Podcasts about dynamic programming

Learn more at TheCityLife.org --- Send in a voice message: https://podcasters.spotify.com/pod/show/citylifeorg/message Support this podcast: https://podcasters.spotify.com/pod/show/citylifeorg/support

Today we're rerunning a conversation Russ had in 2020 with Mykel Kochenderfer, a professor of Aeronautics and Astronautics at Stanford University.Mykel's research has impacted anyone who has been on a plane recently for any kind of travel. His research led to the creation of a program known as the Airborne Collision Avoidance System, or ACAS X , which as he explains in more detail, is a critical tool in keeping air travel safe.Thank you for tuning in, and we hope you enjoy this episode from the archives.Chapter Time Stamps:(00:00:50) ACAS X OriginsRuss Altman sets the stage by revisiting a conversation from 2020 with Professor Mykel Kochenderfer, a pioneer in aeronautics and astronautics at Stanford University. They discuss Mykel's groundbreaking research leading to the creation of the Airborne Collision Avoidance System, ACAS X.(00:03:10) Ensuring Trustworthy Autonomous VehiclesExploring the challenges in building trustworthy autonomous systems, Mykel discusses the complexities of imperfect sensor systems, uncertainty in predicting future trajectory, and the trade-off between safety and operational efficiency.(00:07:20) Dynamic Programming: The Key to ACAS XMykel explains the role of dynamic programming in ACAS X, transforming complex computations into tractable tasks through offline modeling and a lookup table, balancing safety and efficiency.(00:10:30) Balancing Safety and EfficiencyRuss and Mykel delve into the intricate balance between safety and operational efficiency in ACAS X, highlighting the need for AI systems to make sound decisions even in rare, low-probability scenarios.(00:14:15) ACAS X Implementation and Use CasesMykel elaborates on the role of ACAS X in aviation safety, detailing its advisory nature and its integration with air traffic control systems, while addressing the potential automation in specific aircraft models.(00:17:40) Broadening Horizons: Urban Air MobilityExploring the expansion of ACAS X to urban air mobility systems, Russ and Mykel discuss the challenges of modeling and validating models for a wide range of aircraft, and the importance of incorporating human expertise.(00:21:05) Global Collaboration and AI ConsensusMykel emphasizes the collaborative nature of ACAS X implementation, involving different stakeholders, such as the FAA, Eurocontrol, and ICAO, and the role of AI in reaching a consensus on safety objectives.(00:23:30) The Journey AheadAs the conversation draws to a close, Mykel reflects on the evolution of ACAS X, its future applications, and the fusion of AI and human wisdom shaping the skies of tomorrow.

Are you ready to take your water management skills to the next level? Join us for an exclusive webinar to unlock the secrets to mastering water management in the face of scorching heat waves. Discover how JAIN Unity cutting-edge features, including dynamic programming and accurate ET (Evapotranspiration) data, can revolutionize your approach to heat wave preparedness. Gain Insights from YoY Reports and Over/Under Analysis: Learn how to leverage JAIN Unity's comprehensive YoY (Year-over-Year) reports and insightful Over/Under analysis to better understand your water usage patterns. Uncover trends, identify areas for improvement, and make data-driven decisions to optimize your water management strategies. Harness the Power of Accurate ET Data: Temperature changes can significantly impact your water management requirements. Explore the importance of precise ET (Evapotranspiration) data in adapting to temperature variations. Discover how JAIN Unity's advanced ET data integration equips you with the accurate information you need to proactively respond to temperature fluctuations and optimize your irrigation schedules. Unlock Proactive Heat Wave Strategies with Dynamic Programming: Are you prepared to tackle heat waves head-on? JAIN Unity's dynamic programming capabilities enable you to stay ahead of the game. Unleash the power of proactive planning by incorporating heat wave scenarios into your water management algorithms. Learn how to adjust irrigation schedules, manage water resources, and protect your landscapes during extreme temperature events. Maximize Efficiency and Preserve Resources: Water is a precious resource, and efficient water management is essential for sustainable landscapes. Discover how JAIN Unity empowers you to maximize efficiency, conserve water, and minimize waste. Gain practical tips and insights to make the most of your water resources.

