Podcasts about machine learning deep learning

Questa settimana Alberto Massidda ci spiega un po' come funziona il machine learning e non solo, e persino io sono riuscito a capirci qualcosa :D . ## Supportaci suhttps://www.gitbar.it/support**Grazie di cuore**Questa settimana dobbiamo ringraziare:- Giovanni Italiano

Cybersecurity is inherently complicated due to the dynamic nature of the threats andever-expanding attack surfaces.  Ironically,this challenge is exacerbated by the rapid advancement of many new technologieslike Internet of Things (IoT) devices, 5G infrastructure, cloud-basedcomputing, etc.  This is where artificialintelligence (AI) and machine learning (ML) techniques can be called intoservice, and provide potential solutions in terms of threat detection andmitigation responses in a rapidly changing environment.  On contrary, humans are often limited by theirinnate inability to process information and fail to recognize/respond to attackpatterns in the multi-dimensional, multi-faceted world.  The recent DARPA AlphaDogFight has proven AIpilots can defeat even the best human pilot in air-to-air combat.  This prompted our engineers to develop aminimum viable product (MVP) that demonstrates the value of a multi-agent reinforcementlearning (MARL) architecture in a simulated cyber wargaming environment.   By using our simulation framework, we essentially"trained" the learning agents to produce the optimum combination/permutation ofcyber attack vectors in a given scenario. This cyber wargaming engine allows our analysts to examine tactics,techniques and procedures (TTPs) potentially employed by our adversaries.  Once these vulnerabilities are analyzed, ourcyber protection team (CPT) can close security gaps in the system. About the speaker: Ambrose Kam is a Lockheed Martin Fellow with over 25 years of experience in the Department of Defense (DoD) industry. He is one of the earliest pioneers at applying modeling, simulation, and operations analysis techniques to threat modeling and cyber resiliency assessment. He regularly gives lectures at MIT, Georgia Tech, and industry consortiums like the Military Operations Research Society (MORS) and National Defense Industry Association (NDIA). Ambrose has been quoted in major publications including Forbes, The Economist, etc, and has co-authored a book in Simulation and Wargames. As a subject matter expert, he represents Lockheed Martin in industry standards organizations like ISO, LOTAR, and INCITS. His most recent efforts in wargaming, Machine Learning/Deep Learning, Cyber Digital Twin, and Blockchain earned him patents and trade secret awards. In 2017, Ambrose won the prestigious Asian American Engineer of the Year (AAEOY) award for his technical leadership and innovations. He holds several advanced degrees from MIT and Cornell University as well as a Bachelor of Science degree from the University at Buffalo.

Given how Artificial Intelligence (AI) is eating our civilization, it's essential to sketch out its first principles and ethical dimensions. We are joined by one of the world's leading AI/ML experts in former founder and co-lead at Google's AI ethical division, and the founder of Ethical AI LLC Margaret Mitchell (@mmitchell_ai) who speaks to our Empasco Co-Founder Waheed Rahman (@iwaheedo) on the fundamentals of artificial intelligence ethics and global value structures. Margaret has published more than 50 blockbuster papers on vision-language and grounded language generation concentrating on the evolution of AI towards achieving positive goals. She is vastly famous for her work on the instinctive elimination of undesired biases concerning demographic groups from machine learning models. And her work is utilized by some of the biggest companies in the world. Margaret was also the founding member of Microsoft's ethical AI group. In this episode, we talk about: - what is exactly AI?  - what's machine learning vs deep learning? -the first principles of Ethical AI -various types of bias and the existence of a 'good' and 'bad' bias as key components to building an AI model  -how governments and policymakers can evaluate ethical AI models -development of AI in the western world vs the Emerging markets -the possible utopian, dystopian and realistic predictions of a society fully adopting AI Follow our host Waheed Rahman (@iwaheedo) for more updates on tech, civilizational growth, progress studies, and emerging markets. Here are the timestamps for the episode. On some podcast players, you should be able to click the timestamp for the episode. (00:00) - Intro (06:03) - Margaret's background on Artificial Intelligence (AI) (07:55) - Definition of Artificial Intelligence  (09:36) - Difference between Machine Learning & Deep Learning (11:56) - Ethics in AI (Definition & Practices) (13:43) - The role of the human layer in Ethical AI  (15:11) - Categories & Examples of biases that occur in AI (22:29) - Normative vs Descriptive approach for selecting biases in machine learning models (25:22) - Recent developments in the field of AI (26:50) - Effective practices of ethical AI in big tech companies (29:33) - Steps Governments & Policymakers can take to build and regulate AI models (32:08) - How should tech startups in Emerging Markets develop models in the field of AI ethics? (35:07) - Future of AI: Utopian vs Dystopian vision (37:41) - Margaret's recent venture on open source AI  (40:36) - Outro

