All ITE students to learn artificial intelligence in first year to take on future jobs

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Finland and China tackle medical AI together

Finland and China are joining forces to study the usefulness of artificial intelligence and machine learning in the medical sector.

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How artificial intelligence makes travel safer during Covid-19 and city commuting easier | South China Morning Post

AI can help computers recognise speech, predict people’s behaviour, detect signs of illness in passengers at airport check-ins and reduce fraudulent business transactions

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Bing to improve overall search result quality using artificial intelligence (AI)

Ahead of rebranding, Bing continues to make use of Microsoft’s Turing Natural Language Generation (T-NLG) and Turing Natural Language Representation (T-NLR) models to bring search results diversity. Bing sees hundreds of millions of search queries every day and these queries are a mixed bag of intent the users are seeking to fulfill, in addition to the languages and regions where these search queries originate from.

Bing brings AI-based predictions

“Advancements in NLP continue to happen at a very rapid pace and Microsoft Turing models, both for language representation and generation, are at the cutting edge bringing the very best of deep learning capabilities and Microsoft’s innovation into its product family,” Bing said in its blog post.

However, dealing with such a vast range of dynamic usage is not easy. As a result, Microsoft realizes the importance in evolving and expanding the scale of its AI models. Microsoft AI models are the subset of the Turing NLR model that plays an important role in Bing’s question answering and ranking systems.

Microsoft has outlined how its AI model advancements are helping them build better search experiences in Bing. Interested users can preview the Turing NLR model for their own business scenarios:

“Although much of the latest AI research has been focused on English languages, we believe it is incredibly important to build AI technology that is inclusive of everyone,” Bing said.

Autosuggest (AS) prediction system promises to improve the Bing search experience by suggesting the most relevant completed queries that match the partial query entered by the user. Similarly, the People Also Ask (PAA) feature that allows users to explore answers to related queries.

Microsoft is bringing these features and upgrades to Bing users around the world. Let’s hope Bing offers more trustworthy answers irrespective of the language they speak and where are they are located.

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Future through the watchful eye of Artificial Intelligence in the world of Online Remote Proctoring Exams – The Economic Times

Future through the watchful eye of Artificial Intelligence in the world of Online Remote Proctoring Exams

Future through the watchful eye of Artificial Intelligence in the world of Online Remote Proctoring Exams

ET Spotlight

Remote Proctoring, an opportunity, which is now widely used by both public and private sector organizations.

ET Spotlight

The New Normal

Proctoring, or invigilation, is no longer restricted to a scheduled time and a physical exam center/campus anymore. The COVID-19 lockout led to a rapid revolution in the way the exams are now conducted. This created a demand for a variety of Online & Remote Proctoring Solutions and supporting technologies. Today, educators and students are increasingly adopting virtual platforms to remotely proctor and take the examinations from home resulting in modernizing the way of conducting assessments which is now
“The New Normal”.

Here, what has not changed is the fundamental need to align security demands with a positive candidate’s experience making Online & Remote Proctoring, an opportunity, which is now widely used by both public and private sector organizations.

Deep Dive to the World of Online & Remote Proctoring

Wheebox NewET Spotlight

Online & Remote Proctoring is an effective, simple and technology-based alternative to traditional on-site proctoring where the integrity of the test is preserved with the assistance of AI-enabled monitoring tools that ensure a high level of safety & holistic experience for both educators and students using:

  • Voice/Audio Recognition: To pick up background noise and match it with speech patterns to identify potential malpractice.
  • Facial Recognition: To authenticate both that the student signed up for a given online examination is the one taking it.
  • Object Detection: To identify objects on screen apart from registered face viz. mobile phone, books, unnecessary gadgets to avoid using malpractice during the exam.
  • Eye Movement Detection: A regular pattern of even the most subtle motions of the eyes may suggest wrongdoing. As such this technology is vital to our AI system’s accuracy.

