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What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.
Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖
In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.
Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.
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Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.
Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.
To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.
Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.
On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.
Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.
One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.
Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.
To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.
In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!
Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.
How the Rumors about Q* Started
There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.
It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.
The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.
Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.
A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.
Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.
It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.
OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.
In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.
In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.
The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.
Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.
This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.
If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.
AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.
By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.
Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.
In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon
Improving Q* (SoftMax with Hierarchical Curiosity)
Combining efficiency in handling large action spaces with curiosity-driven exploration.
Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity
Softmaxwithhierarchicalcuriosity
Adaptive Softmax with Hierarchical Curiosity
This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.
Adaptive Softmax
Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.
Hierarchical Curiosity
Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.
Combining Adaptive Softmax and Hierarchical Curiosity
By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.
Here’s the proposed algorithm:
Initialize the Q-values for all actions in all states.
At each time step:
a. Observe the current state s.
b. Select an action a according to an exploration policy that balances exploration and exploitation.
c. Execute action a and observe the resulting state s’ and reward r.
d. Update the Q-value for action a in state s:
Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))
where α is the learning rate and γ is the discount factor.
e. Update the curiosity bonus for state s:
curio(s) = β * |r – Q(s, a)|
where β is the curiosity parameter.
f. Update the probability distribution over actions:
p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))
Repeat steps 2a-2f until the termination criterion is met.
The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.
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submitted by /u/DumbMoneyMedia [link] [comments]
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Recently i saw a lot of clips where people add motion to the images. And not just move the camera around to imitate motion - hair, clouds, a lot of active elements move, like in this example: https://youtu.be/7A-yO7t0H20 But they never say what kind of ai is used to animate this. Would be also cool if it wasn't paid only. And yes, i tried using google, but the result was underwhelming - lots of paid services that only offer something like slight camera shifts, that distort image a low, and only allowing commercial use for subscribers. submitted by /u/ElvenNeko [link] [comments]
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Hi, fellow AI experts. I currently have an API key for Perplexity AI. Even though I have a background in technology, I still can't understand which AI models are best for what purposes and where the differences lie. Perplexity has a short page listing available models that work with its AI engine but no explanation as to which does what best. I've spent hours testing them, but I'm still not sure which one to go for (I don't want to switch it every time). The models are: Perplexity: sonar-small-chat sonar-small-online sonar-medium-chat sonar-medium-online Open Source: llama-3-8b-instruct llama-3-70b-instruct codellama-70b-instruct mistral-7b-instruct mixtral-8x7b-instruct mixtral-8x22b-instruct Before that, I used GPT-4, which is a great allrounder, but these models don't seem like that. I use AI mainly for code-related questions and explanations (if GitHub Copilot doesn't satisfy my answers or I don't want to launch my IDE all the time to access it), translations, factual debates, and advisors. Pretty mixed, I'd say. With advisors, I mean things like giving it a prompt to act, for example, as a lawyer who knows a lot about the laws of, let's say, Germany. Some models respond to things I never even asked, others don't take my previous prompts into account, and some of them do a pretty decent job but aren't really good for other purposes. I hope you guys can point me to some resources where I can learn more about the distinctions of each of these models, the best use cases and so on, or shed some light on it in the comments. Your help would be much appreciated. I'd also be grateful if someone could explain to me in simple terms what exactly the parameter count and the context length mean from a user perspective. I have a general idea but no definitive answer. If it matters: I'm using TypingMind and set up Perplexity as a custom model. Bonus points if you can point me to an alternative since I'm not a huge fan of the interface design. macOS only, please. submitted by /u/Mavrokordato [link] [comments]
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This story is wild. I think we are going to keep seeing things like this. As an IT person, I'm not sure how I can go about even preparing our top Execs if this happens to them. https://www.thebaltimorebanner.com/education/k-12-schools/eric-eiswert-ai-audio-baltimore-county-YBJNJAS6OZEE5OQVF5LFOFYN6M/ submitted by /u/baconisgooder [link] [comments]
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- Real-time Object Detection with Kinesis Video Streams and SageMakerby /u/krunal_bhimani_ (Artificial Intelligence Gateway) on April 26, 2024 at 10:39 am
Imagine a system that can identify objects in live video feeds as they happen. This powerful capability is within reach using Amazon's Kinesis Video Streams and SageMaker. Kinesis acts as the data pipeline, ingesting the live video stream. SageMaker, the machine learning powerhouse, analyzes the video frames using your trained object detection model. The result? Real-time insights into what's happening in your video feed. This system is perfect for applications like security monitoring, traffic analysis, and even industrial automation. https://www.seaflux.tech/blogs/live-recognition-system-using-Kinesis-and-SageMaker submitted by /u/krunal_bhimani_ [link] [comments]
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submitted by /u/DumbMoneyMedia [link] [comments]
- Image generation with GPT4 & Dalle 3by /u/No-Transition3372 (Artificial Intelligence Gateway) on April 26, 2024 at 4:10 pm
submitted by /u/No-Transition3372 [link] [comments]
- What are the good AI services to animate pictures?by /u/ElvenNeko (Artificial Intelligence Gateway) on April 26, 2024 at 4:08 pm
Recently i saw a lot of clips where people add motion to the images. And not just move the camera around to imitate motion - hair, clouds, a lot of active elements move, like in this example: https://youtu.be/7A-yO7t0H20 But they never say what kind of ai is used to animate this. Would be also cool if it wasn't paid only. And yes, i tried using google, but the result was underwhelming - lots of paid services that only offer something like slight camera shifts, that distort image a low, and only allowing commercial use for subscribers. submitted by /u/ElvenNeko [link] [comments]
- Perplexity AI (and others): Confusion about which LLM model to chooseby /u/Mavrokordato (Artificial Intelligence Gateway) on April 26, 2024 at 3:18 pm
Hi, fellow AI experts. I currently have an API key for Perplexity AI. Even though I have a background in technology, I still can't understand which AI models are best for what purposes and where the differences lie. Perplexity has a short page listing available models that work with its AI engine but no explanation as to which does what best. I've spent hours testing them, but I'm still not sure which one to go for (I don't want to switch it every time). The models are: Perplexity: sonar-small-chat sonar-small-online sonar-medium-chat sonar-medium-online Open Source: llama-3-8b-instruct llama-3-70b-instruct codellama-70b-instruct mistral-7b-instruct mixtral-8x7b-instruct mixtral-8x22b-instruct Before that, I used GPT-4, which is a great allrounder, but these models don't seem like that. I use AI mainly for code-related questions and explanations (if GitHub Copilot doesn't satisfy my answers or I don't want to launch my IDE all the time to access it), translations, factual debates, and advisors. Pretty mixed, I'd say. With advisors, I mean things like giving it a prompt to act, for example, as a lawyer who knows a lot about the laws of, let's say, Germany. Some models respond to things I never even asked, others don't take my previous prompts into account, and some of them do a pretty decent job but aren't really good for other purposes. I hope you guys can point me to some resources where I can learn more about the distinctions of each of these models, the best use cases and so on, or shed some light on it in the comments. Your help would be much appreciated. I'd also be grateful if someone could explain to me in simple terms what exactly the parameter count and the context length mean from a user perspective. I have a general idea but no definitive answer. If it matters: I'm using TypingMind and set up Perplexity as a custom model. Bonus points if you can point me to an alternative since I'm not a huge fan of the interface design. macOS only, please. submitted by /u/Mavrokordato [link] [comments]
- Our plan on building a better tomorrow with Artificial Intelligence!by /u/unknownstudentoflife (Artificial Intelligence Gateway) on April 26, 2024 at 3:17 pm
submitted by /u/unknownstudentoflife [link] [comments]
- GPT4 prompts for Dalle-3: Deep image creationby /u/No-Transition3372 (Artificial Intelligence Gateway) on April 26, 2024 at 2:49 pm
submitted by /u/No-Transition3372 [link] [comments]
- New GPT4 prompts for GPT-Teams and GPT Enterpriseby /u/No-Transition3372 (Artificial Intelligence Gateway) on April 26, 2024 at 2:05 pm
submitted by /u/No-Transition3372 [link] [comments]
- AD used AI to clone boss's voiceby /u/baconisgooder (Artificial Intelligence Gateway) on April 26, 2024 at 12:24 pm
This story is wild. I think we are going to keep seeing things like this. As an IT person, I'm not sure how I can go about even preparing our top Execs if this happens to them. https://www.thebaltimorebanner.com/education/k-12-schools/eric-eiswert-ai-audio-baltimore-county-YBJNJAS6OZEE5OQVF5LFOFYN6M/ submitted by /u/baconisgooder [link] [comments]
- Company Wants To Address Euro Teacher Shortage With AI By Using Avatars To Teach Mathsby /u/vinaylovestotravel (Artificial Intelligence) on April 26, 2024 at 10:42 am
