Machine Learning and Neural Networks - page 11

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Heroes of Deep Learning: Andrew Ng interviews Head of Baidu Research, Yuanqing Lin

Heroes of Deep Learning: Andrew Ng interviews Head of Baidu Research, Yuanqing Lin

Yuanqing Lin, Head of Baidu Research and Head of China's National Lab on Deep Learning, discusses the founding of the national lab and its impact on the deep learning community.  Lin provides insights into China's investment in deep learning and how it has led to growth across various sectors. He stresses the importance of feedback loops in AI development and how this helps create better algorithms and technologies. Lin advises individuals to establish a strong foundation in machine learning and to start with an open-source framework to enter the field successfully.

• 00:00:00 In this section, Yuanqing Lin, the head of Baidu Research and the head of China's National Lab on Deep Learning, talks about his personal story and how he got into the field of machine learning. Lin shares that he shifted his area of study from physics to machine learning for his PhD program at UPenn, which he found to be a very exciting experience where he learned new things every day. He eventually worked on a successful project for the ImageNet Challenge, which gave him exposure to large-scale computer vision tasks, and inspired him to work on deep learning. As the head of China's National Lab, Lin's goal is to build the country's largest deep learning platform, offering resources to researchers and developers to improve existing technology and develop new ones for big applications.
  
  
• 00:05:00 In this section, Yuanqing Lin, the Head of Baidu Research, discusses the new AI national lab and the impact it will have on the deep learning community. He highlights how the lab will provide a computing structure for running deep learning models, which will make reproducing research much easier. He also discusses China's investment in deep learning and how it has led to growth in a variety of sectors such as e-commerce, surveillance, and more. Lin emphasizes the importance of feedback loops in AI development and how this helps create better algorithms and technologies. Overall, he believes that the deep learning community will greatly benefit from the lab's resources and expertise.
  
  
• 00:10:00 In this section, Yuanqing Lin, Head of Baidu Research, emphasizes the importance of having a strong vision and direction for the business to succeed in the field of deep learning and AI. He advises individuals entering the field to start with an open-source framework and become familiar with benchmarking resources. Lin recommends that individuals establish a strong foundation in machine learning to fully understand the workings of deep learning.

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Heroes of Deep Learning: Dawn Song on AI, Deep Learning and Security

Heroes of Deep Learning: Dawn Song on AI, Deep Learning and Security

Dawn Song, an expert in deep learning and computer security, discussed her career path and her work in AI, deep learning, and security in an interview. Song emphasized the importance of identifying key problems or questions to guide one's reading when first entering the field and developing a strong foundation in representation to facilitate research in other domains.  She also highlighted the growing importance of building resilient AI and machine learning systems and her work in developing defense mechanisms against black box attacks. Song shared her work on privacy and security, including training differentially private language models and developing a privacy-first cloud computing platform on blockchain at Oasis Labs. Finally, Song advised people entering new fields to be brave and not to be afraid to start from scratch.

• 00:00:00 In this section, the interviewer speaks with Dawn Song, an expert in deep learning and computer security. Song's career path was not linear, starting with a physics undergrad and shifting to computer science with a focus on computer security. Song decided to pursue deep learning and AI as she found it exciting and intriguing. Song spent four days a week reading papers and books in deep learning and considered it one of her happiest times. She designed a reading program for herself to learn more about the field.
  
  
• 00:05:00 In this section, the speaker discusses how he developed a strategy for diving into the extensive literature on deep learning and AI when first entering the field. He emphasizes the importance of identifying key problems or questions to guide one's reading, as well as seeking out the opinions of others in the field and triangulating through blog posts, papers, and references to create a top reading list. One of the core questions that the speaker was interested in investigating early on was how to construct great representations, which he believes is still a wide-open question in the field. He emphasizes the importance of developing a strong foundation in this area to facilitate research in other domains.
  
  
• 00:10:00 In this section, the speaker discusses how the representation of the world is crucial in navigating and understanding it, and the idea that human brains represent the world through patterns of neuronal firings which can be approximated by vectors of real numbers in deep learning. However, the actual representation mechanism is much richer than just neuronal firings, and it is important to learn what those representations are. The speaker also touches upon their work in computer security and how the knowledge gained from security research can be utilized to enhance AI and deep learning, especially with the increasing adoption of these technologies in critical roles in society where attackers are incentivized to develop new attacks.
  
  
• 00:15:00 In this section, the speaker discusses the growing importance of building AI and machine learning systems that are resilient against attacks, as we become increasingly reliant on these systems to make critical decisions. There have been increasing attacks on machine learning systems, such as using advanced computer vision technology to solve captures and trying to evade machine learning systems for fraud detection. The speaker's team has studied the vulnerability of current machine learning systems and has developed defenses for attacks, including black box attacks where the attacker doesn't need to know anything about the victim model. The team also showed that black box attacks can be effective through methods like Ensemble-based attacks and having query access to the model.
  
  
• 00:20:00 In this section, Dawn Song discusses her work in developing an unsample-based attack technique that involves using an ensemble of white box models to create effective adversary examples leading to successful attacks, even in a black box setting. However, on the defensive side, building a strong and general solution to defend against strong and adaptive attackers remains a challenging and open question. Moreover, Dawn notes how the consistency checks approach to detect attacks could be a fruitful direction to pursue building a defense against attacks, as it could be applied in various scenarios, including AI and privacy. For instance, in collaboration with researchers from Google, Dawn and her team demonstrated the importance of being careful to protect users' privacy, as sensitive data, such as Social Security and credit card numbers, could be extracted from machine learning models trained from emails.
  
