I’ll be giving the talk below to an audience of oligarchs in Los Angeles next week. This is a video version I made for fun. It cuts off at 17min even though the whole talk is ~25min, because my team noticed that I gave away some sensitive information 🙁 Slides: https://drive.google.com/open?id=1NcsgvWUKBmlr_qcgL0VXN5H0JVmsJDdJ A Brief History of the (Near) Future: How AI and Genomics Will Change What It Means To Be Human AI and Genomics are almost certain to have huge impacts on markets, health, society, and even what it means to be human. These are not two independent trends; they interact in important ways, as I will explain. Computers now outperform humans on most narrowly-defined tasks, such as face recognition, voice recognition, Chess, and Go. Using AI methods in genomic prediction, we can (for example) estimate the height of a human based on DNA alone, plus or minus an inch. Almost a million babies are born each year via IVF, and it is possible now to make nontrivial predictions about them (even, about their cognitive ability) from embryo genotyping. I will describe how AI, Genomics, and AI+Genomics will evolve in the coming decades. Short Bio: Stephen Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech.

Richard Socher is the Chief Scientist at Salesforce and one of the leading researchers in deep learning and natural language processing as an adjunct professor at Stanford University’s Department of Computer Science. In this video he presents at our recent AirTree Speaker Series event in partnership with the Melbourne ML/AI community. First up, Richard takes us through his team’s recent research on the Natural Language Decathlon: Multitask Learning as Question Answering (https://arxiv.org/abs/1806.08730​), a new paradigm for natural language learning that casts all tasks as question answering over a context. Then he sits down with John Henderson, one of the partners at AirTree Ventures, to talk about the global landscape for AI including recent big milestones, what’s hype vs reality, and what the opportunities are for start-ups in the space.

“Hey do you want to go paramotoring?” This may not be the typical way an IT conversation starts, but it just might be the way you begin an interaction with Richard Socher. Especially if you’re near a beautiful vista or in any gorgeous outdoor setting. But paramotoring is only Richard’s hobby, his job is Chief Scientist for Salesforce. As the Chief Scientist, Richard wears a couple of different hats. He and his team focus on publishing new research data, doing applied research, incubating, and working on the capabilities of Salesforce’s platform. The type of work Richard and his team do extends across many fields, but lately, they have been narrowing in on A.I. and all things Salesforce Einstein Voice Assistant. On this episode of IT Visionaries, Richard talks about his origins, including the work he did as the founder, CEO and CTO of MetaMind, the current and future state of affairs of A.I. and voice, and yes, his love of paramotoring. Key Takeaways: How A.I. can have a positive impact What is the future of voice technology? What out-of-the-box features are available IT Visionaries is brought to you by the Salesforce Customer 360 Platform – the #1 cloud platform for digital transformation of every experience. Build connected experiences, empower every employee, and deliver continuous innovation – with the customer at the center of everything you do. Learn more at salesforce.com/pla

Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us! —–

Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce. Richard, who has been at the forefront of Salesforce’s AI Research since they acquired his startup Metamind in 2016, and his team have been publishing a ton of great projects as of late, including CTRL: A Conditional Transformer Language Model for Controllable Generation, and ProGen, an AI Protein Generator, both of which we cover in-depth in this conversation. We explore the balancing act between investments, product requirement research and otherwise at a large product-focused company like Salesforce, the evolution of his language modeling research since being acquired, and how it ties in with Protein Generation. The complete show notes for this episode can be found at twimlai.com/talk/372.

AI is changing the way we interact with the world, forcing us to consider how we create AI-powered technology that is human-first and has positive ethical implications. Salesforce Research is committed to advancing the state of AI research by building intuitive, contextual, and conversational experiences that power the Salesforce products you know and love. Join Salesforce’s Chief Scientist Dr. Richard Socher and his team of AI experts as they introduce exciting new AI breakthroughs that promise to fundamentally change the way you work and improve the state of the world. #Salesforce #CRMMarketing #Richard Socher Subscribe to Salesforce: http://bit.ly/SalesforceSubscribe Learn more about Salesforce: Website: https://www.salesforce.com/ Facebook: https://www.facebook.com/salesforce/ Twitter: https://www.twitter.com/salesforce Instagram: https://www.instagram.com/salesforce/ LinkedIn: https://www.linkedin.com/company/salesforce/ About Salesforce: Salesforce is a customer relationship management solution that brings companies and customers together. It’s one integrated CRM platform that gives all your departments — including marketing, sales, commerce, and service — a single, shared view of every customer.

