Category Archives: Machine Learning
Adaptive Insights CPO on Why Machine Learning Is Disrupting Financial Services – Toolbox
Machine learning, in-memory computing, and democratization of data are important trends that are helping organizations operate with greater agility.
Data Analytics has helped organizations make informed business decisions. The increased usage of data analytics has resulted in the mainstreaming of enterprise-grade data management, data preparation, and data visualizations tools. In an interview with Toolbox, Bhaskar Himatsingka, Chief Product Officer at Adaptive Insights, talks how data-driven planning can help companies achieve strategic objectives, cloud performance management solutions and the importance of cloud-based planning systems. He also spotlights how machine learning (ML)has revolutionized cloud-based planning, and upcoming trends in data visualization.
In high school, I realized I wasnt smart enough to be a career mathematician, so I turned to Computer Science. After I came to the U.S. for post-graduate studies, I joined Oracle working on the database kernel team. Over the years, I moved from technical staff positions to CTO at Ariba, a SaaS procurement software company now owned by SAP, and later CTO at Change Healthcare. In 2016, I joined Adaptive Insights, where I lead the engineering, product management, and technical operations teams.
Too often, people learn about a certain technology and think, We need this. I believe thats the wrong approach. I like to use the analogy that when you go to the hardware store to buy a quarter-inch drill bit, what you really want is a quarter-inch hole. So, for a CTO or CIO, the focus should be on going to all the relevant stakeholders across the business and asking, What are we trying to accomplish? What problem are we trying to solve?
Ive seen firsthand that data-driven planning can help companies achieve strategic objectives and solve a lot of ongoing inefficiencies, but you have to start with the goal in mind.
Learn More: How AI Has Transformed the Role of a Chief Data Officer: LinkedIn CDO
It depends on what youre trying to achieve. It might be saving days or even weeks on a budget or monthly close, improving data integrity, increasing the number of people participating in planning and reporting, cutting in the time required to produce a report, and expanding the number of people throughout your organization actively modeling what-if scenarios and participating in decision-making. You can also tie many of these KPIs to key business metrics, like higher gross margin and reduced operating expenses.
The classic data readiness best practices are always advisable whenever implementing a new solution. Youll want to establish key metrics to ensure your data is relevant and business-ready; implement a data quality strategy; and then determine your integration strategy.
Data is often locked in separate systems and data lakes. Those silos live in your ERP, HCM, and elsewhere in your enterprise stack. So, any cloud-based CPM system absolutely must be platform-agnostic and offer easy and automated integration of data from all your various sources.
Learn More: CFO as Visionary: Using Digital Analytics to Power Strategy
When you process data entirely in memory, youre able to create and calculate far more complex models so people in a business can understand the implications of their actions. In-memory computing helps a cloud platform scale to meet the demands of a business as it grows. But memory isnt a blunt instrument; you need to use it efficiently to get the maximum value from it. For instance, when updating a large, complex model, its far better to recalculate only those cells that have changed. Doing it the old wayrecalculating the entire modelis unacceptably slow and a waste of resources.
Learn More: Top 6 Analytics Trends To Drive Data-Driven Decision Making
There have been so many. One great example is reporting. Only a few years ago, reporting was far more difficult and often involved a request to finance. Thats changed, and its become an empowering development in modern businesses. Another example is augmented analytics to automatically identify trends and opportunities, empowering both finance and business users with the capability to harness data in a way previously reserved for data scientists.
Cloud-based planning systems have transformed and democratized reporting. Now virtually anyone in the business can generateon demanda report that visualizes operational and financial performance for their function.
Learn More: Data Visualization Will Change How You Use Data
Were working to make machine learning (ML) a foundational aspect of modern, cloud-based planning. A powerful practical use of ML is its ability to serve as a reliable prediction engine for business planning.
Were seeing Machine Learning improve the ability of managers in finance or operations to accurately anticipate outcomes and even identify anomalies in your data that could otherwise lead you to make a bad decision. Scenarios like changing the price of a product or service, remapping sales territories, or adding headcountthese are important decisions in any business, and making them with confidence helps those businesses operate with agility.
Learn More: How Can Machine Learning Improve Risk Management?
