK has the second-highest number of AI companies globally, after the US, but most of those companies are small, making them a popular target for acquisition by the tech giants.

Germany is a powerhouse when it comes to the uses of AI in the manufacturing, automotive, and industrial sectors. In 2018, France announced that it would invest 1.5bn ($1.8bn) in AI research until the end of 2022.

Other countries that consider AI an important strategic initiative include South Korea, Russia, Canada, Israel, India, Sweden, Australia, and Singapore.

To best track the emergence and use of artificial intelligence in power, GlobalData tracks patent filings and grants, as well as companies that hold most patents in the field of artificial intelligence.

Power Technology monitors live power company job postings mentioning artificial intelligence or those requiring similar skills.

Jobs postings by power companies mentioning artificial intelligence in recent months. AI jobs tracker in the power sector looks at jobs posted, closed and active in the sector.

As illustrated by the value chain, big data extremely large, diverse data sets that, when analysed in aggregate, reveal patterns, trends, and associations, especially relating to human behaviour and interactions plays a significant role in the development of AI technology.

Big data is produced by all forms of digital activity: phone calls, emails, sensors, payments, social media posts, and much more.

It is also produced by machines, both hardware and software, in the form of machine-to-machine exchanges of data.

These exchanges are particularly important in the IoT (Internet of Things) era, where devices talk to each other without any form of human prompting.

Once collected, big data is typically managed in data centres, either in the public cloud, in corporate data centres, or on end devices. Big data is covered in more detail in our Big Data report.

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Artificial intelligence companies leading the way in the power industry - Power Technology

Objectives of the marketing research are kept in mind while preparing this market research report. This report deals with plentiful aspects of this industry. A comprehensive market study and analysis of trends in consumer and supply chain dynamics underlined in this report assists businesses in drawing the strategies for sales, marketing, advertising, and promotion. It not only assists you with informed decision-making but also helps with smart working. This market research report is prepared by keeping in mind todays business needs and advancements in technology.

The CAGR values covered here estimate the fluctuation of the rise or fall of demand for the specific forecasted period with respect to investment. Market analysis, market definition, currency and pricing, key developments, and market categorization along with detailed research methodology are the key factors of this market report. This market research report gives clear idea about the strategic analysis of mergers, expansions, acquisitions, partnerships, and investments. Skillful capabilities and excellent resources in research, data collection, development, consulting, evaluation, compliance, and regulatory services come together to form this world-class market research report.

Data Bridge Market Research analyses theartificial intelligence in aviation marketwill exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.84 million by 2029.

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Top Players Analysed in the Report are:

Some of the major players operating in the artificial intelligence in aviation market are IBM, Microsoft, Amazon Web Services, Inc., Airbus S.A.S., Xilinx, NVIDIA Corporation, Intel Corporation, General Electric, Micron Technology, Inc., Garmin Ltd., Lockheed Martin Corporation, SAMSUNG, Thales Group, MINDTITAN, Mitsubishi Electric Corporation, OMRON Corporation, TAV Technologies and IRIS Automation, among others.

Porters five forces model in the report provides insights into the competitive rivalry, supplier and buyer positions in the market and opportunities for the new entrants in the global Artificial Intelligence in Aviation market over the period. Further, Growth Matrix gave in the report brings an insight into the investment areas that existing or new market players can consider.

Research Methodology

A) Primary Research

Our primary research involves extensive interviews and analysis of the opinions provided by the primary respondents. The primary research starts with identifying and approaching the primary respondents, the primary respondents are approached include

Key Opinion Leaders Internal and External subject matter experts Professionals and participants from the industry

Our primary research respondents typically include

Executives working with leading companies in the market under review Product/brand/marketing managers CXO level executives Regional/zonal/ country managers Vice President level executives.B) Secondary Research

Secondary research involves extensive exploring through the secondary sources of information available in both the public domain and paid sources. Each research study is based on over 500 hours of secondary research accompanied by primary research. The information obtained through the secondary sources is validated through the crosscheck on various data sources.

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Who Will Get Advantage of This Report?

The prime aim of theGlobal Artificial Intelligence in Aviation Marketis to provide industry investors, private equity companies, company leaders and stakeholders with complete information to help them make well-versed strategic decisions associated to the chances in the Concealed Door Closer market throughout the world.

The secondary sources of the data typically include

Company reports and publications Government/institutional publications Trade and associations journals Databases such as WTO, OECD, World Bank, and among others Websites and publications by research agencies

Key Market Segmentation:

On the basis of technology, the artificial intelligence in aviation has been segmented into computer vision, machine learning, context awareness computing and natural language processing. Machine learning is further segmented into deep learning, supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning.