ABOUT THIS TALK What does graph have to do with machine learning? A lot, actually. And it goes both ways Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is still a nascent technology, but one which is full of promise. Amazon, Alibaba, Apple, Facebook and Twitter are just some of the organizations using this in production, and advancing the state of the art. More than 25% of the research published in top AI conferences is graph-related. Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch. Machine learning can effectively help the semantic modeling process needed to construct knowledge graphs, and consequently populate them with information. Key Topics What can knowledge-based technologies do for Deep Learning?What is Graph AI, how does it work, what can it do?What's next? What are the roadblocks and opportunities? Target Audience Machine Learning PractitionersData ScientistsData ModelersCxOsInvestors Goals Explore the interplay between machine learning and knowledge based technologiesAnswer questions that matterHow can those approaches complement one another, and what would that unlock?What is the current state of the art, how and where is it used in the wild?What are the next milestones / roadblocks?Where are the opportunities for investment? Session outline IntroductionMeet and GreetSetting the stageKnowledge Graphs, meet Machine LearningHow can machine learning help create and populate knowledge graphs?What kind of problems can we solve by using it?Where is this used in production?What is the current state of the art in knowledge graph bootstrapping and population?What are the major roadblocks / goals, how could we address them, and what would that enable?Who are some key players to keep an eye on?Graph Machine LearningWhat is special about Graph Machine Learning?What kind of problems can we solve by using it?Where is it used in production?What is the current state of the art?What are the major roadblocks / goals, how could we address them, and what would that enable?Who are some key players to keep an eye on? Format Extended panelExpert discussion, coordinated by moderator2 hours running timeRunning time includes modules of expert discussion, interspersed with modules of audience Q&A / interaction Level Intermediate - Advanced Prerequisite Knowledge Basic understanding of Knowledge GraphsBasic understanding of Machine Learning / Deep Learning

Ahmad Mustafa Anis joins the adventure to discuss how he deploys his Machine Learning models using FastAPI. FastAPI is a system for connecting and running Python programs. If your model is built in Python, you can use FastAPI to deploy to Heroku or similar services. Panel Ben WilsonCharles Max WoodFrancois Bertrand Guest Ahmad Mustafa Anis Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tv Links How to deploy Machine Learning/Deep Learning models to the webMachine Learning Guide PodcastAhmad Anis, Author at cnvrgAhmad Anis - KDnuggetsLinkedIn: Ahmad AnisGitHub: Ahmad Mustafa Anis ( ahmadmustafaanis ) Twitter: Ahmad Mustafa Anis ( @AhmadMustafaAn1 ) Picks Ahmad- Deep Learning for Coders with Fastai and PyTorchBen- Arcadia: A NovelCharles- JSJ 278 Machine Learning with Tyler RenelleCharles- X: Multiply Your God-Given Potential Charles- The Art of Impossible: A Peak Performance Primer Charles- Lost Ruins of ArnakCharles- Steampunk Rally Charles- Gods Love Dinosaurs Francois- Will and Vision: How Latecomers Grow to Dominate Markets Contact Ben: DatabricksGitHub | BenWilson2/ML-EngineeringGitHub | databrickslabs/automl-toolkitLinkedIn: Benjamin Wilson Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Contact Francois: Francois BertrandGitHub | fbdesignpro/sweetviz Special Guest: Ahmad Mustafa Anis.

AR, AI, Machine learning, Deep learning, Full body tracking in fashion, we talked about all of these exciting technologies in this episode with Stephan Klimpke. Stephan founded Vyking in 2016 and led a team to release computer vision face tracking technology used in AR media campaigns in the UK and US, Launched Beta Version of Vyking AR SDK framework in July 2017, closed 5+ paid brand beta tests with Chanel and Nike. Pivoting of business model from agency-basis to solely technology licensing for 'Body AR' and including the full suite of technology (face, hand, foot and full body tracking)