Warren Powell is Chief Analytics Officer of Optimal Dynamics and Professor Emeritus from Princeton University, where he taught and served as a faculty member in the Department of Operations Research and Financial Engineering since 1981.  In the 1980s Powell designed and wrote SYSNET, an interactive optimization model for load planning at Yellow Freight System, where it is still in use after 25 years. He is the founder of Princeton Transportation Consulting Group, which marketed the model as SuperSPIN, stabilizing an industry where 80% of companies went bankrupt in the first post-deregulation decade. SuperSPIN was used in the planning of American Freightways (which became FedEx Freight) and Overnight Transportation (which became UPS Freight). In 1990 Powell founded CASTLE Laboratory which spans research in computational stochastic optimization with applications initially in transportation and logistics. In 2011 he then founded the Princeton laboratory for ENergy Systems Analysis (PENSA) to tackle the rich array of problems in energy systems analysis, and in 2013: this morphed into “CASTLE Labs,” focusing on computational stochastic optimization and learning. In 2017 Powell founded Optimal Dynamics, helping carriers to automate and optimize trucking networks using AI.  Motivated by these applications, he developed a method for bridging dynamic programming with math programming to solve very high-dimensional stochastic, dynamic programs using the modeling and algorithmic framework of approximate dynamic programming. He identified four fundamental classes of policies for solving sequential decision problems, integrating fields such as stochastic programming, dynamic programming (including approximate dynamic programming/reinforcement learning), robust optimization, optimal control and stochastic search (to name a few). This work identified a new class of policy called a parametric cost function approximation. His work in industry is balanced by contributions to the theory of stochastic optimization, and machine learning.

This episode is also available as a blog post: Python Maths Series: Dynamic Programming - Karate Coder

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubVenkat Subramaniam - Author of "Programming Kotlin"Hadi Hariri - VP of Developer Advocacy at JetBrainsDESCRIPTION“Developers don't just like Kotlin, they LOVE it.”Understand why in this discussion with Venkat Subramaniam, author of “Programming Kotlin: Create Elegant, Expressive, and Performant JVM and Android Applications 1st Edition” and Hadi Hariri, VP of developer advocacy at JetBrains. They give you reasons to learn more about Kotlin, whether you like dynamic or functional languages, and why you should always be learning something new.The interview is based on Venkat's book "Programming Kotlin": https://amzn.to/2MIC8D1Read the full transcription of the interview here:https://gotopia.tech/bookclub/episodes/programming-with-kotlinRECOMMENDED BOOKSVenkat Subramaniam • Programming Kotlin • https://amzn.to/2MIC8D1Venkat Subramaniam• Functional Programming in Java • https://amzn.to/3bzFNNQVenkat Subramaniam • Pragmatic Scala • https://amzn.to/3oIEq35Venkat Subramaniam • Test-Driving JavaScript Applications • https://amzn.to/3i9CbmWhttps://twitter.com/GOTOconhttps://www.linkedin.com/company/goto-https://www.facebook.com/GOTOConferencesLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket at https://gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily.https://www.youtube.com/user/GotoConferences/?sub_confirmation=1

In this episode of the Thoughtful Software Podcast, Andrew and Fahad discuss team building in software, hiring methods for creating the best team possible, ways to screen for the best cross-functional candidates, and proven methods for instilling quality leadership to help train and mentor junior developers to be the next wave of industry leaders. Show NotesHow to build teams for application and development. How should development teams be built from a non-technical founder or executive standpoint?Start by looking at the size and maturity of the organization. Is there a technical founder? What is the maturity of that technical founder? Small organizations and startups should begin with Dynamic Programming languages such as Python or JavaScript. While these languages enable speed they do not often work at scale (number of coders working). Hiring top talent without paying them what Google or Netflix can offer requires new and exciting programming languages like Rust where people can come and learn.Once hiring is figured out then the question becomes "How fast do we want to move?" Senior developers are going to move more slowly than junior developers because they often make better decisions and take their time because they have suffered through bad decisions before. Larger companies can afford to move slower whereas startups may not.The last thing in hiring that must be considered is leadership. First are People Leaders - Managers, Directors, VP's. Second, are Tech Leaders - Engineers, Architects. Most startups need one technical leader.The people being hired in a startup must have a growth mindset or it will crush your team.Hiring at the enterprise level is designed to throw bodies at a problem, they are building to hedge against risk and return money to their shareholders. How can you screen for good candidates? Asking the right questions at the start: "Tell me why you picked this one technology and give me your reasons for doing so?"  Asking someone to defend their choices is the best to understand why people make choices.How important is it now for developers to be cross-functional and understand the context of their code, not just the latest and greatest languages? Understanding the business you are in is important. As a leader, you have to understand where the company is going and where the market might force you to go. Any single agenda can't supersede that of the business. What Does a Good Technical Mentorship Look Like? Pairing on programming is very helpful and explaining and working through errors together with junior engineers rather than leaving a note is a great learning experience. Enabling engineers to confidently sell their ideas and training them to execute. Let people make the mistakes and then help them learn from those mistakes is the constructive loop needed. Great leaders are always training their replacements.Hiring Remote will help optimizing business. Thanks for listening! What did you think about this episode? Drop us a comment and let us know how we're doing. We take the time to read and respond to every comment and email. Visit our Insights page to hear some previous episodes of our podcast and our Articles written by The Skiplist Team.We’d love to know what you took away from our conversation. Follow us at @fahsho12 and @andrewwwolfe and share your insights and questions with the #thoughtful software.