Among the most important trends involve technology that helps organizations operate with greater agility. Weve already mentioned a couplemachine learning and in-memory computing. Another is the democratization of data that allows business users across the enterprise to collaborate in ways they never could before. This is all part of making planning intelligent and anticipatorymoving from simply understanding whats happening right now to predicting with a high degree of confidence whats likely to happen in the future.
About Bhaskar Himatsingka:
Bhaskar leads Adaptive Insights engineering, product management, and technical operations teams. Bhaskar brings more than 20 years of experience building teams, technology, and products. He joined Adaptive Insights from Change Healthcare where, as CTO, he developed a new technology roadmap, led the engineering team to re-architect the way the company stored and analyzed data, and oversaw the launch of successful new products. Prior to that, he spent more than 12 years at Ariba where, as CTO, Bhaskar led the effort to re-architect the companys on-premises products to launch its multi-tenant enterprise software as a service (SaaS) platform.
Bhaskar earned a masters degree in computer science from the University of Minnesota at Minneapolis, and he holds a Bachelor of Technology degree in computer science and engineering from the Indian Institute of Technology in Kanpur, India.
About Adaptive Insights:
Adaptive Insights, a Workday company, is powering a new generation of business planning. Driving business agility in a fast-moving world, Adaptive Insights Business Planning Cloud leads the way for people in companies to collaborate, gain insights, and make smarter decisions faster. Powerful modeling for any size organization, yet so easy for everybody who plans. Adaptive Insights is headquartered in Palo Alto, CA.
About Tech Talk:
Tech Talk is a Toolbox Interview Series with notable CTOs and senior executives from around the world. Join us to share your insights and research on where technology and data are heading in the future. This interview series focuses on integrated solutions, research and best practices in the day-to-day work of the tech world.
Do you think machine learning has played a major role in transforming business planning? Comment below or let us know on LinkedIn, Twitter, or Facebook. Wed love to hear from you!
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Adaptive Insights CPO on Why Machine Learning Is Disrupting Financial Services - Toolbox
Insights into the E-Commerce Fraud Detection Solutions Market Overview – Machine Learning Tools Have Significantly Changed the Way Fraud is Detected -…
DUBLIN--(BUSINESS WIRE)--The "E-Commerce Fraud Detection Solutions: Market Overview" report has been added to ResearchAndMarkets.com's offering.
This report provides a foundational framework for evaluating fraud detection technologies in two categories. The first category includes 18 suppliers that have been identified as implementing more traditional systems that monitor e-commerce websites and payments, evaluating shopping, purchasing, shipping, payments, and disputes to detect fraud.
The second category includes 37 service providers that the publisher has identified as specializing in identity and authentication often utilizing biometrics as well as behavioral biometric data collected across multiple websites to establish risk scores and to detect account takeover attempts and bots. Note, however, that companies in both of these categories are adopting new technologies and their solutions are undergoing rapid change.
Machine learning tools have significantly changed the way fraud is detected. Even as machine learning technology advances at a dizzying rate, so do the models that fraud detection platforms deploy to recognize fraud. These models can now monitor and learn from activity across multiple sites operating the same platform or even from data received directly from the payment networks.
This ability to model and detect fraud activity across multiple merchants, multiple geographies, and from the payment networks enables improved detection and inoculation from new types of fraud attack as soon as they are discovered. What is more important is that this technology starts to connect identity, authentication, behavior, and payments in ways never possible before.
E-commerce fraud rates continue to increase at a rapid rate, with synthetic fraud growing faster than other fraud types. It is time for merchants to reevaluate the tools they currently deploy to prevent fraud, commented Steve Murphy, Director, Commercial and Enterprise Payments Advisory Service, co-author of the report.
Highlights of the report include:
Key Topics Covered:
1. Introduction
2. Determining the Cost of Fraud
3. The Business of Fraud
4. A Framework for Evaluating E-Commerce Fraud Detection Solutions
5. Selecting the Appropriate Tools
6. The Fraud Prevention Landscape
7. Conclusions
8. References
Companies Mentioned
For more information about this report visit https://www.researchandmarkets.com/r/th2kms
Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.
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Insights into the E-Commerce Fraud Detection Solutions Market Overview - Machine Learning Tools Have Significantly Changed the Way Fraud is Detected -...
