Daily Archives: June 3, 2022

Last month, DeepMind, a subsidiary of technology giant Alphabet, set Silicon Valley abuzz when it announced Gato, perhaps the most versatile artificial intelligence model in existence. Billed as a generalist agent, Gato can perform over 600 different tasks. It can drive a robot, caption images, identify objects in pictures, and more. It is probably the most advanced AI system on the planet that isnt dedicated to a singular function. And, to some computing experts, it is evidence that the industry is on the verge of reaching a long-awaited, much-hyped milestone: Artificial General Intelligence.

Unlike ordinary AI, Artificial General Intelligence wouldnt require giant troves of data to learn a task. Whereas ordinary artificial intelligence has to be pre-trained or programmed to solve a specific set of problems, a general intelligence can learn through intuition and experience.

An AGI would in theory be capable of learning anything that a human can, if given the same access to information. Basically, if you put an AGI on a chip and then put that chip into a robot, the robot could learn to play tennis the same way you or I do: by swinging a racket around and getting a feel for the game. That doesnt necessarily mean the robot would be sentient or capable of cognition. It wouldnt have thoughts or emotions, itd just be really good at learning to do new tasks without human aid.

This would be huge for humanity. Think about everything you could accomplish if you had a machine with the intellectual capacity of a human and the loyalty of a trusted canine companion a machine that could be physically adapted to suit any purpose. Thats the promise of AGI. Its C-3PO without the emotions, Lt. Commander Data without the curiosity, and Rosey the Robot without the personality. In the hands of the right developers, it could epitomize the idea of human-centered AI.

But how close, really, is the dream of AGI? And does Gato actually move us closer to it?

For a certain group of scientists and developers (Ill call this group the Scaling-Uber-Alles crowd, adopting a term coined by world-renowned AI expert Gary Marcus) Gato and similar systems based on transformer models of deep learning have already given us the blueprint for building AGI. Essentially, these transformers use humongous databases and billions or trillions of adjustable parameters to predict what will happen next in a sequence.

The Scaling-Uber-Alles crowd, which includes notable names such as OpenAIs Ilya Sutskever and the University of Texas at Austins Alex Dimakis, believes that transformers will inevitably lead to AGI; all that remains is to make them bigger and faster. As Nando de Freitas, a member of the team that created Gato, recently tweeted: Its all about scale now! The Game is Over! Its about making these models bigger, safer, compute efficient, faster at sampling, smarter memory De Freitas and company understand that theyll have to create new algorithms and architectures to support this growth, but they also seem to believe that an AGI will emerge on its own if we keep making models like Gato bigger.

Call me old-fashioned, but when a developer tells me their plan is to wait for an AGI to magically emerge from the miasma of big data like a mudfish from primordial soup, I tend to think theyre skipping a few steps. Apparently, Im not alone. A host of pundits and scientists, including Marcus, have argued that something fundamental is missing from the grandiose plans to build Gato-like AI into full-fledged generally intelligent machines.

If you put an AGI on a chip and then put that chip into a robot, the robot could learn to play tennis the same way you or I do: by swinging a racket around and getting a feel for the game.

I recently explained my thinking in a trilogy of essays for The Next Webs Neural vertical, where Im an editor. In short, a key premise of AGI is that it should be able to obtain its own data. But deep learning models, such as transformer AIs, are little more than machines designed to make inferences relative to the databases that have already been supplied to them. Theyre librarians and, as such, they are only as good as their training libraries.

A general intelligence could theoretically figure things out even if it had a tiny database. It would intuit the methodology to accomplish its task based on nothing more than its ability to choose which external data was and wasnt important, like a human deciding where to place their attention.

Gato is cool and theres nothing quite like it. But, essentially, it is a clever package that arguably presents the illusion of a general AI through the expert use of big data. Its giant database, for example, probably contains datasets built on the entire contents of websites such as Reddit and Wikipedia. Its amazing that humans have managed to do so much with simple algorithms just by forcing them to parse more data.

In fact, Gato is such an impressive way to fake general intelligence, it makes me wonder if we might be barking up the wrong tree. Many of the tasks Gato is capable of today were once believed to be something only an AGI could do. It feels like the more we accomplish with regular AI, the harder the challenge of building a general agent appears to be.

