Podcasts about genetic algorithms

Sean Moriarity, creator of the Axon deep learning framework, co-creator of the Nx library, and author of Machine Learning in Elixir and Genetic Algorithms in Elixir, published by the Pragmatic Bookshelf, speaks with SE Radio host Gavin Henry about what deep learning (neural networks) means today. Using a practical example with deep learning for fraud detection, they explore what Axon is and why it was created. Moriarity describes why the Beam is ideal for machine learning, and why he dislikes the term “neural network.” They discuss the need for deep learning, its history, how it offers a good fit for many of today's complex problems, where it shines and when not to use it. Moriarity goes into depth on a range of topics, including how to get datasets in shape, supervised and unsupervised learning, feed-forward neural networks, Nx.serving, decision trees, gradient descent, linear regression, logistic regression, support vector machines, and random forests. The episode considers what a model looks like, what training is, labeling, classification, regression tasks, hardware resources needed, EXGBoost, Jax, PyIgnite, and Explorer. Finally, they look at what's involved in the ongoing lifecycle or operational side of Axon once a workflow is put into production, so you can safely back it all up and feed in new data. Brought to you by IEEE Computer Society and IEEE Software magazine. This episode sponsored by Miro.

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs. The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively. Topics discussed: Using pre-trained models in Bumblebee for Elixir projects Training models using Python and SQL The importance of data preprocessing before building models Popular tools used for machine learning in different languages Getting started with ML by picking a personal project topic of interest Resources for ML aspirants, such as online courses, tutorials, and books The potential for Elixir to train more customized models in the future Similarities between ML approaches in different languages Collaboration opportunities across programming communities Choosing the right ML approach for the problem you're trying to solve Productionalizing models like fine-tuned LLM's The need for hands-on practice for learning ML skills Continued maturation of tools like Bumblebee in Elixir Katelynn's upcoming CodeBeam talk on advanced motion tracking Links mentioned in this episode https://launchscout.com/ https://www.cars.com/ Genetic Algorithms in Elixir (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) by Sean Moriarity Machine Learning in Elixir (https://pragprog.com/titles/smelixir/machine-learning-in-elixir/) by Sean Moriarity https://github.com/elixir-nx/bumblebee https://github.com/huggingface https://www.docker.com/products/docker-hub/ Programming with MATLAB (https://www.mathworks.com/products/matlab/programming-with-matlab.html) https://elixirforum.com/ https://pypi.org/project/pyspark/  Machine Learning Course (https://online.stanford.edu/courses/cs229-machine-learning) from Stanford School of Engineering Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) by Aurélien Géron Data Science for Business (https://data-science-for-biz.com/) by Foster Provost & Tom Fawcett https://medium.com/@carscomtech  https://github.com/k-burns  Code Beam America (https://codebeamamerica.com/) March, 2024 Special Guests: Alexis Carpenter and Katelynn Burns.

This interview was recorded for the GOTO Book Club.gotopia.tech/bookclubRead the full transcription of the interview hereSean Moriarity - Author of "Genetic Algorithms in Elixir" & "Machine Learning in Elixir"  Bruce Tate - President at Groxio & Author of many BooksRESOURCESSeanseanmoriarity.com@sean_moriaritygithub.com/seanmor5Brucegrox.io@redrapidslinkedin.com/in/bruce-tateDESCRIPTIONFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms.* Book description: © The Pragmatic BookshelfThe interview is based on the book "Genetic Algorithms in Elixir"RECOMMENDED BOOKSSean Moriarity • Genetic Algorithms in ElixirSean Moriarity • Machine Learning in ElixirBruce Tate • Programmer Passport: ElixirBruce Tate • Programmer Passport: PrologBruce Tate,  Ian Dees, Frederic Daoud & Jack Moffitt • Seven More Languages in Seven WeeksBruce Tate • Seven Languages in Seven WeeksSvilen Gospodinov • Concurrent Data Processing in ElixirIan Goodfellow, Yoshua Bengio & Aaron Courville • Deep LearningFrancois Chollet • Deep Learning with PythonTwitterInstagramLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily

Frances Buontempo joins Phil and returning guest co-host, Matt Godbolt. Frances talks to us about her new book on modern C++ as well as her the topic of her previous book on machine learning. We discuss the differences between LLM-based AI and more statistical approaches, as well as where random numbers fit into all this and the limitations of their current support in C++. Show Notes News CppCon programme announced C++ on Sea videos "Inside STL" - The Old New Things Open source C++ projects that use modern C++ features (Reddit) Links "C++ Bookcamp" (title may change) - Frances' new book "Genetic Algorithms and Machine Learning for Programmers" - Frances' previous book Overload issues (submit articles on the main ACCU site) Frances' paper bag escapology certificate Shannon's mind reading paper ERNIE (Electronic Random Number Indicator Equipment) P2059R0 - "Make Pseudo-random Numbers Portable" (defunct) "Using, Generating and Testing with Pseudo-Random Numbers" - Frances' ACCU 2023 talk "Program your way out of a paper bag" series: "How to program your way out of a paper bag" (slides) "How to Evolve Your Way Out of a Paper Bag" (video) "Diffuse your way out of a paper bag" (video) "How to Evolve Your Way Out of a Paper Bag" (video) "Crowd Your Way Out of a Paper Bag" (video)

This Flashback Friday is from episode 479 published last February 18, 2015. In today's Creating Wealth introduction, Jason Hartman reads out loud about a Realty Times article about Zillow's evaluations and gives his comments on this. He also talks about military drones, regular drones, and reminds the audience that you can still purchase Meet the Master home study courses on JasonHartman.com!  Dr. David E. Goldberg is today's Creating Wealth guest. David has a background in civil engineering and is also an author who has written several books about engineering and computer algorithms. Jason talks to David about his most recent book, A Whole New Engineer as well as genetic algorithms, why there is a decline in engineers, and more on today's episode.    Key Takeaways: 3:58 Jason talks about when he first started in the real estate business.  7:58 Single family homes appreciate a lot better than other real estate classes.  14:06 Jason reads out loud a Realty Times article about Zillow,  17:41 Zillow agents say their estimates are 'just a good starting point', but what does that mean?  19:38 Reminder: if you have any comments or questions for Jason, you can now leave voice messages on the website.  20:36 Appraisals and CMAs show the data points, Zillow does not.  27:41 Jason introduces his guest, Dr. David E. Goldberg.  30:56 Designing a kidney by hand is almost impossible, but by using nature's genetic algorithms as a base, you can speed up the process.  35:36 There will always be a good and bad side to technological advances.  40:26 At one point in our history, engineers were seen as rockstars.  46:26 Engineering is fairly uninviting and there's bigger paychecks else where. 51:36 When students feel trusted, they end up achieving a lot more.    Mentioned In This Episode: Zillow.com http://realtytimes.com/consumeradvice/sellersadvice1/item/32910-20150218-starting-with-zillows-zestimate-may-not-get-you-very-far The Singularity is Near by Ray Kurzweil  NoFlyZone.org  DoNotCall.gov The Visible Hand by Alfred Chandler http://bigbeacon.org/   http://www.amazon.com/David-E.-Goldberg/e/B000APHEJU   Follow Jason on TWITTER, INSTAGRAM & LINKEDIN Twitter.com/JasonHartmanROI Instagram.com/jasonhartman1/ Linkedin.com/in/jasonhartmaninvestor/ Call our Investment Counselors at: 1-800-HARTMAN (US) or visit: https://www.jasonhartman.com/ Free Class:  Easily get up to $250,000 in funding for real estate, business or anything else: http://JasonHartman.com/Fund CYA Protect Your Assets, Save Taxes & Estate Planning: http://JasonHartman.com/Protect Get wholesale real estate deals for investment or build a great business – Free Course: https://www.jasonhartman.com/deals Special Offer from Ron LeGrand: https://JasonHartman.com/Ron Free Mini-Book on Pandemic Investing: https://www.PandemicInvesting.com

