Artificial Intelligence (AI) is no longer sci-fi. From driverless cars to the use of machine learning algorithms to improve healthcare services and the financial industry, AI and algorithms are shaping our daily practices, and a fast-growing number of fundamental aspects of our societies. This can lead to dangerous situations in which vital decision making is automated – for instance in credit scoring, or sentencing – but limited policies exist for citizens subject to such AI technologies embedded in our social institutions to seek redress. Similarly, well-intended technologists might release AI into society that is ethically unsound. A growing body of literature on improving the auditability and transparency of algorithms is being developed. Yet, more is needed to develop a shared understanding about the fundamental issues at the heart of the debate on AI, algorithms, the law, and ethics. These issues taken together are leading to a renewed focus on, and increasing concern about, the ethical and legal impact of AI on our societies. In this panel we bring together five thought leaders on AI from the corporate sector, academia, politics, and civil society to discuss. We will hear from Paul Nemitz, Monica Beltrametti, Alan Winfield, Vidushi Marda and Sandra Wachter, the conversation will be moderated by Corinne Cath. Panel: – Paul Nemitz – Director responsible for Fundamental rights and Union citizenship in the Directorate-General Justice of the European Commission – Monica Beltrametti – Director at NAVER Labs Europe – Alan Winfield – Professor of Robot Ethics University of the West [More]
Advances in Artificial Intelligence are changing everything around us. Are art and creativity immune from the perceived AI takeover? In this talk, Dr. Ahmed Elgammal will highlight the symbiotic relationship between AI and art. He will argue why investigating perceptual and cognitive tasks related to human creativity is essential to advancing AI and how AI is changing the way art is made. Dr Ahmed Elgammal is the director of the Art and Artificial Intelligence Laboratory, and a professor of computer science at Rutgers University. He is the founder and CEO of Artrendex, a startup that builds innovative AI technology for the art market. Dr. Elgammal research on knowledge discovery in art history and AI art generation, received wide international media attention, including reports on the Washington Post, New York Times, NBC News, the Daily Telegraph, Science News, New Scientist, and many others. In 2017, an Artsy editorial acclaimed AICAN as “the biggest artistic achievement of the year”. In 2016, a TV segment about his research, produced for PBS, won an Emmy award. AICAN art has been shown in several technology and art venues in Los Angeles, Frankfurt, San Francisco, and New York City. Looking for more? Check out EGG On Air! https://bit.ly/37GhXMY CHECK OUT DATAIKU: https://bit.ly/36XBlpK BRIGHTTALK WEBINARS: https://bit.ly/33TIRjn DATA SCIENCE PIONEERS DOCUMENTARY: https://bit.ly/36V3rBF PARTNER ECOSYSTEM: https://bit.ly/3oCbk5k DATAIKU ACADEMY: https://bit.ly/2LjsEgZ DATAIKU COMMUNITY: https://bit.ly/2K8lOtV DATA SCIENCE AND ANALYTICS MEETUPS: https://bit.ly/3n0ar5R BANANA DATA PODCAST: https://bit.ly/36UFgDs Linkedin: https://bit.ly/3lXyRMb Twitter: @dataiku Instagram: @dataiku Turn on our channel notifications for the latest data science and [More]
Support my work on Patreon: https://www.patreon.com/whatsai A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code. Full list on Medium: https://medium.com/towards-artificial-intelligence/2020-a-year-full-of-amazing-ai-papers-a-review-c42fa07aff4b The GitHub repository: https://github.com/louisfb01/Best_AI_paper_2020 ►Subscribe to my newsletter: http://eepurl.com/huGLT5 Interested in computer vision? Here is my top 10 CV research papers of 2020: https://youtu.be/CP3E9Iaunm4 AI Debate 2 – Hosted by Montreal AI: https://youtu.be/VOI3Bb3p4GM Chapters: 0:00 Hey! Tap the Thumbs Up button and Subscribe. You’ll learn a lot of cool stuff in 2021, I promise. 0:28 2020, A year in review 9:06 Where do you want AI to go? #StateOfAI #ArtificialIntelligence #AI2020
In Lecture 15, guest lecturer Song Han discusses algorithms and specialized hardware that can be used to accelerate training and inference of deep learning workloads. We discuss pruning, weight sharing, quantization, and other techniques for accelerating inference, as well as parallelization, mixed precision, and other techniques for accelerating training. We discuss specialized hardware for deep learning such as GPUs, FPGAs, and ASICs, including the Tensor Cores in NVIDIA’s latest Volta GPUs as well as Google’s Tensor Processing Units (TPUs). Keywords: Hardware, CPU, GPU, ASIC, FPGA, pruning, weight sharing, quantization, low-rank approximations, binary networks, ternary networks, Winograd transformations, EIE, data parallelism, model parallelism, mixed precision, FP16, FP32, model distillation, Dense-Sparse-Dense training, NVIDIA Volta, Tensor Core, Google TPU, Google Cloud TPU Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. [More]