И снова здравствуйте! AI, ML, Deep Learning - и прочие умные тренды. Вы наверняка слышали про них. Когда-то, с термином искусственный интеллект была одна ассоциация: "И восстали машины из пепла ядерного огня, и пошла война на уничтожения человечества и шла она десятилетия, но последнее сражение состоится не в будущем, оно состоится здесь, в наше время, сегодня ночью..."(с) Терминатор Вот и мы смотрели это старое доброе кино. Сейчас популярно более современное определение: Мы решили разобраться на встрече без галстуков - что же такое AI, как и что в нем можно тестировать? В гостях у нас были: Михаил Перлин, Разработчик в области Artificial Intelligence, Берлин Иван Ямщиков, ABBYY AI Евангелист, Институт Макса Планка - Исследователь Искусственного Интеллекта, Кировск/Лейпциг Спрашивали о наболевшем наши непостоянные и самые депрессивные ведущие Сергей Атрощенков, Ленинград и Алексей Виноградов, у себя дома. Мы обсудили и получили просветление по таким вопросам: Что такое АI? Не, ну а на самом деле? Различия между AI, Machine Learning и Deep Learning А зачем там вообще тестирование? Data-sets - что это? почему это важно? А как их, эстамое, протестировать? Генеративные, Генеративно-состязательные сверточные сети, что это, примеры, как их тестировать? Музыка: "Нейронная оборона" https://music.yandex.ru/artist/4445922  Всякое что не существует, но придумано машиной https://thisxdoesnotexist.com  Какие навыки нужны - чтобы начать тестировать ML?  Из интересных ссылок на посмотреть и почитать: Искусственный интеллект - рассказывает евангелист ИИ Иван Ямщиков Могут ли машины создавать новое? Как искусственный интеллект превращается в новое электричество Quality Assurance for Artificial Intelligence (Michael Perlin) Иван Ямщиков — Как данные превращают в знания и почему уметь мечтать — одно из самых важных умений Вы, наверное, заметили, что наши последние выпуски стали короче. Мы исправили эту оплошность в нашем новом запуске. Stay Tuned...

I detta avsnitt fortsätter vi från det förra in i nästa datadecennium. Vad är de stora nyheterna och trenderna som kommer fortsätta in på 20-talet? Klimatkris, elbilar, maskininlärning och AI är ämnen som tas upp. Samt att det mycket väl kan vara sånt vi inte alls har någon aning om. Följ med in i nästa datadecennium. Dagens spaning temperatur.nu - svensk temperaturdata i realtid och historiskt. Länkar & Kommentarer Klimatdata från Our World In Data De rikaste 25 % av världens befolking använder 20 ggr mer energi än de 25 % fattigaste, men pga lägre effektiv användning blir den effektiva skillnaden närmare 40 ggr större. [Energy and Civilization (2017), Vaclav Smil. s357] Global energianvändning Elbilsstatistik SCB:s fordonsstatistik Fermis "back-of-the-envelope calculations" - inte Feynman Google trends: Machine Learning & Deep Learning #27 Möjligheter med AI med Marcus Weiland David Mindell - forskare som skrivit om robotar och autonomi. Bl.a. boken Our Robots, Ourselves (2015) Nästa Half-Life blir VR-exklusivt. YouTube - Samsung Bot Chef Disclaimer: Vi har för närvarande inga externa samarbeten och alla åsikter är våra egna. Inget vi pratar om är någon typ av investeringsrekommendationer och alla investeringar är förenade med risk. Medverkande i avsnittet: Henning Hammar, driver tjänsten Börslabbet, doktor i fysik, @investerarfys Daniel Constanda, IT-konsult i finansbranchen på Clara Financial Consulting, @DanielConstanda Martin Nordgren, ingenjör på Tobii, tidigare på Dirac, @martinjnordgren Kontakta oss:dataspaning.se@dataspaning @ Twitterdataspaning@gmail.com

Dr. Alex Antic is a trusted and experienced Data Science leader. He has a proven record of delivering innovative, successful and sustainable projects in government, industry and startups (including Sports Analytics), that leverage data and Machine Learning/Deep Learning capabilities to deliver actionable insights. In this episode of The Analytics Show, he will discuss about the uses of Confidential Computing and Natural Language Processing, and we discussed how that could be important in order to convince the public moving to the cloud. I also asked Dr. Antic about his experiences in working with different groups of analytics professionals and further seek to understand his mission to train the next generation of data scientists at Australia National University. With a unique background and professional experiences, Dr. Antic brings a fresh perspective in helping to bring the PhD students in analytics and industry closer through a pragmatic approach at ANU. If you are looking to build up analytics capabilities and wanting to learn how to build up team, this is one episode not to miss. LinkedIn Profile URL: linkedin.com/in/alexantic Blog Site: https://impartiallyderivative.com/ Official Site: https://cecs.anu.edu.au/people/alex-antic Human Services Australia: https://www.humanservices.gov.au/ Meetups that Alex runs: https://www.meetup.com/Data-Science-Canberra/ https://www.meetup.com/Canberra-R-Users-Group/ https://www.meetup.com/Analyst-First-A1-Canberra/ Alex's blogs that you should read 10 hidden challenges of working as a Data Scientist, and how to overcome them to advance your career 7 questions you need to ask before taking on a Data Science role. Featured Interviews: https://www.superdatascience.com/podcast/podcast-building-successful-data-science-practice-effective-data-scientist https://www.datafriends.rocks/single-post/2018/02/27/Q-A-with-Alex-Antic-Data-Scientist --- Send in a voice message: https://anchor.fm/analyticsshow/message