Rapid Industrialization to Boost Machine Learning Courses Growth by 2019-2025 – Keep Reading
The global Machine Learning Courses market reached ~US$ xx Mn in 2018 and is anticipated grow at a CAGR of xx% over the forecast period 2019-2029. In this Machine Learning Courses market study, the following years are considered to predict the market footprint:
The business intelligence study of the Machine Learning Courses market covers the estimation size of the market both in terms of value (Mn/Bn USD) and volume (x units). In a bid to recognize the growth prospects in the Machine Learning Courses market, the market study has been geographically fragmented into important regions that are progressing faster than the overall market. Each segment of the Machine Learning Courses market has been individually analyzed on the basis of pricing, distribution, and demand prospect for the following regions:
Each market player encompassed in the Machine Learning Courses market study is assessed according to its market share, production footprint, current launches, agreements, ongoing R&D projects, and business tactics. In addition, the Machine Learning Courses market study scrutinizes the strengths, weaknesses, opportunities and threats (SWOT) analysis.
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On the basis of age group, the global Machine Learning Courses market report covers the footprint, and adoption pattern of the segments including
The key players covered in this study
EdX
Ivy Professional School
NobleProg
Udacity
Edvancer
Udemy
Simplilearn
Jigsaw Academy
BitBootCamp
Metis
DataCamp
Market segment by Type, the product can be split into
Rote Learning
Learning From Instruction
Learning By Deduction
Learning By Analogy
Explanation-Based Learning
Learning From Induction
Market segment by Application, split into
Data Mining
Computer Vision
Natural Language Processing
Biometrics Recognition
Search Engines
Medical Diagnostics
Detection Of Credit Card Fraud
Securities Market Analysis
DNA Sequencing
Market segment by Regions/Countries, this report covers
United States
Europe
China
Japan
Southeast Asia
India
Central & South America
The study objectives of this report are:
To analyze global Machine Learning Courses status, future forecast, growth opportunity, key market and key players.
To present the Machine Learning Courses development in United States, Europe and China.
To strategically profile the key players and comprehensively analyze their development plan and strategies.
To define, describe and forecast the market by product type, market and key regions.
In this study, the years considered to estimate the market size of Machine Learning Courses are as follows:
History Year: 2014-2018
Base Year: 2018
Estimated Year: 2019
Forecast Year 2019 to 2025
For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.
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What insights readers can gather from the Machine Learning Courses market report?
The Machine Learning Courses market report answers the following queries:
Why Choose Machine Learning Courses Market Report?
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Rapid Industrialization to Boost Machine Learning Courses Growth by 2019-2025 - Keep Reading
Machine Learning in Finance Market Size 2020 Global Industry Share, Top Players, Opportunities And Forecast To 2026 – 3rd Watch News
Machine Learning in Finance Market report profile provides top-line qualitative and quantitative summary information including: Market Size (Production, Consumption, Value and Volume 2014-2019, and Forecast from 2020 to 2026). The Machine Learning in Finance Market profile also contains descriptions of the leading topmost manufactures/players like (Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture, ZestFinance) which including Capacity, Production, Price, Revenue, Cost, Gross, Gross Margin, Growth Rate, Import, Export, Market Share and Technological Developments. Besides, this Machine Learning in Finance market covers Type, Application, Major Key Players, Regional Segment Analysis Machine Learning in Finance, Industry Chain Analysis, Competitive Insights and Macroeconomic Analysis.
Some of The Major Highlights Of TOC Covers: Development Trend of Analysis of Machine Learning in Finance Market; Marketing Channel; Direct Marketing; Indirect Marketing; Machine Learning in Finance Customers; Machine Learning in Finance Market Dynamics; Opportunities; Market Drivers; Challenges; Influence Factors; Research Programs/Design; Machine Learning in Finance Market Breakdown; Data Triangulation and Source.
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Scope of Machine Learning in Finance Market:The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice.
Split by Product Types, this report focuses on consumption, production, market size, share and growth rate of Machine Learning in Finance in each type, can be classified into:
Supervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning
Split by End User/Applications, this report focuses on consumption, production, market size, share and growth rate of Machine Learning in Finance in each application, can be classified into:
Banks Securities Company Others
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Machine Learning in Finance Market Regional Analysis Covers:
The Study Objectives Of This Machine Learning in Finance Market Report Are:
To analyze the key Machine Learning in Finance manufacturers, to study theProduction, Capacity, Volume, Value, Market Size, Shareand development plans in future.