In Lecture 12 we discuss methods for visualizing and understanding the internal mechanisms of convolutional networks. We also discuss the use of convolutional networks for generating new images, including DeepDream and artistic style transfer. Keywords: Visualization, t-SNE, saliency maps, class visualizations, fooling images, feature inversion, DeepDream, style transfer Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences between CPUs and GPUs. Keywords: CPU vs GPU, TensorFlow, Keras, Theano, Torch, PyTorch, Caffe, Caffe2, dynamic vs static computational graphs Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
11.23.19 | Technology is advancing faster than ever, and it’s not slowing down. This decade was the era of smart phones, streaming, and the internet of things. But with 5G and AI on the rise, high-tech executive Jeff Brown believes 2030 will be a new world. Brown is an early-stage tech investor and analyst who’s seen the modern technological revolution firsthand. He describes quantum computing as a moon landing and 5G as game-changing. He predicts a near future full of artificial intelligence, self-driving cars, wireless surgeries, genetic healing, cryptocurrencies, and more! But with equal advances in encryption hacking and the AI tracking abilities of Google, Facebook, and even China, we must control our own data! ► Click HERE to subscribe to Glenn Beck https://bit.ly/2UVLqhL ►Click HERE to subscribe to BlazeTV: https://www.blazetv.com/glenn Connect with Glenn on Social Media: http://twitter.com/glennbeck http://instagram.com/glennbeck http://facebook.com/glennbeck
Artificial intelligence has been progressing at exponential rates and it’s just a matter of time until AI replaces tens of millions of blue-collar jobs and even millions of white-collar jobs. However, this is not as concerning as it might sound as artificial intelligence will also create millions of jobs not only in existing AI-related industries but also in brand new industries. Taking a look at a longer time period, AI will eventually replace all the jobs that we know today and put us in a position where everyone can just do nothing and live off welfare checks. However, people will never choose to do this. As AI takes over entire industries, it’s simply fewer things for humans to do, thus giving us more time and energy to take on bigger tasks like lengthening our lives and engaging in interplanetary travel. This video explains the various impacts of AI on the job market and why artificial intelligence will never be able to replace the role of humans. Timestamps: 0:00 – The Eventual Dominance Of AI 0:40 – Jobs Transition, Don’t Disappear 2:36 – Jobs That Cannot Be Replaced 3:54 – Long Term AI Dominance 5:15 – Distribution Of Scarce Resources 6:46 – Why AI Will Never Replace Humans 8:01 – Smaller Work Weeks 9:08 – Focus On A Sci-Fi Future 10:37 – Overall Impact Of AI Resources: https://pastebin.com/KpMWWz0q
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take. In this course, you will learn: – The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science – What AI realistically can–and cannot–do – How to spot opportunities to apply AI to problems in your own organization – What it feels like to build machine learning and data science projects – How to work with an AI team and build an AI strategy in your company – How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. Like, Subscribe & share Support our Channel Tuitions Tonight
These superheroes are as real as they get. They are, in fact, scientists, doctors and clinicians continuously tinkering in tech labs across Singapore. Meet the individuals facing off against some of the world’s most pressing health issues — from cancer to heart disease and diabetic retinopathy, as they harness the incredible power of Artificial Intelligence. The Hidden Layer: Healthcare Trailblazers ============== #CNAInsider #TheHiddenLayerHealthcareTrailblazersCNA #HealthTech #ArtificialIntelligence #Documentary #Singapore #Cancer #HeartDisease #Diabetics #AI SUBSCRIBE to CNA INSIDER for more informative content: https://cna.asia/insideryoutubesub Follow CNA INSIDER on: Instagram: https://www.instagram.com/cnainsider/ Facebook: https://www.facebook.com/cnainsider/ Website: https://cna.asia/cnainsider