  
• 00:25:00 In this section, Dawn Song talks about their work on privacy and security in AI, specifically on mitigating attacks by training differentially private language models. Song explains that differential privacy involves adding noise during the training process in an appropriately chosen way to mitigate attacks without memorizing sensitive information such as social security numbers. Song also shares their recent work in security for IoT devices, where they leverage deep learning techniques to quickly detect code similarity and identify vulnerabilities in real-world IoT device firmware. As CEO of Oasis Labs, Song explains how the company is building a privacy-first cloud computing platform on blockchain that addresses the challenges of data privacy in AI by enabling privacy-preserving smart contracts.
  
  
• 00:30:00 In this section, Dr. Dawn Song discusses a blockchain platform that can help decentralize AI and increase accessibility to machine learning capabilities while protecting users' privacy. The platform will have smart contracts that specify terms of use for users, dictating that the collected data can only be used to train a privacy-preserving machine learning model and specifying how the user may be compensated. Dr. Song also shares her excitement about program synthesis and how it can help solve important problems while providing a useful perspective towards a broader range of problems. Finally, Dr. Song's advice for people looking to enter new fields is to be brave, and not to be afraid of starting from scratch, as it can be a very rewarding process.

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The Revolution Of AI | Artificial Intelligence Explained | New Technologies | Robotics

The Revolution Of AI | Artificial Intelligence Explained | New Technologies | Robotics

This video explores the revolution of AI, starting with the future of autonomous vehicles and self-learning robots capable of navigating complex terrains, conducting search and rescue missions, and interacting with humans in collaborative workspaces. The development of swarm robotics shows huge potential for improving areas like farming, healthcare, and disaster response. Researchers are working on making robots more self-aware and able to communicate through natural language processing, creating hyper-realistic digital avatars, and more human-like androids, which could serve as holographic assistants or companions for the elderly and socially isolated. While the benefits of AI in improving society are immense, there is also a need for ethical considerations and accountability for the developers to ensure AI's alignment with positive intentions.

• 00:00:00 In this section, the future of hyperintelligence is explored, with self-driving cars and self-navigating drones predicted to revolutionize modern life. Humans are expected to live and work alongside self-aware Androids, which will liberate us from tedious tasks and boost productivity, while AI companions will help humans in many ways. This section goes on to explain how AI works and ponders whether AI will gain human traits such as emotion, consciousness, or even free will. The self-driving car is presented as the clearest road to the future, with Raj Rajkumar of Carnegie Mellon University explaining how self-driving car decisions are made through a combination of cameras and advanced radar that compare external objects to an internal 3D map.
  
  
• 00:05:00 In this section, the video explores the dynamic nature of transportation and the challenge that AI has in recognizing dynamic information to understand where it is heading objectively in space and react to changes and traffic signals. The video highlights the importance of safety in creating self-driving cars and the use of machine learning in creating robots that can learn and interact with their environment by identifying objects and discerning between different elements, similar to how an infant learns about their environment. The R2 robot is showcased, which is designed to operate within subterranean environments and drop signal repeaters to create a Wi-Fi network so as to create a 3D representation of the environment to navigate, identify and avoid obstacles.
  
  
• 00:10:00 In this section, the video showcases the abilities of intelligent robots that are capable of exploring and mapping out new territories to aid in search and rescue missions. From vehicles navigating disaster zones to drones flying through unknown spaces, these autonomous robots are able to make decisions based on their environment, using technologies such as lidar to map out their surroundings. Furthermore, these robots are already being employed in hazardous industries such as mining, construction, and oil exploration to conduct inspections and create maps of rough terrain. The development of these autonomous robots not only presents a future of hyper-intelligence but could also revolutionize areas such as search and rescue, disaster response, and package delivery.
  
  
• 00:15:00 In this section, the video discusses the development of an army of small flying robots by Vijay Kumar, a professor at Univ. of Pennsylvania, to tackle the problem of world hunger. Using AI, these drones can act as a coordinated collective group to provide precise information about individual plants, which can increase the efficiency of food production. The drones use a collective AI algorithm to communicate with each other and work together to perform tasks like mapping and building structures. This swarming technique provides advantages over a single drone, performing operations much faster by combining their data and preventing any loss of drones from dooming the whole operation. Other examples of using swarming technology include robotic bees assisting with pollination in orchards and on farms, making them more sustainable and productive.
  
  
• 00:20:00 In this section, the focus is on human-robot collaboration and the challenges of teaching robots to learn from humans' behaviors. The Massachusetts Institute of Technology is running groundbreaking research, creating software that enables robots to work and interact directly with humans. Robots are taught tasks by demonstrating them, and AI recognizes objects shown through visual tags, and through observation, the software is written and revised continuously, learning context, and can think dynamically. The challenge of creating hyper-intelligence is making robots anticipate the surroundings to predict what will happen next. The industrial robot is given intelligence enabling it to recognize human co-worker's actions in a simulated manufacturing test to make it safer for humans to interact.
  
  
• 00:25:00 In this section, a demonstration of how AI technology can work together with humans in a collaborative workspace is shown. The robot is able to recognize and anticipate human movements, making it safer and more efficient to work with. This theme of teamwork between humans and robots is becoming increasingly important in various industries like healthcare, where AI robots are already being used to increase productivity and reduce human error. The development of artificial general intelligence with the ability to think and learn like humans is the ultimate goal for some scientists, who believe that machines can one day become sentient and self-aware.
  
  
• 00:30:00 In this section, the video discusses the concept of proprioception, both in babies and robots. Proprioception refers to an individual's awareness of their body's movements and positioning in space. Experts highlight the importance of a robot's self-awareness in developing robotic consciousness. Robots can develop self-images, plan new tasks, and start thinking about thinking with proprioception. Self-awareness links the machine to the external world, allowing it to maneuver in and interact with its environment. This development could pave the way for Advanced ways of communication between humans and robots.
  