The book begins by positing a scenario in which AI has exceeded human intelligence and become pervasive in society. Tegmark refers to different stages of human life since its inception: Life 1.0 referring to biological origins, Life 2.0 referring to cultural developments in humanity, and Life 3.0 referring to the technological age of humans. The book focuses on “Life 3.0”, and on emerging technology such as artificial general intelligence that may someday, in addition to being able to learn, be able to also redesign its own hardware and internal structure. The summary is in Hindi. Please like and share this video. #life3.0 #AILife #AI #ArtificialIntelligence #IndianAI #IndiaAI #BookSummary www.indianai.in

#ArtificalIntelligence #MachineLearning #collegesuggest #KnowYourCourse Welcome to College Suggest! Let’s take a look at one of the most innovative and widely popular courses available today – CSE with a specialization in Artificial Intelligence and Machine Learning, which offers great opportunities due to plenty of demand for skilled professionals. 00:00 Intro 01:10 What exactly is artificial intelligence? 1:54 What to study? 02:57 Core components of AI and ML 03:34 Where to study? 04:54 Why Artificial Intelligence? 06:02 Job roles 06:59 Salary trends 07:38 Top Recruiters 08:22 Salary 09:16 Tips 09:58 How to stay updated? 10:12 Reasons to study AI & ML

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For any type of advertisement Contact us : knowledgetv460@gmail.com भारत की जनता के लिए शुद्ध देसी Knowledge वो भी हिंदी में To open Mothilal Oswal free demat account – https://bit.ly/36wD2KH क्लिक करे उपर दिए लिंक पर और अभी शुरू कीजिए स्टॉक ट्रेंडिंग में बेहतरीन मुनाफे कमाना चैनल को सब्सक्राइब कीजिये हर छोटी मोटी जानकारी पाने के लिए https://www.youtube.com/user/specialDocumentary10?sub_confirmation=1

The full automated Toolchain from 360° Camera to simulation for digital twin generation The use of a digital twin in the production process will be used, for example, for proactive planning, analysis of existing systems or process-parallel monitoring. Many companies, especially small and medium-sized enterprises, use the technology incorrectly or not. From your point of view, the generation of a digital twin is cost-, time- and resource-intensive. With our approach, these obstacles can be overcome quickly and easily, and the production layout and production logic (e.g. machine types, etc.) can be captured with a 360° Camera. The identification of CAD models and the transfer of geometric and other object data (e.g. machine types) from a reference library significantly reduces the recording of production. With the recognized machines, their properties are also known. With the comparison of the future production program, any simulation of the production can be created. Especially in the planning phase for investments, well-founded results based on a simulation model are indispensable for a target-oriented decision in today’s world. In the presentation we will show you the procedure from scanning, object recognition to simulation and its challenges out of the way.

Artificial Intelligence is a driving a digital transformation in healthcare. At RSNA 2018, our experts discuss our vision for two digital twins – an AI-powered, personalized bio-psysiological model and a workflow simulation model. #RSNA18 #AI #DigitalTwin

The Pentagon’s research arm has pumped $1 million into a contract to build an AI tool meant to decode and predict the emotions of allies and enemies. It even wants the AI app to advise generals on major military decisions. DARPA’s backing is the starting pistol for a race with the government and startups to use AI to predict emotions but the science behind it is deeply controversial. Some say it’s entirely unproven, making military applications that much riskier. The previously-unreported work is being carried out under a DARPA project dubbed PRIDE, short for the Prediction and Recognition of Intent, Decision and Emotion. The aim is to create an AI that can understand and predict reactions of a group, rather than an individual, and then offer guidance on what to do next. Think of a military leader who wants to know how a political faction or a whole country would react should he or she take an aggressive action against their leader. In PRIDE, the emotion detection is not for an individual. It’s more as a collective group and even at a national level,” says Dr. Kalyan Gupta, president and founder of Knexus. “To think about, you know, whether a nation state is either angry or agitated.” And it’s no small fry initiative; the plan is for PRIDE to provide recommendations for “international courses of action,” according to a contract description. Whilst DARPA’s project is largely looking at sentiment elicited from text and information posted online, a handful of startups, [More]

Despite the great progress made in artificial intelligence, we are still far from having a natural interaction between man and machine, because the machine does not understand the emotional state of the speaker. Speech emotion detection has been drawing increasing attention, which aims to recognize emotion states from speech signal. The task of speech emotion recognition is very challenging, because it is not clear which speech features are most powerful in distinguishing between emotions. We utilize deep neural networks to detect emotion status from each speech segment in an utterance and then combine the segment-level results to form the final emotion recognition results. The system produces promising results on both clean speech and speech in gaming scenario.