Call me old fashioned, but when a developer tells me their plan is to wait for an AGI to magically emerge from the miasma of big data like a mudfish from primordial soup, I tend to think theyre skipping a few steps.

For those reasons, Im skeptical that deep learning alone is the path to AGI. I believe well need more than bigger databases and additional parameters to tweak. Well need an entirely new conceptual approach to machine learning.

I do think that humanity will eventually succeed in the quest to build AGI. My best guess is that we will knock on AGIs door sometime around the early-to-mid 2100s, and that, when we do, well find that it looks quite different from what the scientists at DeepMind are envisioning.

But the beautiful thing about science is that you have to show your work, and, right now, DeepMind is doing just that. Its got every opportunity to prove me and the other naysayers wrong.

I truly, deeply hope it succeeds.

Tristan Greene is a futurist who believes in the power of human-centered technology. Hes currently the editor of The Next Webs futurism vertical, Neural.

Original post:

Opinion: The Long, Uncertain Road to Artificial General Intelligence - Undark Magazine

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Mandel Ngan/AP hide caption

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system.

Child welfare officials in Oregon will stop using an algorithm to help decide which families are investigated by social workers, opting instead for a new process that officials say will make better, more racially equitable decisions.

The move comes weeks after an Associated Press review of a separate algorithmic tool in Pennsylvania that had originally inspired Oregon officials to develop their model, and was found to have flagged a disproportionate number of Black children for "mandatory" neglect investigations when it first was in place.

Oregon's Department of Human Services announced to staff via email last month that after "extensive analysis" the agency's hotline workers would stop using the algorithm at the end of June to reduce disparities concerning which families are investigated for child abuse and neglect by child protective services.

"We are committed to continuous quality improvement and equity," Lacey Andresen, the agency's deputy director, said in the May 19 email.

Jake Sunderland, a department spokesman, said the existing algorithm would "no longer be necessary," since it can't be used with the state's new screening process. He declined to provide further details about why Oregon decided to replace the algorithm and would not elaborate on any related disparities that influenced the policy change.

Hotline workers' decisions about reports of child abuse and neglect mark a critical moment in the investigations process, when social workers first decide if families should face state intervention. The stakes are high not attending to an allegation could end with a child's death, but scrutinizing a family's life could set them up for separation.

From California to Colorado and Pennsylvania, as child welfare agencies use or consider implementing algorithms, an AP review identified concerns about transparency, reliability and racial disparities in the use of the technology, including their potential to harden bias in the child welfare system.

U.S. Sen. Ron Wyden, an Oregon Democrat, said he had long been concerned about the algorithms used by his state's child welfare system and reached out to the department again following the AP story to ask questions about racial bias a prevailing concern with the growing use of artificial intelligence tools in child protective services.

"Making decisions about what should happen to children and families is far too important a task to give untested algorithms," Wyden said in a statement. "I'm glad the Oregon Department of Human Services is taking the concerns I raised about racial bias seriously and is pausing the use of its screening tool."

Sunderland said Oregon child welfare officials had long been considering changing their investigations process before making the announcement last month.

He added that the state decided recently that the algorithm would be completely replaced by its new program, called the Structured Decision Making model, which aligns with many other child welfare jurisdictions across the country.

Oregon's Safety at Screening Tool was inspired by the influential Allegheny Family Screening Tool, which is named for the county surrounding Pittsburgh, and is aimed at predicting the risk that children face of winding up in foster care or being investigated in the future. It was first implemented in 2018. Social workers view the numerical risk scores the algorithm generates the higher the number, the greater the risk as they decide if a different social worker should go out to investigate the family.

But Oregon officials tweaked their original algorithm to only draw from internal child welfare data in calculating a family's risk, and tried to deliberately address racial bias in its design with a "fairness correction."

In response to Carnegie Mellon University researchers' findings that Allegheny County's algorithm initially flagged a disproportionate number of Black families for "mandatory" child neglect investigations, county officials called the research "hypothetical," and noted that social workers can always override the tool, which was never intended to be used on its own.

Wyden is a chief sponsor of a bill that seeks to establish transparency and national oversight of software, algorithms and other automated systems.