Join the BeamRadio panel for a riveting talk with Sean Moriarity on Elixir and machine learning! https://seanmoriarity.com Machine Learning in Elixir – Coming Soon! Genetic Algorithms in Elixir (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) We want to connect with you! Twitter: @BeamRadio1 Send us your questions via Twitter @BeamRadio1 #ProcessMailbox Keep up to date with our hosts on Twitter @akoutmos @lawik @meryldakin @RedRapids @smdebenedetto @StevenNunez and on Mastodon @akoutmos@fosstodon.org @lawik@fosstodon.org @redrapids@genserver.social @steven@genserver.social Sponsored by Groxio (https://grox.io) and Underjord (https://underjord.io)

In this episode, we explore the fascinating world of Evolutionary Computation and Evolutionary Algorithms (EAs) and their real-world applications. We dive into the fundamental concepts of EAs, such as natural selection, mutation, and recombination, while discussing various types of algorithms, including Genetic Algorithms, Evolutionary Programming, and Genetic Programming. Learn how these powerful optimization techniques have been applied to diverse domains such as function optimization, evolutionary art and music, and neural network evolution. Join us in this captivating journey to understand how EAs can be used to solve complex problems and unlock new possibilities.Support the Show.Keep AI insights flowing – become a supporter of the show!Click the link for details

Sean Moriarity, author of Genetic Algorithms in Elixir and creator of the Axon Library, joins Elixir Wizards Sundi Myint and Bilal Hankins to discuss Elixir's role in the future of machine learning and AI. He explains the difference between artificial intelligence, chat models, machine learning, deep learning systems, and neural networks. Large language models have great potential for code generation, education tools, streamlining workflow, and more. Deployment, development experience, and real-time processing make Elixir an ideal programming language for creating and improving machine learning tools. Key Topics Discussed in this Episode: The difference between machine learning and artificial intelligence How Axon builds on top of the Nx library for deep learning in Elixir Why logic cannot fully define characteristics that identify golden retrievers How Google Translate uses machine learning with a unified language model The difficulties in translating concepts with no direct counterpart between languages Data cleaning and labeling challenges How Sean's interest in sports betting led to exploring machine learning Why Sean's NBA betting model recommended betting $0 to maximize profit Getting started with machine learning and Elixir projects Attention mechanisms in neural networks Bias and exceptions in machine translation models How hummus preference was used to determine Sundi's Hogwarts house Sean's work on a LiveView interface for ChatGPT Why Elixir's deployment story, development experience, and real-time processing are good fits for machine learning applications Links Mentioned: Genetic Algorithms in Elixir by Sean Moriarity: https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/ Axon Deep Learning in Elixir: https://seanmoriarity.com/2021/04/08/axon-deep-learning-in-elixir/ Nx Axon: https://github.com/elixir-nx/axon Sean's Twitter: https://twitter.com/seanmoriarity Weston the Golden's IG: https://www.instagram.com/westonthegolden/ Sean's Github: https://github.com/seanmor5 Bumblebee: https://github.com/elixir-nx/bumblebee Sal Khan's TedTalk about AI in Education: https://www.ted.com/talks/salkhanhowaicouldsavenotdestroyeducation/c Publicly Available Datasets/Intro to Machine Learning: https://www.kaggle.com/ Use code WIZARD for $100 off your ticket to Empex NYC in Brooklyn, NY on June 9, 2023 https://ti.to/empex-ny/empex-nyc-2023 Special Guest: Sean Moriarity.

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Neural Networks Optimized by Genetic Algorithms in Cosmology by Isidro Gómez-Vargas et al. on Wednesday 07 September The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions. However, in some cases, their use still remains controversial becasue their ease of producing inaccurate results when the hyperparameters are not carefully selected. In this paper, to find the optimal combination of hyperparameters that describe the artificial neural networks, we propose to take advantage of the genetic algorithms. As a proof of the concept, we analyze three different cosmological cases to test the performance of the new architecture achieved with the genetic algorithms and compare the output with the standard process, consisting of a grid with all possible configurations. First, we carry out a model-independent reconstruction of the distance modulus using a Type Ia Supernovae compilation. Second, the neural networks learn to solve dynamical system of the Universe's content, and finally with the latest Sloan Digital Sky Survey data release we train the networks for the classification of astronomical objects. We found that the genetic algorithms improve considerably the generation of the architecture, which can ensure more confidence in their physical results because of the better performance in the metrics with respect to the grid method. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2209.02685v1

Neural Networks Optimized by Genetic Algorithms in Cosmology by Isidro Gómez-Vargas et al. on Wednesday 07 September The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions. However, in some cases, their use still remains controversial becasue their ease of producing inaccurate results when the hyperparameters are not carefully selected. In this paper, to find the optimal combination of hyperparameters that describe the artificial neural networks, we propose to take advantage of the genetic algorithms. As a proof of the concept, we analyze three different cosmological cases to test the performance of the new architecture achieved with the genetic algorithms and compare the output with the standard process, consisting of a grid with all possible configurations. First, we carry out a model-independent reconstruction of the distance modulus using a Type Ia Supernovae compilation. Second, the neural networks learn to solve dynamical system of the Universe's content, and finally with the latest Sloan Digital Sky Survey data release we train the networks for the classification of astronomical objects. We found that the genetic algorithms improve considerably the generation of the architecture, which can ensure more confidence in their physical results because of the better performance in the metrics with respect to the grid method. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2209.02685v1

In episode 41 of The Gradient Podcast, Andrey Kurenkov speaks to Professor Jeff Clune.Jeff is an Associate Professor of Computer Science at the University of British Columbia and a Faculty Member of the Vector Institute. Previously, he was a Research Team Leader at OpenAI and before that a Senior Research Manager and founding member of Uber AI Labs, and prior to that he was an Associate Professor in Computer Science at the University of Wyoming.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterThe Gradient is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Outline:(00:00) Intro(01:05) Path into AI(08:05) Studying biology with simulations(10:30) Overview of genetic algorithms(14:00) Evolving gaits with genetic algorithms(20:00) Quality-Diversity Algorithms(27:00) Evolving Soft Robots(32:15) Genetic algorithms for studying Evolution(39:30) Modularity for Catastrophic Forgetting(45:15) Curiosity for Learning Diverse Skills(51:15) Evolving Environments (58:3) The Surprising Creativity of Digital Evolution(1:04:28) Hobbies Outside of Research(1:07:25) Outro Get full access to The Gradient at thegradientpub.substack.com/subscribe