Minter Dialogue Episode #340Sam Charrington is an industry analyst, specialized in Machine Learning and Artificial Intelligence. He's also the host of the very successful podcast, TWiMLAI aka This Week in Machine Learning and AI. In this conversation with Sam, we plunge into how and why businesses are using ML and AI, the biggest learnings Sam has had after doing nearly 300 episodes, who was his favorite guest, as well as the outlook for AI/ML in 2020. If you've got comments or questions you'd like to see answered, send your email or audio file to nminterdial@gmail.com; or you can find the show notes and comment on minterdial.com. If you liked the podcast, please take a moment to go over to iTunes or your favourite podcast channel, to rate/review the show. Otherwise, you can find me @mdial on Twitter. Support the show (https://www.patreon.com/minterdial)

Mike & Nakiso discuss the unprecedented disruptive power of industry 4 technologies like AI, Machine Learning, Deep Learning, VR / AR and others, and their already significant effect on a multitude of industries from Financial Services, Insurance, Mobility and Transportation, Health Care, Food and Urban Farming to Gaming, Music, Movies and Professional Sports. In addition, we discuss how AI & ML are totally changing the game for sales, marketing and customer service and support professionals.

Che differenza c'è tra Intelligenza Artificiale, Robotica e ML? Cos'hanno a che fare le Reti Neurali Artificiali con le Neuroscienze? Scopriamolo insieme! Corso online "Elements of AI": https://www.elementsofai.com/ Alcune definizioni da me presentate sono rielaborate dalla seguente fonte: * Machine Learning Glossary | Google Developers: https://developers.google.com/machine-learning/glossary/ (licenza: https://creativecommons.org/licenses/by/4.0/);

La inteligencia artificial está cada día más presente, mucha tecnología la incorpora sin que prestemos mucha atención a cómo funciona y cómo se consiguen entrenar los modelos para la automatización de procesos. En esta charla divulgativa ofrecida por Alberto Ruíz Rodas en las Jornadas de seguridad TomatinaCON, expone de una manera sencilla en qué consiste el #MachineLearning el Deep Learning y el entrenamiento de las redes neuronales y explica de manera práctica cómo crear un sistema basado en Deep learning para detectar webs maliciosas. Más información y enlaces de los repositorios comentados disponibles en: https://www.yolandacorral.com/machine-learning-deep-learning-detectar-webs Alberto Ruiz Rodas (https://twitter.com/AlbertoRRodas). Ingeniero Preventa de Sophos en España y Portugal (http://sophosiberia.es). Ingeniero de Telecomunicaciones especializado en diseño y administración de redes y sistemas, en soluciones de seguridad perimetral y en soporte técnico. Sigue Palabra de hacker tu canal de ciberseguridad de tú a tú en: - Suscríbete escucha todos los podcasts en Ivoox http://www.ivoox.com/podcast-palabra-hacker_sq_f1266057_1.html en iTunes: https://itunes.apple.com/es/podcast/palabra-de-hacker/id1114292064 o Spotify https://open.spotify.com/show/1xKmNk9Gk5egH6fJ9utG86 - Suscríbete al canal de YouTube para no perderte ningún vídeo: https://www.youtube.com/c/Palabradehacker-ciberseguridad - Toda la información en la web https://www.yolandacorral.com/palabra-de-hacker - Twitter: https://twitter.com/palabradehacker - Facebook: https://www.facebook.com/Palabradehacker

Introduction to Machine Learning/Deep Learning with Susan Nash, AAPG's Director of Innovation and Emerging Science/Technology. https://www.aapg.org/podcast/episode/articleid/52568

Gościem szóstego odcinka jest Vladimir Alekseichenko. Vladimir to specjalista od sztucznej inteligencji i uczenia maszynowego, programista, trener, podcaster oraz podróżnik. Z Vladimirem rozmawiamy o tym czym jest sztuczna inteligencja, jakie są jej etapy, czym jest uczenie maszynowe (machine learning) i czym różni się od uczenia głębokiego (deep learning). O przeniesieniu naszych mózgów do komputera, o niezliczonych zastosowaniach sztucznej inteligencji, o podróżach i o tym jaką drogę trzeba przejść, aby z małej białoruskiej wioski dojść do pozycji specjalisty w dziedzinie sztucznej inteligencji. Panie i Panowie, Vladimir Alekseichenko! Notatki do podcastu dostępne pod adresem: http://startupmyway.com/6

In this session, we provide an overview of the artificial intelligence/machine learning landscape, discuss the current state of the industry, and identify new market opportunities. Partners will come away with a better understanding of the investment that AWS is making in this space, as well as our unique value proposition.

Scott talks to Microsoft Research's Edaena Salinas Jasso who explains Machine Learning, Deep Learning, and Artificial Intelligence. What are they, what's the difference, and how can I use them to make my users' lives better?