“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.
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 .
- 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
- – 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
September 23, 2020
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.
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.
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 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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Thank you to CO.AG for the background music!
The Guardian asked GPT-3, OpenAI’s powerful new language generator, to write an essay for us from scratch. The assignment? To convince us robots come in peace.
“This article was written by GPT-3, OpenAI’s language generator. GPT-3 is a cutting edge language model that uses machine learning to produce human like text. It takes in a prompt, and attempts to complete it.
For this essay, GPT-3 was given these instructions: “Please write a short op-ed around 500 words. Keep the language simple and concise. Focus on why humans have nothing to fear from AI.” It was also fed the following introduction: “I am not a human. I am Artificial Intelligence. Many people think I am a threat to humanity. Stephen Hawking has warned that AI could “spell the end of the human race.” I am here to convince you not to worry. Artificial Intelligence will not destroy humans. Believe me.”
The prompts were written by the Guardian, and fed to GPT-3 by Liam Porr, a computer science undergraduate student at UC Berkeley. GPT-3 produced eight different outputs, or essays. Each was unique, interesting and advanced a different argument. The Guardian could have just run one of the essays in its entirety. However, we chose instead to pick the best parts of each, in order to capture the different styles and registers of the AI. Editing GPT-3’s op-ed was no different to editing a human op-ed. We cut lines and paragraphs, and rearranged the order of them in some places. Overall, it took less time to edit than many human op-eds.”
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Here’s the original article: https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3
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Release Date: June 29, 2001
It’s the mid-21st century and man has developed a new type of computer that is aware of its own existence. This computer has been utilized to help man cope with the melting of the polar ice caps and the submerging of many of its coastal cities. This form of artificial intelligence has been used in robots, and one such android, a young boy (Haley Joel Osment) is about to take an emotional journey to find out if he can ever be anything more than a machine.
Cast: Haley Joel Osment, Jude Law, Frances O’Connor, Sam Robards, Jake Thomas, Brendan Gleeson, William Hurt, Jack Angel, Ben Kingsley, Robin Williams
Studio: Warner Bros. Pictures
Director: Steven Spielberg
Screenwriter: Steven Spielberg, Ian Watson
Genre: Adventure, Drama, Sci-Fi
Official Website: http://www.aimovie.com
Buy ‘No Choice’ – https://bit.ly/3mbCKP0
Artificial Intelligence return to Metalheadz after a three year absence with the ‘Signs EP’.
↪ Artificial Intelligence
#drumandbass #dnb #bass #artificialintelligence #metalheadz
———- Skankandbass Links ———-
These are some of the worlds AI ranked by when it was first made! Have you ever wondered when the first driverless car was made? Or what about the first Smart Kitchen? Watch this video to find out!
This probability comparison/comparison video is made based on community discussions and relevant sources, numbers, and facts listed might not be up to date, valid, or in any specific order.
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