Based on offering, the artificial intelligence in aviation market has been segmented into hardware, software and services. Hardware is further segmented into processors, memory and networks. Software is further segmented into AI solutions and AI platforms. Services are further segmented into deployment and integration and support and maintenance.

On the basis of application, the artificial intelligence in aviation market has been segmented into dynamic pricing, virtual assistants, flight operations, smart maintenance, manufacturing, surveillance, training and other applications. Manufacturing is further segmented into material movement, predictive maintenance and machinery inspection, production planning, quality control and reclamation.

Key Elements that the report acknowledges:

Market size and growth rate during the forecast period. Key factors driving this market Key market trends cracking up the growth of this market Challenges to market growth Key vendors of this market Detailed SWOT analysis Opportunities and threats face by the existing vendors in this global market Trending factors influencing the market in the geographical regions Strategic initiatives focusing on the leading vendors PEST analysis of the market in the five major regions

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Global Artificial Intelligence in Aviation Market Key Drivers, Trends, Latest Innovations, Business Scenario, Demand With Outlook Designer Women -...

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Dublin, June 20, 2022 (GLOBE NEWSWIRE) -- The "Global Medical Imaging Equipment Market: Analysis By Product By End User Size and Trends with Impact of COVID-19 and forecast up to 2026" report has been added to ResearchAndMarkets.com's offering.

In 2021, global medical imaging equipment market was valued at US$38.36 billion in 2021, and is expected to reach up to US$48.58 billion in 2026. The market is expected to expand at a CAGR of 4.70% over the forecasted period of 2022-2026.

Medical imaging describes the technology which uses radiation, sound waves or flexible optical instrument equipped with a small camera to visualize internal structure of a body to perform accurate diagnosis. Medical imaging equipment has become a significant instrument for doctors, dentists, surgeons and physical therapists with the objective of offering better care for their patients.

Benefits associated with medical imaging has made it a popular choice amongst medical professionals. As of now, medical imaging is going through significant changes with corporations helping in establishing digital workflow keeping medical imaging in center and with government deploying funds to build strong healthcare infrastructure.

Global Medical Imaging Equipment Market Dynamics

Growth Drivers: The aging population is a prominent growth factor of medical imaging market. The aging population in developed economies is compelling government to invest in healthcare services and provide affordable healthcare services. The inclusion of equity into the industry would create growth opportunities for the market. Other than this, rapid rise in demand for point of care diagnostics is also providing growth opportunities to the market.

With rising awareness about the benefits of diagnose of cancer in stage 1 has given people a hope to save the lives of their loved ones, and point of care diagnostic is an umbrella term for medical imaging tools, hence becoming the growth driver of the market. Furthermore, factors like rising healthcare expenditure, growing cases of death due to chronic disease, accelerating obese population, etc. would also help the market to grow across the globe.

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Challenges: The market also have to deal with some of the challenges such as shortage of helium, high cost involved, and lack of skilled radiologists. These challenges are expected to hamper market growth in coming years. The medical imaging devices are very costly in terms of purchasing as well as installing. The high cost of equipment makes customers reluctant in purchasing them. Such situations are more common in developing countries where per capita income is very low in comparison to developed countries.

Trends: Artificial intelligence is set to revolutionize the industry by overcoming certain limitations associated with the conventional devices such as time consuming examination, high dependency on technicians to acquire and interpret images, etc. Furthermore, other notable trends such as 3D printing in medical imaging, emerging 4D & 5D ultrasound imaging technology and cryogen-free MRI imaging system would also provide significant growth opportunities to the market.

Market Segmentation Analysis:

In 2021, ultrasound segment dominated the market by absorbing more than one-fourth of the market, as it is considered the safest technology for the diagnose procedures because it does not utilize ionizing radiation and magnetic field. Ultrasound not only used to determine health status of fetus and mother but it also used to diagnose liver tumor, breast cyst, kidney stone, gallstones, size of ovaries and health of uterus in a patient suffering from PCOS/PCOD, pancreas, etc.

In 2021, Hospital segment held the highest share by covering more than 40% of the market and it is also expected to be the fastest growing segment. Hospital segment is likely to continue to register decent growth during the forecast period because of M&A deals between market players and hospitals. For instance, Philips and Zhejiang University's First Affiliated Hospital, has signed a multi-year contract to support the expansion by combining clinical research and education.

Asia Pacific dominated the market in 2021 by occupying almost 45% share of the global market. The most important factor driving the market in Asia Pacific is the rising demand for advanced diagnostic equipment, and presence of most populous countries of the world with rapidly changing demographic.