Sean Moriarity, the author of Genetic Algorithms in Elixir, lays out Machine Learning in the Elixir space. We talk about where it is today and where it's going in the future. Sean talks more about his book, how that led to working with José Valim which then led to the creation of Nx. He fills us in on recent ML events with Google and Facebook and shows us how Elixir fits into the bigger picture. It's a fast developing area and Sean helps us follow the important points even if we aren't doing ML ourselves… because our teams may still need it. Show Notes online - http://podcast.thinkingelixir.com/102 (http://podcast.thinkingelixir.com/102) Elixir Community News - https://github.com/phoenixframework/phoenixliveview/blob/v0.17.10/CHANGELOG.md (https://github.com/phoenixframework/phoenix_live_view/blob/v0.17.10/CHANGELOG.md) – Phoenix LiveView gets a minor release v0.17.10 with formatting improvements - https://www.rakeroutes.com/2022/05/18/let-s-write-an-elixir-livebook-smart-cell (https://www.rakeroutes.com/2022/05/18/let-s-write-an-elixir-livebook-smart-cell) – Creating custom Livebook Smart Cells - https://twitter.com/evadne/status/1527651328188723209 (https://twitter.com/evadne/status/1527651328188723209) – Etso was updated to work with the latest Ecto - https://github.com/evadne/etso (https://github.com/evadne/etso) – Etso library Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Discussion Resources - https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/ (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) – Genetic Algorithms in Elixir - https://github.com/elixir-nx/nx (https://github.com/elixir-nx/nx) – Numerical Elixir (Nx) - https://github.com/elixir-nx/axon (https://github.com/elixir-nx/axon) – Nx-powered Neural Networks for Elixir. - https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/ (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) – Book - Genetic Algorithms in Elixir - https://scala-lang.org/ (https://scala-lang.org/) - https://www.quora.com/ (https://www.quora.com/) - https://pragprog.com/titles/elixir16/programming-elixir-1-6/ (https://pragprog.com/titles/elixir16/programming-elixir-1-6/) - https://pragprog.com/titles/phoenix14/programming-phoenix-1-4/ (https://pragprog.com/titles/phoenix14/programming-phoenix-1-4/) - https://www.linkedin.com/in/briancardarella/ (https://www.linkedin.com/in/briancardarella/) - https://dockyard.com/ (https://dockyard.com/) - https://dockyard.com/blog/authors/sean-moriarity (https://dockyard.com/blog/authors/sean-moriarity) – Sean's blog posts on Dockyard blog - https://numpy.org/ (https://numpy.org/) - https://llvm.org/ (https://llvm.org/) - https://en.wikipedia.org/wiki/Softmax_function (https://en.wikipedia.org/wiki/Softmax_function) - https://en.wikipedia.org/wiki/Naturallanguageprocessing (https://en.wikipedia.org/wiki/Natural_language_processing) - https://xkcd.com/1897/ (https://xkcd.com/1897/) – XKCD comic - https://www.image-net.org/ (https://www.image-net.org/) - https://www.deeplearningbook.org/ (https://www.deeplearningbook.org/) - https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html (https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) - https://erlef.org/wg/machine-learning (https://erlef.org/wg/machine-learning) – Erlang Eco-system foundation machine working group Guest Information - https://twitter.com/sean_moriarity (https://twitter.com/sean_moriarity) – on Twitter - https://github.com/seanmor5/ (https://github.com/seanmor5/) – on Github - https://seanmoriarity.com (https://seanmoriarity.com) – Blog Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - David Bernheisel - @bernheisel (https://twitter.com/bernheisel) - Cade Ward - @cadebward (https://twitter.com/cadebward)

Welcoming crypto art legend and valued Interdependence subscriber Harm van den Dorpel on the podcast to discuss scarcity, genetic algorithms, managing expectations, limit experiences and his upcoming work, Markov's DreamFollow Harm: https://twitter.com/harmvddorpelCheck out Harm's Work and writing: https://harm.work/Check out Markov's Dream: https://harm.work/work/markovs-dreamChemsex Benelux / Indiscreet Units: https://benelux.chem.sex/ 

#security #censorship #ai Most of us conceive the internet as a free and open space where we are able to send traffic between any two nodes, but for large parts of the world this is not the case. Entire nations have large machinery in place to survey all internet traffic and automated procedures to block any undesirable connections. Evading such censorship has been largely a cat-and-mouse game between security researchers and government actors. A new system, called Geneva, uses a Genetic Algorithm in combination with Evolutionary Search in order to dynamically evade such censorship and adjust itself in real-time to any potential response by its adversaries. In this video, I talk to Security researcher Kevin Bock, who is one of Geneva's main contributors and member of the Breakerspace project. We talk about the evolution of internet censorship, how to evade it, how to mess with the censors' infrastructure, as well as the broader emerging connections between AI and Security. OUTLINE: 0:00 - Intro 3:30 - What is automated censorship in networks? 7:20 - The evolution of censorship vs evasion 12:40 - Why do we need a dynamic, evolving system? 16:30 - The building blocks of Geneva 23:15 - Introducing evolution 28:30 - What's the censors' response? 31:45 - How was Geneva's media reception? 33:15 - Where do we go from here? 37:30 - Can we deliberately attack the censors? 47:00 - On responsible disclosure 49:40 - Breakerspace: Security research for undergrads 50:40 - How often do you get into trouble? 52:10 - How can I get started in security? Learn more at: - Geneva (& more) project page: https://censorship.ai - Open Observatory of Network Interference: https://ooni.org - Censored Planet: https://censoredplanet.org - Breakerspace: https://breakerspace.cs.umd.edu Links: Merch: store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Credit Assignment Problem, published by Abram Demski on the AI Alignment Forum. This post is eventually about partial agency. However, it's been a somewhat tricky point for me to convey; I take the long route. Epistemic status: slightly crazy. I've occasionally said "Everything boils down to credit assignment problems." What I really mean is that credit assignment pops up in a wide range of scenarios, and improvements to credit assignment algorithms have broad implications. For example: Politics. When politics focuses on (re-)electing candidates based on their track records, it's about credit assignment. The practice is sometimes derogatorily called "finger pointing", but the basic computation makes sense: figure out good and bad qualities via previous performance, and vote accordingly. When politics instead focuses on policy, it is still (to a degree) about credit assignment. Was raising the minimum wage responsible for reduced employment? Was it responsible for improved life outcomes? Etc. Economics. Money acts as a kind of distributed credit-assignment algorithm, and questions of how to handle money, such as how to compensate employees, often involve credit assignment. In particular, mechanism design (a subfield of economics and game theory) can often be thought of as a credit-assignment problem. Law. Both criminal law and civil law involve concepts of fault and compensation/retribution -- these at least resemble elements of a credit assignment process. Sociology. The distributed computation which determines social norms involves a heavy element of credit assignment: identifying failure states and success states, determining which actions are responsible for those states and who is responsible, assigning blame and praise. Biology. Evolution can be thought of as a (relatively dumb) credit assignment algorithm. Ethics. Justice, fairness, contractualism, issues in utilitarianism. Epistemology. Bayesian updates are a credit assignment algorithm, intended to make high-quality hypotheses rise to the top. Beyond the basics of Bayesianism, building good theories realistically involves identifying which concepts are responsible for successes and failures. This is credit assignment. Another big area which I'll claim is "basically credit assignment" is artificial intelligence. In the 1970s, John Holland kicked off the investigation of learning classifier systems. John Holland had recently invented the Genetic Algorithms paradigm, which applies an evolutionary paradigm to optimization problems. Classifier systems were his attempt to apply this kind of "adaptive" paradigm (as in "complex adaptive systems") to cognition. Classifier systems added an economic metaphor to the evolutionary one; little bits of thought paid each other for services rendered. The hope was that a complex ecology+economy could develop, solving difficult problems. One of the main design issues for classifier systems is the virtual economy -- that is, the credit assignment algorithm. An early proposal was the bucket-brigade algorithm. Money is given to cognitive procedures which produce good outputs. These procedures pass reward back to the procedures which activated them, who similarly pass reward back in turn. This way, the economy supports chains of useful procedures. Unfortunately, the bucket-brigade algorithm was vulnerable to parasites. Malign cognitive procedures could gain wealth by activating useful procedures without really contributing anything. This problem proved difficult to solve. Taking the economy analogy seriously, we might want cognitive procedures to decide intelligently who to pay for services. But, these are supposed to be itty bitty fragments of our thought process. Deciding how to pass along credit is a very complex task. Hence the need for a pre-specified solution such as bucke...