As more exams are proctored with AI and machine learning, the systems are becoming smarter. AI systems will continue to learn and become wise enough over time to make judgements on the seriousness of their own findings.

The variations of Remote Proctoring

There are different types of online proctoring, depending on each institution’s traditional requirements, such as budget, comprehensive experience, risk levels and safety testing. In broader terms, Remote Proctoring can be divided in three methods:

Wheebox New 2ET Spotlight
  • Auto proctoring: Highly supported by AI, this flags suspicious events and provides the vast ability to continuously track a larger number of candidates. The program can discover the occurrences on the screen and what is happening in front of the camera. For example, if a test-taker opens a browser and browses another website or application during the test, the remote proctoring system takes screenshots of this activity if it occurs without letting the candidate know.
  • Live proctoring: Live proctoring offers the best of both worlds by incorporating a “real human” with the technology. An invigilator sits and controls the video feeds with live proctoring, and can interfere in real-time if necessary in a candidate’s exam, giving the proctors an additional level of security and feedback to candidates as they go through a test or review. A live proctoring may be used along with either auto proctoring or AI-assisted notifications.
  • Record and review proctoring: This proctoring method not only enables additional protection but also provides flexibility to candidates who do not need to fix time and coordinate with the availability of the surveillant. Instead the candidates’ audio-visual and screen-sharing feeds are recorded during the examination, a proctor then reviews the recorded exam, and flags any dubious activity if occurs.

Benefits of Remote World of Online Proctoring

Wheebox New 3ET Spotlight

The major reason why Online & Remote Proctoring is becoming more important & common is because of the ability to meet candidates’ and invigilator’s expectation for evaluation of tests anywhere at any time, this includes:

  • Reducing turnaround time while marking & publishing results.
  • Go beyond geographical gap barriers to enter new markets.
  • Cutting & optimizing logistics costs
  • Integrate with your existing infrastructure using online proctoring that provides an extension to improve efficiency of an educational organization.

The Future is Now at Wheebox:

Wheebox New 4ET Spotlight

As new innovations are becoming more accessible and available, our algorithms also evolve and adapt these regular upgrades to provide:

  • Enhanced & un-matched security
  • Scalability & seamless exam experience
  • Robust platform with advanced analytics using Microsoft BI tools.

Wheebox brings powerful and customizable solutions in time to help the delivery of any type of online examination certified by CERT-In (National Agency for Cyber Security) for application security that helps educational organizations to identify, & build cost effective online examinations with a higher accuracy that enables them to conduct the examinations according to the needs of assessment and test-takers alike.

(This article is generated and published by ET Spotlight team. You can get in touch with them on etspotlight@timesinternet.in)

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The push towards artificial intelligence in Africa – Next Einstein Forum

“Despite the support, many of us still have trouble making it to conferences. I have had papers accepted at meetings but been unable to attend because Western countries such as Australia denied me a visa, even though I was already settled and working professionally in Europe.

We need more efforts to overcome these barriers and to ensure that the benefits of AI arrive globally,” Google’s head of AI Accra Moustapha Cisse.

He has long been concerned that AI is a missed opportunity for improving African lives, and that the AI industry is missing out on talent from African nations, because they do not have access to the right education.

Today people often have to travel out of the continent in order to gain the IT skills they need, before returning to Africa to try to build new businesses.

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Interview with Deep Learning Researcher and The GANfather: Dr. Ian Goodfellow