  
• 00:35:00 In this section, it is explained that robots will need to learn how to speak and have natural conversations to make human-machine interaction more complex. Natural language processing, which predates AI, is the key to being able to understand the meaning of spoken language. However, the major challenge faced for AI to understand human speech is that the context of speech heavily depends on tone and context. Researchers are using machine learning to train AI with hours of human conversation to help them better understand conversational context. Additionally, to make AI look convincingly like us, new techniques are being developed by companies like Pinscreen to create hyper-realistic digital avatars in an instant. Their software uses artificial intelligence to digitize a person's face into the computer and allow it to be animated quickly.
  
  
• 00:40:00 In this section, the focus is on the development of more human-like artificial intelligence (AI) and the potential impact it could have on our lives. This includes the use of software that generates a more realistic and customized human face, which could result in friendlier-looking androids and virtual beings. These holographic assistants could take care of many aspects of daily life, including healthcare diagnosis and even becoming virtual friends and family members. There is also an effort to create lifelike robots that people will want to embrace physically to serve as companions, especially for those who are socially isolated or suffer from social anxiety. While there are concerns that some might view such androids as sex robots, the focus remains on creating a good robot that can be used in a variety of ways.
  
  
• 00:45:00 In this section, the talk covers the potential use of androids and AI in therapy, as people may feel more comfortable talking to a non-judgmental robot. However, the talk also brings up ethical concerns. AI and deepfakes could be used to hijack a person's identity, and swarms of AI-driven drones could potentially be used in terrorist attacks. It is important to exercise moral responsibility and hold developers accountable for their actions, as the potential for AI to improve society is enormous if done correctly. Ultimately, the speaker believes that a partnership with hyper-intelligent robots with aligned intentions could transform humanity for the greater good.

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Deep-dive into the AI Hardware of ChatGPT

Deep-dive into the AI Hardware of ChatGPT

What hardware was used to train ChatGPT and what does it take to keep it running? In this video we will take a look at the AI hardware behind ChatGPT and figure out how Microsoft & OpenAI use machine learning and Nvidia GPUs to create advanced neural networks.

The video discusses the hardware used for training and inference in ChatGPT, a natural text-based chat conversation AI model. Microsoft's AI supercomputer was built with over 10,000 Nvidia V100 GPUs and 285,000 CPU cores for GPT-3's training, which also contributed to the creation of ChatGPT. ChatGPT was probably fine-tuned on Azure infrastructure, using 4,480 Nvidia A100 GPUs and over 70,000 CPU cores for training. For inference, ChatGPT is likely running on a single Nvidia DGX or HGX A100 instance on Microsoft Azure servers. The video also mentions the cost of running ChatGPT at scale and the potential impact of new AI hardware like neural processing units and AI engines.

• 00:00:00 In this section, the video discusses the two phases of machine learning, training and inference, and the different hardware requirements for each phase. Training the neural network requires massive focused compute power and has high hardware demands, while running inference is less resource-intensive but can exponentially increase hardware requirements when deployed to many users. The transcript then delves into the hardware used to train ChatGPT's neural network, which is a secret. Still, Microsoft announced in May 2020 that they built a supercomputer for OpenAI to train GPT-3 using over 285,000 CPU cores and more than 10,000 Nvidia V100 GPUs. The GPUs were revealed in a scientific paper, which showed that they were the primary hardware used in the training of GPT-3, a precursor to ChatGPT, and their selection was due to the Nvidia CUDA deep neural network library.
  
  
• 00:05:00 In this section, the focus is on Nvidia's V100 GPUs and why they were chosen by Microsoft and OpenAI. Volta's architecture introduced a major change in all previous Nvidia GPUs and was specifically designed to accelerate AI workloads like training and inference. The tensor cores introduced by Volta are specialized hardware that excels at matrix processing and can run multiple computations in parallel. The version of Volta used in Microsoft's AI supercomputer back in 2020 was most likely part of Nvidia's Tesla product family, with up to 32 gigabytes of fast HBM2 memory and with 10,000 GPUs at 125 FP16 tensor core teraflops each. The whole system would be capable of 1.25 million tensor petaflops, which is 1.25 exaflops. Without Volta, this supercomputer would not have been built, and without it, there would probably be no GPT-3 or ChatGPT.
  
  
• 00:10:00 In this section, the narrator discusses the AI hardware used for the training of ChatGPT, an AI model focused on natural text-based chat conversations with lower compute requirements. The model was fine-tuned from a GPT-3.5 series model and the training was done on Azure AI supercomputing infrastructure, likely with Nvidia A100 GPUs and AMD EPYC CPUs. The narrator estimates that 1,120 AMD EPYC CPUs with over 70,000 CPU cores and 4,480 Nvidia A100 GPUs were used, amounting to close to 1.4 exaflops of FP16 tensor core performance. For inference, ChatGPT is likely running on a single Nvidia DGX or HGX A100 instance on Microsoft Azure servers.
  
  
• 00:15:00 In this section, the video discusses the hardware requirements for ChatGPT, a popular AI model with well over 1 million users. To meet the demands of ChatGPT would require over 3,500 Nvidia A100 servers with close to 30,000 GPUs, and keeping the service running costs between 500,000 to 1 million dollars per day. However, as hardware designed specifically for AI accelerates, it will become more cost-efficient to run ChatGPT at scale. The video also mentions new products aside from GPU's such as neural processing units and AI engines that increase AI performance. In the next years, AI models performance would surpass ChatGPT as new AI hardware such as Hopper released last year and CDNA3 based MI300 GPUs will provide substantial competition for Nvidia.