Cynthia Rudin presents “Simpler Models Exist and How Can We Find Them?” at the AI Seminar (December 4, 2020). The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored. Bio: Cynthia Rudin is a professor of computer science, electrical and computer engineering, and statistical science at Duke University. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. Her degrees are from the University at Buffalo and Princeton University. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award. She has served on committees for INFORMS, the National Academies, the American Statistical Association, DARPA, the NIJ, and AAAI. She is a fellow of both the American Statistical Association and Institute of Mathematical Statistics. She was a Thomas Langford Lecturer at Duke University for 2019-2020. Abstract: While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to understand by adding interpretability constraints. These constraints shrink the hypothesis space; that is, they make the model simpler. Statistical learning theory suggests that generalization may be improved as a [More]
Mauro Martino from the Northeastern University Mauro is a pioneer in the use of the artificial neural network in the sculpture field and talks about “Beautiful Data Science”. His talk is titled “Data Exploration Through Artificial Intelligence and Dataviz”. Shooting and editing: Frieder Aurin for 2SPOT production, www.2spot.tv
As part of Qubit’s continued deep-dive into how machine learning, and artificial intelligence, can change the way that marketers engage with and communcate with their customers, we hosted a panel discussion with Gerry Brown, Ana Sanandres and Bud Goswami. To find out more about how Qubit can help you understand how to leverage machine learning for market segmentation: http://www.qubit.com/machine-learning To download the co-authored research between IDC and Qubit: http://www.qubit.com/research/machine-learning-revolutionizes-segmentation-practices
Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=rIpUf-Vy2JA Please support this podcast by checking out our sponsors: – Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin – Codecademy: https://codecademy.com and use code LEX to get 15% off – Linode: https://linode.com/lex to get $100 free credit – NetSuite: http://netsuite.com/lex to get free product tour – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free GUEST BIO: Joscha Bach is a cognitive scientist, AI researcher, and philosopher. PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 SOCIAL: – Twitter: https://twitter.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – Medium: https://medium.com/@lexfridman – Reddit: https://reddit.com/r/lexfridman – Support on Patreon: https://www.patreon.com/lexfridman
Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=rIpUf-Vy2JA Please support this podcast by checking out our sponsors: – Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin – Codecademy: https://codecademy.com and use code LEX to get 15% off – Linode: https://linode.com/lex to get $100 free credit – NetSuite: http://netsuite.com/lex to get free product tour – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free GUEST BIO: Joscha Bach is a cognitive scientist, AI researcher, and philosopher. PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 SOCIAL: – Twitter: https://twitter.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – Medium: https://medium.com/@lexfridman – Reddit: https://reddit.com/r/lexfridman – Support on Patreon: https://www.patreon.com/lexfridman
Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=rIpUf-Vy2JA Please support this podcast by checking out our sponsors: – Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin – Codecademy: https://codecademy.com and use code LEX to get 15% off – Linode: https://linode.com/lex to get $100 free credit – NetSuite: http://netsuite.com/lex to get free product tour – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free GUEST BIO: Joscha Bach is a cognitive scientist, AI researcher, and philosopher. PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 SOCIAL: – Twitter: https://twitter.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – Medium: https://medium.com/@lexfridman – Reddit: https://reddit.com/r/lexfridman – Support on Patreon: https://www.patreon.com/lexfridman
Joscha Bach is a cognitive scientist, AI researcher, and philosopher. Please support this podcast by checking out our sponsors: – Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin – Codecademy: https://codecademy.com and use code LEX to get 15% off – Linode: https://linode.com/lex to get $100 free credit – NetSuite: http://netsuite.com/lex to get free product tour – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free EPISODE LINKS: Joscha’s Twitter: https://twitter.com/Plinz Joscha’s Website: http://bach.ai PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 – Introduction 0:33 – Life is hard 2:56 – Consciousness 9:42 – What is life? 19:51 – Free will 33:56 – Simulation 36:06 – Base layer of reality 51:42 – Boston Dynamics 1:00:01 – Engineering consciousness 1:10:30 – Suffering 1:19:24 – Postmodernism 1:23:43 – Psychedelics 1:36:57 – GPT-3 1:45:40 – GPT-4 1:52:05 – OpenAI Codex 1:54:20 – Humans vs AI: Who is more dangerous? 2:11:04 – Hitler 2:16:01 – Autonomous weapon systems 2:23:29 – Mark Zuckerberg 2:29:04 – Love 2:43:18 – Michael Malice and anarchism 3:00:15 – Love 3:04:23 – Advice for young people 3:09:00 – Meaning of life SOCIAL: – Twitter: https://twitter.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – Medium: https://medium.com/@lexfridman – Reddit: https://reddit.com/r/lexfridman – Support on Patreon: https://www.patreon.com/lexfridman