To analyze the key regions Machine Learning in Finance market potential andAdvantage, Opportunity and Challenge, Restraints and Risks.
Focuses on the key manufacturers, to define, describe and analyze the marketCompetition Landscape, SWOT Analysis.
To define, describe and forecast the Machine Learning in Finance market by type, application and region.
To analyze the opportunities in the Machine Learning in Finance market forStakeholders by Identifying the High Growth Segments.
To analyze competitive developments such as Expansions, Agreements, New Product Launches, And Acquisitions in the Machine Learning in Finance Market.
To strategically analyze each submarket with respect to individualGrowth Trend and Their Contribution to the Machine Learning in Finance Market.
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Machine Learning Definition – Investopedia
What Is Machine Learning?
Machine learning is theconcept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computers built-in algorithms current regardless of changes in the worldwide economy.
Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as big data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information. As such, artificial intelligence measures are being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly utilized for big data processing is machine learning.
The various data applications of machine learning are formed through a complex algorithm or source code built into the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the model shouldnt change.
Machine learning is used in different sectors for various reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be tuned to provide accurate and personalized recommendations to their users based on the users internet search history or previous transactions. Lending institutions can incorporate machine learning to predict bad loans and build a credit risk model. Information hubs can use machine learning to cover huge amounts of news stories from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital-savvy era is endless as businesses and governments become more aware of the opportunities that big data presents.
How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. However, some pertinent information may not be widely publicized by the media and may be privy to only a select few who have the advantage of being employees of the company or residents of the country where the information stems from. In addition, theres only so much information humans can collect and process within a given time frame. This is where machine learning comes in.
An asset management firm may employ machine learning in its investment analysis and research area. Say the asset manager only invests in mining stocks. The model built into the system scans the web and collects all types of news events from businesses, industries, cities, and countries, and this information gathered makes up the data set. The asset managers and researchers of the firm would not have been able to get the information in the data set using their human powers and intellects. The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set. Saya mining company XYZ just discovered a diamond mine in a small town in South Africa, the machine learning app would highlight this as relevant data. The model could then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period, or which mining stocks are likely to increase in value at a certain time. This information is relayed to the asset manager to analyze and make a decision for his portfolio. The asset manager may make a decision to invest millions of dollars into XYZ stock.
In the wake of an unfavorable event, such as South African miners going on strike, the computer algorithm adjusts its parameters automatically to create a new pattern. This way, the computational model built into the machine stays current even with changes in world events and without needing a human to tweak its code to reflect the changes. Because the asset manager received this new data on time, they are able to limit his losses by exiting the stock.
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Machine Learning Definition - Investopedia
Qeexo is making machine learning accessible to all – Stacey on IoT
A still from a Qeexo demonstration video for a package monitoring.
Every now and then I see technology thats so impressive, I cant wait to write about it, even if no one else finds it cool. I had that experience last week while watching a demonstration of a machine learning platform built byQeexo. In the demo, I watched CEO and Co-Founder Sang Won Lee spend roughly five minutes teaching Qeexos AutoML software to distinguish between the gestures associated with playing the drums and playing a violin.
The technology is designed to take data from existing sensors, synthesize the information in the cloud, and then spit out a machine learning model that could run on a low-end microcontroller. It could enable normal developers to train some types of machine learning models quickly and then deploy them in the real world.
The demonstration consisted of the Qeexo software running on a laptop, anSTMicroelectronics SensorTile.box acting as the sensor to gatherthe accelerometer and gyroscope data and sending it to the computer, and Lee holding the SensorTile and playing the air drums or air violin. First, Lee left the sensor on the table to get background data, and saved that to the Qeexo software. Then he played the drums for 20 seconds to teach the software what that motion looked like, and saved that. Finally, he played the violin for 20 seconds to let the software learn that motion and saved that.
After a little bit of processing, the models were ready to test. (Lee turned off a few software settings that would result in better models for the sake of time, but noted that in a real-world setting these would add about 30 minutes to the learning process.) I watch as the model easily switched back and forth, identifying Lees drumming hands or violin movements instantly.