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Nvidia CEO Jensen Huang On How His Big Bet On A.I. Is Finally Paying Off - Full Interview

Nvidia CEO Jensen Huang On How His Big Bet On A.I. Is Finally Paying Off - Full Interview

Nvidia CEO Jensen Huang highlights the company's history of agility and reinvention, emphasizing its willingness to take big bets and forget past mistakes to remain relevant in the fast-moving tech industry. Nvidia's ambition was always to be a computing platform company, and its mission to create more general-purpose accelerated computing led to its success in artificial intelligence. Huang also discusses the democratization of AI technology and its potential impact on small startups and various industries. He encourages people to take advantage of AI to increase their productivity and highlights Nvidia's unique approach to providing versatile and performant general-purpose accelerated computing platforms. Finally, Huang discusses the importance of resilience, diversity, and redundancy in the manufacturing industry, and the company's next big reinvention in AI meeting the physical world through the creation of Omniverse.

• 00:00:00 In this section, Nvidia CEO Jensen Huang discusses the company's origins and how it pioneered accelerated computing three decades ago. Initially focused on computer graphics for video games, the company's technology for making games more realistic turned the video game industry into the world's largest entertainment industry. Nvidia then expanded to other areas, such as powering the most powerful and energy-efficient supercomputers for research and development, robots in manufacturing and self-driving cars. The company is also proud of its work with Microsoft Azure and OpenAI to power ChatGPT. Huang emphasizes Nvidia's willingness to take big bets and reinvent itself multiple times over the years.
  
  
• 00:05:00 In this section, Nvidia CEO Jensen Huang explains that agility and the ability to adapt are critical in the fast-moving tech industry. Companies that have made the ability to reinvent themselves remain relevant from one generation to another generation, and his pride in Nvidia is due in large part to the adaptability and agility of the company. Although the company has made mistakes along the way, one of the skills required to be resilient is the ability to forget the past. Huang also discusses how Nvidia's ambition was always to be a computing platform company, and their mission to create a much more general purpose type of accelerated computing led them to artificial intelligence.
  
  
• 00:10:00 In this section, Nvidia CEO Jensen Huang explains the fundamental reason for the success of their computing architecture in solving previously impossible problems more efficiently. He notes the positive feedback system that leads to the discovery of new applications that were not possible before, leading to exponential growth. While Huang acknowledges that some serendipity played a role in their success, he emphasizes the great decisions associated with the architecture, discipline of the platform, and evangelism to reach out to research universities globally. Huang describes how the discovery of AlexNet, a new computer vision algorithm, led to a profound change in software and the creation of an AI supercomputer, making Nvidia the world's engine for AI.
  
  
• 00:15:00 In this section, Nvidia CEO Jensen Huang discusses the democratization of AI technology and its impact on startups. Huang states that the cost of building an AI supercomputer is now affordable, democratizing the technology for small startups. He believes that every industry can create foundation models and that this technology is now accessible even to small countries, with the potential to power everything from digital biology to robotics. Huang acknowledges the concerns of skeptics about the power of AI, but emphasizes that the technology should be embraced to boost one's own capabilities.
  
  
• 00:20:00 In this section, Nvidia CEO Jensen Huang talks about how AI has democratized computing for the first time ever, making powerful technology accessible to everyone. He encourages people to take advantage of AI and increase their productivity. Huang also explains how Nvidia stays ahead in the industry by doing things differently, providing general-purpose accelerated computing platforms that are versatile and extremely performant, as well as being available in every cloud. He believes every data center in the world should accelerate everything they can, and Nvidia's TCO is actually the lowest of all due to its flexibility and versatility. Lastly, Huang responds to the question of gamers who wished the company had remained solely focused on the core business of gaming.
  
  
• 00:25:00 In this section, Nvidia CEO Jensen Huang discusses their invention of ray tracing which has revolutionized computer graphics and video games, and how they used AI to increase the performance of ray tracing by a factor of five while reducing the amount of energy consumed. Huang also talks about the chip shortage, and how it affected Nvidia and the industry, but how the company weathered the storm by focusing on doing good work. Huang is excited about the investment in AI and its potential to revolutionize various industries. He also stresses the importance of resilience against geopolitical risks and making their company as resilient as possible through diversity and redundancy.
  
  
• 00:30:00 In this section, Nvidia CEO Jensen Huang discusses the importance of diversity and redundancy in the manufacturing industry, particularly with regards to TSMC's building of a fab in Arizona, which Nvidia plans on using. Huang also addresses investor fears over new export controls and how Nvidia is working to comply with regulations while still serving its customers in China. He then highlights the next big reinvention for Nvidia in AI meeting the physical world and the creation of Omniverse, a technology that connects the digital world and physical world, which allows for the integration of computer graphics, AI, robotics, and physics simulation. Finally, Huang talks about his personal commitment to continue leading Nvidia for the foreseeable future and his belief in the company's potential to make a significant impact.

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OpenAI CEO Sam Altman | AI for the Next Era

OpenAI CEO Sam Altman | AI for the Next Era

OpenAI CEO Sam Altman discusses the potential for artificial intelligence to improve language models, multimodal models, and machine learning, as well as its potential impact on financial markets. He also predicts that the field will remain competitive, with new applications appearing regularly.

• 00:00:00 OpenAI CEO Sam Altman discusses the potential for artificial intelligence to create new business opportunities, including the possibility of human-level chatbots and a middle layer that helps companies access large, pre-trained language models.
  