How informed do you feel about the future of Artificial Intelligence? Have we addressed its challenges and biases, or do we need more conversations to improve its trajectory? In this episode of Humans, Now and Then, I speak to B Cavello, Research Program Lead at the Partnership on AI, on the opportunities to improve AI, the importance of educating the public on its use, and why it’s so important to bring more people together to discuss its future. Bio: B Cavello is a Program Lead at the Partnership on AI (PAI), a non-profit multi-stakeholder initiative focused on advancing the benefits and addressing the challenges of machine intelligence. PAI was founded by Amazon, Apple, DeepMind, Facebook, Google, IBM, and Microsoft in partnership with leading academic and civil society organizations such as the American Civil Liberties Union (ACLU), the MacArthur Foundation, and OpenAI. B leads PAI research in areas such as fairness, transparency, AI’s impact on labor, and works closely with PAI Partner organizations. Previously, B served as Senior Engagement Lead for IBM’s AI division, the Watson Group, where they led strategic conversations with world leaders about implementing Artificial Intelligence in the real world. In June 2017, B was recognized as an IBM LGBT+ ‘Out Role Model’ for championing diversity and inclusion in and beyond IBM. Prior to IBM, B was both Product Development and Community Director at Exploding Kittens, a record-breaking crowdfunded card game startup. B also co-led Phenomenon Media 501c3, an arts and education nonprofit, developing an educational tool for [More]
In this podcast episode, Ryan has the pleasure of talking with Gihan Perera, who is an author, conference speaker and consultant. As a futurist, Gihan researches trends that will impact the future of work and brings these insights to audiences in a relevant way. Gihan talks about one of the biggest trends at the moment, artificial intelligence, and explains how it is already everywhere in our lives, how it helps us do our jobs better, and how it will continue to evolve. He also discusses digital disruption and the challenges around it, skills leaders of today need to have, and innovation. He shares his insights on creating a high performance culture characterised by diversity and inclusion, as well as the importance of learning from the younger generations like Gen Z. Lastly, he talks about his book “The Future of Leadership” and some of the insights that can be learned from it. Links Read more about or get in touch with Gihan Perera here Where else you can find us Website: https://thebreakthrough.co LinkedIn: http://www.linkedin.com/company/the-breakthrough-company Facebook: https://www.facebook.com/thebreakthroughco/ Podcast: https://thebreakthrough.co/podcast/ Blogs: https://thebreakthrough.co/blog/ YouTube: https://www.youtube.com/thebreakthroughco
S2 E376 Toshie Takahashi, Professor, Institute of AI and Robotics, Waseda University, Tokyo 00:00 What is AI and Machine Learning and why are they used together? 03:45 Strong AI vs Weak AI 06:06 AI and the common man 07:19 Are robots going to take our jobs? 08:33 Which governments are encouraging the use of AI and which governments are resisting it? 11:14 Ethical issues of AI, ML, and Robotics 13:44 Will machines take over our world? 16:02 Toshie’s new research 20:07 Toshie’s book 21:58 What does success mean to Toshie and what inspires her? 24:48 Core values Toshie believes in —————————————————————————————————————————————————————- Diversity and Inclusion have become the need of the hour. In this fast-paced digital world, how can one ensure that our new technology like artificial intelligence and AI also embraces people from all walks of life? The Brand Called You brings you a professor and researcher from Harvard and Cambridge, Toshie Takahashi. Toshie takes us through the basics of Artificial Intelligence, Machine Learning, Robotics, and their possible future. She talks about all the good AI can do and her contribution to make AI more inclusive. Tune in to learn more about the future of Artificial Intelligence and humanity. Do not forget to like and subscribe! ————————————————————————————————————————————————————– Follow us on Facebook – http://facebook.com/followtbcy/ Twitter – http://twitter.com/followtbcy/ Instagram – http://instagram.com/followtbcy/ #ArtificialIntelligence #Technology #Robotics
How many times a day do you interact with AI in your everyday things? Four leading figures in the future of AI discuss the responsibilities and opportunities for designers using data as material to create social impact through a more inclusive design of products and services. When considering the future of design leveraging artificial intelligence, the mantra can no longer be “move fast and break things”. Featuring: Jennifer Bove, Head of Design for B2B Payments, Capital One Dr. Jamika D. Burge, Head of AI Design Insights, Capital One Co-Founder, blackcomputeHER Ruth Kikin-Gil, Responsible AI strategist and Senior UX Designer, Microsoft Molly Wright Steenson, Senior Associate Dean for Research, College of Fine Arts, Carnegie Mellon University Dive deeper into this issue: https://onblend.tealeaves.com/diversity-bias-ethics-in-ai/ Register for future Nature X Design Events: https://onblend.tealeaves.com/naturexdesign/​ Get to know TEALEAVES Our Sustainability: ​https://www.tealeaves.com/pages/our-ethos Facebook: http://www.facebook.com/TealeavesCo​​ Twitter: http://www.twitter.com/TealeavesCo​​ Instagram: http://www.instagram.com/TealeavesCo
Clarifai Community brings together the world’s best AI resources, including state-of-the-art models from organizations like Google, Facebook, and HuggingFace. These models can then become the building blocks for your own AI using Clarifai Mesh, which lets you create workflows by chaining together models to solve complex problems. And, of course – you can also share your favorite workflows with the public. Read our blog: www.clarifai.com/blog/clarifai-launches-the-worlds-ai-community Learn more at: https://www.clarifai.com/
Matthew Zeiler, Founder and CEO of Clarifai, is a machine learning Ph.D. and thought leader pioneering the field of applied artificial intelligence (AI). In this session Matt will run through the latest advancements in computer vision, and how industries are innovating using computer vision. #Clarifai provides a platform for #datascientists, developers, researchers, and enterprises to master the entire #artificialintelligence lifecycle. Try our free API and get started with 1,000 free operations each month. Request a free API key at our website, https://www.clarifai.com/, and start building today. Learn more about Clarifai at: https://www.clarifai.com/. Sign up for a free account: https://portal.clarifai.com/signup
Clarifai provides a fantastic service that brings image recognition into any application that connects to the cloud. Matt Zeiler, CEO and founder of Clarifai, talks about their service and explains the concepts of artificial intelligence, machine learning, deep learning and neural networks. A huge thank you to Matt Zeiler for taking the time to chat about all of this very cool tech and for giving us a really nice demo of what Clarifai can do! If you’d like to try it out yourself, head to https://www.clarifai.com for more info and to try out some of the public demos yourself! — Dev Diner is a website and channel dedicated to helping developers navigate the world of emerging tech – Virtual & Augmented Reality, Artificial Intelligence, the Internet of Things and more! Discover more about what’s happening and how you can get started at: https://devdiner.com — A big thank you to Heartbeat Intensity (http://heartbeatintensity.com) as always for providing our wonderful theme music!
Businesses can benefit from both the efficiency of machine learning (ML) as well as the quality of human judgement. An increasing part of the ML solution is human-in-the-loop (HITL), where human feedback is provided to evaluate the output of ML algorithms, i.e., to determine its validity and help refine the result. An example is image classification, where the task might be too ambiguous for a purely mechanical solution and too vast for even a large team of human experts. In this session, learn how to effectively incorporate human-in-the-loop in your ML projects to achieve higher accuracy and better results with Amazon Mechanical Turk (Mechanical Turk). Learn More – https://aws.amazon.com/machine-learning Catch up on the excitement of re:Invent 2018 with the AWS launchpad featuring launch announcements, demos of newly launched technology, interviews with expert guests and live Q&A. AWS re:Invent is a tech education conference for the global cloud computing community hosted by Amazon Web Services. See all recordings of the AWS Launchpad at re:Invent here: https://www.youtube.com/playlist?list=PLhr1KZpdzukc0WXQruGVXTiNPtct-LLaa and learn more about AWS live streaming here: https://aws.amazon.com/twitch.
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