This is another very special version of the series.
In the past few interviews, I’ve had the chance of interacting with Kaggle Grandmasters , Technical Leaders and Practitioners .
Today, I’m honoured to be talking to the GANFather, the inventor of Generative Adversarial Networks, a pioneer of cutting edge Deep Learning research and author of one of the best theoretical books on Deep Learning: Dr. Ian Goodfellow.
About the Series:
I have very recently started making some progress with my Self-Taught Machine Learning Journey . But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me.
In this Series of Blog Posts, I talk with People that have really inspired me and whom I look up to as my role-models.
The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of learning from.
Sanyam Bhutani: ​ Hello GANFather, Thank you so much for doing this interview.
Dr. Ian Goodfellow: Very welcome! Thank you very much for interviewing me, and for writing a blog to help other students.
Sanyam Bhutani: ​ Today, you’re working as a research scientist at Google . You’re the inventor of the most exciting development in Deep Learning: GAN(s).
Could you tell the readers about how you got started? What got you interested in Deep Learning?
Dr. Ian Goodfellow: I was studying artificial intelligence as an undergrad, back when machine learning was mostly support vector machines, boosted trees, and so on. I was also a hobbyist game programmer, making little hobby projects using OpenGL shader language. My friend Ethan Dreyfuss who works at Zoox now told me about two things: 1) Geoff Hinton’s tech talk at Google on deep belief nets 2) CUDA GPUs, which were new at the time.
It was obvious to me right away that deep learning would fix a lot of my complaints about SVMs. SVMs don’t give you a lot of freedom to design the model. There isn’t an easy way to make the SVM smarter by throwing more resources at it. But deep neural nets tend to get better as they get bigger. At the same time, CUDA GPUs would make it possible to trainer much bigger neural nets, and I knew how to write GPU code already from my game programming hobby.
Over winter break, Ethan and I built the first CUDA machine at Stanford (as far as I know) and I started training Boltzmann machines.
Sanyam Bhutani: You’ve mentioned that you coded the first GAN model just overnight whereas the general belief is that a breakthrough in research might take months if not years.
Could you tell us what allowed you to make the breakthrough just overnight?
Dr. Ian Goodfellow: If you have a good codebase related to a new idea, it’s easy to try out a new idea quickly. My colleagues and I had been working for several years on the software libraries that I used to build the first GAN, Theano, and Pylearn2. The first GAN was mostly a copy-paste of our MNIST classifier from an earlier paper called “Maxout Networks”. Even the hyperparameters from the Maxout paper worked fairly well for GANs, so I didn’t need to do much new. Also, MNIST models train very quickly. I think the first MNIST GAN only took me an hour or so to make.
Sanyam Bhutani: Since their inception, we have seen tremendous growth in GAN(s), which one are you most excited about?
Dr. Ian Goodfellow: It’s hard to choose. Emily Denton and Soumith Chintala’s LAPGAN was the first moment I really knew GANs were going to be big. Of course, LAPGAN was just a small taste of what was to come.
Sanyam Bhutani: Apart from GAN(s), what other domains of Deep Learning research do you find to be really promising?
Dr. Ian Goodfellow: I spend most of my own time working on robustness to adversarial examples. I think this is important for being able to use machine learning in settings where security is a concern. I also hope it will help us understand machine learning better.
Sanyam Bhutani: For the readers and the beginners who are interested in working on Deep Learning with the dreams of working at Google someday. What would be your best advice?
Dr. Ian Goodfellow: Start by learning the basics really well: programming, debugging, linear algebra, probability. Most advanced research projects require you to be excellent at the basics much more than they require you to know something extremely advanced. For example, today I am working on debugging a memory leak that is preventing me from running one of my experiments, and I am working on speeding up the unit tests for a software library so that we can try out more research ideas faster. When I was an undergrad and early PhD student I used to ask Andrew Ng for advice a lot and he always told me to work on thorough mastery of these basics. I thought that was really boring and had been hoping he’d tell me to learn about hyperreal numbers or something like that, but now several years in I think that advice was definitely correct.
Sanyam Bhutani: Could you tell us what a day at Google research is like?
Dr. Ian Goodfellow: It’s very different for different people, or even for the same person at different times in their career. I’ve had times when I mostly just wrote code, ran experiments, and read papers. I’ve had times when I mostly just worked on the deep learning book. I’ve had times when I mostly just went to several different meetings each day checking in on many different projects. Today I try to have about a 60–40 split between supervising others’ project and working firsthand on my own projects.
Sanyam Bhutani: It’s a common belief that you need major resources to produce significant results in Deep Learning.
Do you think a person who does not have the resources that someone at Google might have access to, could produce significant contributions to the field?
Dr. Ian Goodfellow: Yes, definitely, but you need to choose your research project appropriately. For example, proving an interesting theoretical result probably does not require any computational resources. Designing a new algorithm that generalizes very well from an extremely small amount of data will require some resources but not as much as it takes to train on a very large dataset. It is probably not a good idea to try to make the world’s fastest-training ImageNet classifier if you don’t have a lot of hardware to parallelize across though.
Sanyam Bhutani: Given the explosive growth rates in research, How do you stay up to date with the cutting edge?
Dr. Ian Goodfellow: Not very long ago I followed almost everything in deep learning, especially while I was writing the textbook. Today that does not seem feasible, and I really only follow topics that are clearly relevant to my own research. I don’t even know everything that is going on with GANs.
Sanyam Bhutani: Do you feel Machine Learning has been overhyped?
Dr. Ian Goodfellow: In terms of its long-term potential, I actually still think machine learning is still underhyped, in the sense that people outside of the tech industry don’t seem to talk about it as much as I think they should. I do think machine learning is often “incorrectly hyped”: people often exaggerate how much is possible already today, or exaggerate how much of an advance an individual project is, and so on.
Sanyam Bhutani: Do you feel a Ph.D. or Masters level of expertise is necessary or one can contribute to the field of Deep Learning without being an “expert”?
Dr. Ian Goodfellow: I do think that it’s important to develop expertise but I don’t think that a PhD is the only way to get this expertise. The best PhD students are usually very self-directed learners, and it’s possible to do this kind of learning in any job that gives you the time and freedom to learn.
Sanyam Bhutani: Before we conclude, any advice for the beginners who feel overwhelmed to even get started with Deep Learning?
Dr. Ian Goodfellow: Start with an easy project, where you are just re-implementing something that you already know should work, like a CIFAR-10 classifier. A lot of people want to dive straight into doing something new first, and then it’s very hard to tell whether your project doesn’t work because your idea doesn’t work, or whether your project doesn’t work because you have a slight misunderstanding of something that is already known. I do think it’s important to have a project though: deep learning is a bit like flying an airplane. You can read a lot about it but you also need to get hands-on experience to learn the more intuition-based parts of it.
Sanyam Bhutani: Thank you so much for doing this interview.
If you found this interesting and would like to be a part of My Learning Path , you can find me on Twitter here .
If you’re interested in reading about Deep Learning and Computer Vision news, you can checkout my newsletter here .

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Post Graduation in Artificial Intelligence Program (#1 Training Institute) Noida

  • Understanding Tableau user Interface.
  • Exploring Tableau File Types
  • Understanding green and blue fills
  • Working with available data sources.
  • Working with extracts.
  • How to connect to the data sources.
  • How to join the various data sources.
  • How to create data visualization using
  • Tableau feature ‘show me’ Reorder &
  • remove visualization fields.
  • How to sort & filter data.
  • How to create a calculated field
  • How to perform operations using cross tab
  • Working with workbook
  • data and worksheets.
  • How to create a packaged workbook.
  • Creating various charts
  • Creating maps & setting map options
  • Creation dashboards &
  • working with dashboard
  • Overview
  • – RDBMS Concepts
  • – Databases
  • – Syntax
  • – Data Types
  • – Operators
  • – Expressions
  • – Create Database
  • – Drop Database
  • – Select Database
  • – Create Table
  • – Drop Table
  • – Insert Query
  • – Select Query
  • – Where Clause
  • – AND & OR Clauses
  • – Update Query
  • – Delete Query
  • – Like Clause
  • – Top Clause
  • – Order By
  • – Group By
  • – Distinct Keyword
  • – Sorting Results
  • – Constraints
  • – Using Joins
  • – Unions Clause
  • – NULL Values
  • – Alias Syntax
  • – Indexes
  • – Alter Command
  • – Truncate Table
  • – Using Views
  • – Having Clause
  • – Transactions
  • – Wildcards
  • – Date Functions
  • – Temporary Tables
  • – Clone Tables
  • – Sub Queries
  • – Using Sequences
  • – Handling Duplicates
  • – Injection