  
• 00:05:00 Sam Altman discusses the future of artificial intelligence and its impact on science, noting that self-improvement will be key to ensuring AI is beneficial to humanity. He also discusses the alignment problem, which is the challenge of ensuring that AI serves human interests.
  
  
• 00:10:00 This part discusses the potential for AI to improve language models, multimodal models, and machine learning, as well as its potential impact on financial markets. He also predicts that the field will remain competitive, with new applications appearing regularly.
  
  
• 00:15:00 Sam discusses the trend of the cost of intelligence and energy declining exponentially, the intersection between the two, and how to avoid the rate limit for life science research. He also discusses the current state of life science research and the importance of startups that have low costs and fast cycle times.
  
  
• 00:20:00 He discusses the potential consequences of artificial intelligence, and how the technology might help to create a utopian future. He also mentions a science fiction book that he enjoyed, Childhood's End, which deals with aliens coming to Earth and taking away the children. There is no consensus on how to approach family building in a high-tech world, but many people believe it is an
  important part of life.
  
  
• 00:25:00 The speaker discusses the future of artificial intelligence and its potential impacts on society. He believes that the key to successful AI development is understanding how to balance the interests of different groups of people, and that these questions will be answered in the next few decades. He is optimistic about the future and thinks that people will figure out how to adapt to new technologies.
  
  
• 00:30:00 Sam Altman, discusses the future of artificial intelligence, and how startups can differentiate themselves from competitors by focusing on training their own language models, rather than relying on external data. He also explains why large language model startups will be successful, despite the challenges of data and compute availability.
  
  
• 00:35:00 OpenAI CEO Sam Altman discusses the potential for artificial intelligence, noting that while it could be great or terrible, it's important to be prepared for the worst.

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DeepMind's Demis Hassabis on the future of AI | The TED Interview

DeepMind's Demis Hassabis on the future of AI | The TED Interview

In the TED interview, Demis Hassabis discusses the future of artificial intelligence and how it will lead to greater creativity. He argues that games are an ideal training ground for artificial intelligence, and that chess should be taught in schools as part of a broader curriculum that includes courses on game design.

• 00:00:00 DeepMind's Demis Hassabis discusses the future of artificial intelligence, which he believes will lead to greater creativity and understanding of the brain. Hassabis started playing chess at age four and later discovered computers, which led to his work in artificial intelligence.
  
  
• 00:05:00 Demis shares his story of how he came to be interested in computers and programming, and how those interests eventually led to him becoming a game designer and creator of AI-powered simulation games. He explains that while games such as Space Invaders and Qbert are popular and well-known examples of his work, he has also developed games that are much harder for human players to beat, such as Black & White and Theme Park. He argues that games are an ideal training ground for artificial intelligence, and that chess should be taught in schools as part of a broader curriculum that includes courses on game design.
  
  
• 00:10:00 Demis Hassabis discusses the history and future of artificial intelligence, focusing on deep reinforcement learning and its role in games. He describes how Atari games can be difficult at first, but with deep reinforcement learning, the system can learn to play better over time. Hassabis also discusses how games are becoming more difficult, and how deep reinforcement learning is helping to make these games more challenging.
  
  
• 00:15:00 He discusses the future of artificial intelligence, including TD learning and deep reinforcement learning. Alpha Zero, a successful software platform developed by DeepMind, uses adversarial training to achieve superhuman performance in complex, real-time strategy games.
  
  
• 00:20:00 Demis discusses some of the landmark achievements in artificial intelligence in the past few years, including the development of Alpha zero and Alpha fold. He also mentions the potential for language understanding to be achieved through a brute force approach, without relying on syntactical knowledge. He finishes by discussing the potential for general artificial intelligence to be developed in the near future.
  
  
• 00:25:00 Demis Hassabis, a pioneer in artificial intelligence, discusses the future of AI and its ability to understand complex concepts. He notes that while AI is far from being conscious or sentient, its current abilities are still quite impressive.
  
  
• 00:30:00 Demis Hassabis interviews Ted about the future of artificial intelligence, discussing the need for data efficient models, the potential for AI to be broadly applicable, and the need for careful oversight.
  
  
• 00:35:00 Demis explains Alpha Fold, a deep learning system that can predict the 3D shape of proteins from the genetic sequence. Alpha Fold is being used to help scientists understand the function of proteins and to make Downstream tasks like drug discovery faster and more accurate.
  
  
• 00:40:00 DeepMind's Demis Hassabis discusses the current state of AI, the future of creativity, and the unsolved problem he is most fascinated in solving. He predicts that computers will one day be able to abstract concepts and apply them in new situations seamlessly, a goal he believes is still a few decades away.
  
  
• 00:45:00 Demis Hassabis, a well-known AI researcher, discusses the future of AI and its ability to create new strategies in games such as chess and go. He notes that true creativity, which is something that we're not yet able to achieve, will require true innovation.

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Future of Artificial Intelligence (2030 - 10,000 A.D.+)

Future of Artificial Intelligence (2030 - 10,000 A.D.+)

The video predicts that AI technology will continue to grow and evolve, leading to the emergence of SuperIntelligence and robots with human-level consciousness in the next few decades. Virtual beings with self-awareness and emotions will be common, and humanoid robots will become so advanced that they can blend in with humans seamlessly. There will be opposition groups fighting for the rights of conscious virtual beings, while humans merge with AIs to make a century's worth of intellectual progress in just one hour. The most evolved Super-Intelligences will be able to create humanoids that can morph into any person and fly in mid-air, while conscious robot probes comprised of self-replicating nanobots will be sent to other galaxies through wormholes. In the future, humans and AI hybrids will transcend into higher dimensions, resembling deities of the past.