James is a Dynamic Programming Language Denier but realizes only a part of his code is actually statically typed. And programming languages so full of quirks that you have to hold the quick reference book in your teeth. Discuss this episode: https://discord.gg/nPa76qF

Ciamac is Professor of Business in the Decision, Risk, and Operations Division of the Graduate School of Business at Columbia University, where he has been since 2007. He also develops quantitative trading strategies at Bourbaki LLC, a quantitative investment advisor. A high school dropout, he received degrees at MIT, Cambridge, and Stanford. In this podcast, we discuss: Types of quant investing – prediction vs risk premia. Why machine learning is impacting finance more slowly than other domains (like vision and text). The pros and cons of using linear regressions. The advantages of machine learning in non-linear and complex markets. How to think about alternative and big data. Portfolio construction and combining signals. The importance of incorporating costs. Understanding time horizons of different markets. The trend to winner-takes-all with quant investors. Why bitcoin and crypto technology is special. Books that influenced Ciamac: The Elements of Statistical Learning (Hastie and Tibshirani), Dynamic Programming and Optimal Control: books 1 and 2 (Bertsekas), Active Portfolio Management (Grinold and Kahn). You can follow Ciamac on Twitter here and his work here

How do you jump on your path of purpose? How do you navigate the internal and external challenges that emerge with such a journey? Yes, here is the challenge... Society and cultural norms don't support you to follow your heart's calling. You are taught to be safe, secure, and 'responsible'? But what happens if you are called to so much more? That's what this episode of the awaken your business podcast is all about. Teaching you how to navigate the fear, confusion, and uncertainty, especially if something goes 'wrong'. If you are ready to grow and contribute at the level you know you can, this is the episode for you. Enjoy :) If you want to join other heart-centered business owners to connect and collaborate to grow as one, use the link below to join the serving circle: https://www.facebook.com/groups/theservingcircle/

Anthony Corso is a Ph.D. student in the Aeronautics and Astronautics Department at Stanford University where he is advised by Professor Mykel Kochenderfer in the Stanford Intelligent Systems Laboratory (SISL). He studies approaches for the validation of safety-critical autonomous systems with an emphasis on interpretability and scalability. In this podcast he talked about safety-validation of autonomous systems. The latter includes systems such as robots, cars, aircraft, and planetary rovers equally. In May he published a paper which deals with different algorithms for black-box safety validation. One of the approaches is to use reinforcement learning, which was discussed in the podcast in more detail. He also briefly introduced the Next-Generation Airborne Collision Avoidance System ACAS X, in which development Professor Kochenderfer was heavily involved. ACAS X takes advantage of Dynamic Programming, an algorithm for optimal decision making. The mentioned papers, further readings and an interesting podcast can be found here: The paper mentioned above: A Survey of Algorithms for Black-Box Safety Validation The paper on Adaptive Stress Testing (AST): Adaptive stress testing with reward augmentation for autonomous vehicle validation The AST toolbox mentioned in the podcast: AST-Toolbox The CARLA simulator mentioned in the podcast: CARLA A paper on ACAS X: Next-Generation Airborne Collision Avoidance System An episode of Standford University's podcast The Future of Everything with Mykel Kochenderfer where he talkes about ACAS X and Artificial Intelligence (AI) in safety-critical systems: Mykel Kochenderfer: AI and safety-critical systems --- Send in a voice message: https://anchor.fm/safetycorner/message

In this last episode of the season we continue our discussion of dynamic programming, and show just how efficient it can be by using the Fibonacci sequence! Based on Vaidehi Joshi's blog post, "Less Repetition, More Dynamic Programming".

