Today we’re joined by Richard Socher, the CEO of In our conversation with Richard, we explore the inspiration and motivation behind the search engine, and how it differs from the traditional google search engine experience. We discuss some of the various ways that machine learning is used across the platform including how they surface relevant search results and some of the recent additions like code completion and a text generator that can write complete essays and blog posts. Finally, we talk through some of the projects we covered in our last conversation with Richard, namely his work on Salesforce’s AI Economist project. The complete show notes for this episode can be found at Subscribe: Apple Podcasts: Spotify: Google Podcasts: RSS: Full episodes playlist: Subscribe to our Youtube Channel: Podcast website: Sign up for our newsletter: Check out our blog: Follow us on Twitter: Follow us on Facebook: Follow us on Instagram:
📝 TOPICS COVERED: 1- Natural Language Processing AKTU BTech First Year PDF Notes Download 2- Artificial Intelligence for Engineering KMC 101 201 PDF Notes Download 3- Artificial Intelligence for Engineers AKTU 4- Natural Language Processing B.Tech AKTU with PDF Notes 5- Unit 3 Natural Language Processing AKTU 1st Year 5- AI B.Tech 1st year Unit 3 6- Artificial Intelligence B.Tech first year AKTU 7- Artificial Intelligence AKTU 1st Year Lectures Watch Full Video to know, Where is the FREE F* Notes? Buy E-Book: Syllabus – Unit 3: Natural Language Processing 3.1 Speech recognition 3.2 Natural language understanding 3.3 Natural language generation 3.4 Chatbots 3.5 Machine Translation AI Full Playlist: #KrazyKaksha #ArtificialIntelligenceForEngineering #ArtificialIntelligenceForEngineers ……………………………………………………… 🛒 Buy my Unboxed Products at Crazy Price: 🛍️ LOOT Deals & Coupons: 📱Mobile, Accessories & Gadgets- ……………………………………………………… 💰EARN FREE RECHARGE: 1- PhonePe – 2- Google Pay – ……………………………………………………… 🎥MORE CHANNELS: 😎 Tech Videos: 😂 Fun, Vlog & More: 🤑Free Online Earning: 🤓Online Education: ……………………………………………………… 💰 BUSINESS: 🔗 WEBSITE: ……………………………………………………… 💬 FOLLOW FOR MORE & CHAT: Instagram – Twitter – Facebook – ……………………………………………………… DISCLAIMER: Some contents are used for educational purpose under fair use. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational, or personal use [More]
Mechanical Engineering’s Allen Robinson and several other faculty members explain how artificial intelligence and machine learning affect their research and the curricula they teach, both at the graduate and undergraduate level.
Title: Reverse Engineering Human Morality Abstract: Human morality enables successful and repeated collaboration. We collaborate with others to accomplish together what none of us can do on our own, share the benefits fairly, and trust others to do the same. Even young children play together guided by normative principles that are unparalleled in other animal species. I seek to understand this everyday morality in engineering terms. How do humans flexibly learn and use moral knowledge? How can we apply these principles to build moral and fair machines? I present an account of human moral learning and judgment based on inverse planning and Bayesian inference. Our computational framework explains quantitatively how people learn abstract moral theories from sparse examples, share resources fairly, and judge others actions as right or wrong. Bio: Dr. Max Kleiman-Weiner is a post-doctoral fellow at the Data Science Institute and Center for Research on Computation and Society (CRCS) within the computer science and psychology departments at Harvard. He did his PhD in Computational Cognitive Science at MIT advised by Josh Tenenbaum where he was a NSF and Hertz Foundation Fellow. His thesis won the 2019 Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science. He also won best paper at RLDM 2017 for models of human cooperation and the William James Award at SPP for computational work on moral learning. Max serves as Chief Scientist of Diffeo a startup building collaborative machine intelligence. Previously, he was a Fulbright Fellow in Beijing, earned an MSc in [More]
Apple Revenue (2019): US$ 260.2 Billion Google Revenue (2019): US$ 160 Billion Microsoft Revenue (2019): US$ 125.8 Billion Facebook Revenue (2019): US$ 70.7 Billion All these Giants have started their companies with an idea, which has changed our world. My idea is a proposal “Free-of-charge Online Healthcare Technology Platform”, which will grant free subscription for more than 160,000 hospitals worldwide (2017) and organize an expected $8.2 trillion healthcare market (End 2020). This platform will give enormous savings “large profit” for the hospitals worldwide with free subscription, similar to any online free account (Facebook, Twitter, Instagram, LinkedIn…etc). The platform’s fortune will be a big data representing global healthcare market “$8.2 trillion”. To understand my proposal “$$$$ Billion”, you need just to watch this 11 minutes video on my YouTube Channel. “Engineering The Future OF Healthcare Technology” LIU Team: Mohamad Abou Ali, Hassan Nasser & Mohamad Hajj-Hassan This material has been prepared and presented by the Biomedical Engineering Department at the Lebanese International University (LIU), Lebanon. Best Regards, Authors: Mohamad Abou Ali (Instructor, BENG – LIU) Mohamad Hajj-Hassan (Dean, BENG – LIU ) Department of Biomedical Engineering (BENG), Lebanese International University ( #HealthcareTechnologyPlatform #AIBigData #SmartInvestment