My guest today is musician and inventor of experimental musical instruments and musical robots, Gil Weinberg. Weinberg is a professor of musical technology at Georgia Tech and founding director of the Georgia Tech Center for Music Technology. In this interview, we spoke about: • Shimon, the incredible robot that can sing, improvise, write music and more • How algorithms are implemented in Shimon • Working with drummer Jason Barnes to develop his prosthetic hand that allowed him to play the drums again • Finding patterns in music • Defining rules over data to allow it to be meaningful • How Gil resets his mind to allow for a fresh perspective on problems • How Machine Learning is used to train, for example, Shimon • The Game of Life and how it can be used to create musicListen to Shimon Sings on Spotify here: https://open.spotify.com/album/49mqgxoLXFGP5NnBB5PQAU?si=QXBCoWCrQJaCX8WmIZ-skA

------------------Support the channel------------ Patreon: https://www.patreon.com/thedissenter PayPal: paypal.me/thedissenter PayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuy PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao ------------------Follow me on--------------------- Facebook: https://www.facebook.com/thedissenteryt/ Twitter: https://twitter.com/TheDissenterYT This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/ Dr. Roman V. Yampolskiy is a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering at the University of Louisville. He is the founding and current director of the Cyber Security Lab and an author of many books including Artificial Superintelligence: a Futuristic Approach. Dr. Yampolskiy is a Senior member of IEEE and AGI; Member of Kentucky Academy of Science, and Research Advisor for MIRI and Associate of GCRI. Dr. Yampolskiy's main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. In this episode, we talk about artificial intelligence. We start by discussing what AI is, and how it compares to natural intelligence. We then go into some of the issues we have to worry about, like the ones related to security, controllability, and unexplainability of AI. We talk about the Singularity, the concept, and what it could be like. -- A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: KARIN LIETZCKE, ANN BLANCHETTE, PER HELGE LARSEN, LAU GUERREIRO, JERRY MULLER, HANS FREDRIK SUNDE, BERNARDO SEIXAS, HERBERT GINTIS, RUTGER VOS, RICARDO VLADIMIRO, CRAIG HEALY, OLAF ALEX, PHILIP KURIAN, JONATHAN VISSER, JAKOB KLINKBY, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, JOHN CONNORS, PAULINA BARREN, FILIP FORS CONNOLLY, DAN DEMETRIOU, ROBERT WINDHAGER, RUI INACIO, ARTHUR KOH, ZOOP, MARCO NEVES, COLIN HOLBROOK, SUSAN PINKER, PABLO SANTURBANO, SIMON COLUMBUS, PHIL KAVANAGH, JORGE ESPINHA, CORY CLARK, MARK BLYTH, ROBERTO INGUANZO, MIKKEL STORMYR, ERIC NEURMANN, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, BERNARD HUGUENEY, ALEXANDER DANNBAUER, FERGAL CUSSEN, YEVHEN BODRENKO, HAL HERZOG, NUNO MACHADO, DON ROSS, JONATHAN LEIBRANT, JOÃO LINHARES, OZLEM BULUT, NATHAN NGUYEN, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, J.W., JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, IDAN SOLON, ROMAIN ROCH, DMITRY GRIGORYEV, TOM ROTH, DIEGO LONDOÑO CORREA, YANICK PUNTER, ADANER USMANI, CHARLOTTE BLEASE, NICOLE BARBARO, ADAM HUNT, PAWEL OSTASZEWSKI, AL ORTIZ, NELLEKE BAK, KATHRINE AND PATRICK TOBIN, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, NICK GOLDEN, PAULO TOLENTINO, JOÃO BARBOSA, JULIAN PRICE, EDWARD HALL, HEDIN BRØNNER, DOUGLAS P. FRY, AND FRANCA BORTOLOTTI! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, IAN GILLIGAN, LUIS CAYETANO, TOM VANEGDOM, CURTIS DIXON, BENEDIKT MUELLER, VEGA GIDEY, AND THOMAS TRUMBLE! AND TO MY EXECUTIVE PRODUCERS, MICHAL RUSIECKI, ROSEY, JAMES PRATT, MATTHEW LAVENDER, SERGIU CODREANU, AND JASON PARTEE!

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In this episode, Lloyd discusses Genetic Algorithms and the unique framework that allows them to be effective. Episode Guide: 1:34 - Intro to Genetic Algorithms 3:13 - Survival of the Fittest 6:08 - Genetic Encodings 8:54 - Quantitative Breeding 13:17 - Limitations & Drawbacks More Info: Visit us at aiexperience.org Brought to you by ICED(AI) Host - Lloyd Danzig

Sebastian Risi : What Are Research Directions look Most Promising For Genetic Algorithms & Neuroevolution?

The newest guest on the pod is Carla García Medina: a researcher in Computer Science and Engineering!

Today's Heavy Networking dives into a research project, Geneva, that uses genetic algorithms to evade Internet censorship. The project was developed at the University of Maryland. We drill into how it works with guests Dr. David Levin and graduate student Kevin Bock from the University of Maryland. The post Heavy Networking 488: Using Genetic Algorithms To Avoid Internet Censorship appeared first on Packet Pushers.

Today's Heavy Networking dives into a research project, Geneva, that uses genetic algorithms to evade Internet censorship. The project was developed at the University of Maryland. We drill into how it works with guests Dr. David Levin and graduate student Kevin Bock from the University of Maryland. The post Heavy Networking 488: Using Genetic Algorithms To Avoid Internet Censorship appeared first on Packet Pushers.

Today's Heavy Networking dives into a research project, Geneva, that uses genetic algorithms to evade Internet censorship. The project was developed at the University of Maryland. We drill into how it works with guests Dr. David Levin and graduate student Kevin Bock from the University of Maryland. The post Heavy Networking 488: Using Genetic Algorithms To Avoid Internet Censorship appeared first on Packet Pushers.

Artificial intelligence is better than humans at playing chess or go, but still has trouble holding a conversation or driving a car. A simple way to think about the discrepancy is through the lens of “common sense” — there are features of the world, from the fact that tables are solid to the prediction that a tree won’t walk across the street, that humans take for granted but that machines have difficulty learning. Melanie Mitchell is a computer scientist and complexity researcher who has written a new book about the prospects of modern AI. We talk about deep learning and other AI strategies, why they currently fall short at equipping computers with a functional “folk physics” understanding of the world, and how we might move forward.Support Mindscape on Patreon.Melanie Mitchell received her Ph.D. in computer science from the University of Michigan. She is currently a professor of computer science at Portland State University and an external professor at the Santa Fe Institute. Her research focuses on genetic algorithms, cellular automata, and analogical reasoning. She is the author of An Introduction to Genetic Algorithms, Complexity: A Guided Tour, and most recently Artificial Intelligence: A Guide for Thinking Humans. She originated the Santa Fe Institute’s Complexity Explorer project, on online learning resource for complex systems.Web siteSanta Fe web pageWikipediaGoogle ScholarComplexity ExplorerAmazon.com author pageTwitter

Brendon and Anish interview Filip Kučerak, a student from Slovakia, about his research on using genetic algorithms to generate realistic, lifelike trees in a simulated environment. We discuss how one develops a realistic simulation that can be optimized using genetic algorithms, the concepts that enable genetic algorithms to work, and how they relate to other machine learning methods. Please send comments to shatteredgradients@gmail.com.