"With the livelihoods and safety of children and families at stake, technology used by the state must be equitable and I will continue to watchdog," Wyden said.

The second tool that Oregon developed an algorithm to help decide when foster care children can be reunified with their families remains on hiatus as researchers rework the model. Sunderland said the pilot was paused months ago due to inadequate data but that there is "no expectation that it will be unpaused soon."

In recent years while under scrutiny by a crisis oversight board ordered by the governor, the state agency currently preparing to hire its eighth new child welfare director in six years considered three additional algorithms, including predictive models that sought to assess a child's risk for death and severe injury, whether children should be placed in foster care, and if so, where. Sunderland said the child welfare department never built those tools, however.

Continued here:

Oregon is dropping an artificial intelligence tool used in child welfare system - NPR

Greece is just one example of a population where the share of older people is expanding, and with it the incidences of neurodegenerative diseases. Among these, Alzheimers disease is the most prevalent, accounting for 70% of neurodegenerative disease cases in Greece. According to estimates published by the Alzheimer Society of Greece, 197,000 people are suffering from the disease at present. This number is expected to rise to 354,000 by 2050.

Dr. Andreas Papadopoulos1, a physician and scientific coordinator at Iatropolis Medical Group, a leading diagnostic provider near Athens, Greece, explains the key role of early diagnosis: The likelihood of developing Alzheimers may be only 1% to 2% at age 65. But then it doubles every five years. Existing drugs cannot reverse the course of the degeneration; they can only slow it down. This is why its crucial to make the right diagnosis in the preliminary stageswhen the first mild cognitive disorder appearsand to filter out Alzheimers patients2.

Diseases like Alzheimers or other neurodegenerative pathologies characteristically have a very slow progression, which makes is difficult to recognize and quantify pathological changes on brain MRI images at an early stage. In evaluating scans, some radiologists describe the process as one of guesstimation, as visual changes in the highly complex anatomy of the brain are not always possible to observe well with the human eye. This is where technical innovations such as artificial intelligence can offer support in interpreting clinical images.

One such tool is the AI-Rad Companion Brain MR3. Part of a family of AI-based, decision-support solutions for imaging, AI-Rad Companion Brain MR is a brain volumetry software that provides automatic volumetric quantification of different brain segments. It is able to segment them from each other: it isolates the hippocampi and the lobes of the brain and quantifies white matter and gray matter volumes for each segment individually. says Dr. Papadopoulos. In total, it has the capacity to segment, measure volumes, and highlight more than 40 regions of the brain.

Calculating volumetric properties manually can be an extremely laborious and time-consuming task. More importantly, it also involves a degree of precise observation that humans are simply not able to achieve. says Dr. Papadopoulos. Papadopoulos has always been an early adopter and welcomed technological innovations in imaging throughout his career. This AI-powered tool means that he can now also compare the quantifications with normative data from a healthy population. And its not all about the automation: the software displays the data in a structured report and generates a highlighted deviation map based on user settings. This allows the user to also monitor volumetric changes manually with all the key data prepared automatically in advance.

Opportunities for more accurate observation and evaluation of volumetric changes in the brain encourages Papadopoulos when he considers how important the early detection of neurodegenerative diseases is. He explains: In the early stages, the volumetric changes are small. In the hippocampus, for example, there is a volume reduction of 10% to 15%, which is very difficult for the eye to detect. But the objective calculations provided by the system could prove a big help.

The aim of AI is to relieve physicians of a considerable burden and, ultimately, to save time when optimally embedded in the workflow. An extremely valuable role for this particular AI-powered postprocessing tool is that it can visualize a deviation of the different structures that might be hard to identify with the naked eye. Papadopoulos already recognizes that the greatest advantage in his work is the objective framework that AI-Rad Companion Brain MR provides on which he can base his subjective assessment during an examination.

AI-Rad Companion4 from Siemens Healthineers supports clinicians in their daily routine of diagnostic decision-making. To maintain a continuous value stream, our AI-powered tools include regular software updates and upgrades that are deployed to the customers via the cloud. Customers can decide whether they want to integrate a fully cloud-based approach into their working environment leveraging all the benefits of the cloud or a hybrid approach that allows them to process imaging data within their own hospital IT setup.

