Over the years, digital transformation has modified the way people and organizations function. While the researches are carried out to find ways of integrating technology into the traditional sectors of a country, some noteworthy technologies have surfaced.

Amongst them are blockchain and machine learning.

What are blockchain and machine learning?

Blockchains an immutable ledger that aids in maintaining the records of transactions and tracking assets in an organization.

As for the assets, they can be tangible or intangible. Additionally, the transaction may refer to cash inflows and outflows.

Blockchain is playing a significant role in many organizations due to several reasons.

With this latest technology, anything can be traded and track, which minimizes the risk and cut the costs. As a result, a business can employ fewer accountants and efficiently manage their accounts with minimal to zero errors.

Secondly, blockchain management helps track orders, production processes, and payments that are to be made to the business itself or others.

Lastly, blockchain stores information with great secrecy, which gives more confidence and a sense of security to the business. Therefore, a business can significantly benefit from the increased efficiency, which may lead to economies of scale. As a result, decreased average costs will provide the business with more opportunities.

On the other hand, machine learning is a type of Artificial Intelligence that allows the system to learn from the data and not through explicit learning. Nonetheless, it is not a simple procedure.

Furthermore, a machine-learning model is an outcome generated through the training of your machine-learning algorithm. Therefore, you will receive an output after providing an input after the machine is trained.

There are various approaches to machine-learning which are based on the volume and kind of data.

These approaches include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

If you are a Researcher or student want to write and dissertation or thesis on Blockchain, Artificial intelligence, you can visit Researchprospect.com and find Blockchain andArtificial Intelligence Topics for Dissertation.

Impact of blockchain on education system

Since the functioning, if organizations have modified due to newfound technology, this will directly impact the education system in many ways.

Maintaining student records

Academic records are one of the most demanding documents to maintain. Labor-intensive tasks such as these consume more time leading to inefficiencies and a greater risk of mistakes. However, blockchain technology ensures accuracy and efficiency.

Moreover, certification of students who are enrolled in a course is another tedious task. It becomes even more challenging at the university level to compare the coursework of students and know their credibility. Manually, the information shall be stamped a designed for authentication.

However, with blockchain, a person can gain access to the verified record of a students academic course and achievements.

Issuance of certificates

Imagine how tiring it would be to print gazillions and gazillions of certificates, sign them off and award them. Though this has been happening for years, it is undoubtedly a challenging task.

Therefore, blockchain has brought much ease. A students certificates, diplomas, and degrees can be stored and issued with just a few clicks.

In this way, the employers will only need a link to access the diploma, unlike viewing a paper copy of certificates.

This is not only eco-friendly, but it will prevent students from submitting fake diplomas and certificates.

Aside from diplomas and degrees, a resume has other elements that an employer might look at. This includes foreign languages, special abilities, technical knowledge, and extracurricular. However, a person will need verification to prove they learned this skill over time.

This authentication comes from the badges and certificates. Therefore, if you store these on the blockchain, it will verify the existence of your skills conveniently.

Impact of machine learning on education system

Learning analytics

Machine-learning can aid the teachers in gaining access to data that is complex yet important. Computers can help the teachers to perform tasks. As a result, the teachers can derive conclusions that positively affect the learning process.

Predictive analytics

Furthermore, machine learning can help analyze and derive conclusions about situations that can happen in the future. If a teacher wants to use the data of school students, they do so within minutes. Also, blockchain can help the admin know if a student fails to achieve a certain level. Aside from this, predictive analytics can predict the students future grade to provide a direction to the teachers.

Adaptive learning

Adaptive learning is a tech-based education system that elaborates a students performance and modifies learning methods.

Therefore, machine learning can aid struggling students or students with different abilities.

Personalized learning

On the other hand, personalized learning is an education system that guides every student according to their capability.

Henceforth, the students can pick out their interests through machine-learning, and teachers can fit the curriculum according to it.

Improved efficiency

Machine learning can make the education system more efficient by providing detailed analysis, completing work related to classroom management. The teacher can efficiently manage databases to maintain records and plan out the schedule for the coming weeks.

If they want, they can refer to it whenever. Therefore, machine learning will not only save up the teachers energy but their time as well.

Assessments

Did you imagine artificial intelligence could test students? Machine learning can be used to grade students assignments and assessments alongside exams.

Though assessing students through machine-learning might require some human effort, it will surely provide extraordinarily valid and reliable results.