Zeff.ai strategies for short term business projects with high return on investment. ”High ROI projects are easy to measure in the short term. ”

GUEST BIO: Fran Buontempo is editor of the ACCU’s (Association of C and C++ Users) Overload magazine.  Fran has been programming in C++ for over a decade and also knows C# and Python.  Fran has also written a book about how to program your way out of a paper bag. EPISODE DESCRIPTION: Phil’s guest on today’s show is Fran Buontempo. She is a C and C++ expert who is the editor of the ACCU’s (Association of C and C++ Users) Overload magazine. Fran also works with C# and Python. She is a conference speaker, blogger, and author. Her first book, Genetic Algorithms and Machine Learning for Programmers (Pragmatic Programmers), has been well received. In it, she shares several ways to code your way out of a paper bag, as a fun way of providing an insight into emerging machine learning tech. KEY TAKEAWAYS: (0.53) – So firstly, I want to ask you about is your role as editor of the overload magazine? How long have you been doing that? Fran can’t remember exactly but she thinks it has been between 5 and 6 years. Becoming the editor happened slowly, almost by accident. Fran got involved with code critiques, writing book reviews and writing or editing one or two articles. So, when the editor stepped down she volunteered. (1.40) - In terms of the following of the magazine, what's its reach? It reaches a worldwide audience of around a thousand people. A magazine is produced each month. One month it is the member-only version. The next month a different version is published, which anyone can read. (2.38) In terms of your book, how did learning to program your way out of a paper bag come about? Fran was involved in interviewing candidates for a job. One interviewee was so bad that one of their colleagues said that they couldn’t “code their way out of a paper bag.” A throwaway comment that struck a chord with Fran and inspired her to dig deeper into machine learning and improve her skill set too. This led to her writing, her book, Genetic Algorithms and Machine Learning for Programmers (Pragmatic Programmers). In the book, she goes through several AI learning techniques using the example of escaping from a paper bag to illustrate what she was sharing. It was a great way to catch people’s attention and engage them. She was also able to include examples from some of the conference talks and articles she had written. (3.44) - So you're confident now that you can program your way out of a paper bag, presumably. Fran says yes, and she has the certificate to prove it. She gave her first talk at the ACCU conference on that very subject. For fun, she asked the audience to sign a certificate if they thought she had done well enough, which they did. (4.01) – Can you please share a unique career tip with the I.T. career audience?  Fran’s advice is to start seeing imposter syndrome as a positive thing. You get the feeling you are not sure what you are talking about when you put your head above the parapet and do something that stretches you. Feeling like that helps you to identify the holes in your knowledge and fill them. So, that is a positive thing. At this stage, Phil points out that imposter syndrome is simply a different way to describe self-doubt. (5.07) – Can you tell us about your worst career moment? And what you learned from that experience. Very early in Fran’s IT career, she was in the registry at the command prompt and accidentally deleted Windows from a work laptop. She panicked, but it all worked out OK. Not long after she was working in a team of seven that was reduced to just two, overnight. The next day everything broke. Fortunately, Fran was able to sort things out fairly quickly. But, it was a bad situation to find herself in. (6.46) – What was your best career moment? For Fran, her career highlights have come about mainly from human interactions. Being able to mentor people is something she finds to be particularly exciting and fulfilling. It feels great to watch them grow. Being thanked by someone you have helped on somewhere like stack overflow also feels good. Positive feedback from conferences and book reviews, also give her a lift. Of course, the comments are not always positive. Sometimes people do not agree with you or see the value of what you are offering. When that happens, it is important to handle things in a Zen way. Use it as a learning opportunity and see if there is something you could have done better. (8.30) – Can you tell us what excites you about the future of the IT industry and careers? The pace of innovation is exciting. It is especially good to see a new wave of young programmers becoming interested in C++. Version 11 has made a huge difference to how popular the language is, at the moment. Fran is also fascinated by what is happening with AI and machine learning. People are now achieving things that just 10 years ago would have been impossible. As new technologies emerge and advance, this is going to continue to happen, at an even faster rate (10.01) – What drew you to a career in IT? Fran responds that it was unemployment. As a teenager, she had done a little programming, using her Dad’s computer. But, she studied maths and philosophy at university. For 3 years she taught secondary school maths but ended up becoming unemployed. That is when she realized that she already had some of the skills she needed to work in the IT industry. So, she went to a local college and got a City and Guild qualification in C programming. It only took a few weeks to complete that course. Yet, that qualification was enough to land her an IT job. Fairly quickly, Fran learned C++. At which point, she was able to become far more productive. (11.31) – What is the best career advice you have ever received? One of her managers suggested that she join ACCU. That turned out to be great advice for Fran. Finding a group of like-minded people who are willing to help you makes a huge difference. (12.09) - Conversely, what is the worst career advice you've ever received? Fran loves coding, so wants to carry on doing that. Climbing the promotional ladder usually leads to you having less time available to actually program which is not what she wants. So, for her, the advice to move into management is bad advice. It is something she has been asked to do several times. But, it is something she is not likely to want to do. (12.50) – If you were to begin your IT career again, right now, what would you do? From the start, Fran would find a supportive group. Joining the ACCU made a huge difference to her. So, she would definitely do something like that early on. There is now plenty of good quality support available for anyone who uses or wants to learn how to use C++. (13.49) – What are your current career objectives? Recently, Fran has been daydreaming about retiring. But, she is currently fascinated with how AI can be used to speed up the programming process. At the recent ACCU conference, she demonstrated how to get AI to automatically generate FizzBuzz code. The code produced was pretty awful and it took ages to come out with the right tests. But, it did inspire her to try to do more things with AI. She is currently experimenting with genetic programming. AI has the potential to be used for all kinds of things, in particular, to create and help with test cases. Using AI you can dig deep and seek out numbers or strings that will fail the cases. Even established systems could benefit from being crash tested using AI. It could also be used for mutation testing. Fran thinks there is a lot of potential. (15.50) - What do you do to keep your own career energized? Fran finds that editing the Overload magazine keeps her energized. It makes it easier for her to stay up to date and pushes her to explore tech she would not otherwise notice. She also finds speaking at and attending conferences to be an energizing experience. Sitting back and listening is a much easier way to learn. Plus, you get to speak to the people delivering the talk afterward, which is a good way to learn more. (16.19) - What do you do in your spare time away from technology? Fran has a lot of interests outside of IT. She likes to do things that ground her. For example, she used to read dystopian cyberpunk sci-fi books as a way of switching off. These days, cooking, making bread, enjoying her garden and walking all help her to recharge her batteries (17.00) – Phil asks Fran to share a final piece of career advice with the audience. While listening to Mike Feathers, last year, at the Software Craftsmanship Conference Fran picked up a great piece of career advice. He reminded everyone that they have an amazing set of skills. Their abilities are in high demand. So, there is absolutely no reason to be unhappy in their career. If you are not happy, switch jobs or innovate. Coming up with a problem to solve and launching a start-up is always a possibility. It guarantees that you will be doing something that interests you BEST MOMENTS: (4.35) FRAN – "Imposter syndrome is really conscious incompetence from the four stages of learning." (7.53) FRAN – "You need to be quite Zen about how you read feedback." (8.15) PHIL – "For every extreme, ardent follower of yours, you're going to get somebody in the opposite end of the spectrum." (12.03) FRAN – "Finding a group of people who will help you is really important." (14.40) FRAN – "There's an overlap going on between the AI machine learning community and the tech community. If we talk to each other better, we can help each other out." (17.19) FRAN – "You have an amazing set of skills. So, you don't have to be unhappy in your career" CONTACT FRAN: Twitter: https://twitter.com/fbuontempo LinkedIn: https://about.me/frances_buontempo Personal Website: https://about.me/frances_buontempo

This week we chat with Frances Buontempo and Andy Balaam about Machine Learning, Artificial Intelligence and Genetic Algorithms. We learn how ML is mostly just 'multiplying and adding up' with a bit of 'randomly trying stuff out' but that you might need a kill switch - except when you don't. We also revive the 'C++ Lamentations' debate and try to make an iota of difference.

