Mediaboss Marketing

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value.

However, the challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment.

In response, new solutions have emerged to deliver key capabilities in areas including visualization, self-service, and real-time analytics. Along with the rise of DataOps, greater collaboration, and automation have been identified as key success factors.

DBTA recently hosted a special roundtable webinar featuring Alyssa Simpson Rochwerger, VP of AI and data, Appen; Doug Freud, SAP platform and technology global center of excellence, VP of data science; and Robert Stanley, senior director, special projects, Melissa Informatics, who discussed new technologies and strategies for expanding data science and machine learning capabilities.

According to a Gartner 2020 CIO survey, only 20% of AI projects deploy, Rochwerger said. The top challenges are skills of staff, understanding the benefits and uses of AI, and the data scope and quality.

She said businesses need to start out by clarifying a goal so they can then know where the data is coming from. Once organizations know where the data is coming from, they can find and fill in the gaps. Having a diverse team of humans can make it easier to sift and combine data.

According to Data2020: State of Big Data Study Regina Corso Consulting 2017, 86% of companies arent getting the most out of their data and they are limited by data complexity and sprawl, Freud explained.

SAP Data Intelligence can meet companies in the middle, Freud said. The platform boasts that its enterprise AI meets intelligent information management.

The platform features benefits that include:

Stanley took another approach by introducing the concept of data quality (DQ) fundamentals with AI. AI can be useful for DQ, particularly with unstructured or more complex data, bringing competitive advantage.

Using AI (MR and ML), more efficient methods for identification, extraction and normalization has been developed. AI on clean data enables pattern recognition, discovery and intelligent action.

Machine reasoning (MR) relies on knowledge captured and applied within ontologies using graph database technologies - most formally, using SDBs, he explained.

Machine reasoning can make sense out of incomplete or noisy data, making it possible to answer difficult questions. MR delivers highly confident decision-making by applying existing knowledge and ontology-enable logic to data, Stanley noted.

An archived on-demand replay of this webinar is available here.

Read the original post:
Going Deeper with Data Science and Machine Learning - Database Trends and Applications

Researchers involved in aWestern University-led international study have found that the most reliable predictor of a relationships success is partners belief that the other person is fully committed.

A statement issued by the university, which is located in London Ont., said this is the first-ever systematic attempt at using machine-learning algorithms to predict peoples relationship satisfaction.

Satisfaction with romantic relationships has important implications for health, well-being and work productivity, said Western psychology professor Samantha Joel.

But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.

The machine-learning study is conducted by Joel, Paul Eastwick from University of California, Davis, as well as 84 other scholars internationally.

Story continues below advertisement

More than 11,000 couples participated.

In the study, an application of artificial intelligence (AI) is used to comb through various combinations of predictors to find the most robust predictors of relationship satisfaction.

Trending Stories

It provides answers to the question: What predicts how happy I will be with my relationship partner?

According to the study, relationship-specific predictors such as perceived partner commitment, appreciation, and sexual satisfaction account for nearly half of variance in relationship quality.

Individual characteristics, which describe a partner rather than a relationship, explains 21 per cent of variance in relationship quality, the study said.

The top five individual characteristics with the strongest predictive power for relationship quality are satisfaction with life, negative affect, depression, avoidant attachment and anxious attachment.

Story continues below advertisement

Joel notes she was surprised the study showed that one partners individual differences predictors like life satisfaction, depression or agreeableness explained only five per cent of variance in the other partners relationship satisfaction.

In other words, relationship satisfaction is not well-explained by your partners own self-reported characteristics, Joel said.

The current datasets were sampled from Canada, the United States, Israel, the Netherlands, Switzerland and New Zealand.

2020 Global News, a division of Corus Entertainment Inc.

Go here to read the rest:
Bees do it, machines know it: Western University-led study hints at key to relationship satisfaction - Globalnews.ca

Private learning group AdvTech has announced the launch of a new online school for grades R to 9.

AdvTech is the largest private education provider in Africa, and its schools division includes major brands such as Crawford Schools, Trinityhouse and Abbotts.

Its new school, which is called Evolve Online School (Evolve), will begin operations from 1 January 2021 and will offer a curriculum mapping system developed by MIT.

This IEB-aligned mapping curriculum allows learners to progress at their own deliberate or accelerated pace, Evolve states.

In this rapidly changing society, the one-size-fits-all method of teaching no longer makes any sense, said Principal Colin Northmore. Evolve starts by answering the question of how we can make learning an adventure for each child?

This system places students within subjects according to their abilities, letting them progress up to their potential in each subject.

The result is that each students learning experience is tailored to their specific needs, and they are encouraged to grow at a pace that suits their ability and enthusiasm, the school states.

One of the key features touted by the Evolve Online School is its use of machine learning, which it says is employed to:

Evolve also offers a range of forward-looking subjects that differ depending on which phase the student is in.

The school separates students into three phases Foundation Phase, Intermediate Phase, and Senior Phase. These comprise students from Grades R-3, Grades 4-6, and Grades 7-9, respectively.

Evolve said that it plans to add a phase which caters to Grades 10-12 from 2022.