In China, medical imaging equipment installation would increase because after COVID-19, government strongly focus on healthcare infrastructure to be better prepare for any future healthcare emergency. Europe medical imaging equipment market provides lucrative opportunities in the coming years. Various reasons such as introduction of technological advanced systems and the increasing demand for early diagnosis, are expected to drive the growth of the market in Europe.

Impact Analysis of COVID-19 and Way Forward:

The pandemic negative affect was also felt by medical imaging industry as market participants reported negative numbers in their income statement. The disruptions caused in logistics, with drop in manufacturing and postponements in installation put the industry at a difficult situation. Whereas, the demand for computed tomography witnessed a rapid rise because of it providing beneficial diagnosis results.

Pandemic also brought forth the benefits of mobile and portable imaging systems as they are easy to handle and can diagnose sooner and easier in remote locations that were set up nationwide. Most of the hospitals find it easier to sterilize a mobile DR or ultrasound system than cleaning a CT room for 30-50 mins. The pandemic also encouraged hard-hit country like China, Italy, India, the US, and many others to implement strong healthcare policies, which would support healthcare requirement of its citizens while building a strong healthcare infrastructure in the country.

Competitive Landscape:

Global medical imaging equipment market is concentrated in nature, which place few of the players at the top. Collaborations and partnerships between local and global companies, innovative product releases, and rising focus on developing multimodal imaging devices are some of the primary strategies used by companies in global medical imaging equipment market.

Siemens Healthineers, Koninklijke Philips N.V. and General Electric Company (GE) hold more than 75% share in the industry, and rest is being covered by Hitachi, Canon, Crestream Health, Inc. and others. Siemens Healthineers and General Electric Company (GE) both operate in diagnostic imaging industry through their subsdiaries, aka, Siemens AG, and GE Healthcare.

Both companies extensively invest in R&D to offer technically enhanced products to their targeted audience. Even though Siemen Healthineers is as big as General Electric Company (GE), but the former less diversified portfolio helped latter to stand out in this front.

Further, Companies such as GE Healthcare, Siemens Healthineers, Canon Medical, Esaote, Samsung and others have been implementing various organic growth strategies that have helped the growth of the company and in turn have brought about various changes in the market. Whereas, Companies such Siemens AG, Shimadzu Medical Systems, Hitachi, Canon Medical, Carestream Health and other companies have also been implementing various inorganic developments that have bought about dynamic improvements in the market they are operating.

Market Dynamics

Growth Drivers

Growing Geriatric Population

Rising Healthcare Spending

Accelerating Obese Population

Rising Cases of Death due to Chronic Disease

Upsurge in Demand for Point Of Care Diagnostics

Challenges

Market Trends

3D Printing in Medical Imaging

Integration of Artificial Intelligence (AI) with Medical Imaging Equipment

Emerging 4D & 5D Ultrasound Imaging Technology

Cryogen-Free MRI Imaging System

The key players of medical imaging equipment market are:

Siemens Healthineers

Koninklijke Philips N.V.

Canon Medical Systems Corporation

General Electric Company (GE)

FujiFilm Corporation

Shimadzu Corporation

Hologic, Inc.

Onex Corporation (Carestream Health, Inc)

Samsung Electronics (Samsung Medison)

Esaote SpA

For more information about this report visit https://www.researchandmarkets.com/r/ond6b7

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Global Medical Imaging Equipment Market Report 2022: A $48.58 Billion Market in 2026 - Integration of Artificial Intelligence (AI) with Medical...

SUNNYVALE, Calif.--(BUSINESS WIRE)--Juniper Networks, a leader in secure, AI-driven networks, today announced the findings of a global research project that shows an increase in enterprise artificial intelligence (AI) adoption over the last 12 months is yielding tangible benefits to organizations. However, a shortage of human talent still exists and governance policies continue to lack in maturity both of which are needed to responsibly manage AIs growth when considering privacy issues, regulation compliance, hacking and AI terrorism.

Juniper partnered with Wakefield Research to conduct a survey of 700 senior IT leaders around the world with direct involvement in their organizations AI and/or machine learning (ML) plans or deployments. The survey gauges sentiment around the value of AI, the perceived maturity of deployments and where challenges still exist.

This years survey found that enterprises have largely moved past proof-of-concepts and limited trials of AI and are now implementing AI across their organizations, thanks to pandemic-related digital acceleration and the maturation of AI tools available. While Junipers 2021 report previously showed only 6% of C-level leaders had adopted AI-powered solutions across their organizations (citing technological, skillset and governance challenges), this year, 63% of company leaders surveyed say they are at least most of the way to their planned AI adoption goals.