Ameen Kazerouni is the Lead Data Scientist at Zappos Family of Companies and Founding Partner at Bumblebee Analytics. Connect with him on LinkedIn: https://www.linkedin.com/in/ameenkazerouni/ Ameen earned his Bachelor of Science degree in Computer Science with a minor in Biology at the University of West Georgia. He then earned his Master of Science degree in Computer Science with a concentration in Computational Life Sciences from Emory University.  

Dr. Eric Durant earned a PHD in Electrical Engineering from the University of Michigan and his Bachelor of Science in Electrical and Computer Engineering from the Milwaukee School of Engineering– MSOE and is currently a professor at MSOE Dr. Durant has done research in Genetic Algorithms which is a form of Artificial Intelligence and adaptive signal processing focusing on applying mathematics in engineering. He is now digging into Deep Learning for Audio Processing. Going into college he did not plan to get his PHD or become a professor, he got involved in digital signal processing in undergrad via elective courses and learned that it was an area he could learn more about during graduate school and went on to pursue his PHD. The standard advice is to get your PHD from a different university than your undergrad, but not a requirement by any means. Many times as a professor, your summers are open for you to work in industry, do research, your summers can be very flexible. MSOE is very focused on teaching and is not an R1 Research University – something to ask when you are taking your college tours to make sure the school is a good fit for you. He is really fired up about deep learning as an engineering tool and especially how it applies to signal processing – this is a whole new realm. Ah-ha moment – as a sophomore in college struggling with an assembly language problem in lab, he learned how to debug problems as he struggled to figure out why it was not working. He just stuck with it and finally figured out the small problem he had and that was a very powerful moment. But some advice is when you are not making progress take a break and give your mind a rest and then come back an hour or so later and if still stuck ask a friend or a professor. He tries to think outside the box, but be you need to be very laser focused on a task but then be able to break away. He likes Feedly to get access to RSS feeds and a book recommendation is Why We Sleep by Matthew Walker. You can get a free book from Audible at www.stemonfirebook.com and can cancel within 30 days and keep the book of your choice with no cost. Free Audio Book from Audible.

We speak with David Kopec, professor & iOS developer consultant, about his book "Classic Computer Science Problems in Swift". What can you learn from solving classic CS problems such as search, constraint-satisfaction, graph problems and more? David gives a brief explanation of some of the interesting problems in the book such as K-Means clustering and Genetic algorithms. Use promo code 'pckopec' at https://www.manning.com/books/classic-computer-science-problems-in-swift to purchase the book for half price! Wanna chat with other smart iOS developers? Sign up for our free forum: https://forum.insideiosdev.com

What role does cyber security play in alignment and safety? What is AI completeness? What is the space of mind design and what does it tell us about AI safety? How does the possibility of machine qualia fit into this space? Can we leak proof the singularity to ensure we are able to test AGI? And what is computational complexity theory anyway? AI Safety, Possible Minds, and Simulated Worlds is the third podcast in the new AI Alignment series, hosted by Lucas Perry. For those of you that are new, this series will be covering and exploring the AI alignment problem across a large variety of domains, reflecting the fundamentally interdisciplinary nature of AI alignment. Broadly, we will be having discussions with technical and non-technical researchers across areas such as machine learning, AI safety, governance, coordination, ethics, philosophy, and psychology as they pertain to the project of creating beneficial AI. If this sounds interesting to you, we hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, or your preferred podcast site/application. In this podcast, Lucas spoke with Roman Yampolskiy, a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. Dr. Yampolskiy’s main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. He is an author of over 100 publications including multiple journal articles and books.  Topics discussed in this episode include: -Cyber security applications to AI safety -Key concepts in Roman's papers and books -Is AI alignment solvable? -The control problem -The ethics of and detecting qualia in machine intelligence -Machine ethics and it's role or lack thereof  in AI safety -Simulated worlds and if detecting base reality is possible -AI safety publicity strategy

Добрый день уважаемые слушатели. Представляем новый выпуск подкаста RWpod. В этом выпуске: Ruby Refactoring views with Ruby on Rails' ActiveSupport helpers и Ruby on Rails – your own slow query log, no sql configuration required Using Genetic Algorithms in Ruby, Grpc Tutorial With Ruby и Write your own scss-compiler RabbitMQ is more than a Sidekiq replacement и Encrypted Credentials in Rails 5.2 JavaScript Standardizing lessons learned from AMP, AMP is not the issue, it's Google, The Problem with Webpack and Why It Is (Kind of) Our Fault и Choosing Between Progressive Web Apps, React Native & NativeScript in 2018 How I organize CSS in large projects using UFOCSS - Part 1 и Get Started with ngUpgrade: Going from AngularJS to Angular AppBandit - Web Security Proxy in NodeJS and Electron, Driver - a light-weight, no-dependency, vanilla JavaScript engine to drive the user's focus across the page, Risk - an implementation of the popular board game Risk, Coördinator - turn an SVG into XY coördinates, Mutag - a simple MP3 file tag parser и Awaity.js - a functional, lightweight alternative to bluebird.js, built with async / await in mind

Sind Genetische Algorithmen der nächste logische Schritt in der KI-Forschung? Wie funktioniert Evolution bei Algorithmen? Was sind die Vorteile und die Grundidee von Artificial Life? In dieser Folge von "Black Box: Tech" diskutieren Joel Kaczmarek und Johannes Schaback mit NaturalMotion-Gründer Torsten Reil über Genetische Algorithmen sowie über die Frage, wie sich Erkenntnisse der Evolutionsforschung und Biologie auf Algorithmen und die KI-Forschung applizieren lassen. Du erfährst... 1) …was Genetic Algorithms sind und wie sie funktionieren 2) …was die Grundidee von Artificial Life ist 3) …wie Mutationen und Rekombinationen bei Genetic Algorithms funktionieren

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

In this rerun of episode 24 we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hopes of evolving into more intelligent and smarter learning machines. This leads us to the topic of stochastic model search and evaluation. Check out the blog with additional technical references at: www.learningmachines101.com 

Computation is a nascent science, and as such, looks towards the other sciences for inspiration. Whether it be physics, as in simulated annealing, or, as now is popular, biology, as in neural networks, computer science has shown repeatedly that it can learn great things from other sciences. Genetic algorithms are one such method that is inspired, of course, by biological evolution. So what are genetic algorithms used for? What have they taught us about the natural process of evolution? And how can we use them to improve our world? --- Support this podcast: https://anchor.fm/breakingmathpodcast/support

As it turns out, survival of the fittest applies as much to algorithms as it does to amoebas, at least when we're talking about genetic algorithms. We recently interviewed Dr. Jay Perrret, CTO of Aria Networks, a company that uses genetic algorithm-based technology for solving some of industry's toughest problems, from optimization of business networks to pinpointing genetic patterns correlated with specific diseases. Dr. Perrett has been working for years in this domain, testing algorithms that use variations of parameters in order to gradually arrive at a best result, when there's no simple way to program a solution. In this episode, Dr. Perrett discusses how genetic algorithms (GA) work and ways that they can be tested and applied in a business context. He provides two very useful case studies, including a recent example with Facebook that involved planning out an optimal (and massive) data network.  