Sentiment analysis is an active research field where researchers aim to automatically determine the polarity of text [1], either as a binary problem or as a multi-class problem where multiple levels of positiveness and negativeness are reported. Recently, there is an increasing interest in going beyond sentiment, and analyzing emotions such as happiness, fear, anger, surprise, sadness and others. Emotion detection has many use cases for both enterprises and consumers. The best-known examples are customer service performance monitoring [2], and social media analysis [3]. In this talk, we present a new algorithm based on deep learning, which not only outperforms state-of-the-art method [4] in emotion detection from text, but also automatically decides on length of emotionally-intensive text blocks in a document. Our talk presents the problem by examples, with business motivations related to the Microsoft Cognitive Services suite. We present a technique to capture both semantic and syntactic relationships in sentences using word embeddings and Long Short-Term Memory (LSTM) based modeling. Our algorithm exploits lexical information of emotions to enrich the data representation. We present empirical results based on ISAER and SemEval-2007 datasets [5,6]. We then motivate the problem of detecting emotionally-intensive text blocks of various sizes, along with an entropy-based technique to solve it by determining the granularity on which the emotions model is applied. We conclude with a live demonstration of the algorithm on diverse types of data: interviews, customer service, and social media.

Tests of the Emotion Detector created in Python. As it can be seen, the detector prioritizes neutral and happiness expressions (this was due to the datasets employed in the creation of the detector; they had many more samples of these expressions than the others). The complete source code (under MIT license) is available in Github: https://github.com/luigivieira/emotions The original video used in the tests is the Human Emotions, used and reproduced here with the kind authorization from the folks of the Imagine Pictures video production company (thank you guys!). Original URL of the video: https://www.youtube.com/watch?v=UjZzdo2-LKE

Using only speech samples, machine learning can detect emotions in a speaker’s voice. This session will outline modeling challenges including label uncertainty and robustness to non-emotional latent factors, and present an adversarial auto-encoder learning approach that can be applied to a wide range of models. Session Speakers: Viktor Rozgic, Chao Wang (Session A07)

Artificial intelligence and machine learning are changing the world. Today, business leaders and developers have open-source AI/ML learning systems at their disposal to build intelligent environments. The question is, which one suits your needs the best? We’ve got the top 10 Open-Source AI/ML Learning Systems for you to consider right here.

Deep learning is a key technology driving the current artificial intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider applying to your engineering and science applications? MATLAB and Simulink developers have purpose-built the MATLAB deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market. The aim of the session is to provide an overview of how MATLAB enables you to take advantage of disruptive technologies like deep learning. We will: • Show where deep learning is being applied in engineering and science, and how it is driving MATLAB’s development. • Demonstrate a workflow for how you can research, develop and deploy your own deep learning application. • Outline what MATLAB and Simulink engineers can do to help support you achieve success with deep learning. Demo files (note: this is a large download at 433 MB): https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/company/events/post-event-email/3228951-Presentation.zip Check out these other great resources: * See if your school has a MATLAB campus license: https://bit.ly/33hvREb * Get a free product trial: https://bit.ly/2SeH5mA * MATLAB EXPO 2020 On Demand: https://bit.ly/3n8KgKL * Join the Simulink Student Challenge: https://bit.ly/30iLVUb * Learn more about MATLAB: https://bit.ly/3l5xkDR * Learn more about Simulink: https://bit.ly/36lYuSw * See what’s new in MATLAB and Simulink: https://bit.ly/33iHRp0

🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: http://goo.gl/O2vo13 🔹Check Edureka’s Blog playlist here: https://bit.ly/3gfNuZr ——————————————————————————————– 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]