When he stopped, the software identified the background setting. Its unclear how much subtlety the platform is capable of (drumming is very different from playing an imaginary violin), but even at relatively blunt settings, the opportunities for Qeexo are clear. You could use the technology to teach software to turn on a light with a series of knocks, as Qeexo did inthis video. You could use it to train a device to recognize different gestures (Lee says the company is in talks with a toy company to create a personal wand for which people could build customized gestures to control items in their home). And in industrial settings, it could be used for anomaly detection developed in-house, which would be especially useful for older machines or in companies where data scientists are hard to find. Lee says that while Qeexo has raised $4.5 million in funding so far, it is already profitable from working with clients, so its clear there is real demand for the platform.
The company started out trying to provide machine learning for companies, but quickly realized that the way it was trying to solve client problems wasnt scalable, so it transitioned to building a platform that could learn. It has been active since 2016, providing software that tracks various types of a finger touch on phone screens for Huawei. One of its competitive advantages is that the software takes what it learns and recompiles the Python code generated by the original models into C code, which is smaller and can run on constrained devices.
Lee says the models are designed to run on chips that have as little as 100 kilobytes of memory. Today those chips are only handling inference, or actually matching behavior against an existing model on the chip, but Lee says that the plan is to offer training on the chip itself later this year.
Thats a pretty significant claim, as it would allow someone to place the software on a device and do away with sending data to the cloud, which reduces the need for connectivity and helps boost privacy. For the last few years, it has been the holy grail of machine learning at the edge, but so far it hasnt been done. It will be, though, and well see if Qeexo is the one that will make it happen.
Related
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Qeexo is making machine learning accessible to all - Stacey on IoT
With launch of COVID-19 data hub, the White House issues a call to action for AI researchers – TechCrunch
In a briefing on Monday, research leaders across tech, academia and the government joined the White House to announce an open data set full of scientific literature on the novel coronavirus. The COVID-19 Open Research Dataset, known as CORD-19, will also add relevant new research moving forward, compiling it into one centralized hub. The new data set is machine readable, making it easily parsed for machine learning purposes a key advantage according to researchers involved in the ambitious project.
In a press conference, U.S. CTO Michael Kratsios called the new data set the most extensive collection of machine readable coronavirus literature to date. Kratsios characterized the project as a call to action for the AI community, which can employ machine learning techniques to surface unique insights in the body of data. To come up with guidance for researchers combing through the data, the National Academies of Sciences, Engineering, and Medicine collaborated with the World Health Organization to come up with high priority questions about the coronavirus related to genetics, incubation, treatment, symptoms and prevention.
The partnership, announced today by the White House Office of Science and Technology Policy, brings together the Chan Zuckerberg Initiative, Microsoft Research, the Allen Institute for Artificial Intelligence, the National Institutes of Healths National Library of Medicine, Georgetown Universitys Center for Security and Emerging Technology, Cold Spring Harbor Laboratory and the Kaggle AI platform, owned by Google.
The database brings together nearly 30,000 scientific articles about the virus known as SARS-CoV-2. as well as related viruses in the broader coronavirus group. Around half of those articles make the full text available. Critically, the database will include pre-publication research from resources like medRxiv and bioRxiv, open access archives for pre-print health sciences and biology research.
Sharing vital information across scientific and medical communities is key to accelerating our ability to respond to the coronavirus pandemic, Chan Zuckerberg Initiative Head of Science Cori Bargmann said of the project.
The Chan Zuckerberg Initiative hopes that the global machine learning community will be able to help the science community connect the dots on some of the enduring mysteries about the novel coronavirus as scientists pursue knowledge around prevention, treatment and a vaccine.
For updates to the CORD-19 data set, the Chan Zuckerberg Initiative will track new research on a dedicated page on Meta, the research search engine the organization acquired in 2017.
The CORD-19 data set announcement is certain to roll out more smoothly than the White Houses last attempt at a coronavirus-related partnership with the tech industry. The White House came under criticism last week for President Trumps announcement that Google would build a dedicated website for COVID-19 screening. In fact, the site was in development by Verily, Alphabets life science research group, and intended to serve California residents, beginning with San Mateo and Santa Clara County. (Alphabet is the parent company of Google.)