My guest for the most recent episode was an AI expert Roman Yampolskiy. While listening to our conversation, you will fine-tune your understanding of AI from a safety perspective. Those of you who have decision- making authority in the IT Security world will appreciate Roman's viewpoint on AI Safety. Major Take-Aways From This Episode: 1) Wire heading or Mental Illness with Machines - Miss aligned objectives/incentives for example what happens when a sales rep is told to sell more new customers, but ignores profits. Now you have more customers but less profit. Or you tell your reps to sell more products and possibly forsake the long term relationship value of the customer. There are all sorts of misaligned incentives and Roman makes this point with AIs. 2) I can even draw a parallel with coaching my girls' teams where I have incented them to combine off each other because I want this type of behavior. This can also go against you because you end up becoming really good at passing but not scoring goals to win. 3) AI Decision making: The need for AIs to be able to explain themselves and how they arrived at their decisions. 4) The IT Security implications of AI Chat bots and Social Engineering attacks. 5) The real danger of Human Level AGI Artificial General intelligence. 6) How will we communicate with systems that are smarter than us? We already have a hard time communicating with dogs, for example, how will this work out with AIs and humans? 7) Why you can't wait to develop AI safety mechanisms until there is a problem.....We should remember that seat belts were a good idea the day the first car was driven down the road, but weren't mandated till 60 years after... 8) The difference between AI safety and Cybersecurity. About Roman Yampolskiy Dr. Roman V. Yampolskiy is a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. He is the founding and current director of the Cyber Security Lab and an author of many books including Artificial Superintelligence: a Futuristic Approach. During his tenure at UofL, Dr. Yampolskiy has been recognized as: Distinguished Teaching Professor, Professor of the Year, Faculty Favorite, Top 4 Faculty, Leader in Engineering Education, Top 10 of Online College Professor of the Year, with many other distinctions too numerous to mention. Dr. Yampolskiy's main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. Dr. Yampolskiy is an author of over 100 publications including multiple journal articles and books. His research has been cited by 1000+ scientists and profiled in popular magazines both American and foreign (New Scientist, Poker Magazine, Science World Magazine), dozens of websites (BBC, MSNBC, Yahoo! News), Dr. Yampolskiy's research has been featured 250+ times in numerous media reports in 22 languages. Read full transcript here. How to get in touch with Roman Yampolskiy: LinkedIn Twitter Facebook Resources: http://cecs.louisville.edu/ry/ J.B. Speed School of Engineering Profile Books/ Publications: Artificial Superintelligence: A Futuristic Approach Full List of Published Books This episode is sponsored by the CIO Scoreboard, a powerful tool that helps you communicate the status of your IT Security program visually in just a few minutes. Credits: * Outro music provided by Ben’s Sound Other Ways To Listen to the Podcast iTunes | Libsyn | Soundcloud | RSS | LinkedIn Leave a Review If you enjoyed this episode, then please consider leaving an iTunes review here Click here for instructions on how to leave an iTunes review if you're doing this for the first time. About Bill Murphy Bill Murphy is a world renowned IT Security Expert dedicated to your success as an IT business leader. Follow Bill on LinkedIn and Twitter.

I’m sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps aren't. In this episode we’ll be talking with Perry Kaufman about strategy development and more specifically some of the issues that can catch us out when creating trading strategies. Perry raises some interesting points about optimization that may not be well known plus he shares loads of tips to creating more robust strategies. Perry writes extensively on markets and strategies, having published fourteen books and has just released a new book on building algorithmic trading strategies, which we'll be discussing in this episode. He has worked and consulted to a number of successful CTA, investment and prop trading groups, creating systematic trading and hedging programs. This is also his 2nd appearance on the podcast, appearing as a guest way back in Episode 10. Topics discussed The most robust type of systems How your choice of optimization values could be misrepresenting your results and how to choose parameters that give a more accurate picture The mistakes traders make when analyzing optimization runs and tips to doing it properly How to really determine if a new trading rule is robust Reducing risk by using multiple parameters What the number of profitable runs in an optimization can tell you about the robustness of a strategy Why diversifying across strategies instead of across markets could be a better approach The challenges of building robust strategies using Genetic Algorithms and Neural Networks

Dr. David E. Goldberg is an Emeritus Professor of Engineering at the University of Illinois and a pioneer in genetic algorithm. He is the founder and president of Big Beacon and he also hosts the radio show, Big Beacon Radio. He believes that the future is more optimistic than many people think it will be and that engineering is the key to enhancing education reform.   Key Takeaways: [2:22] Evolutionary computation is the idea of combining Darwinian survival of the fittest and ideas from genetics. [7:49] What impact does evolutionary engineering have on humans, the economy and the systems we use? [11:53] The future can be more optimistic than what people assume it will turn out to be. [14:06] Engineers used to be rock stars, just look at the ebb and flow of the popularity of historical engineers. [16:14] After World War II, we turned engineers into ‘worker bees' and now we want them to be charismatic leaders, like Steve Jobs. [19:58] One of the interesting things we found when researching ‘A Whole New Engineer' is that Asia may no longer be a breeding ground for engineers. [24:12] Contact information for Dr. David Goldberg. [25:10] Empower students with trust and they will find the courage to take the initiative which leads to real learning.   Mentioned in This Episode: Jason Hartman Big Beacon A Whole New Engineer

    His New Book on Artificial Superintelligence - Amazon.   Biography of Dr. Roman V. Yampolskiy Dr. Roman V. Yampolskiy is a Tenured Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. He is the founding and current director of the Cyber Security Lab and an author of many books including Artificial Superintelligence: a Futuristic Approach. During his tenure at UofL, Dr. Yampolskiy has been recognized as: Distinguished Teaching Professor, Professor of the Year, Faculty Favorite, Top 4 Faculty, Leader in Engineering Education, Top 10 of Online College Professor of the Year, and Outstanding Early Career in Education award winner among many other honors and distinctions. Yampolskiy is aSenior member of IEEE and AGI; Member of Kentucky Academy of Science, and Research Advisor for MIRI and Associate of GCRI.   Roman Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. He was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London,College of London. He had previously conducted research at the Laboratory for Applied Computing (currently known as Center for Advancing the Study of Infrastructure) at theRochester Institute of Technology and at the Center for Unified Biometrics and Sensors at the University at Buffalo. Dr. Yampolskiy is an alumnus of Singularity University(GSP2012) and a Visiting Fellow of the Singularity Institute (Machine Intelligence Research Institute).   Dr. Yampolskiy’s main areas of interest are AI Safety, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, Digital Forensics, Games, Genetic Algorithms, and Pattern Recognition. Dr. Yampolskiy is an author of over 100 publications including multiple journal articles and books. His research has been cited by 1000+ scientists and profiled in popular magazines both American and foreign (New Scientist, Poker Magazine, Science World Magazine), dozens of websites (BBC, MSNBC, Yahoo! News), on radio (German National Radio, Swedish National Radio, Alex Jones Show) and TV. Dr. Yampolskiy’s research has been featured 250+ times in numerous media reports in 22 languages.