The upcoming software version of AI-Rad Companion Brain MR will contain new algorithms that are capable of segmenting, quantifying, and visualizing white matter hyperintensities (WMH). Along with the McDonald criteria, reporting WHM aids in multiple sclerosis (MS) evaluation.

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Evaluating brain MRI scans with the help of artificial intelligence - MIT Technology Review

A new study finds that an artificial intelligence model can predict whether Crohns disease will recur after surgery.

A deep learning model trained to analyze histological images of surgical specimens accurately classified patients with and without Crohns disease recurrence, investigators report in The American Journal of Pathology.

According to researchers, more than 500,000 individuals in the United States have Crohns disease. Crohns disease is a chronic inflammatory bowel disease that damages the digestive system lining. It can cause digestive system inflammation, which may result in abdominal pain, severe diarrhea, exhaustion, weight loss, and malnutrition.

Many people end up needing surgery to treat their Crohns disease. Even after a successful operation, recurrence is common. Now, researchers are reporting that their AI tool is highly accurate at predicting the postoperative recurrence of Crohns disease. It also linked recurrence with the histology of subserosal adipose cells and mast cell infiltration.

Using an artificial intelligence (AI) tool that simulates how humans visualize and is trained to identify and categorize pictures, researchers created a model that predicts the postoperative recurrence of Crohns disease with high accuracy by evaluating histological images. The AI tool also identified previously unknown differences in adipose cells and substantial disparities in the degree of mast cell infiltration in the subserosa, or outer lining of the gut, when comparing individuals with and without disease recurrence. Elseviers The American Journal of Pathology published the findings.

The 10-year rate of postoperative symptomatic recurrence of Crohns disease, a chronic inflammatory gastrointestinal illness, is believed to be 40%. Although there are scoring methods to measure Crohns disease activity and the existence of postoperative recurrence, no scoring system has been devised to predict whether Crohns disease will return.

Sixty-eight patients with Crohns disease were classified according to the presence or absence of postoperative recurrence within two years. The investigators performed histological analysis of surgical specimens using deep learning EfficientNet-b5, a commercially available AI model designed to perform image classification. They achieved a highly accurate prediction of postoperative recurrence (AUC=0.995) and discovered morphological differences in adipose cells between the two groups. Credit: The American Journal of Pathology

Most of the analysis of histopathological images using AI in the past have targeted malignant tumors, explained lead investigators Takahiro Matsui, MD, Ph.D., and Eiichi Morii, MD, Ph.D., Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan. We aimed to obtain clinically useful information for a wider variety of diseases by analyzing histopathology images using AI. We focused on Crohns disease, in which postoperative recurrence is a clinical problem.

The research involved 68 Crohns disease patients who underwent bowel resection between January 2007 and July 2018. They were divided into two groups based on whether or not they had postoperative disease recurrence within two years after surgery. Each group was divided into two subgroups, one for training and the other for validation of an AI model. Whole slide pictures of surgical specimens were cropped into tile images for training, labeled for the presence or absence of postsurgical recurrence, and then processed using EfficientNet-b5, a commercially available AI model built to perform image classification. When the model was tested with unlabeled photographs, the findings indicated that the deep learning model accurately classified the unlabeled images according to the presence or absence of disease occurrence.

Following that, prediction heat maps were created to identify areas and histological features from which the machine learning algorithm could accurately predict recurrence. All layers of the intestinal wall were shown in the photos. The heatmaps revealed that the machine learning algorithm correctly predicted the subserosal adipose tissue layer. However, the model was less precise in other regions, such as the mucosal and proper muscular layers. Images with the greatest accurate predictions were taken from the non-recurrence and recurrence test datasets. The photos with the greatest predictive results all had adipose tissue.

Because the machine learning model achieved accurate predictions from images of subserosal tissue, the investigators hypothesized that subserosal adipose cell morphologies differed between the recurrence and the non-recurrence groups. Adipose cells in the recurrence group had a significantly smaller cell size, higher flattening, and smaller center-to-center cell distance values than those in the nonrecurrence group.

These features, defined as adipocyte shrinkage, are important histological characteristics associated with Crohns disease recurrence, said Dr. Matsui and Dr. Morii.