Machine Learning courses and articles from coursera, edX, Udacity, dataCamp to udemy. Here are the resources link collection: —- R Language —- 1. https://cognitiveclass.ai/courses/machine-learning-r/ 2. https://www.datacamp.com/community/tutorials/machine-learning-in-r —- Python Language —- 1. https://www.coursera.org/learn/machine-learning-with-python 2. https://www.coursera.org/learn/python-machine-learning —- Java Language —- 1. https://skymind.ai/wiki/java-ai 2. EdX : Course Lists https://www.edx.org/learn/java 3. Coursera: Course List https://www.coursera.org/courses?query=java —- Machine Learning fundamentals —- 1. EdX: https://www.edx.org/course/machine-learning-fundamentals 2. Udacity: https://in.udacity.com/course/intro-to-machine-learning–ud120-india 3. Udemy: https://www.udemy.com/machine-learning-for-beginners/ 4. Coursera: https://www.coursera.org/learn/ml-foundations 5. Google https://developers.google.com/machine-learning/crash-course/prereqs-and-prework —- ML Tools and packages —- 1. NumPy, SciPy, matplotlib a. EdX https://www.edx.org/course/python-data-science-uc-san-diegox-dse200x 2. TensorFlow a. Coursera : https://www.coursera.org/learn/intro-tensorflow b. EdX : https://www.edx.org/course/deep-learning-with-tensorflow 3.Scikit Learn a. https://www.datacamp.com/courses/supervised-learning-with-scikit-learn b. Udemy https://www.udemy.com/machine-learning-with-scikit-learn/ c. https://www.dataschool.io/machine-learning-with-scikit-learn/ 4. Pandas a. https://www.udemy.com/data-analysis-with-pandas/ —- Nano Degrees —- 1. IBM : https://imarticus.org/machine-learning-prodegree 2. Coursera https://www.coursera.org/specializations/data-science-python 3. EdX Microsoft : https://www.edx.org/microsoft-professional-program-artificial-intelligence 4. Nanodegree https://in.udacity.com/course/python-foundation-nanodegree–nd002-inpy —- Machine Learning Maths —- 1. https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 2. EdX https://www.edx.org/course/essential-math-machine-learning-python 3. Coursera https://www.coursera.org/specializations/mathematics-machine-learning Udemy 4. https://www.udemy.com/calculus1/ 5. https://www.udemy.com/statshelp/ 6. https://www.udemy.com/integralcalc-algebra I am sure this will help you and please share this video — Follow me — : Twitter – https://twitter.com/bitfumes Facebook – https://www.facebook.com/Bitfumes/ Instagram – https://www.instagram.com/bitfumes/ (ask me questions!) — QUESTIONS? — Leave a comment below and I or someone else can help you. For quick questions you may also want to ask me on Twitter, I respond almost immediately. Email me support@bitfumes.com Thanks for all your support!

Since 1999, colleges and universities in the National Centers of Academic Excellence in Cyber Defense (CAE-CD) program have educated our nation’s cyber first responders. Learn more about the role of CAE-CD schools in developing the tools and talent needed to help defend our national security.

Cyber & Defence: Digital transformation and UK defence: A discussion with Major General Tom Copinger-Symes Recently, we’ve seen £16 billion injected into British defence — the biggest investment since the end of the Cold War. What does this mean for the UK’s standing against new superpowers? In this session, we look at how the sector is being transformed and the critical role that emerging technologies will play. Featuring: Major General Tom Copinger-Symes – Director Strategy and Military Digitsation – Ministry of Defence Grace Cassy – Co-Founder – CyLon #CogX2021 #JoinTheConversation

The NSA Cyber Exercise (NCX) is a near full spectrum cyber operations program that helps develop and test the cybersecurity skills of U.S. Service Academies’ cadets and midshipmen. The 2018 NCX will take place 19-21 March 2018 at the U.S. Naval Academy in Annapolis, Maryland. Transcript: https://www.nsa.gov/resources/everyone/digital-media-center/video-audio/general/assets/files/ncx-2018-preview-transcript-01.pdf

#technicalastra #defenseupdates #news Top 5 Latest Indian Defence News Headlines on Today’s “Defence Updates” episode 04-07-2021 follows : Defense updates : Defense Corridor Aligarh, GRSE from Bangladesh, Indra 2021, Indian cyber security strategy, Ukraine’s Motor Sich T-129 helicopters Jaisalmer: Army gears up for exercise ‘Indra’ Ukraine’s Motor Sich To provide engines for Turkish T-129 helicopters Govt To Approve 6 #Project18 Next Generation Stealth Destroyers In 2022. Indian Army chief to visit UK & Italy #FRCV competitors. Defense Corridor: 55 hectares of land allotted to 19 companies in Aligarh, companies will invest more than 1,245 crore GRSE Bags Export Order Worth $1.82 Mn USD From Bangladesh Indian Army gears up for exercise Indra 2021 exercise government will soon release the national cyber security strategy cyber security coordinator. All images/footage in this video are owned by their owners. About : Technical Astra is a No. 1 Indian Defence YouTube Channel where you will find videos related to the Great Indian Defence – Indian Army, Indian Navy & Indian Air Force and other related updates In Hindi. Indian Defence Updates Latest – Indian Defence Updates 2021 – Defence Updates 2021 – Indian Defence Updates India – Indian Defence News – technical astra | Indian Defence Hindi. Official Page :- FACEBOOK – https://www.facebook.com/Technicalastra/ Instagram : https://www.instagram.com/technicalastra/ Kishan Chand :- FACEBOOK – https://www.facebook.com/kishanachand.nanwal Note- NOTE : ALL THE IMAGES/PICTURES SHOWN IN THE VIDEO BELONGS TO THE RESPECTED OWNERS AND NOT ME.. I AM NOT THE OWNER OF ANY PICTURES SHOWED IN THE VIDEO ——————————————————————————————————— Copyright [More]