The site, now live, offers risk screening through an online questionnaire to direct high-risk individuals toward local mobile testing sites. At this time, the project has no plans for a nationwide rollout.
Google later clarified that the company is undertaking its own efforts to bring crucial COVID-19 information to users across its products, but that may have become conflated with Verilys much more limited screening site rollout. On Twitter, Googles comms team noted that Google is indeed working with the government on a website, but not one intended to screen potential COVID-19 patients or refer them to local testing sites.
In a partial clarification over the weekend, Vice President Pence, one of the Trump administrations designated point people on the pandemic, indicated that the White House is working with Google but also working with many other tech companies. Its not clear if that means a central site will indeed launch soon out of a White House collaboration with Silicon Valley, but Pence hinted that might be the case. If that centralized site will handle screening and testing location referral is not clear.
Our best estimate is that some point early in the week we will have a website that goes up, Pence said.
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With launch of COVID-19 data hub, the White House issues a call to action for AI researchers - TechCrunch
AI and machine learning algorithms have made aptitude tests more accurate. Here’s how – EdexLive
The rapid advancements of technologies within the spheres of communication and education have enriched and streamlined career counselling services across the globe. One area that has gone from strength to strength is psychometric assessment. As a career coach, one is now able to gain profound insights into their clients personalities. The most advanced psychometric assessments are able to map the test takers across numerous dimensions, such as intellectual quotient, emotional quotient, and orientation style, just to name a few.
Powered by Artificial Intelligence and Machine Learning algorithms, psychometric and aptitude tests are now able to accurately gauge the test takers aptitudes and subsequently generate result reports that enable them to career counsellors to identify the best-suited career trajectories for their clients.
Technology has allowed professionals in the domain of career counselling to expand their horizons and reach larger audiences. Some of the ways that they are using to connect with their includes the following:
Dont let scepticism bog you down
With Artificial Intelligence and Machine Learning continuing to influence career counselling services, one may ponder the requirement for human intervention in the highly automated process. Are we required to partake in the process? Is our input important? Such questions might bother you. The simple answer to such nagging questions is YES!
Given the might of AI and ML, it is natural to grow sceptical about the nature of career counselling. However, be mindful that you, and only you, have the unique ability to empathize with other individuals. This is what gives us the upper hand over machines when it comes to counselling. Having said that, the intersection of advanced technologies and human thought is where career counselling thrives.
The best of both worlds
Leveraging this synergy, Mindler, an EdTech startup headquartered in New Delhi, is revolutionizing career counselling services and empowering individuals to enter this fulfilling line of work.
Their proprietary psychometric and aptitude assessment, that maps students across 56 dimensions and is being hailed as Indias most advanced psychometric assessment, coupled with the interactive career counselling sessions convened by eminent career coaches makes for a nourishing package that guides students to their ideal careers.In a nutshell, Mindler has identified a sweet spot that harnesses powerful technologies and synthesizes that with expert advice from seasoned career counsellors. Therefore, the startup is ahead of its time and promises a bright future for the young learners of this nation.
(Eesha Bagga is the Director (Partnerships & Alliances) of Mindler,a career guidance and mapping platform)
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AI and machine learning algorithms have made aptitude tests more accurate. Here's how - EdexLive
Hey, Sparky: Confused by data science governance and security in the cloud? Databricks promises to ease machine learning pipelines – The Register
Databricks, the company behind analytics tool Spark, is introducing new features to ease the management of security, governance and administration of its machine learning platform.
Security and data access rights have been fragmented between on-premises data, cloud instances and data platforms, Databricks told us. And the new approach allows tech teams to manage policies from a single environment and have them replicated in the cloud, it added.
David Meyer, senior veep of product management at Databricks, said:
"Cloud companies have inherent native security controls, but it can be a very confusing journey for these customers moving from an on-premise[s] world where they have their own governance in place, controlling who has access to what, and then they move this up to the cloud and suddenly all the rules are different."
The idea behind the new features is to allow users to employ the controls they are familiar with, for example, Active Directory to control data policies in Databricks. The firm then pushes those controls out into the cloud, he said.
The new features include user-owned revocable data encryption keys and customised private networks run in cloud clusters, allowing companies to tailor the security services to their enterprise and compliance requirements.