The investigators also hypothesized that the differences in adipocyte morphology between the two groups were associated with some degree or type of inflammatory condition in the tissue. They found that the recurrence group had a significantly higher number of mast cells infiltrating the subserosal adipose tissue, indicating that the cells are associated with the recurrence of Crohns disease and the adipocyte shrinkage phenomenon.

To the investigators knowledge, these findings are the first to link postoperative recurrence of Crohns disease with the histology of subserosal adipose cells and mast cell infiltration. Dr. Matsui and Dr. Morii observed, Our findings enable stratification by the prognosis of postoperative Crohns disease patients. Many drugs, including biologicals, are used to prevent Crohns disease recurrence, and proper stratification can enable more intensive and successful treatment of high-risk patients.

Reference: Deep Learning Analysis of Histologic Images from Intestinal Specimen Reveals Adipocyte Shrinkage and Mast Cell Infiltration to Predict Postoperative Crohn Disease by Hiroki Kiyokawa, Masatoshi Abe, Takahiro Matsui, Masako Kurashige, Kenji Ohshima, Shinichiro Tahara, Satoshi Nojima, Takayuki Ogino, Yuki Sekido, Tsunekazu Mizushima and Eiichi Morii, 28 March 2022, The American Journal of Pathology.DOI: 10.1016/j.ajpath.2022.03.006

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Artificial Intelligence Model Can Successfully Predict the Reoccurrence of Crohns Disease - SciTechDaily

A new study finds that utilizing artificial intelligence could allow scientists to detect arthritis earlier.

Researchers have been able to teach artificial intelligence neural networks to distinguish between two different kinds of arthritis and healthy joints. The neural network was able to detect 82% of the healthy joints and 75% of cases of rheumatoid arthritis. When combined with the expertise of a doctor, it could lead to much more accurate diagnoses. Researchers are planning to investigate this approach further in another project.

This breakthrough by a team of doctors and computer scientists has been published in the journal Frontiers in Medicine.

There are many different varieties of arthritis, and determining which type of inflammatory illness is affecting a patients joints may be difficult. Computer scientists and physicians from Friedrich-Alexander-Universitt Erlangen-Nrnberg (FAU) and Universittsklinikum Erlangen have now taught artificial neural networks to distinguish between rheumatoid arthritis, psoriatic arthritis, and healthy joints in an interdisciplinary research effort.

Within the scope of the BMBF-funded project Molecular characterization of arthritis remission (MASCARA), a team led by Prof. Andreas Maier and Lukas Folle from the Chair of Computer Science 5 (Pattern Recognition) and PD Dr. Arnd Kleyer and Prof. Dr. Georg Schett from the Department of Medicine 3 at Universittsklinikum Erlangen was tasked with investigating the following questions: Can artificial intelligence (AI) recognize different forms of arthritis based on joint shape patterns? Is this strategy useful for making more precise diagnoses of undifferentiated arthritis? Is there any part of the joint that should be inspected more carefully during a diagnosis?

Currently, a lack of biomarkers makes correct categorization of the relevant form of arthritis challenging. X-ray pictures used to help diagnosis are also not completely trustworthy since their two-dimensionality is insufficiently precise and leaves room for interpretation. This is in addition to the challenge of placing the joint under examination for X-ray imaging.

To find the answers to its questions, the research team focused its investigations on the metacarpophalangeal joints of the fingers regions in the body that are very often affected early on in patients with autoimmune diseases such as rheumatoid arthritis or psoriatic arthritis. A network of artificial neurons was trained using finger scans from high-resolution peripheral quantitative computer tomography (HR-pQCT) with the aim of differentiating between healthy joints and those of patients with rheumatoid or psoriatic arthritis.

HR-pQCT was selected as it is currently the best quantitative method of producing three-dimensional images of human bones in the highest resolution. In the case of arthritis, changes in the structure of bones can be very accurately detected, which makes precise classification possible.

A total of 932 new HR-pQCT scans from 611 patients were then used to check if the artificial network can actually implement what it had learned: Can it provide a correct assessment of the previously classified finger joints?