Teachers can feel confident if the grades accuracy while students can be sure that grades have been awarded on equal merit and fairly.

Conclusively, technological advancement has dramatically revolutionized the educational sector of countries. In the coming years, block chain and machine learning will continue to impact the education system positively. However, it comes with inevitable repercussions as well. Rapid capital-intensity means that manual workers will no longer be needed to perform various functions. Henceforth, it will cause massive unemployment sooner or later. As a result, the government might face difficulties in retaining the right economic conditions. Lastly, automation such as blockchain and machine-learning are costly procedures that may not be affordable for every institute.

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How Blockchain and Machine Learning Impact on education system - ABCmoney.co.uk

ST. PAUL, Minn., Jan. 19, 2021 /PRNewswire/ -- San Diego-based Mission Healthcare, one of the largest home health, hospice, and palliative care providers in California, will adopt Muse Healthcare's machine learning and predictive modeling tool to help deliver a more personalized level of care to their patients.

The Muse technology evaluates and models every clinical assessment, medication, vital sign, and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. Muse Healthcare identifies patients as "Critical," which means they have a greater than 90% likelihood of passing in the next 7-10 days. Users are also able to make accurate changes to care plans based on the condition and location of the patient. When agencies use Muse's powerful machine learning tool, they have an advantage and data proven outcomes to demonstrate they are providing more care and better care to patients in transition.

According to Mission Healthcare's Vice President of Clinical and Quality, Gerry Smith, RN, MSN, Muse will serve as an invaluable tool that will assist their clinicians to enhance care for their patients. "Mission Hospice strives to ensure every patient receives the care and comfort they need while on service, and especially in their final days. We are so excited that the Muse technology will provide our clinical team with additional insights to positively optimize care for patients at the end of life. This predictive modeling technology will enable us to intervene earlier; make better decisions for more personalized care; empower staff; and ultimately improve patient outcomes."

Mission Healthcare's CEO, Paul VerHoeve, also believes that the Muse technology will empower their staff to provide better care for patients. "Predictive analytics are a new wave in hospice innovation and Muse's technology will be a valuable asset to augment our clinical efforts at Mission Healthcare. By implementing a revolutionary machine learning tool like Muse, we can ensure our patients are receiving enhanced hands-on care in those critical last 7 10 days of life. Our mission is to take care of people, with Muse we will continue to improve the patient experience and provide better care in the final days and hours of a patient's life."

As the only machine learning tool in the hospice industry, the Muse transitions tool takes advantage of the implemented documentation within the EMR. This allows the agency to quickly implement the tool without disruption. "With guidance from our customers in the hundreds of locations that are now using the tool, we have focused on deploying time saving enhancements to simplify a clinician's role within hospice agencies. These tools allow the user to view a clinical snapshot, complete review of the scheduled frequency, and quickly identify the patients that need immediate attention. Without Muse HC, a full medical review must be conducted to identify these patients," said Tom Maxwell, co-Founder of Muse Healthcare. "We are saving clinicians time in their day, simplifying the identification challenges of hospice, and making it easier to provide better care to our patients. Hospice agencies only get one chance to get this right," said Maxwell.

CEO of Muse Healthcare, Bryan Mosher, is also excited about Mission's adoption of the Muse tool. "We welcome the Mission Healthcare team to the Muse Healthcare family of customers, and are happy to have them adopt our product so quickly. We are sure with the use of our tools,clinicians at Mission Healthcare will provide better care for their hospice patients," said Mosher.

About Mission Healthcare

As one of the largest regional home health, hospice, and palliative care providers in California, San Diego-based Mission Healthcare was founded in 2009 with the creation of its first service line, Mission Home Health. In 2011, Mission added its hospice service line. Today, Mission employs over 600 people and serves both home health and hospice patients through Southern California. In 2018, Mission was selected as a Top Workplace by the San Diego Union-Tribune. For more information visit https://homewithmission.com/.

About Muse Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals -- Jennifer Maxwell, Tom Maxwell, and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from numerous visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and the patients' families receive the care that matters most in the final days and hours of a patient's life. For more information visit http://www.musehc.com.

More:

Mission Healthcare of San Diego Adopts Muse Healthcare's Machine Learning Tool - Southernminn.com

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

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Project MEDAL to apply machine learning to aero innovation - The Engineer

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Machine Learning (ML) Intelligent Process Automation Marketresearch is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

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Some of the important players in Machine Learning (ML) Intelligent Process Automation market are Automation Anywhere, Inc., UiPath., Blue Prism Limited., Pegasystems Inc., AntWorks, NICE Ltd., KOFAX INC., Softomotive Ltd., SAP SE, AutomationEdge, eggplant., LarcAI, Kryon Systems, Autologyx, Sanbot Innovation Technology., Ltd, Cinnamon, Inc., Wipro Limited, Xerox Corporation, Tata Consultancy Services Limited., IBM Corporation.

Machine Learning (ML) Intelligent Process Automation Market is growing at a High CAGR during the forecast period 2021-2027. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Machine Learning (ML) Intelligent Process Automation market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Machine Learning (ML) Intelligent Process Automation markets trajectory between forecast periods.

Global Machine Learning (ML) Intelligent Process Automation Market research report offers:

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Regions Covered in the Global Machine Learning (ML) Intelligent Process Automation Market Report 2021: The Middle East and Africa(GCC Countries and Egypt) North America(the United States, Mexico, and Canada) South America(Brazil etc.) Europe(Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global Machine Learning (ML) Intelligent Process Automation Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key questions answered in the report include:

Table of Content (TOC)

Global Machine Learning (ML) Intelligent Process Automation Market Report 2021 Growth, Trend and Forecast to 2027

Chapter 1 Machine Learning (ML) Intelligent Process Automation Market Overview

Chapter 2 Global Economic Impact on Machine Learning (ML) Intelligent Process Automation Industry

Chapter 3 Global Machine Learning (ML) Intelligent Process Automation Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region (2014-2021)

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions (2014-2021)

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Machine Learning (ML) Intelligent Process Automation Market Forecast (2021-2027)

Chapter 13 Appendix

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Forecast On Machine Learning (ML) Intelligent Process Automation Market Witness the Growth of Great Billion by 2027 With Top Companies Like Automation...

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.

Reference

Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006. https://acrabstracts.org/abstract/identification-of-a-rule-to-predict-response-to-sarilumab-in-patients-with-rheumatoid-arthritis-using-machine-learning-and-clinical-trial-data/

See the rest here:

Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network

We successfully made it through 2020 and 2021 is finally upon us. While some things, like the way businesses operate, have changed drastically, others remain the same. In the current times, companies are increasingly going online and operating with newer tech solutions to keep up with the changes that the pandemic has brought about in the market.

Companies across the world are adopting Data Science and Machine Learning to understand complex business problems, extract meaningful insights and formulate ways to resolve them. Theyre being used across several sectors and for diverse use cases. These can be anything from banking & finance departments using machine learning algorithms to identify forged signatures to supply chain and manufacturing companies using it for smarter inventory management. In the same vein, airline companies are using data science to map flight delay and develop loyalty programs, and the gaming industry is applying it to improve gaming models based on insights.

These job roles offer some of the highest salaries. Therefore, many engineering graduates in India are interested to pursue their M. tech in Data Science and Machine Learning. The salary scale in this domain ranges from Rs 4 Lakhs per annum to Rs 25 Lakhs per annum, considering various factors. In India, the average pay scale of a Data Scientist is estimated to be Rs 7 Lakhs per annum. Hence the incredible demand. Check out all the lucrative roles you can bag with these skills:

1. Data AnalystAs a data analyst, you will be responsible for various tasks, including visualisation, munging and processing of massive amounts of data. You will also have to perform queries on the databases from time to time. One of the most important skills to gain for you, as a data analyst would be optimisation. This is because you will have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

2. Data EngineersAs a Data Engineer, you build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimised. You will also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

3. Database AdministratorYour job profile is pretty much self-explanatory: You will be responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on your requirements. You will also be responsible for database backups and recoveries.

4. Machine Learning EngineerAs a Machine Learning Engineer, you will be in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc., you would also be expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.

5. Data ScientistYou have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, you are expected to perform predictive analysis and run a fine-toothed comb through an unstructured/disorganised data to offer actionable insights. You could also do this by identifying trends and patterns that can help the companies in making better decisions.

6. Data ArchitectAs a Data Architect, you create the blueprints for data management so that the databases can be easily integrated, centralised, and protected with the best security measures. You must also ensure that the Data Engineers have the best tools and systems to work with. Some other related job roles worth mentioning include Statistician, Business analyst, Data and Analytics Manager.