The subjects included in each phase are described as follows, according to the schools website:

Instead of teachers, Evolve states that its students will be taught by learning activators, which draw from master teachers across the country to develop curriculum content.

There will be a strong focus on foundational, social, and emotional learning skills. Our team of life coaches will focus exclusively on these skills. Our children are growing up in a world very different from the one in which we grew up, Northmore said.

Things that we, as adults, deal with and take in our stride they are already facing at a very young age. Our life coaches will play a very important role in teaching students how to deal with issues such as stress and anxiety, helping them develop coping mechanisms, resilience and a growth mindset.

Registrations for the 2021 academic year open in September, with Evolves school year set to start in 2021.

The Evolve 2021 fee structure is shown below.

It should be noted that a non-refundable registration fee of R300 is payable at the start of the online application process, and the school will supply each childs iPad with all the required books and apps they will need.

Excerpt from:
New South African online school uses machine learning to teach children Here is how much it costs - MyBroadband

In recent years, entire industries have popped up that rely on the delicate interplay between human workers and automated software. Companies like Facebook work to keep hateful and violent content off their platforms usinga combination of automated filtering and human moderators. In the medical field, researchers at MIT and elsewhere have used machine learning to help radiologistsbetter detect different forms of cancer.

What can be tricky about these hybrid approaches is understanding when to rely on the expertise of people versus programs. This isnt always merely a question of who does a task better; indeed, if a person has limited bandwidth, the system may have to be trained to minimize how often it asks for help.

To tackle this complex issue, researchers from MITs Computer Science and Artificial Intelligence Lab (CSAIL) have developed a machine learning system that can either make a prediction about a task, or defer the decision to an expert. Most importantly, it can adapt when and how often it defers to its human collaborator, based on factors such as its teammates availability and level of experience.

The team trained the system on multiple tasks, including looking at chest X-rays to diagnose specific conditions such as atelectasis (lung collapse) and cardiomegaly (an enlarged heart). In the case of cardiomegaly, they found that their human-AI hybrid model performed 8 percent better than either could on their own (based on AU-ROC scores).

In medical environments where doctors dont have many extra cycles, its not the best use of their time to have them look at every single data point from a given patients file, says PhD student Hussein Mozannar, lead author with David Sontag, the Von Helmholtz Associate Professor of Medical Engineering in the Department of Electrical Engineering and Computer Science, of a new paper about the system that was recently presented at the International Conference of Machine Learning. In that sort of scenario, its important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary.

The system has two parts: a classifier that can predict a certain subset of tasks, and a rejector that decides whether a given task should be handled by either its own classifier or the human expert.

Through experiments on tasks in medical diagnosis and text/image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and with far fewer training data samples.

Our algorithms allow you to optimize for whatever choice you want, whether thats the specific prediction accuracy or the cost of the experts time and effort, says Sontag, who is also a member of MITs Institute for Medical Engineering and Science. Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa.

The systems particular ability to help detect offensive text and images could also have interesting implications for content moderation. Mozanner suggests that it could be used at companies like Facebook in conjunction with a team of human moderators. (He is hopeful that such systems could minimize the amount of hateful or traumatic posts that human moderators have to review every day.)

Sontag clarified that the team has not yet tested the system with human experts, but instead developed a series of synthetic experts so that they could tweak parameters such as experience and availability. In order to work with a new expert its never seen before, the system would need some minimal onboarding to get trained on the persons particular strengths and weaknesses.

In future work, the team plans to test their approach with real human experts, such as radiologists for X-ray diagnosis. They will also explore how to develop systems that can learn from biased expert data, as well as systems that can work with and defer to several experts at once.For example, Sontag imagines a hospital scenario where the system could collaborate with different radiologists who are more experienced with different patient populations.

There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability, says Sontag. We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms.

Mozanner is affiliated with both CSAIL and the MIT Institute for Data, Systems and Society (IDSS). The teams work was supported, in part, by the National Science Foundation.

Link:
An automated health care system that understands when to step in - MIT News

This detailed market study covers machine learning chip market growth potentials which can assist the stake holders to understand key trends and prospects in machine learning chip market identifying the growth opportunities and competitive scenarios. The report also focuses on data from different primary and secondary sources, and is analyzed using various tools. It helps to gain insights into the markets growth potential, which can help investors identify scope and opportunities. The analysis also provides details of each segment in the global machine learning chip market

Click Here to Get Sample of the Premium Report @https://www.quincemarketinsights.com/request-sample-64000?utm_source=MP&utm_medium=Arshad

According to the report, the machine learning chip market report points out national and global business prospects and competitive conditions for machine learning chip. Market size estimation and forecasts were given based on a detailed research methodology tailored to the conditions of the demand for machine learning chip. The machine learning chip market has been segmented by chip type (gpu, asic, fpga, cpu, others), by technology (system-on-chip, system-in-package, multi-chip module, others), by industry vertical (media & advertising, bfsi, it & telecom, retail, healthcare, automotive & transportation, others). Historical background for the demand of machine learning chip has been studied according to organic and inorganic innovations in order to provide accurate estimates of the market size. Primary factors influencing the growth of the demand machine learning chip have also been established with potential gravity.