Session Chair: Federica Sarro Perf-AL: Performance Prediction for Configurable Software through Adversarial Learning – (Technical Paper) Yangyang Shu, Yulei Sui, Hongyu Zhang and Guandong Xu Presenter: Yangyang Shu Learning Features that Predict Developer Responses for iOS App Store Reviews – (Technical Paper) Kamonphop Srisopha, Daniel Link, Devendra Swami and Barry Boehm Presenter: Kamonphop Srisopha Automatic Identification of Code Smell Discussions on Stack Overflow: A Preliminary Investigation – (Emerging Result and Vision Paper) Sergei Shcherban, Peng Liang, Amjed Tahir and Xueying Li Presenter: Peng Liang GASSER: Genetic Algorithm for teSt Suite Reduction – (Emerging Result and Vision Paper) Carmen Coviello, Simone Romano, Giuseppe Scanniello and Giuliano Antoniol Presenter: Carmen Coviello
Welcome to our event celebrating the launch of Machine Learning Engineering for Production (MLOps) Specialization featuring AI leaders in MLOps. Topics we plan to cover: -To what extent does the role of Data Scientist or MLE involve MLOps? -How is MLOps actually implemented in an industry setting? Is there some kind of a framework people use? -Is MLOps suitable for early-stage startups or only teams with enough resources as the big tech companies do? -The latest trends on MLOps and how will the future of it evolve. -What do you see as the biggest challenges for MLOps adoption? -Apart from taking courses, what are some of the other resources or activities might recommend to learners interested in gaining practical experience with MLOps? Speakers: -Andrew Ng, Founder, DeepLearning.AI -Robert Crowe, TensorFlow Developer Engineer, Google -Laurence Moroney, AI Advocate, Google -Chip Huyen, Adjunct Lecturer, Stanford University -Rajat Monga, co-founder, Stealth Startup; Former lead of TensorFlow, Google -Event moderator: Ryan Keenan, Director of Product, DeepLearning.AI Let us know what you think of the event by filling out a quick survey here: To learn more about DeepLearning.AI and sign up for future events: To sign up for Machine Learning Engineering for Production (MLOps),
On September 11, 2017, the Deming Center at Columbia Business School partnered with the School for Engineering and Applied Science to host an event on Artificial Intelligence just for the children of faculty, staff and friends of the Center. More than 50 kids ages 6 to 16 gathered to hear Professor of Mechanical Engineering and Director of the Creative Machines Lab Hod Lipson speak to the past, present and future of AI. Impressing on them that their lives would be profoundly impacted by advances in AI and Machine Learning, Professor Lipson took the children on a journey through the history of the field, bringing them up to speed on the incredible advances of the last 50 years. He then explored all of the ways in which AI is being used today from simply playing a game of Tic Tac Toe and painting portraits to diagnosing diseases and synthesizing the vast amounts of data being generated by computers, algorithms and images that affect our daily lives. They then turned their attention to all of the ways in which AI would permeate their lives in the near future from driverless cars to robotic traffic police. The children asked creative, probing questions throughout compelling the adults in the room to think outside-of-the-box and consider the deep nuances their questions asked. From 6-year old Aziz who was curious as to whether Artificial Intelligence can exist in water to 12-year old Christina’s question about how AI will affect terrorism and war in the future, children [More]
Artificial Intelligence has come a long way from being a component of science fiction to reality. Today, we have a host of intelligent machines like self-driving cars, smart virtual assistants, chatbots, and surgical robots, to name a few. Since AI became a mainstream technology in the present industry and a part of the common man’s daily life, it has sparked a debate – Artificial Intelligence vs. Human Intelligence.  The Important Points covered in this Video: 1. Artificial Intelligence vs. Human Intelligence: Definition 2. Artificial Intelligence vs. Human Intelligence: A comparison 3. Artificial Intelligence vs. Human Intelligence: What will the future hold? 4. Conclusion #ArtificialintelligencevsHumanintelligence
WRT-1025: Using AI/ML Design Patterns for Digital Twins and Model-Centric Engineering – Dr. Mark Austin, University of Maryland Presented on November 18, 2020 at the 12th Annual SERC Sponsor Research Review. Through various keynotes and breakout sessions, the SSRR focuses on the latest research results from SERC researchers aligned with the emerging and critical research needs of sponsors. EVENT PAGE:
Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains, but the field mostly concentrates on accuracy of results. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities). Follow @OReillyAI on Twitter for news and updates about artificial intelligence.
WRT-1025: Using AI/ML Design Patterns for Digital Twins and Model-Centric Engineering – Dr. Mark Austin, University of Maryland Presented on November 18, 2020 at the 12th Annual SERC Sponsor Research Review. Through various keynotes and breakout sessions, the SSRR focuses on the latest research results from SERC researchers aligned with the emerging and critical research needs of sponsors. EVENT PAGE:
The 7 Most Disruptive Software Engineering Trends of 2021 (disruptive innovation & 2021 tech trends) As we begin our journey into 2021, we thought we’d investigate the software engineering trends that are most likely to be at the forefront of innovation in the coming months. To help us do so, we tapped the experience and knowledge of Dr. Jeff Jensen (CEO & Co-Founder of Keto AI) and Martin Do (Software Solutions Architect, and Ex-Microsoft Engineer). SUBSCRIBE to Kofi Group: ____________ 00:00 – Intro 01:14 – Think-Less and No-Code 02:17 – IoT 03:19 – React & Flutter 04:06 – AI & Machine Learning 06:44 – Cloud & DevOps 08:08 – Remote Work 10:18 – Cyber Security 13:03 – How these trends will impact software engineering 13:55 – How to take advantage of these trends 15:19 – Unpopular Opinions * Website: Blog article version: Remote jobs: Kofi Group helps startups outcompete FAANG (Facebook, Amazon, Apple, Netflix, Google) and big tech in the highly competitive, war for talent. Our videos cover hiring tips and strategies for startups, software engineering and machine learning interview preparation, salary negotiation best practices, compensation analysis, computer science basics, artificial intelligence, tips for other recruiters, and much more! Hit the SUBSCRIBE button and we’ll see you in the comments! __________ Music – Throwaway 2 by XIAO-NIAO __________ #kofigroup #disruptiveinnovation #softwareengineer #softwareengineering #machinelearning #cybersecurity #reactjs #flutter
Machine Learning for Engineering and Science Applications – Intro Video
Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division, making him NASA’s senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006.
This video describes the reasons to become a software engineer! Credit: A ride in a Waymo driverless car: Full Self-Driving: ITRI shows OLED Lighting, Chess playing robot “Turk”: Artificial justice: would robots make good judges?: This video is for non-commercial use.
With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale for near real-time decision support. This talk will discuss these complexities in the context of building an example system. First, you must have failure data to train a good model, but equipment failures can be expensive to introduce for the sake of building a data set! Instead, physical simulations can be used to create large, synthetic data sets to train a model with a variety of failure conditions. These systems also involve high-frequency data from many sensors, reporting at different times. The data must be time-aligned to apply calculations, which makes it difficult to design a streaming architecture. These challenges can be addressed through a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka. The sensor data must then be synchronized for further signal processing before being passed to a machine learning model. As these architectures and software stacks mature in areas like manufacturing, it is increasingly important to enable engineers and domain experts in this workflow to build and deploy the machine learning models and work with system architects on the system integration. This talk also highlights the benefit of using apps and exposing the functionality through API layers to help make these systems more accessible and extensible across the workflow. This session will focus on building [More]