The results showed that AI detected 82% of the healthy joints, 75% of the cases of rheumatoid arthritis, and 68% of the cases of psoriatic arthritis, which is a very high hit probability without any further information. When combined with the expertise of a rheumatologist, it could lead to much more accurate diagnoses. In addition, when presented with cases of undifferentiated arthritis, the network was able to classify them correctly.

We are very satisfied with the results of the study as they show that artificial intelligence can help us to classify arthritis more easily, which could lead to quicker and more targeted treatment for patients. However, we are aware of the fact that there are other categories that need to be fed into the network. We are also planning to transfer the AI method to other imaging methods such as ultrasound or MRI, which are more readily available, explains Lukas Folle.

Whereas the research team was able to use high-resolution computer tomography, this type of imaging is only rarely available to physicians under normal circumstances because of restraints in terms of space and costs. However, these new findings are still useful as the neural network detected certain areas of the joints that provide the most information about a specific type of arthritis which is known as intra-articular hotspots. In the future, this could mean that physicians could use these areas as another piece in the diagnostic puzzle to confirm suspected cases, explains Dr. Kleyer. This would save time and effort during the diagnosis and is already in fact possible using ultrasound, for example. Kleyer and Maier are planning to investigate this approach further in another project with their research groups.

Reference: Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape PatternsHow Neural Networks Can Tell Us Where to Deep Dive Clinically by Lukas Folle, David Simon, Koray Tascilar, Gerhard Krnke, Anna-Maria Liphardt, Andreas Maier, Georg Schett and Arnd Kleyer, 10 March 2022, Frontiers in Medicine.DOI: 10.3389/fmed.2022.850552

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Early Detection of Arthritis Now Possible Thanks to Artificial Intelligence - SciTechDaily

DUBLIN--(BUSINESS WIRE)--The "Graphene Electronics Market Report - Global Industry Data, Analysis and Growth Forecasts by Type, Application and Region, 2021-2028" report has been added to ResearchAndMarkets.com's offering.

Graphene Electronics market illustrates an attractive growth rate during the forecast period with the advancements in technologies. Latest developments in Artificial Intelligence and machine learning abilities to expand Graphene Electronics applications and drive demand during the forecast period to 2028.

The pandemic COVID 19 has a significant impact on the manufacturers of Graphene Electronics due to disruptions in the supply chain and frequent lockdowns. Further, the economic slowdown and geopolitical matters have limited the Graphene Electronics market growth in 2020. As the market recovers from the pandemic, we forecast the growth trajectory to vary across regions with some countries offering huge growth potential while others reporting limited profit margins.

New generation Graphene Electronics with improved performance offering higher accuracy and flexibility, with easy integration into systems spur the growth in Graphene Electronics industry. However, a paradigm shift towards a connected world and growing requirement for miniaturization are necessitating further advancement in the Graphene Electronics market and develop smarter products.

Research and development in the Graphene Electronics industry to drive down costs and improve functionality are expected to advance in the medium term. Autonomous vehicles poised to hit the mainstream alongside rapid growth in AI computing capabilities with improving commercials are offering enormous opportunities in the Graphene Electronics market. Over the forecast period to 2028, we forecast the Graphene Electronics market to regain growth momentum, mainly with support from developing markets.

Graphene Electronics market competitive landscape

On the Graphene Electronics market structure front, consolidation observed in 2020 is expected to be continued in 2021. Mergers and acquisitions are primarily intended to acquiring new technologies, strengthening portfolios, and leveraging capabilities.

Companies operating in the Graphene Electronics market were hard hit by the adverse effects of COVID, with the major difficulty being the supply chain management. Managing production with shortages in supply and man force has limited the profitability of companies in 2020 and created the need to adapt to more agile methods of working.

However, growing trends of online work and education along with the exponential development of the e-commerce industry facilitate companies to regain their market share. Detailed profiles of top companies in the Graphene Electronics industry along with their key strategies to 2028 are provided in the report.

Impact of COVID 19 on Graphene Electronics Industry

The global Graphene Electronics market study carefully examines the deviation in the global outlook due to COVID-19 considering its impact on supply chain, economy, and consumer preferences by country and region.

The report identifies competitive strategies being implemented and planned by key companies in the Graphene Electronics market to counter adverse effects and take advantage of the new opportunities created by the pandemic situation. Different scenarios based on expected containment of the virus in the medium to long term are considered to provide Graphene Electronics market forecasts.

Graphene Electronics market segmentation

The research estimates global Graphene Electronics market revenues in 2021 with a detailed market share and penetration of different types, technologies, applications, and geographies in the Graphene Electronics market to 2028.

The study identifies current trends along with potential drivers and challenges leading to growth or decline in their market share, for each segment during the outlook period.

Key Topics Covered:

1. Executive Summary

1.1 Graphene Electronics Market Overview, 2021

1.1 Graphene Electronics Fastest-Growing Types, 2021-2028

1.2 Graphene Electronics Leading Application Segments, 2021-2028

1.3 Graphene Electronics High Potential markets, 2021-2028

2. Market Insights and Strategic Analysis

2.1 Key Market trends

2.2 Market Drivers

2.3 Market Challenges

2.4 Industry Attractiveness - Porter's Five Forces Analysis

2.5 Impact of COVID-19 on the Market

3. Global Graphene Electronics Market Outlook

3.1 Global Graphene Electronics Market Outlook by Type, 2021-2028

3.2 Global Graphene Electronics Market Outlook by Application, 2021-2028

3.3 Global Graphene Electronics Market Outlook by Country, 2021-2028

4. Asia Pacific Graphene Electronics Market Outlook

4.1 Key Snapshot, 2021

4.2 Asia Pacific Graphene Electronics Market Outlook by Type, 2021-2028

4.3 Asia Pacific Graphene Electronics Market Outlook by Application, 2021-2028

4.4 Asia Pacific Graphene Electronics Market Outlook by Country, 2021-2028

5. Europe Graphene Electronics Market Outlook and Growth Opportunities

5.1 Key Snapshot, 2021

5.2 Europe Graphene Electronics Market Outlook by Type, 2021-2028

5.3 Europe Graphene Electronics Market Outlook by Application, 2021-2028

5.4 Europe Graphene Electronics Market Outlook by Country, 2021-2028

6. North America Graphene Electronics Market Outlook and Growth Opportunities

6.1 Key Snapshot, 2021

6.2 North America Graphene Electronics Market Outlook by Type, 2021-2028

6.3 North America Graphene Electronics Market Outlook by Application, 2021-2028

6.4 North America Graphene Electronics Market Outlook by Country, 2021-2028

7. South and Central America Graphene Electronics Market Outlook and Growth Opportunities

7.1 Key Snapshot, 2021

7.2 South and Central America Graphene Electronics Market Outlook by Type, 2021-2028

7.3 South and Central America Graphene Electronics Market Outlook by Application, 2021-2028

7.4 South and Central America Graphene Electronics Market Outlook, 2021-2028

8. Middle East Africa Graphene Electronics Market Outlook and Growth Opportunities

8.1 Key Snapshot, 2021

8.2 Middle East Africa Graphene Electronics Market Outlook by Type, 2021-2028

8.3 Middle East Africa Graphene Electronics Market Outlook by Application, 2021-2028

8.4 Middle East Africa Graphene Electronics Market Outlook by Country, 2021-2028

9. Competitive Analysis

9.1 Leading Companies in Graphene Electronics Market

9.2 Business Profiles of Leading Graphene Electronics Companies

Introduction

SWOT Analysis

Financial Analysis

10. Latest News and Developments in Global Graphene Electronics Market

For more information about this report visit https://www.researchandmarkets.com/r/4yrb4z

Excerpt from:

Global Graphene Electronics Market Report 2021-2028: Developments in Artificial Intelligence and Machine Learning Abilities to Expand Graphene...

SPOILER ALERT: Do not read unless you have watched Top Gun: Maverick, in theaters now.

Top Gun fans knew ahead of time that Val Kilmer would be reprising his role of Tom Iceman Kazansky in the sequel, but the specifics of the actors return were a question mark considering Kilmer lost the ability to speak after undergoing throat cancer treatment in 2014. The script for Top Gun: Maverick pulls from Kilmers real life, with Iceman also having cancer and communicating through typing. Kilmer gets to say one brief line of dialogue. In real life Kilmers speaking voice has been revived courtesy of artificial intelligence.