Regional segmentation and analysis to understand growth patterns:The market has been segmented in major regions to understand the global development and demand patterns of this market.

By region, the machine learning chip market has been segmented in North America, Europe, Asia Pacific, Middle East, and Rest of the World. The North America and Western Europe regions are estimated to register a stable demand during the forecast period with market recovery from recent slowdowns. North America region includes the US, Canada, and Mexico. The US is estimated to dominate this market with a sizeable share followed by Canada, and Mexico. The industrial sector is a major contributor to the US and Canada economies overall. Hence, the supply of advanced materials in production activities is critical to the overall growth of industries in this region.

Make an Enquiry for purchasing this Report @https://www.quincemarketinsights.com/enquiry-before-buying/enquiry-before-buying-64000?utm_source=MP&utm_medium=Arshad

Western Europe region is dominated by Germany, the UK, France, Italy, and Spain. These countries also have a strong influence on the industrial sector resulting in sizeable demand for machine learning chip market . Asia Pacific is estimated to register the highest CAGR by region during the forecast period.

The presence of some of the high growth economies such as China and India is expected to propel the demand in this region. Besides, this region has witnessed strategic investments by major companies to increase their market presence. The Middle East and Eastern Europe are estimated to be other key regions for the machine learning chip market with a strong market potential during the forecast period. Rest of the World consisting of South America and Africa are estimated to be emerging markets during the forecast period.

This report provides:1) An overview of the global market for machine learning chip market and related technologies.2) Analysis of global market trends, yearly estimates and annual growth rate projections for compounds (CAGRs).3) Identification of new market opportunities and targeted consumer marketing strategies for global machine learning chip market .4) Analysis of R&D and demand for new technologies and new applications5) Extensive company profiles of key players in industry.

The researchers have studied the market in depth and have developed important segments such as product type, application and region. Each and every segment and its sub-segments are analyzed based on their market share, growth prospects and CAGR. Each market segment offers in-depth, both qualitative and quantitative information on market outlook.

With an emphasis on strategies there have been several primary developments done by major companies such as AMD (Advanced Micro Devices), Google Inc., Intel Corporation, NVIDIA, Baidu, Bitmain Technologies, Qualcomm, Amazon, Xilinx, Samsung.

Market Segmentation:By Chip Type:o GPUo ASICo FPGAo CPUo Others

By Technology:o System-on-chipo System-in-packageo Multi-chip moduleo Others

By Industry Vertical:o Media & Advertisingo BFSIo IT & Telecomo Retailo Healthcareo Automotive & Transportationo Others

By Region:North America Machine Learning Chip Marketo North America, by Countryo USo Canadao Mexicoo North America, by Chip Typeo North America, by Technologyo North America, by Industry Vertical

Europe Machine Learning Chip Marketo Europe, by Countryo Germanyo Russiao UKo Franceo Italyo Spaino The Netherlandso Rest of Europeo Europe, by Chip Typeo Europe, by Technologyo Europe, by Industry Vertical

Asia Pacific Machine Learning Chip Marketo Asia Pacific, by Countryo Chinao Indiao Japano South Koreao Australiao Indonesiao Rest of Asia Pacifico Asia Pacific, by Chip Typeo Asia Pacific, by Technologyo Asia Pacific, by Industry Vertical

Middle East & Africa Machine Learning Chip Marketo Middle East & Africa, by Countryo UAEo Saudi Arabiao Qataro South Africao Rest of Middle East & Africao Middle East & Africa, by Chip Typeo Middle East & Africa, by Technologyo Middle East & Africa, by Industry Vertical

South America Machine Learning Chip Marketo South America, by Countryo Brazilo Argentinao Colombiao Rest of South Americao South America, by Chip Typeo South America, by Technologyo South America, by Industry Vertical

Reasons to Buy This Report:o Provides niche insights for decision about every possible segment helping in strategic decision making process.o Market size estimation of the machine learning chip market on a regional and global basis.o A unique research design for market size estimation and forecast.o Identification of major companies operating in the market with related developmentso Exhaustive scope to cover all the possible segments helping every stakeholder in the machine learning chip

Customization:This study is customized to meet your specific requirements:o By Segmento By Sub-segmento By Region/Countryo Product Specific Competitive Analysis

Contact:Quince Market InsightsAjay D. (Knowledge Partner)Office No- A109Pune, Maharashtra 411028Phone: US +1 208 405 2835 UK +44 121 364 6144 APAC +91 706 672 4848Email:[emailprotected]Web:www.quincemarketinsights.com

ABOUT US:QMI has the most comprehensive collection of market research products and services available on the web. We deliver reports from virtually all major publications and refresh our list regularly to provide you with immediate online access to the worlds most extensive and up-to-date archive of professional insights into global markets, companies, goods, and patterns.

See the original post here:
Machine Learning Chip Market Growth Accelerated by Healthy CAGR, Upcoming Trends and Key Companies Analysis | AMD (Advanced Micro Devices), Google...