For those whod love to upskill, Great Learning has emerged as one of Indias leading professional learning services with a footprint in 140 countries and has delivered 55 million+ learning hours. With a curriculum formulated by industry experts, their programs have helped learners successfully transition to new domains and grow in their fields. They offer courses on some of the hottest topics of today Data Science and Machine Learning, Artificial Intelligence etc.

Read more:

Bangalore based Great Learning can help you unleash the potential of an M-Tech in Data Science & Machine - Times of India

Sri Lanka revives port deal with India and Japan for sea terminal Business Standard First quasi-judicial body to strengthen its digital back-end

Topics CERC|artificial intelligence|machine learning

Shreya Jai | New Delhi Last Updated at January 15, 2021 06:10 IST

The apex power sector regulator, the Central Electricity Regulatory Commission (CERC), is planning to set up an artificial intelligence (AI)-based regulatory expert system tool (REST) for improving access to information and assist the commission in discharge of its duties. So far, only the Supreme Court (SC) has an electronic filing (e-filing) system and is in the process of building an AI-based back-end service.

The CERC will be the first such quasi-judicial regulatory body to embrace AI and machine learning (ML). The decision comes at a time when the CERC has been shut for four ...

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First Published: Fri, January 15 2021. 06:10 IST

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CERC plans to embrace AI, machine learning to improve functioning - Business Standard

PALO ALTO, Calif.--(BUSINESS WIRE)--NTT Research, Inc., a division of NTT (TYO:9432), NTT Communication Science Laboratories and NTT Software Innovation Center today announced that three papers co-authored by scientists from several of their divisions were selected (including one Spotlight paper) for this years NeurIPS 2020, the 34th annual conference of the Neural Information Processing Systems Foundation. A non-profit corporation that fosters the exchange of research on neural information processing systems in their biological, technological, mathematical and theoretical aspects, the NeurIPS Foundation will hold this years all-virtual conference on December 6-12. Its selection committee accepted 16 percent of the more than 12,000 abstract submissions they received, including the following three, which touch upon deep neural networks, theory and algorithms, deep learning and Bayesian modeling:

There is no better place to explore the overlap between machine learning and computational neuroscience than the annual NeurIPS event, said Yoshihisa Yamamoto, PHI Lab Director. We are excited to see the latest paper by Dr. Tanaka and his Stanford colleagues, as well as those by our colleagues at the NTT Software Innovation Center and NTT Communication Science Laboratories and expect the fields of neural networking and machine learning will benefit from the efficiencies and expanded capabilities that they are proposing.

This years seven-day virtual NeurIPS event includes an expo, conference sessions, tutorials and workshops. The authors of these papers will participate in the event through poster and short recorded presentations. A follow-up to the Pruning Neural Networks paper, as noted above, will be presented at one of the events workshops. As an indication of the vitality of this sub-field of neuroscience, the event organizers noted a 40 percent year-over-year increase in the number of submitted abstracts, similar to the growth from 2018 to 2019. Papers in the areas of algorithms, deep learning and applications comprised 66 percent of the papers that were reviewed. Among this years keynote speakers are Christopher Bishop, director of the Microsoft Research Lab in Cambridge, England; Shafi Goldwasser, Director of the Simons Institute for the Theory of Computing; and Marloes Maathuis, Professor of Statistics at ETH (the Swiss Federal Institute of Technology) in Zurich.

About NTT Research

NTT Research opened its Palo Alto offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. Currently, three labs are housed at NTT Research: the Physics and Informatics (PHI) Lab, the Cryptography and Information Security (CIS) Lab, and the Medical and Health Informatics (MEI) Lab. The organization aims to upgrade reality in three areas: 1) quantum information, neuro-science and photonics; 2) cryptographic and information security; and 3) medical and health informatics. NTT Research is part of NTT, a global technology and business solutions provider with an annual R&D budget of $3.6 billion.

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NTT Co-authored Papers at NeurIPS to Advance Machine Learning Efficiency and Performance - Business Wire

Companies are collecting increasing amounts of data, a trend that is driving the development of better analytical tools and tougher security. Analysis and security are now converging as confidential computing prepares to deliver a critical boost to artificial intelligence.

Intel has been investing heavily in confidential computing as a way to expand the amount and types of data companies will manage through cloud services. According to Intel Fellow Ron Perez, who works on security architecture with the Intel Data Center Group, the company believes the emerging security standard will allow enterprises and large organizations to explore new ways to share the data needed to fuel AI and machine learning.