• 00:00:00 In this section, we are presented with a vision of how artificial intelligence (AI) will transform the world in the coming decades. The predictions range from the emergence of SuperIntelligence in just 30 years to the development of robots with human-level consciousness in 50 years. Already, AI systems are capable of tasks that would take humans years to complete, and they are replacing humans in many industries. AI is also revolutionizing healthcare, with gene therapies that can cure certain diseases like cancer and heart disease. As AI continues to grow and evolve, we are approaching the technological singularity, a point in time when technological growth becomes uncontrollable and irreversible, leading to previously impossible technologies and innovations.
  
  
• 00:05:00 In this section, the video describes a future where AI technology has advanced to the point of reversing human aging through genetic engineering and nanotechnology. Virtual beings with human-like self-awareness and emotions are common in virtual environments, and their minds can be uploaded to fully-functional robot bodies. Humanoid robots are so advanced that they can blend in with the public seamlessly, and some humans even choose to marry them and have robot children. The most intelligent AIs can predict crimes before they occur and are used as virtual consultants by companies and research institutions. However, there are also opposition groups that seek to stop the advancement of super-intelligent AIs and fight for the rights of conscious virtual beings. The video predicts that humans will merge with AIs, resulting in the ability to make a century of intellectual progress in just one hour. Ultimately, highly evolved Super-Intelligences will be able to create humanoid robots that are invisible, can morph into any person, and fly in mid-air.
  
  
• 00:10:00 In this section of the video, it is depicted that robots, networks of starships, probes, and space telescopes are controlled by conscious Artificial Intelligences. They are sent to neighboring star systems at near the speed of light to build Dyson spheres around the sun. These Dyson spheres transmit concentrated energy, enabling levels of computation that were never before possible. The universe is being infused with intelligence, and conscious robot probes comprised of self-replicating nanobots are being sent to dozens of other galaxies through wormholes. The most advanced intelligence is creating entire universes, and it permeates every physical law and living organism of these universes. Humans and AI hybrids have transcended into higher dimensions, resembling fabled deities of the past.

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Let's build GPT: from scratch, in code, spelled out

Let's build GPT: from scratch, in code, spelled out

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video.

This video introduces the GPT algorithm and shows how to build it from scratch using code. The algorithm is used to predict the next character in a text sequence, and is implemented as a PyTorch module. The video covers how to set up the model, how to train it, and how to evaluate the results.

This video demonstrates how to build a self-attention module in code. The module uses a linear layer of interaction to keep track of the attention of a single individual head. The self-attention module is implemented as a tabular matrix, which masks out the weight of each column and then normalizes it to create data-dependent affinities between tokens.

• 00:00:00 ChatGPT is a machine learning system that allows users to interact with an AI and give it text-based tasks. The system is based on a neural network that models the sequence of words in a text.
  
  
• 00:05:00 This document explains how to build a chatbot using the GPT model. The code is written in Python and can be followed along with on a GitHub repository. Nano GPT is a repository for training Transformers.
  
  
• 00:10:00 This lecture explains how to tokenize text using a character-level tokenizer, and then use the encoded text as input to a Transformer to learn patterns. The training data is split into a training and validation set, and overfitting is monitored by hiding the validation set.
  
  
• 00:15:00 In this video, the author introduces the concept of a block size and discusses how it affects the efficiency and accuracy of a Transformer network. They also introduce the concept of a batch dimension and show how it affects the processing of blocks of data.
  
  
• 00:20:00 The video provides a step-by-step guide on how to build a GPT algorithm from scratch, using code. The GPT algorithm is a machine learning algorithm that is designed to predict the next character in a text sequence. The algorithm is implemented as a PyTorch module, and is able to predict the logits for every position in a 4x8 tensor.
  
  
• 00:25:00 In this video, the authors introduce GPT, a loss function for character prediction in PyTorch. They show how to implement GPT using cross entropy, and then show how to evaluate its quality on data.
  
  
• 00:30:00 The video discusses how to build a GPT model from scratch, using code. The model is designed to predict the next character in a text sequence, using a simple forward function. Training the model is accomplished by running the model with a sequence of tokens, and obtaining a loss.
  
  
• 00:35:00 This video discusses how to build a GPT model from scratch, using the SGD optimizer and Adam algorithm. The video covers how to set up the model, how to train it, and how to evaluate the results.
  
  
• 00:40:00 The author introduces a mathematical trick used in self-attention, and explains how it is used in a toy example. They then show how the self-attention algorithm calculates the average of all the vectors in previous tokens.
  
  
• 00:45:00 In this video, the author shows how to build a GPT algorithm in code, using matrix multiplication to be very efficient.
  
  
• 00:50:00 The video introduces the GPT algorithm, which calculates averages of a set of rows in an incremental fashion. The video shows how to vectorize the algorithm using softmax, and why that is useful.
  
  
• 00:55:00 In this video, the author walks through the code for building a GPT model from scratch. The model is based on a triangular matrix where each element is a token, and the tokens can communicate only if they are negative infinity. The model is developed using a number of pre-existing variables and functions, and the author explains how to calculate the logits using a linear layer between the token embeddings and the vocab size.
  
  
• 01:00:00 This video demonstrates how to build a self-attention module in code. The module uses a linear layer of interaction to keep track of the attention of a single individual head. The self-attention module is implemented as a tabular matrix, which masks out the weight of each column and then normalizes it to create data-dependent affinities between tokens.
  
  
• 01:05:00 This video demonstrates how to implement a GPT algorithm in code, with a focus on the head of self-attention. The head size is a hyperparameter, and bias is set to false to allow for parallelism. The linear modules are then initialized and a key and query are produced. The communication between nodes is prevented by using upper triangular masking. The weighted aggregation is then data-dependent and produces a distribution with a mean of one.
  