submitted by /u/vinaylovestotravel [link] [comments]
- Real-time Object Detection with Kinesis Video Streams and SageMakerby /u/krunal_bhimani_ (Artificial Intelligence Gateway) on April 26, 2024 at 10:39 am
Imagine a system that can identify objects in live video feeds as they happen. This powerful capability is within reach using Amazon's Kinesis Video Streams and SageMaker. Kinesis acts as the data pipeline, ingesting the live video stream. SageMaker, the machine learning powerhouse, analyzes the video frames using your trained object detection model. The result? Real-time insights into what's happening in your video feed. This system is perfect for applications like security monitoring, traffic analysis, and even industrial automation. https://www.seaflux.tech/blogs/live-recognition-system-using-Kinesis-and-SageMaker submitted by /u/krunal_bhimani_ [link] [comments]
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic
- Irregular bone marrow cells may increase heart disease riskby /u/Passervore on April 26, 2024 at 2:55 pm
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- ‘Real hope’ for cancer cure as personal mRNA vaccine for melanoma trialledby /u/Well_Socialized on April 26, 2024 at 2:53 pm
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- 20% of grocery store milk has traces of bird flu, suggesting wider outbreak | The milk is still considered safe, but disease experts are alarmed by the prevalence.by /u/chrisdh79 on April 26, 2024 at 2:48 pm
submitted by /u/chrisdh79 [link] [comments]
- "Teachers and family dismissed my cry for help—it was almost too late"by /u/newsweek on April 26, 2024 at 12:41 pm
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- A new kind of gene-edited pig kidney was just transplanted into a personby /u/Sariel007 on April 26, 2024 at 12:32 pm
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Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
- TIL A group of horses were trained to communicate whether they wanted a jacket. All horses in the group successfully communicated that they did want a jacket when it was cold and did not want a jacket when it was hot.by /u/PunnyBanana on April 26, 2024 at 2:20 pm
submitted by /u/PunnyBanana [link] [comments]
- TIL when the artists arrived to record "We Are the World," Stevie Wonder told them that if the song wasn't finished in one take, he and Ray Charles would drive them home.by /u/RequirementSouth4482 on April 26, 2024 at 1:53 pm
submitted by /u/RequirementSouth4482 [link] [comments]
- TIL E.T was a 12-year-old disabled boy in a suitby /u/DiaBoloix on April 26, 2024 at 12:43 pm
submitted by /u/DiaBoloix [link] [comments]
- TIL that a politician gave a food review of kebab while speaking in parliament. Australian Senator Sam Dastyari gave a "10 out of 10" rating to the kebab snack pack sold at King Kebab House, and advised others to also enjoy "a great Australian tradition of meat in a box".by /u/TMWNN on April 26, 2024 at 11:47 am
submitted by /u/TMWNN [link] [comments]
- TIL the infamous "Jump the Shark" episode of Happy Days (Season 5, Episode 3) was created as a way to showcase Henry Winkler's real-life water skiing skills. The episode drew over 30 million viewers.by /u/ColeBelthazorTurner on April 26, 2024 at 11:36 am
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Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Researchers have developed a nanomaterial that could be used to treat neurodegenerative diseases, such as Alzheimer's or Parkinson's. The new "protein-like polymer" has been shown to alter the interaction between two key brain proteins in cell cultures, releasing an important antioxidant on demand.by /u/alexbeadlesci on April 26, 2024 at 3:34 pm
submitted by /u/alexbeadlesci [link] [comments]
- EV drivers need to transition from the “monitor fuel gauge model” (driver refuels when fuel is running out) which represents how most people refuel a petrol or diesel car, to the “event-triggered model” (driver plugs in as soon as arriving home or work) which is optimum for EV use, finds new study.by /u/mvea on April 26, 2024 at 1:49 pm
submitted by /u/mvea [link] [comments]
- Recent research challenges the common belief that childhood trauma affects the experience of ayahuasca, a plant-based psychedelic. Surprisingly, the study finds no connection between prior childhood trauma and the intensity of challenges faced when under the influence of ayahuasca.by /u/mvea on April 26, 2024 at 1:29 pm
submitted by /u/mvea [link] [comments]
- ‘Uncharted territory’: Dual fusion breakthrough in generating denser and safer plasmaby /u/Cleancoolenergy on April 26, 2024 at 1:12 pm
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- Researchers have found a fast, and inexpensive way to create geometric patterns in carbon nanotube films. The resulting films turned out to have superior properties for manufacturing components for 6G communication devices and flexible and transparent electronics — such as wearable health trackers.by /u/Skoltech_ on April 26, 2024 at 11:56 am
submitted by /u/Skoltech_ [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Falcons GM explains shocking selection of Michael Penix Jr. that left Kirk Cousins 'disappointed'by /u/Oldtimer_2 on April 26, 2024 at 1:45 pm
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- Joel Embiid scores 50 points to lead 76ers past Knicks 125-114 to cut deficit to 2-1by /u/Oldtimer_2 on April 26, 2024 at 1:13 pm
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- 49ers excited to have Aiyuk, Deebo, Pearsall togetherby /u/Oldtimer_2 on April 26, 2024 at 1:12 pm
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- Nuggets breeze past Lakers, take 3-0 series leadby /u/Oldtimer_2 on April 26, 2024 at 1:10 pm
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- Brazil legend Marta to retire from international footballby /u/PrincessBananas85 on April 26, 2024 at 1:08 pm
submitted by /u/PrincessBananas85 [link] [comments]