Still, only 9% of IT leaders consider their AI governance and policies, such as establishing a company-wide AI leader or responsible AI standards and processes, to be fully mature. At the same time, more leaders see governance as a priority: 95% agree having proper AI governance in place is important to stay ahead of future legislation, up from 87% in 2021. Despite leadership recognizing the importance of AI governance and having policies in place to manage, govern and maintain, almost half of respondents (48%) think more needs to be done to effectively govern AI.

The disparity the data shows between the substantial increase in AI implementation in the enterprise and the immaturity of AI governance and policies is staggering. It will be critical for governance to pick up pace so that the positives of AI deployment overshadow existing fears of whether AI can be effectively controlled. This is a challenge not unique to AI, but all emerging technologies.

Sharon Mandell, SVP and CIO, Juniper Networks

The research also found:

AI is ultimately designed to perform tasks on par with humans but at higher scale via automation. Many of Junipers own customers are leveraging cloud AI in their networks to dramatically cut support tickets, which frees up IT teams from the drudgery of tactical issues, allowing them to focus on improving end users experiences. But with all the positives, enterprises need to responsibly manage AIs growth with proper governance to stay ahead of regulation and minimize potential negative impacts. In Europe, for instance, we are seeing regulators starting to classify certain AI use cases as risky and requiring CE certification. AI regulation is changing quickly and business leaders must make AI governance a strategic priority.

Bob Friday, Chief AI Officer, Juniper Networks

Additional resources

Methodology

In April 2022, Juniper Networks conducted primary research of organizations to assess company positions in the AI market. The survey (700 respondents), with a title of Senior Manager or higher, was conducted across more than 20 industries, including Technology, Healthcare, Retail, Automotive, Manufacturing, and Engineering/Construction, and covered AI market usage, acceptance, opportunities for growth, challenges in adoption, AI governance and strategic decisioning.

About Juniper Networks

Juniper Networks challenges the inherent complexity that comes with networking and security in the multicloud era. We do this with products, solutions and services that transform the way people connect, work and live. We simplify the process of transitioning to a secure and automated multicloud environment to enable secure, AI-driven networks that connect the world. Additional information can be found at Juniper Networks (www.juniper.net) or connect with Juniper on Twitter, LinkedIn and Facebook.

Juniper Networks, the Juniper Networks logo, Juniper, Junos, and other trademarks listed here are registered trademarks of Juniper Networks, Inc. and/or its affiliates in the United States and other countries. Other names may be trademarks of their respective owners.

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Juniper Networks Research Finds Artificial Intelligence Adoption Expands Tenfold Across Enterprises While Governance Lags - Business Wire

This report studies the Artificial Intelligence in Blockchain Market with many aspects of the industry like the market size, market status, market trends and forecast, the report also provides brief information of the competitors and the specific growth opportunities with key market drivers. Find the complete Artificial Intelligence in Blockchain Market analysis segmented by companies, region, type and applications in the report.

The report offers valuable insight into the Artificial Intelligence in Blockchain market progress and approaches related to the Artificial Intelligence in Blockchain 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: Alpha Networks, Blaize, BurstIQ, Chainhaus, Core Scientific, Cyware, Fetch.ai, Gainfy Healthcare Network, NetObjex, Neurochain, PrimaFelicitas, Ripple, ScienceSoft, SoluLab, Stowk, Talla, Verisart, Vytalyx, and WealthBlock

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The global Artificial Intelligence in Blockchain market is segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global Artificial Intelligence in Blockchain 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 2022-2026.

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)

Artificial Intelligence in Blockchain Breakdown Data by Type

Softwares, Platforms and Tools

Services

Artificial Intelligence in Blockchain Breakdown Data by Application

Healthcare and Life Sciences

Manufacturing

Media and Entertainment

Others

Research objectives:

To study and analyze the global Artificial Intelligence in Blockchain market size by key regions/countries, product type and application, history data from 2013 to 2017, and forecast to 2027.

To understand the structure of Artificial Intelligence in Blockchain market by identifying its various sub segments.

Focuses on the key global Artificial Intelligence in Blockchain players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in next few years.

To analyze the Artificial Intelligence in Blockchain with respect to individual growth trends, future prospects, and their contribution to the total market.

To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

To project the size of Artificial Intelligence in Blockchain submarkets, with respect to key regions (along with their respective key countries).

To analyze competitive developments such as expansions, agreements, new product launches and acquisitions in the market.

To strategically profile the key players and comprehensively analyze their growth strategies.

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.

Key questions answered in this report

What will the market size be in 2027 and what will the growth rate be?

What are the key market trends?

What is driving this market?