In this episode we talk about different paradigms and approaches to algorithmic design: the Divide and Conquer Algorithm, the Greedy Algorithm, and the Dynamic Programming Algorithm, which remembers the subproblems that it has seen and solved before so as not to repeat doing the same thing over again. Based on Vaidehi Joshi's blog post, "Less Repetition, More Dynamic Programming".

In this episode, I talk about how I am dynamically during my programming day to day and some of the things I think about when structuring my training for each day. Social Medias: Bitchute: https://www.bitchute.com/channel/jimwinkelman/ YouTube - youtube.com/jimwinkelman This Podcast - The teacher weightlifter podcast Instagram - @jimthethe --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/theteacherweightlifter/support

Hello everyone!  This week, I’d like to introduce Sam Gavis-Hughson.  He’s here to help with technical interviews and developing the skills to work through the pressure of a large technical company and startup coding interviews.  He shared SO MUCH good stuff with us on this episode, I think you’ll appreciate how down to earth he is. ... The post EP016: Dynamic Programming, Coding Interviews, and How to do them with Sam Gavis-Hughson appeared first on Angle Free IT.

Joy Clark talks with Alex Miller about Clojure. Topics include the Clojure language and how it compares to other languages as far as features and maintainability are concerned. The benefits of dynamic languages are also discussed, and clojure.spec is introduced as a way to gain the benefits of statically typed languages. Alex also talks about ways to structure Clojure code and gives a great list of tools and materials for getting started with Clojure.

In this recitation, problems related to dynamic programming are discussed.

In this lecture, Professor Devadas introduces the concept of dynamic programming.

In this lecture, Professor Demaine covers different algorithmic solutions for the All-Pairs Shortest Paths problem.

This lecture covers rewards for Markov chains, expected first passage time, and aggregate rewards with a final reward. The professor then moves on to discuss dynamic programming and the dynamic programming algorithm.

Recitation video covering dynamic programming, costs, and heuristics.

This lecture covers dynamic programming, optimal path, overlapping subproblems, specifications, restrictions, efficiency, and pseudo-polynomials.

In this recitation, the class wraps up with dynamic programming and specifically the technique of memoization. Many code examples are presented to show all the applications of this technique.

This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods.

This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods.

This recitation looks at player positions in the Dance Dance Revolution game, along the lines of the guitar fingering example shown in lecture.

This recitation discusses the knapsack problem and polynomial time vs. pseudo-polynomial time.

This recitation uses dynamic programming to find subsequences in the card game Crazy Eights, and to find the shortest path in a graph.

This recitation revisits the perfect-information blackjack problem that was covered in lecture.

This lecture introduces dynamic programming, in which careful exhaustive search can be used to design polynomial-time algorithms. The Fibonacci and shortest paths problems are used to introduce guessing, memoization, and reusing solutions to subproblems.

This lecture starts with a five-step process for dynamic programming, and then covers text justification and perfect-information blackjack. The lecture also describes how parent pointers are used to recover the solution.

Lecture 17 covers dynamic programming for the shortest path problem in a weighted directed graph, as well as negative edge weights allowed but no negative cycles.

Lecture 16 deals with the solution to the RNA folding problem using dynamic programming.

In Lecture 15, Gusfield finishes the discussion of interval selection, and then introduces the RNA folding problem and talks about recurrences for it.

Lecture 14 reviews memoization; introduction to dynamic programming (DP) for the weighted interval problem and traceback in DP.

In Lecture 13, Gusfield introduces recursive programming and memoization through the problem of computing the maximum weight set of pairwise non-overlapping intervals.

Finish the discussion of HMMs for CpG islands. Introduction to the Vitterbi algorithm (really dynamic programming) to find the most likely Markov Chain generating a given sequence.

Start of discussion on Multiple Sequence Alignment. sum-of-pairs objective function. Dynamic program solution for three sequences. Program MSA

End-gap-free alignment using dynamic programming. Example from whole-genome shotgun sequencing.

In depth treatment of local alignment using dynamic programming.

Continuation of the discussion of how to compute similarity and optimal sequence alignment using dynamic programming. Local as well as global alignment.

In this class, we move from the visual alignment graph to a purely symbolic treatment of how to compute sequence similarity and optimal alignment using dynamic programming.

Introduction to computational efficiency. Introduction to how we actually compute sequence similarity efficiently.

IBM software engineer and musician Paul Reiners previews his featured article on Dynamic programming and sequence alignment, looking at how computer science aids molecular biology. Also, check out Paul's dW Space on music programming and algorithmic composition.

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.

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.