Kilmer announced in August 2021 that he had partnered with Sonantic to create an A.I.-powered speaking voice for himself. The actor supplied the company with hours of archival footage featuring his speaking voice that was then fed through the companys algorithms and turned into a model. According to Fortune, this process was used again for the actors Top Gun: Maverick appearance. However, a studio sources tells Variety no A.I. was used in the making of the movie.

In the end, we generated more than 40 different voice models and selected the best, highest-quality, most expressive one, John Flynn, CTO and cofounder of Sonantic, said in a statement to Forbes about reviving Kilmers voice. Those new algorithms are now embedded into our voice engine, so future clients can automatically take advantage of them as well.

Im grateful to the entire team at Sonantic who masterfully restored my voice in a way Ive never imagined possible, Kilmer originally said in a statement about the A.I. As human beings, the ability to communicate is the core of our existence and the side effects from throat cancer have made it difficult for others to understand me. The chance to narrate my story, in a voice that feels authentic and familiar, is an incredibly special gift.

As Fortune reports: After cleaning up old audio recordings of Kilmer, [Sonantic] used a voice engine to teach the voice model how to speak like Kilmer. The engine had around 10 times less data than it would have been given in a typical project, Sonantic said, and it wasnt enough. The company then decided to come up with new algorithms that could produce a higher-quality voice model using the available data.

Top Gun: Maverick is now playing in theaters nationwide.

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Val Kilmers Return: A.I. Created 40 Models to Revive His Voice Ahead of Top Gun: Maverick - Variety

A research group from the Graduate School of Informatics, Nagoya University, has taken a big step towards creating a neural network with metamemory through a computer-based evolution experiment.

In recent years, there has been rapid progress in designing artificial intelligence technology using neural networks that imitate brain circuits. One goal of this field of research is understanding the evolution of metamemory to use it to create artificial intelligence with a human-like mind.

Metamemory is the process by which we ask ourselves whether we remember what we had for dinner yesterday and then use that memory to decide whether to eat something different tonight. While this may seem like a simple question, answering it involves a complex process. Metamemory is important because it involves a person having knowledge of their own memory capabilities and adjusting their behavior accordingly.

In order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory, explains lead author Professor Takaya Arita. A truly human-like artificial intelligence, which can be interacted with and enjoyed like a family member in a persons home, is an artificial intelligence that has a certain amount of metamemory, as it has the ability to remember things that it once heard or learned.

When studying metamemory, researchers often employ a delayed matching-to-sample task. In humans, this task consists of the participant seeing an object, such as a red circle, remembering it, and then taking part in a test to select the thing that they had previously seen from multiple similar objects. Correct answers are rewarded and wrong answers punished. However, the subject can choose not to do the test and still earn a smaller reward.

A human performing this task would naturally use their metamemory to consider if they remembered seeing the object. If they remembered it, they would take the test to get the bigger reward, and if they were unsure, they would avoid risking the penalty and receive the smaller reward instead. Previous studies reported that monkeys could perform this task as well.

The Nagoya University team comprising Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki of the Graduate School of Informatics created an artificial neural network model that performed the delayed matching-to-sample task and analyzed how it behaved.

Despite starting from random neural networks that did not even have a memory function, the model was able to evolve to the point that it performed similarly to the monkeys in previous studies. The neural network could examine its memories, keep them, and separate outputs. The intelligence was able to do this without requiring any assistance or intervention by the researchers, suggesting the plausibility of it having metamemory mechanisms.The need for metamemory depends on the user's environment. Therefore, it is important for artificial intelligence to have a metamemory that adapts to its environment by learning and evolving, says Professor Arita of the finding. The key point is that the artificial intelligence learns and evolves to create a metamemory that adapts to its environment.

Creating an adaptable intelligence with metamemory is a big step towards making machines that have memories like ours. The team is enthusiastic about the future, This achievement is expected to provide clues to the realization of artificial intelligence with a human-like mind and even consciousness.

The research results were published in the online edition of the international scientific journal Scientific Reports. The study was partly supported by a JSPS/MEXT Grants-in-Aid for Scientific Research KAKENHI (JP17H06383 in #4903).

Scientific Reports

Evolution of metamemory based on self-reference to own memory in artificial neural network with neuromodulation

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