What are the challenges to market growth?

Who are the key vendors in this market space?

What are the market opportunities and threats faced by the key vendors?

What are the strengths and weaknesses of the key vendors?

Table of Contents: Artificial Intelligence in Blockchain Market

Chapter 1: Overview of Artificial Intelligence in Blockchain 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

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Our marketing research reports comprise of the best market analysis along with putting the right statistical and analytical information on the markets, applications, industry analysis, market shares, technology and technology shifts, important players, and the developments in the market. If you require any specific company, then our company reports collection has countless profiles of all the key industrial companies. All these reports comprise of vital information including the company overview, the company history, the business description, the key products & services, the SWOT analysis, the crucial facts, employee details, the locations and subsidiaries to name a few.

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Artificial Intelligence in Blockchain Market to Garner Brimming Revenues with Comprehensive Analysis and Landscape Outlook to 2028- Alpha Networks,...

Artificial Intelligence in Healthcare marketanalysis report highlights the idea of high level analysis of major market segments and recognition of opportunities. Market analysis and market segmentation has been reviewed here in terms of markets, geographic scope, years considered for the study, currency and pricing, research methodology, primary interviews with key opinion leaders, DBMR market position grid, DBMR market challenge matrix, secondary sources, and assumptions. This market report carries out comprehensive analysis of company profiles of key market players that offers a competitive landscape. Artificial Intelligence in Healthcare market document exhibits important product developments and tracks recent acquisitions, mergers and research in the healthcare industry by the top market players.

The market study and analysis of the large scale Artificial Intelligence in Healthcare report lends a hand to figure out types of consumers, their views about the product, their buying intentions and their ideas for advancement of a product. To attain knowledge of all the above factors, this transparent, extensive and supreme market report is generated. And for the same, the report also describes all the major topics of the market research analysis that includes market definition, market segmentation, competitive analysis, major developments in the market, and excellent research methodology. This Artificial Intelligence in Healthcare market research report has been formed with the vigilant efforts of innovative, enthusiastic, knowledgeable and experienced team of analysts, researchers, industry experts, and forecasters.

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Leading Key Players Operating in the Artificial Intelligence in Healthcare Market Includes:NVIDIA Corporation (US), Intel Corporation (US), IBM (US), Google LLC (US), Microsoft (US), General Vision Inc. (US), Johnson & Johnson Services, Inc. (US), Siemens Healthcare GmbH (Germany), Medtronic (Ireland), CloudMedx Inc. (US)

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Browse Full Report Along With Facts and Figures @https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-healthcare-market?AZ

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Artificial Intelligence in Healthcare Market, By Region:

Global Artificial Intelligence in Healthcare marketis analyzed and market size insights and trends are provided by country, product as referenced above.

The countries covered in the Artificial Intelligence in Healthcare market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Healthcare market because of the rise in the cases of arrhythmic diseases, favorable reimbursement policies for patients, high demand for advanced treatment methods and developed healthcare infrastructure in the region. Asia-Pacific is estimated to grow in the forecast period due to the high prevalence of cardiovascular diseases, increase in adoption of advanced digital devices, large population and launch of new innovative products.

Table of Contents

Global Artificial Intelligence in Healthcare Market Size, status and Forecast to 2029

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Healthcare Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Healthcare market analysis by numerous Regions5 North America Artificial Intelligence in Healthcare by Countries6 Europe Artificial Intelligence in Healthcare by Countries7 Asia-Pacific Artificial Intelligence in Healthcare by Countries8 South America Artificial Intelligence in Healthcare by Countries9 Middle east and Africas Artificial Intelligence in Healthcare by Countries10 Global Artificial Intelligence in Healthcare Market phase by varieties11 Global Artificial Intelligence in Healthcare Market phase by Applications12 Artificial Intelligence in Healthcare Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

Check Complete Table of Contents with List of Table and Figures @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-healthcare-market&AZ

What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Healthcare Market?

Who are the distributors, traders, and merchants in the Artificial Intelligence in Healthcare Market?

What is the analysis of sales, income, and prices of the leading manufacturers in the Artificial Intelligence in Healthcare Market?

What are the Artificial Intelligence in Healthcare market opportunities and threats faced by the global Artificial Intelligence in Healthcare Market vendors?

What are the main factors driving the worldwide Artificial Intelligence in Healthcare Industry?

What are the Top Players in Artificial Intelligence in Healthcare industry?

What is the analysis of sales, income, and prices by type, application of the Artificial Intelligence in Healthcare market?

What is regional sales, income, and price analysis for Artificial Intelligence in Healthcare Market?

Research Methodology: GlobalLiquid Chromatography Devices Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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