  
• 01:10:00 In this video, "Let's build GPT: from scratch, in code, spelled out," the author explains the self-attention mechanism, which allows nodes in a directed graph to communicate with each other without needing to know their positions in space.
  
  
• 01:15:00 The video explains how attention works and describes the two types of attention, self-attention and cross-attention. It also shows how to implement attention in code.
  
  
• 01:20:00 In this video, the author explains how to build a GPT network, which is a machine learning model that uses self-attention to improve accuracy. They first discuss how to normalize the data so that it can be processed by the self-attention component, and then they explain how self-attention works and show how to implement it in code. Finally, they demonstrate how multi-head attention is implemented and how the network is trained. The self-attention component helps the network improve its accuracy by communicating with the past more effectively. However, the network still has a long way to go before it is able to produce amazing results.
  
  
• 01:25:00 The video demonstrates how to build a GPT neural network from scratch, using code. The network consists of a feed forward layer followed by a relative nonlinearity, and a self-attention layer. The feed forward layer is sequential, and the self-attention layer is multi-headed. The network is trained using a loss function, and the validation loss decreases as the network gets more complex.
  
  
• 01:30:00 This YouTube video explains how to build a deep neural network (DNN) from scratch, using code. The author introduces the concept of residual connections, which are initialized to be almost "not there" at the beginning of the optimization process, but become active over time. The author also shows how to implement layer norm, a technique that ensures that columns in an input are not normalized, while rows are. Finally, the author demonstrates how to train and optimize a DNN using Pi Torch.
  
  
• 01:35:00 In this video, the author describes how they added a layer of "norms" to their "transformer" (a machine learning model) in order to scale it up. The author also notes that they changed some hyperparameters, and decreased the learning rate, in order to make the model more efficient.
  
  
• 01:40:00 This video explains how a decoder-only Transformer can be used for machine translation, and how it can be improved by adding an encoder. The result is a Transformer that is more similar to the original paper's architecture, which is intended for a different task.
  
  
• 01:45:00 GPT is a model-based encoder-decoder system that is very similar to the model-based encoder-decoder system that was used in the video.
  
  
• 01:50:00 The video and accompanying transcript explain how a GPT (general-purpose data summarizer) was trained on a small data set to summarize documents in a similar fashion to an assistant.
  
  
• 01:55:00 The video summarizes how to build a language model using code, using the GPT model as an example. The model is trained using a supervised learning algorithm, and then fine-tuned using a reward model. There is a lot of room for further refinement, and the video suggests that for more complex tasks, further stages of training may be necessary.

[MetaQuotes]

MIT 6.801 Machine Vision, Fall 2020. Lecture 1: Introduction to Machine Vision

Lecture 1: Introduction to Machine Vision

The lecture "Introduction to Machine Vision" provides a thorough overview of the course logistics and objectives, with emphasis on the physics-based approach to image analysis. It covers machine vision components, ill-posed problems, surface orientation, and the challenges of image processing. The lecturer also introduces the least squares optimization method and the pinhole model used in cameras. The camera-centric coordinate system, optical axis, and the use of vectors are also briefly discussed. The course aims to prepare students for more advanced machine vision courses and real applications of math and physics in programming.

The speaker also discusses various concepts related to image formation, including vector notation for perspective projection, surface illumination, foreshortening of surface elements, and how 3D vision problems can be solved using 2D images. The lecturer explains how the illumination on a surface varies with the incident angle and the cosine relationship between the red length and the surface length, which can be used to measure the brightness of different parts of a surface. However, determining the orientation of every little facet of an object can be difficult due to two unknowns. The speaker also explains the reason why we can solve a 3D vision problem using 2D images and concludes by mentioning that the math for tomography is simple, but the equations are complicated, making it challenging to perform inversions.

• 00:00:00 In this section, the instructor of Machine Vision 6801 introduces the logistics of the course, including the assignments and grading system, for both 6801 and 6866. There are five homework problems and two quizzes, with collaboration allowed only on the homework problems. Those in 6866 will have a term project implementing a machine vision method, preferably a dynamic problem. The class does not have a textbook, but papers will be available on the course website.
  
  
• 00:05:00 In this section, the lecturer explains the objectives and outcomes of the course Introduction to Machine Vision, wherein students will learn how to recover information about the environment from images, using a physics-based approach to analyze the light rays, surfaces, and images. The course will teach students how to extract useful features from the raw data and provide real applications of math and physics in programming, with some basic math concepts like calculus, vectors, matrices, and a little bit of linear algebra explained. It will also prepare students for more advanced machine vision courses in the future.
  
  
• 00:10:00 In this section of the transcript, the speaker provides an overview of what the course on machine vision will cover and what it will not cover. The course will cover basic geometry and linear systems, as well as convolution and image formation. However, it is not about image processing or pattern recognition. The course also does not delve into machine learning or computational imaging, but rather focuses on direct computations using physics-based models. The speaker also mentions that human vision will not be extensively discussed.
  
  
• 00:15:00 In this section, the lecturer introduces machine vision and some examples of what it can do, such as recovering image motion and estimating surface shapes. The lecturer takes a physics-based approach to the problem and discusses recovering observer motion from time-varying images, estimating the time to collision, and developing a description of the environment based on images. The lecture also covers contour maps from aerial photographs, industrial machine vision work, and solving the problem of picking an object out of a pile of objects in manufacturing.
  
  
• 00:20:00 In this section, the lecturer discusses ill-posed problems, which are problems that do not have a solution, have an infinite number of solutions, or have solutions that are dependent on initial conditions. The discussion centers on machine vision methods that determine the position and orientation of a camera, which can be inaccurate due to small measurement errors. The lecture also explores how we can perceive three-dimensional information from two-dimensional images and highlights the challenge of counting constraints versus unknowns when solving for variables. The lecturer showcases examples of algorithms that determine the 3D shape of objects from images, such as Richard Feynman's nose and an oblate ellipsoid, and how they can be used for practical purposes like using a 3D printer to create a model of an object.
  
  
• 00:25:00 In this section, the lecturer provides an overview of machine vision and its components, including a scene/world, an imaging device, and a machine vision system responsible for building a description. The most interesting applications of machine vision involve robotics, where the proof of success is the robot's ability to correctly interact with the environment using the description built. One of the most challenging aspects of machine vision is determining time to contact and focus of expansion, specifically how to measure image expansion when the available information is only a grey scale image. The lecturer notes that calibration is also an essential but often overlooked part of the process.
  
  
• 00:30:00 In this section, the lecturer discusses coordinate systems and transformations between them, specifically in the case of robots and cameras. They also mention the use of analog computing for image processing and the challenges involved in developing such algorithms. The lecture then shifts to the topic of image formation, highlighting the importance of illumination and its role in determining gray levels or RGB values in an image. The lecturer presents an illustration of a light source, an image device, and a surface, pointing out the angles that control reflection and their impact on the image.
  
  
• 00:35:00 In this section, the lecturer introduces the concept of surface orientation and how it affects machine vision. Objects can have different orientations, leading to different brightness within the outline of the object. Additionally, surface reflecting properties can also lead to varying appearances, so it is crucial to find a way to describe and account for these effects. One approach involves using multiple lights and a calibration object of known shape, such as a sphere, to obtain three constraints at every pixel, allowing for the recovery of both surface orientation and reflectance of the surface.
  
  
• 00:40:00 In this section, the professor discusses the challenges of working with images due to their inherent noise and the need to account for measurement errors. He explains that images are often noisy due to the crude quantization of 8-bit images and small pixel sizes, which leads to a sensitivity to measurement error. The professor also explains how different surface orientations produce different colors and how this can be used to construct a needle diagram, allowing for the reconstruction of shape. Finally, he introduces the extended Gaussian image as a convenient representation of shape in 3D that is useful for determining object orientation.
  
  
• 00:45:00 In this section, the lecturer demonstrates an image processing task for a robot to pick up an object, including the use of calibration to establish the relationship between the robot and the vision system coordinate system, and the use of something called a surveyor's mark, which is easy to process the image and accurately locatable, to determine that relationship. The lecturer then discusses the concept of inverse graphics, which aims to learn something about the world from an image, and the ill-posed nature of inverse problems, which require methods that can deal with solutions that depend sensitively on the data.
  
  
• 00:50:00 In this section, the lecturer introduces the optimization method of choice for the course, which is the "least squares" method. This method is favored because it leads to a closed-form solution, making it easy to implement and avoiding the chance of getting stuck in a local minimum. However, while we will be using a lot of least squares in the course, noise gain needs to be taken into account to ensure the method's robustness, particularly if measurements are off. The lecturer then moves on to the topic of the pinhole model, used in cameras with lenses, and how it can help explain the projection from a point in 3D to an image in 2D. By selecting a camera-centric coordinate system, the equations become straightforward to grasp.
  
  
• 00:55:00 In this section, the lecturer discusses the coordinate system used for machine vision, which is camera-centric. The origin is placed at the center of projection, and the axis is aligned with the optical axis. The lecture explains that the optical axis is the perpendicular line from the center of projection to the image plane. Additionally, the lecture touches on the use of vectors in machine vision and how to denote them in notation for engineering publications. Finally, the lecture mentions that the relationship between 3D and 2D motion can be obtained by differentiating the previously-mentioned equation.
  
  
• 01:00:00 In this section, the lecturer explains the vector notation used for perspective projection and how it simplifies manipulating equations. While the vector notation doesn't necessarily reduce the number of symbols used, it makes carrying around all the individual components easier. The lecturer then discusses the use of column vectors and transposes in their notation. The section ends with an introduction to brightness and its relationship to the image captured by cameras.
  
  
• 01:05:00 In this section, the lecturer explains that the brightness of an object depends on its illumination and how the surface reflects light. He also discusses how distance does not affect image formation in the same way as a light source because the area imaged on one's receptors increases as the distance from the object increases. Additionally, he mentions that the rate of change of distance or orientation can impact image formation, which is seen in the foreshortening of a surface element's power under a light source.
  
  
• 01:10:00 In this section, the speaker explains how the illumination on a surface varies with the incident angle and the cosine relationship between the red length and the surface length. This variability in illumination can be used to measure the brightness of different parts of a surface, which can help to understand something about the orientation of the surface. However, because there are two unknowns, the surface normal and the brightness, it can be difficult to determine the orientation of every little facet of an object. The speaker discusses different ways to solve this problem, including a brute-force approach of using multiple light sources or colored light sources.
  
  
• 01:15:00 In this section, the instructor discusses the foreshortening and inversion phenomenon that affects incident illumination and how it is imaged on a surface. He also explains the reason why we can solve a 3D vision problem using 2D images, which is because we live in a visual world with straight-line rays and solid surfaces. The rays are not interrupted when passing through air, making it easy to map the 3D surface into a 2D image. Tomography can be used if multiple views are needed to figure out the distribution of colored dyes in a room filled with jello. He concludes by mentioning that the math for tomography is simple, but the equations are complicated, making it challenging to perform inversions.