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Merimen® and Claim Genius® to Launch Artificial Intelligence Solution for Auto Claims

Instant AI detection of automobile damage using Claim Genius

Claim Genius™ (http://www.claimgenius.com) a leading AI InsureTech company, and Merimen Technologies (http://www.merimen.com), a market leader in providing SaaS platform for insurance ecosystems, today announced the signing of a strategic agreement for P&C Insurance services enterprises. As part of this agreement, Merimen will be bringing Claim Genius’s real time damage estimates for passenger vehicles into its TrueSight™ suite of analytics products, and introducing it to Merimen’s network of global and regional insurance carriers across 10 countries. This new product, TrueSight™ AI Imaging, will include an integrated workflow solution to drive better efficiencies, speed, accuracy and productivity improvements for the automobile insurance services sector. Once the service is implemented, clients will be able to get an instant estimate for repair of the damaged vehicle by utilizing the initial photographs or videos of the accident vehicles.

“We are very excited to announce this partnership with Merimen”, said Raj Pofale, founder and CEO of Claim Genius. “The auto claims industry is in the midst of a global revolution, driven by advancements in digital and mobile technology, artificial intelligence, and machine learning. Claim Genius is leading the charge of this transformation through our advanced product capabilities and growing list of technology and delivery partnerships across the entire claims ecosystem. Today Claim Genius is working with large customers in 7 different geographies and becoming a global platform this partnership will further enable us to scale this vision and truly make touchless claims a reality for our customers worldwide.”

“Our vision is to reduce claims costs and to drive better efficiencies for our clients,” said Trevor Lok, CEO of Merimen Technologies. “By integrating Claim Genius’s advanced technology into our TrueSight™ analytics suite, Merimen will deliver the industry’s most relevant and reliable AI solution to our global clients, driving improved accuracy and efficiency throughout the claims management process,” he added.

About Merimen®

Merimen is a market leader in providing a collaborative and information exchange platform for the insurance industry in 10 countries across Asia and UAE. As the pioneer in offering Software as a Service (SaaS) for the motor insurance industry, we have successfully deployed this model throughout the insurance ecosystem communities. We have enabled our clients to grow without disproportionate overheads and provided rapid transformation capabilities with lower risks and predictable cost using Merimen’s infrastructure.

About ClaimGenius®

Based in Iselin, New Jersey, USA with development centers in Nagpur & Hyderabad, Claim Genius, Inc is a rapidly emerging leader of AI-based claims solutions for the auto insurance industry. Using Claim Genius’s patent-pending image analysis and predictive analytics tools, carriers can provide instant damage estimates and rapid processing of claims based on uploaded accident photos from its easy to use mobile app. Claim Genius aims to reduce claims processing time, increase carrier profitability, and revolutionize the claims experience for insurance customers worldwide. Claim Genius Makes Touchless Claims A Reality.

This content was originally published here.

This Artificial Intelligence Tried To Crack The Voynich Manuscript And This Is What It Found

This artificial intelligence tried to crack the Voynich manuscript and this is what it found. Today, we take a look at what this artificial intelligence found out about the Voynich Manuscript.

Perhaps one of the world’s most interesting artefacts, the Voynich Manuscript, has been shrouded in mystery from the 15th century, when it was discovered, to the 21st century, where we are none the wiser as to what it says, who it was written by, or even what language it was written in.

This odd manuscript is named after Wilfrid Voynich, an antiquarian bookseller, who bought the manuscript in 1912 from a Jesuit library in Italy, it is now kept in the Beinecke Rare Book and Manuscript Library at Yale University, USA, where it has been held since 1969.

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