To ease administration, users can audit and analyse all the activity in their account, and set policies to administer users, control budget and manage infrastructure.
Meanwhile, the new features allow customers to deploy analytics and machine learning by offering APIs for everything from user management, workspace provisioning, cluster policies to application and infrastructure monitoring, allowing data ops teams to automate the whole data and machine learning lifecycle, according to Databricks.
Meyer added: "All the rules of the workspaces have to be done programmatically because that's the only way you can run things at scale in an organisation."
Databricks is currently available on AWS and Azure, and although plans are in place to launch on Google Cloud Platform, "it was a question of timing," the exec added.
Dutch ecommerce and banking group Wehkamp has been using Databricks since 2016. In the last two years it has introduced a training programme to help users from across the business - from IT operations to marketing - do their own machine learning projects on Spark.
The new security and governance feature will help in support of such a large volume of users without creating a commensurate administration burden, said Tom Mulder, lead data scientist at Wehkamp. "We introduced a new strategy which was about teaching data science to everybody in the company which actually means we have about 400 active users and 600 jobs running in Databricks," Mulder said.
Examples of use cases include onboarding products for resale, by using natural language processing to help the retailer parse data from suppliers into its own product management system, avoiding onerous re-keying and saving time.
Wehkamp said he was looking forward to the new security and governance features to help manage such a wide pool of users. "The way Databricks is working to introduce the enterprise features and all the management tools, that will help a lot."
Managing data and users in a secure way, which complies with company policy and regulations, is a challenge as data science scales up from a back-room activity led by a handful of data scientists to something in which a broader community of users can participate. Databricks is hoping its new features addressing data governance and security will ease punters along that path.
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Hey, Sparky: Confused by data science governance and security in the cloud? Databricks promises to ease machine learning pipelines - The Register
Using Machine Learning to Improve Management of Atrial Fibrillation – Technology Networks
TTP plc (TTP), an independent technology and product development company, has announced that it has established a partnership with NHS Highland and The University of the Highlands and Islands, to use clinical data interpretation and machine learning algorithms to predict which patients with atrial fibrillation (AF) are able to be successfully treated using electrical cardioversion (ECV).
AF is a common heart condition, causing an abnormally fast heart rate and irregular rhythm, which can lead to significant morbidity (e.g. stroke, heart failure), and mortality. Treatment options include drugs and/or ECV, however although morbidity and mortality can be significantly reduced with ECV, it is successful at one year in only 30% of patients. The ECV procedure also carries risk and is expensive to carry out. Consequently, there is an urgent need to predict which patients with AF are most suitable for treatment using ECV. Successfully predicting treatment suitability using remotely collectable data becomes especially valuable in geographies where population density is sparse, and where patients may often travel long distances for treatment.
NHS Highland and The University of the Highlands and Islands will work together to gather clinical data including electrocardiograms (ECGs), age, gender, comorbidities, medications and outcomes, all of which will be anonymized at source. TTP will analyze and interpret the data, using this information to determine any clinical notable ECG-derived factors that may influence short and long-term AF outcomes post ECV. TTP will also use the data to rapidly prototype and train machine learning algorithms for clinical prediction and risk scoring. The output of the project has the potential to be taken forward to be deployed in clinical settings such as the NHS as a risk stratification/clinical decision support tool.
Dr. Michelle Griffin, Clinical Innovator at TTP plc, said: This project will harness TTPs medical understanding as well as our technical capability, enabling us to gain new physiological understanding of the mechanics of ECV as an AF treatment, and why it works or does not work in certain patients. We are delighted to have been chosen as a partner and look forward to working with the NHS and University groups.
Professor Steve Leslie, Consultant Cardiologist, NHS Highland, commented: Currently, decisions on whether to proceed with ECV are based on varying factors and can be fairly subjective. There is a clear need for an evidence-based test to help guide physicians when treating AF, improve patient outcome and reduce unnecessary burden on the NHS.
The NHS Highland and University of the Highlands and Islands research group has received 15k funding for the project via a grant from the Collaborative Campus Challenge Fund, provided by Highlands and Islands Enterprise.
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Using Machine Learning to Improve Management of Atrial Fibrillation - Technology Networks