DNA: Children trapped in Alexa’s magic are in danger Alexa gave such a dangerous challenge to a 10-year-old girl in America, which could have killed the girl due to electric shock. This news is for those people who depend excessively on Artificial Intelligence. DNA: खतरे में हैं Alexa के तिलिस्म में फंसे बच्चे अमेरिका में एक 10 साल की बच्ची को Alexa ने ऐसा खतरनाक चैलेंज दिया जिससे बिजली का झटका लगने से बच्ची की जान जा सकती थी. जो लोग Artificial Intelligence पर जरूरत से ज्यादा निर्भर रहते हैं ये खबर उनके लिए है. #DNA #Alexa #ArtificialIntelligence About Channel: ज़ी न्यूज़ देश का सबसे भरोसेमंद हिंदी न्यूज़ चैनल है। जो 24 घंटे लगातार भारत और दुनिया से जुड़ी हर ब्रेकिंग न्यूज़, नवीनतम समाचार, राजनीति, मनोरंजन और खेल से जुड़ी खबरे आपके लिए लेकर आता है। इसलिए बने रहें ज़ी न्यूज़ के साथ और सब्सक्राइब करें | Zee News is India’s most trusted Hindi News Channel with 24 hour coverage. Zee News covers Breaking news, Latest news, Politics, Entertainment and Sports from India & World. ————————————————————————————————————- Download our mobile app: http://tiny.cc/c41vhz Subscribe to our channel: http://tiny.cc/ed2vhz Watch Live TV : https://zeenews.india.com/live-tv Subscribe to our other network channels: Zee Business: https://goo.gl/fulFdi WION: http://tiny.cc/iq1vhz Daily News and Analysis: https://goo.gl/B8eVsD Follow us on Google news- https://bit.ly/2FGWI01 ————————————————————————————————————- You can also visit our website at: http://zeenews.india.com/ Like us on Facebook: https://www.facebook.com/ZeeNews Follow us on Twitter: https://twitter.com/ZeeNews Follow us on Google News for latest updates: Zee News:- https://bit.ly/2Ac5G60 Zee Business:- https://bit.ly/36vI2xa DNA India:- https://bit.ly/2ZDuLRY WION: [More]
Prof. Rodney Brooks, MIT Robots and People; the Research Challenge (with English Subtitles) English Subtitles by Tan U-Xuan, Arif Rahman, Sherry Chen, Jing Xu, and Zhidong Wang (EPSB and CAB, IEEE Robotics and Automation Society)
Graph Neural Networks (GNN) have produced groundbreaking applications in different fields where data is fundamentally structured as graphs (e.g., chemistry, recommender systems). In the field of computer networks, this new type of neural networks is being rapidly adopted for a variety of relevant networking use cases, particularly for those involving large and complex graphs (e.g., performance modeling, routing optimization, resource allocation in wireless networks). This talk presents the “Graph Neural Networking challenge 2021”. The second edition of this competition brings a fundamental limitation of existing GNNs: their lack of generalization capability to larger graphs. In order to achieve production-ready GNN-based solutions, we need models that can be trained in network testbeds of limited size, and then be able to operate with guarantees in any real customer network, which are often 10x larger in number of nodes. In this challenge, participants are asked to train their GNN models in small network scenarios (up to 50 nodes), and then test their accuracy in networks of increasing size not seen before, up to 300 nodes. Solutions with better scalability properties will be the winners. Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/ITU_AIForGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood
Data Science coding challenge time! The popular Data Science competition website Kaggle has an ongoing competition to solve the problem of earthquake prediction. Given a dataset of seismographic activity from a laboratory simulation, participants are asked to create a predictive model for earthquakes. In this video, I’ll attempt the challenge as a way to teach 3 concepts; the Data Science mindset, Categorical Boosting, and Support Vector Regression models. I’ll be coding this using python from start to finish in the online Google colab environment. Enjoy! Code for this video: https://github.com/llSourcell/Kaggle_Earthquake_challenge Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology Join us at the School of AI: https://theschool.ai/ More learning resources: https://www.kaggle.com/c/LANL-Earthquake-Prediction/data https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/ https://blog.griddynamics.com/xgboost-vs-catboost-vs-lightgbm-which-is-best-for-price-prediction/ https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db https://accio.github.io/machinelearning/2018/05/30/catboost.html http://kernelsvm.tripod.com/ https://www.saedsayad.com/support_vector_machine_reg.htm https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Richard Socher, ex-Chief Scientist at Salesforce, joins us to talk about The AI Economist, NLP protein generation and biggest challenge in making ML work in the real world. Richard Socher was the Chief scientist (EVP) at Salesforce where he lead teams working on fundamental research ( https://einstein.ai/ ), applied research, product incubation, CRM search, customer service automation and a cross-product AI platform for unstructured and structured data. Previously, he was an adjunct professor at Stanford’s computer science department and the founder and CEO/CTO of MetaMind which was acquired by Salesforce in 2016. In 2014, he got my PhD in the CS Department at Stanford. He likes paramotoring and water adventures, traveling and photography. More info: – Forbes article with more info about Richard’s bio https://www.forbes.com/sites/gilpress/2017/05/01/emerging-artificial-intelligence-ai-leaders-richard-socher-salesforce/ – CS224n – NLP with Deep Learning the class Richard used to teach. http://web.stanford.edu/class/cs224n/ – TEDx talk: where AI is today and where it’s going: https://youtu.be/8cmx7V4oIR8 Research: Google Scholar Link: https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies Arxiv link: https://arxiv.org/abs/2004.13332 blog: https://blog.einstein.ai/the-ai-economist/ short video: https://youtu.be/4iQUcGyQhdA ProGen: Language Modeling for Protein Generation: bioRxiv link: https://www.biorxiv.org/content/10.1101/2020.03.07.982272v2 blog: https://blog.einstein.ai/progen/ Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things. Issue11, (**Chemical Science 2020**). https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc06145b#!divAbstract CTRL: A Conditional Transformer Language Model for Controllable Generation: Arxiv link: https://arxiv.org/abs/1909.05858 code pre-trained and fine-tuning: https://github.com/salesforce/ctrl blog: https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/ Genie: a generator of natural language semantic parsers for virtual assistant commands: https://almond-static.stanford.edu/papers/genie-pldi19.pdf Topics Covered: 0:00 intro 0:42 the AI economist 7:08 [More]
In this video we discuss the challenges faced in developing AI that can play any video game you give it. This is the focus of the General Video Game – AI competition. Full details including a link to the written piece at: http://aiandgames.com/gvgai-part1/ In addition, a full list of all my work on AI can be found at: http://www.patreon.com/AI_and_Games No copyright is claimed for the game footage, images and music, to the extent that material may appear to be infringed, I assert that such alleged infringement is permissible under fair use principles in copyright laws. If you believe material has been used in an unauthorized manner, please contact me.
AI Family Challenge is working to bring STEM educational opportunities to families all over the world to promote diversity in STEM fields, particularly artificial intelligence. Existing education systems can’t keep up with this the pace of change in STEM fields, and students are being left behind, particularly those from underrepresented groups. You can join us and support an approach to education that extends far beyond school walls and into homes, research labs, and corporate offices to build a network of learners and mentors. https://curiositymachine.org/aichallenge/
Discussion points: – AI & the Value Loading problem – Improving the discourse around AI – Where have human ethics come from? – Human constraints don’t apply to artificial intelligecnce – An artificial intelligence could change it’s preferences Joscha Bach, Ph.D. is an AI researcher who worked and published about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He earned his Ph.D. in cognitive science from the University of Osnabrück, Germany, and has built computational models of motivated decision making, perception, categorization, and concept-formation. He is especially interested in the philosophy of AI and in the augmentation of the human mind. Joscha has taught computer science, AI, and cognitive science at the Humboldt-University of Berlin and the Institute for Cognitive Science at Osnabrück. His book “Principles of Synthetic Intelligence” (Oxford University Press) is available on amazon now: https://www.amazon.com/Principles-Synthetic-Intelligence-PSI-Architectures/dp/0195370678 Many thanks for watching! Consider supporting SciFuture by: a) Subscribing to the SciFuture YouTube channel: http://youtube.com/subscription_center?add_user=TheRationalFuture b) Donating – Bitcoin: 1BxusYmpynJsH4i8681aBuw9ZTxbKoUi22 – Etherium: 0xd46a6e88c4fe179d04464caf42626d0c9cab1c6b – Patreon: https://www.patreon.com/scifuture c) Sharing the media SciFuture creates: http://scifuture.org Kind regards, Adam Ford – Science, Technology & the Future
In 2012 the Swedish robotics cluster Robotdalen arranged the event Robotics Innovation Challenge. One of the key note speakers was Rodney Brooks, one of the world’s foremost roboticists. Rodney Brooks has a career as a controversial and distinguished researcher within artificial intelligence at Stanford University, Carnegie Mellon University and MIT. In 2008 he left the academic world to launch his new company Rethink Robotics (formerly Heartland Robotics), now ranked as one of the most promising start-ups in the US. A new generation of robots is developed to improve productivity in manufacturing environments and for places that has not been automated before.
In his work with IBM Watson system, Mauro is exploring the brand new landscape made accessible by enormous amount of data and tools that are capable of analyzing it. This terra incognita is full with unexpected discoveries and new kind of challenges, especially how to establish effective interface between the human users and the machine. Mauro leads IBM’s Cognitive Visualization Lab. He is a distinguished Italian artist, designer, inventor, and educator who investigates the impact of artificial intelligence on design. His projects have been shown at international festivals including Ars Electronica, TEDxCambridge Thrive, and Art Galleries including The Serpentine Gallery (London), GAFTA (San Francisco). His work has been featured on the cover of Nature and PNAS, as well as Nature Communication, Nature Physics, Popular Science, The Economist, The Financial Times, WIRED Magazine, The Guardian, BBC News, MIT News, and Harvard News. He is Research associate at Harvard University. Explore more about Mr. Martino at his website http://www.mamartino.com/ This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx