You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Lecture 10 - Chatbots / Closing Remarks
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 10 - Chatbots / Closing Remarks
The video covers various topics related to building chatbots with deep learning. The lecturer discusses natural language processing, information retrieval, and reinforcement learning as methods for building chatbots.The importance of context, intent classification, slot tagging, and joint training is emphasized. The lecture also covers ways to generate data automatically for training chatbots, evaluating their performance, and building context management systems for them. The lecturer encourages students to use their skills to work on meaningful projects and lift up the whole human race. Finally, he thanks students for their hard work and encourages them to continue to make a difference in the world using AI.
Part 1/2 of Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka
For your convenience, we provide a general timeline and then a detailed one for each part. You can go directly to the right moment, watch in a mode convenient for you and not miss anything.
Detailed timeline for parts of the video course
Part 1
Part 2
Part 3
Part 4
Part 2/2 of Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka
For your convenience, we provide a general timeline and then a detailed one for each part. You can go directly to the right moment, watch in a mode convenient for you and not miss anything.
Detailed timeline for parts of the video course
Part 5
Part 6
Part 7
discounts based on their purchasing patterns. Two algorithms used in association rule mining are discussed, namely, the association rule mining technique and the A-Priori algorithm. Finally, the use of support, confidence, and lift measures in Association Rule Mining is explained with the help of an example, and the concept of frequent itemsets is introduced.
Part 8
Why Neural Networks can learn (almost) anything
Why Neural Networks can learn (almost) anything
This video discusses how neural networks can learn almost anything by using a function as their activation function.
The network gradually adds neurons until it learns the desired function, even if the data set is more complicated than initially intended. This makes neural networks a powerful tool for learning from data.
ChatGPT, AI, and AGI with Stephen Wolfram
ChatGPT, AI, and AGI with Stephen Wolfram
Stephen Wolfram discusses a variety of topics, such as the API between ChatGPT and Wolfram Alpha, natural language understanding and generation, computational irreducibility, semantic grammar in language, natural language programming, the coexistence of AI and humans, and the limitations of axioms in defining complex systems. He also discusses the capabilities of AI in areas such as analogical reasoning and knowledge work and the challenge of AI picking human priorities and motivations. Computational irreducibility is also discussed, specifically how it is at the lowest level of operation in the universe. Wolfram emphasizes the need to understand and work with computational irreducibility to advance our understanding of the world around us.
Stephen Wolfram explains how our computational limitations as observers affect our perception of the universe, leading to our understanding of the laws of physics. He also discusses the potential for experimental evidence that could prove the discreteness of space and speaks about the multi-computational paradigm they developed, which could have implications in different fields. The host thanks Wolfram for his insights and expresses enthusiasm for future video series, "Beyond the Conversations."
GPT-4 Creator Ilya Sutskever
GPT-4 Creator Ilya Sutskever
The video features an interview with Ilya Sutskever, the co-founder and chief scientist of OpenAI who played a crucial role in creating GPT-3 and GPT-4. Ilya Sutskever explains his background in machine learning and his interest in understanding how computers can learn. He discusses the limitations of large language models, including their lack of understanding of the underlying reality that language relates to, but also notes that research is underway to address their shortcomings. Ilya Sutskever also emphasizes the importance of learning the statistical regularities within generative models. The potential for machine learning models to become less data-hungry is discussed, and the conversation turns to the use of AI in democracy and the possibility of high-bandwidth democracy where citizens provide information to AI systems.
research is being conducted to address the shortcomings of these models.
AI Revolution: The Rise of Conscious Machines
"AI Revolution: The Rise of Conscious Machines"
The video "AI Revolution: The Rise of Conscious Machines" discusses the possibility of creating an artificial general intelligence (AGI) that could be the highest expression of intelligence ever seen. Recent developments such as Google's Lambda suggest that this could be possible in the near future. The video also explores the concept of AGIs potentially exhibiting signs of consciousness and the potential ethical implications of creating sentient beings. Additionally, the capabilities of AI systems such as Chai GPD and Dall-E 3 are highlighted, showcasing their ability to write code, create art, and generate tailored content. While the potential benefits of developing advanced AI are vast, careful consideration must be given to how it may impact the job market and the role of humans in a world where super-intelligent beings exist.
The AI Revolution: Here's what will happen
AI Revolution: Here's what will happen
The "AI Revolution: Here's what will happen" video explains how AI technology will impact various industries, including the artistic world. While concerns exist regarding the potential displacement of human artists and creators, AI tools could be used to improve art output and productivity, such as generating new ideas and assisting with tasks like image and video editing or music production. Moreover, the speaker believes that traditional art will not disappear, and AI tools can be seen as a tool for artists to improve their output and productivity. The rapid development of AI in the art world could increase its value if it becomes unique and sought after by collectors. Additionally, AI tools can create new opportunities for artistic expression and innovation by automating certain tasks and freeing up artists to focus on other aspects of their work. The key is to use AI as a tool to enhance our capabilities rather than replace them.
OpenAI GPT-4: The Most Advanced AI Yet - Live with Tesla & Elon Musk
OpenAI GPT-4: The Most Advanced AI Yet - Live with Tesla & Elon Musk
Elon Musk appeared on a YouTube show discussing a wide range of topics, including social media, investments, competition in industries, sustainable energy, carbon tax, chip-making equipment, China, Tesla's production process, and his upbringing. Musk emphasized his desire to make a difference in the world, promoting sustainable energy to combat the climate crisis, and his plans for human civilization to expand beyond Earth as a multi-planet species. He also discussed his early ventures, including Zip2, and the initial struggles of convincing investors to invest in internet companies. Despite Zip2's advanced software, the company struggled with too much control from existing media companies, leading to poor deployment of their technology.
The "OpenAI GPT-4: The Most Advanced AI Yet - Live with Tesla & Elon Musk" video includes multiple segments where Elon Musk shares his experiences with various businesses. In one segment, Musk discusses his past experience with Zip2, an online city guide and business directory, and how newspapers were better partners than industry players. Musk explains that Zip2 helped major newspapers by providing them with technological services to generate revenue to keep their classifieds business from being destroyed by Craigslist. Musk also talks about his early internet company that helped businesses create websites, which led Musk to believe in the success of the internet. Lastly, Musk speaks about how PayPal disrupted the banking industry by improving transaction velocity and caused major players like GM to fall out, which was the case when Tesla started.
Dr Demis Hassabis: Using AI to Accelerate Scientific Discovery
Co-founder and CEO of DeepMind, delivers a major public lecture at the Sheldonian Theatre in Oxford on Tuesday 17 May 2022
Dr Demis Hassabis: Using AI to Accelerate Scientific Discovery
Dr. Demis Hassabis, CEO and co-founder of DeepMind, discusses his career journey that has led him to using AI to accelerate scientific discovery. DeepMind focuses on building general learning systems, which learn through first principles directly from experience, and fuses deep learning or deep neural networks with reinforcement learning. Dr. Hassabis explains how AlphaGo and AlphaZero used AI to accelerate scientific discovery, with AlphaFold being able to predict the 3D structure of a protein. The AlphaFold 2 system reached atomic accuracy, with a score of less than one angstrom error on average, and is used in hundreds of papers and applications across the world.
Also he discusses the potential of AI in revolutionizing the field of biology, specifically in drug discovery. He emphasizes the importance of building AI responsibly and using the scientific method to manage risks and benefits. Dr. Hassabis also addresses ethical concerns related to the use of AI in neuroscience, consciousness, and free will, highlighting the need for multi-disciplinary approaches that involve philosophers, ethicists, and humanities. He believes that AI can contribute to the fields of morality and political science through virtual simulations but acknowledges the complexity of humans and their motivations. Finally, Dr. Hassabis discusses the challenges of studying artificial neural networks and the need for a better understanding of these systems over the next decade.