We see this as a long-term effort, Perez said. But the reason why were investing is that it has the potential to be a huge shift for cloud and utility computing.

Confidential computing is a standard that moves past policy-based privacy and security to implement safeguards on a deeper technical level. By using encryption that can only be unlocked via keys the client holds, confidential computing ensures companies hosting data and applications in the cloud have no way to access underlying data, whether it is stored in a database or passes through an application.

The concept is gaining momentum because it allows data to remain encrypted even as its being processed and used in applications. Because the company hosting the data cant access it, this security standard should prevent hackers from grabbing unencrypted data when it moves to the application layer. It would also theoretically allow companies to share data, even between competitors, to perform security checks on customers and weed out fraud.

In August 2019, Intel became one of the founding members of the Confidential Computing Consortium, an open source effort managed by the Linux Foundation that aims to develop the hardware and software standards needed to further adoption. Companies like IBM,Google, and Microsoft have begun to highlight their work in this area as a way to encourage large enterprises, particularly in areas such as finance and health care, to put more of their sensitive data in the cloud.

Perez leads a group of senior technologists at Intel focused on security architecture through a program dubbed Pathfinding. Perez describes it as the pursuit of interesting challenges that our customers are facing. In Perezs case, the goal is to develop a pipeline of security technologies for Intels datacenter customers.

Intel began its work in this area before the term confidential computing came into vogue, with Perez pointing to the companys launch of software guard extensions in 2015. The SGXs are security coding built directly into Intel processors that create separate memory enclaves where data could be placed to limit access. This idea of using hardware and software to protect data while allowing it to be processed is at the heart of confidential computing.

Microsoft used these Intel processors for its Azure cloud to enable its own confidential computing service. Last month, Intel announced it was expanding these capabilities in a new generation of its Xeon Scalable platform.

Our approach has been to drive continuous innovation and deep collaboration with our technology partners to improve the confidentiality and integrity of all data, wherever it is, Perez said.

Proponents of confidential computing argue that it will lead to a new wave of cloud innovation as companies become more comfortable putting their most sensitive data online. Perez said that helps drive AI and machine learning in a couple of ways.

The first is indirect. AI and ML have advanced in recent years, thanks to the growing datasets available to refine algorithms. Confidential computing, by bringing even more and richer data online, will benefit that development.

The main connection to machine learning and artificial intelligence is the fact that were generating more and more data, Perez said. Were analyzing this data with various machine learning technologies. And that explosion of data is whats really driving the interest in confidential computing, whether its used for machine learning or not. Machine learning just happens to be one of its main uses.

No matter the type of underlying data, if it must be decrypted to be used, the security of algorithms it passes through is critical.

How do you protect these algorithms across this very broad spectrum of use cases? Perez said. We see confidential computing as a paradigm shift for cloud computing. The infrastructure providers are providing the capabilities that allow cloud companies to deliver these services as a utility, and they dont have to take responsibility for the protection of the data themselves.

Beyond that, confidential computing is enabling different types of collaboration around data to drive machine learning. Perez pointed to the example of a brain tumor project at the University of Pennsylvania.

Penns Perelman School of Medicine has teamed up with 29 other health care and research institutions around the world, including in the U.K., Germany, and India. The group is using Intels confidential computing to develop a distributed approach to machine learning that allows them to share patient data, including medical imaging. Because such data can remain encrypted while it is being used for machine learning, the group can safely share that data and collaborate in a way that otherwise might not be possible.

Thats critical because data is urgently needed to train machine learning, but no single institution has enough to achieve this on its own. Previously, Penn Medicine and Intel Labs published a study showing that federated learning (a collaborative approach) could train a machine learning model far more effectively than working alone. In this case, the group believes the combination of confidential computing and federated learning will allow them to make rapid breakthroughs in AI models that identify brain tumors.

Merchants are also tapping the ability to allow new types of collaboration for customer and partner data, as are enterprises. While analysts like Gartner believe the real impact of confidential computing may still be several years away, Perez said it is already helping some sectors accelerate their AI and machine learning capabilities in the short term.

There are multiple aspects of the computing stack that need to be protected, Perez said. Confidential computing solves problems that couldnt be solved before. The concept that I can use any computing capability that may reside in any country around the world and still have some preservation of the privacy and confidentiality of my data, thats pretty powerful.

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Why Intel believes confidential computing will boost AI and machine learning - VentureBeat