In this episode we introduce the concept of learning machines that can self-evolve using simulated natural evolution into more intelligent machines using Monte Carlo Markov Chain Genetic Algorithms. Check out: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!

In today's Creating Wealth introduction, Jason Hartman reads out loud about a Realty Times article about Zillow's evaluations and gives his comments on this. He also talks about military drones, regular drones, and reminds the audience that you can still purchase Meet the Master home study courses on JasonHartman.com!    Dr. David E. Goldberg is today's Creating Wealth guest. David has a background in civil engineering and is also an author who has written several books about engineering and computer algorithms. Jason talks to David about his most recent book, A Whole New Engineer as well as genetic algorithms, why there is a decline in engineers, and more on today's episode.    Key Takeaways: 3:45 – Jason talks about when he first started in the real estate business.  7:45 – Single family homes appreciate a lot better than other real estate classes.  13:40 – Jason reads out loud a Realty Times article about Zillow,  17:25 – Zillow agents say their estimates are 'just a good starting point', but what does that mean?  19:12 – Reminder: if you have any comments or questions for Jason, you can now leave voice messages on the website.  20:10 – Appraisals and CMAs show the data points, Zillow does not.  27:15 – Jason introduces his guest, Dr. David E. Goldberg.  30:30 – Designing a kidney by hand is almost impossible, but by using nature's genetic algorithms as a base, you can speed up the process.  35:10 – There will always be a good and bad side to technological advances.  40:00 – At one point in our history, engineers were seen as rockstars.  46:00 – Engineering is fairly uninviting and there's bigger paychecks else where.   51:10 – When students feel trusted, they end up achieving a lot more.    Mentioned In This Episode: Zillow.com http://realtytimes.com/consumeradvice/sellersadvice1/item/32910-20150218-starting-with-zillows-zestimate-may-not-get-you-very-far The Singularity is Near by Ray Kurzweil  NoFlyZone.org  DoNotCall.gov The visible hand by Alfred Chandler http://bigbeacon.org/   http://www.amazon.com/David-E.-Goldberg/e/B000APHEJU

Dr. David E. Goldberg is a professor, writer, and a civil engineer. David has written several books on the topics of engineering and algorithms. Some of these books include The Design of Innovation, Genetic Algorithms in Search, Optimization, and Machine Learning, and, his latest book, A Whole New Engineer. Jason sits down with David to pick his brains on the latest in AI technology, why there's a decline in engineers, and we also get to find out a little bit more about David's most recent book.    Key Takeaways: 2:10 – David jumps right in and talks about AI, Artificial Intelligence, technology.  5:20 – To design a kidney by human hands is impractical, but nature has been able to create one for the past 3.5 billion years and more.  9:15 – As better or new technologies arise, so will the ethical questions.  12:45 – What's happening in engineering education right now?  15:40 – Engineers were seen as heroes and that view reached its apex around World War one and two. 17:55 – Roughly speaking, 6.9 billion of us owe our existence to technology since our agriculture days. 20:30 – It's not just in the US where engineers feel unwanted; it's happening in Asia too.  24:10 – Closing thoughts? Students who feel trusted end up doing the most innovative things.    Mentioned In This Episode:  The visible hand by Alfred Chandler http://bigbeacon.org/ http://www.amazon.com/David-E.-Goldberg/e/B000APHEJU

This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. We briefly discuss how this space is rich with solutions.

MIT OCW: 21M.380 Music and Technology: Algorithmic and Generative Music, Spring 2010

MIT OCW: 21M.380 Music and Technology: Algorithmic and Generative Music, Spring 2010

MIT OCW: 21M.380 Music and Technology: Algorithmic and Generative Music, Spring 2010

This course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.

Rudolf Penninkhof, Edward Seufert, Gerald de Jong and Tom Barbalet discuss if genetic algorithms are all you need for artificial life. This is the live internet radio format for the podcast at 8pm Pacific on Friday weekly. For more information, http://www.biota.org/podcast/

Rudolf Penninkhof, Edward Seufert, Gerald de Jong and Tom Barbalet discuss if genetic algorithms are all you need for artificial life. This is the live internet radio format for the podcast at 8pm Pacific on Friday weekly. For more information, http://www.biotacast.org/

Rudolf Penninkhof, Edward Seufert, Gerald de Jong and Tom Barbalet discuss if genetic algorithms are all you need for artificial life. This is the live internet radio format for the podcast at 8pm Pacific on Friday weekly. For more information, http://www.biota.org/podcast/

Rudolf Penninkhof, Edward Seufert, Gerald de Jong and Tom Barbalet discuss if genetic algorithms are all you need for artificial life. This is the live internet radio format for the podcast at 8pm Pacific on Friday weekly. For more information, http://www.biotacast.org/

In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The combinationof these tools often leads to inappropriate results. In this paper we propose a simultaneous choice of variables and smoothing parameters based on genetic algorithms. Common genetic algorithms have to be modified since inclusion of variables and smoothing have to be coded separately but are linked in the search for optimal solutions. The basic tool for fitting the additive model is the penalized expansion in B-splines.

Gene expression datasets usually have thousends of explanatory variables which are observed on only few samples. Generally most variables of a dataset have no effect and one is interested in eliminating these irrelevant variables. In order to obtain a subset of relevant variables an appropriate selection procedure is necessary. In this paper we propose the selection of variables by use of genetic algorithms with the logistic regression as underlying modelling procedure. The selection procedure aims at minimizing information criteria like AIC or BIC. It is demonstrated that selection of variables by genetic algorithms yields models which compete well with the best available classification procedures in terms of test misclassification error.

Additive models of the type y=f_1(x_1)+...+f_p(x_p)+e where f_j,j=1,...,p, have unspecified functional form, are flexible statistical regression models which can be used to characterize nonlinear regression effects. The basic tools used for fitting the additive model are the expansion in B-splines and penalization which prevents the problem of overfitting. This penalized B-spline (called P-spline) approach strongly depends on the choice of the amount of smoothing used for components f_j. In this paper we treat the problem of choosing the smoothing parameters by genetic algorithms. In several simulation studies our approach of automatically calculation of the smoothing parameters is compared to alternative methods given in literature. In particular functions with different spatial variability are considered and the effect of constant respectively local adaptive smoothing parameters is evaluated.

Model selection in graphical models is still not fully investigated. The main difficulty lies in the search space of all possible models which grows more than exponentially with the number of variables involved. Here, genetic algorithms seem to be a reasonable strategy to find good fitting models for a given data set. In this paper, we adapt them to the problem of model search in graphical models and discuss their performance by conducting simulation studies.

José Diogo Viana is a Full Stack Engineer. He joins the show to talk about, Genetic Algorithms to optimize an Asset Portfolio and his company, "Finiam". He begins by discussing his company, what clients they cater and the services they provide. Being a Fintech company, he also tackles their projects in Finiam and what frameworks they usually use. SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipLinksGenetic Algorithms to optimize an Asset PortfolioFiniam BlogzediogovianaGitHub: zediogovianaLinkedIn: José Diogo VianaTwitter: @zediogovianaReach out to Adi for work opportunities + Founding Engineer roles: aditya7iyengar@gmail.com PicksAdi - Temu Adi - RoborockAdi - Build Your Own FrameworkAllen - World War Z: AftermathDiogo - The Last of UsDiogo - Code BulletDiogo - Range Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy