{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# GENERATIVE AI INTELLIGENCE\n\"\"\"\nIntelligence is a unique “phenomenon” - a miraculous ability that has been described as the coordination of memory, learning, and reasoning in animals. Intelligence has also been defined as the ability of an animal to form associative links between events or objects of which it has had no previous experience [1]. But what happens when we humans started instilling pieces of metal or carbon fiber with this same ability? This could be the definition of Artificial intelligence in its crudest forms.\n\"\"\"\n\n# ARTIFICIAL INTELLIGENCE\n\"\"\"\nArtificial intelligence refers to the capacity of computers to replicate cognitive human functions, such as thinking, reasoning, and memory. AI utilizes mathematical, deduction, and logical principles to mimic human reasoning processes, enabling computer systems to learn from new information and choose from a set of options constantly and continuously till it arrives at the “best” solution.\nSo, while humans learn the skill of decision-making through observation and experience for a greater portion, they also rely on intuition or “gut feeling” – a distinguishing factor between humans and machines so far. But the development of AI is aimed at producing models which can replicate this method of making decisions based on “intuition” and not just data that has been provided to the computer to find trends.\nPresently, artificial intelligence has permeated various aspects of our lives. It is integrated into cell phones, exemplified by popular personal AI assistants like Siri, Alexa, and Cortana. Additionally, AI is utilized in vehicles like Tesla, social media like Snapchat AI, video games, and even language translation on smartphones (such as Google Translate and Google Lens).\nAlthough we have not yet reached the stage of strong AI, where machines possess consciousness, intelligence, emotions, and self-awareness, we are approaching a time when AI can imitate human behaviors convincingly.\nAI has become a critical component of business development. According to MIT, organizations that incorporate AI are projected to experience a 25% increase in customer satisfaction by 2023. Furthermore, a substantial 91.5% of leading businesses consistently invest in AI. It is evident that artificial intelligence is not only a permanent fixture in our lives but is also continually advancing.[2]\n\"\"\"\n\n# GENERATIVE ARTIFICIAL INTELLIGENCE\n\"\"\"\nSuch advancement is what has led to the development of Generative AI. Generative AI, a remarkable subset of AI, has garnered significant attention due to its ability to generate content based on provided data. Unlike classical AI, which undergoes repetitive training with human supervision, Generative AI is built on a machine-learning model that utilizes both training and testing data. By feeding the computer diverse and relevant training data, it is tasked with making predictions based on this information. These predictions are then compared to the testing data, which represents the actual results. The closer the computer's predictions align with the testing data, the better its decision-making abilities become. Through continuous iterations of this process, the AI evolves to generate a wide range of solutions independently, free from inaccuracies, or rely solely on the input data.\nOn November 30, 2022, a groundbreaking Generative AI model was introduced which was adopted by people quickly and on a large scale. This model encompasses algorithms and frameworks capable of generating original content (Chat GPT), spanning from images (DALL-E) and music to text and even entire virtual environments.\nWith all these advancements in action, it’s only fair that we keep ourselves all brushed up on something as fascinating as Generative AI. This essay serves as a centralizing and summarizing account of the notable developments that have transpired in the realm of Generative AI over the past two years```\n\"\"\"\n# GENERATIVE AI INTELLIGENCE\n\"\"\"\nIntelligence is a unique “phenomenon” - a miraculous ability that has been described as the coordination of memory, learning, and reasoning in animals. Intelligence has also been defined as the ability of an animal to form associative links between events or objects of which it has had no previous experience [1]. But what happens when we humans started instilling pieces of metal or carbon fiber with this same ability? This could be the definition of Artificial intelligence in its crudest forms.\n\"\"\"\n\n# ARTIFICIAL INTELLIGENCE\n\"\"\"\nArtificial intelligence refers to the capacity of computers to replicate cognitive human functions, such as thinking, reasoning, and memory. AI utilizes mathematical, deduction, and logical principles to mimic human reasoning processes, enabling computer systems to learn from new information and choose from a set of options constantly and continuously till it arrives at the “best” solution.\nSo, while humans learn the skill of decision-making through observation and experience for a greater portion, they also rely on intuition or “gut feeling” – a distinguishing factor between humans and machines so far. But the development of AI is aimed at producing models which can replicate this method of making decisions based on “intuition” and not just data that has been provided to the computer to find trends.\nPresently, artificial intelligence has permeated various aspects of our lives. It is integrated into cell phones, exemplified by popular personal AI assistants like Siri, Alexa, and Cortana. Additionally, AI is utilized in vehicles like Tesla, social media like Snapchat AI, video games, and even language translation on smartphones (such as Google Translate and Google Lens).\nAlthough we have not yet reached the stage of strong AI, where machines possess consciousness, intelligence, emotions, and self-awareness, we are approaching a time when AI can imitate human behaviors convincingly.\nAI has become a critical component of business development. According to MIT, organizations that incorporate AI are projected to experience a 25% increase in customer satisfaction by 2023. Furthermore, a substantial 91.5% of leading businesses consistently invest in AI. It is evident that artificial intelligence is not only a permanent fixture in our lives but is also continually advancing.[2]\n\"\"\"\n\n# GENERATIVE ARTIFICIAL INTELLIGENCE\n\"\"\"\nSuch advancement is what has led to the development of Generative AI. Generative AI, a remarkable subset of AI, has garnered significant attention due to its ability to generate content based on provided data. Unlike classical AI, which undergoes repetitive training with human supervision, Generative AI is built on a machine-learning model that utilizes both training and testing data. By feeding the computer diverse and relevant training data, it is tasked with making predictions based on this information. These predictions are then compared to the testing data, which represents the actual results. The closer the computer's predictions align with the testing data, the better its decision-making abilities become. Through continuous iterations of this process, the AI evolves to generate a wide range of solutions independently, free from inaccuracies, or rely solely on the input data.\nOn November 30, 2022, a groundbreaking Generative AI model was introduced which was adopted by people quickly and on a large scale. This model encompasses algorithms and frameworks capable of generating original content (Chat GPT), spanning from images (DALL-E) and music to text and even entire virtual environments.\nWith all these advancements in action, it’s only fair that we keep ourselves all brushed up on something as fascinating as Generative AI. This essay serves as a centralizing and summarizing account of the notable developments that have transpired in the realm of Generative AI over the past two years and the experience of people like me and many others who have used generative AI extensively.\n\"\"\"\n\n# HOW I```\n# HOW I CAME ACROSS AI AND GEN AI\n\"\"\"\nI acknowledged the presence of AI in my life for the first time in 2021 when I was made to use tools like Grammarly and PowerPoint maker extensively. At the heights of COVID, I (and half of the planet) developed a sort of affinity towards tech like never before. 2021 was also the year I graduated from high school. Tech had so much influence on me that I ended up majoring in Computer Science with a minor in Artificial Intelligence. With the introduction of pioneering Generative AIs such as GPT – 3 and DALL – E in the latter half of 2022, my purpose of building a disruptive innovation through tech solidified. I joined the best community there is for all aspiring and enacting Data scientists and ML engineers – Kaggle.\nKaggle truly is a great platform for passionate beginners as the platform offers you not just competition opportunities and a multitude of datasets to practice but also a bunch of relevant courses (such as Python, SQL, Intro to ML, Data Visualization, etc.) at zero cost. Learning at Kaggle is fun and motivating. So, if you are a beginner, you know where to go!\n\"\"\"\n\n# WHAT IS GENERATIVE AI OFFERING?\n\"\"\"\n# Chat GPT\n\nIt’s a text-generating machine-learning model that is ready for use. All you need to do is type in a prompt in the message box (sort of like a Google search but better) and you have your questions answered. As a student, I use ChatGPT to work on my coding skills. One of the most impressive things about GPT–3 is its ability to solve high school-level math and even programming languages because AI models are not proficient in deduction and logic. Reminds me of a saying among us programmers – “Humans think whilst the computers execute”. So, seeing a GPT work well with logic and explaining it to me is nothing less than great. One can easily use GPT to comprehend certain topics that they are interested in or are needed to understand.\n\"\"\"\n# DALL-E\n\"\"\"\nIt’s another interesting Generative AI that has won the hearts of millions of people across the planet, including mine. DALL-E generates pictures based on prompts that the user provides. DALL-E doesn’t question your choices or interests and always serves us with the request that we provided to it. Use DALL-E to express your creativity and yourself. Creativity truly knows no bounds, thanks to these generative AI.\n\nThese are just a few ways in which Generative AI is enriching the lives of users on an individual scale. But there’s surely more to come.\n\"\"\"\n\n# TECHNICAL ADVANCEMENTS (PAST TWO YEARS)\n\"\"\"\n“Businesses need to embrace AI. Many industry professionals believe that strong versions of AI will have the capabilities to think, feel, and move like humans, whereas weak AI—or most of the AI we use today—only can think minimally”.[2]\nThis is a statement extracted from the MIT professional page. Whilst, the idea of AI thinking and feeling like humans is fantastic, it is still a distant dream. Right now, AI is still a budding flower. Let’s take a quick look at the advancement generative AI went through in the past two years:\n2021\n- Cerebras Systems used AI to help generate the design for the WSE-2, a single chip the size of a complete silicon wafer with more than 850,000 cores and 2.6 trillion transistors. [3]\n- OpenAI introduced DALL-E, which can generate images from text prompts. The name is a combination of WALL-E, the name of a fictional robot, and the artist Salvador Dali.\"\"\"\n\"\"\"\nThe new tool introduced the concept of contrastive language-image pretraining (CLIP) to rank captions for images found on the internet. [4]\n2022\n- OpenAI released DALL-E 2, a smaller and more efficient image generator that uses a diffusion model to generate images. The AI system can generate images and art from a description in natural language. [5]\n- Nvidia created NGP Instant NeRF code for quickly transforming pictures into 3D images and content. [6]\n- Google DeepMind released a paper on Gato, a general-purpose multimodal AI that can perform more than 600 tasks, including captioning text, generating robot instructions, playing video games, and navigating environments. [7]\n- Researchers from Runway Research, Stability AI, and CompVis LMU released Stable Diffusion as open-source code that can automatically generate image content from a text prompt. This technique is a new method of combining an auto-encoder to transform data into an intermediate format so Diffusion models can process it more efficiently. [8]\n- OpenAI released ChatGPT in November to provide a chat-based interface to its GPT 3.5 LLM. It attracted more than 100 million users within two months, representing the fastest-ever consumer adoption of a service. [9]\n2023\n- Getty Images and a group of artists separately sued several companies that implemented Stable Diffusion for copyright infringement. Their suit claimed Stability AI, Midjourney, and DeviantArt, among others, scraped Getty's content without consent. [10]\n- Microsoft integrated a version of ChatGPT into its Bing search engine. Google quickly followed with plans to release the Bard chat service based on its Lamda engine. And the controversy over detecting AI-generated content heated up. [11]\n- OpenAI released GPT-4 multimodal LLM, which can receive both text and image prompts. A who's who of technology leaders, including Elon Musk, Steve Wozniak, and thousands more signatories, called for a pause on the development of advanced AI systems \"more powerful than GPT-4.\" [12]\n- New tool helps people choose the right method for evaluating AI models May 31, 2023 (the complexity of these models often surpasses the understanding of even the scientists who design them. To address this issue, scientists at MIT and IBM Research have developed saliency methods, which aim to explain the inner workings of these models.) [13]\n- A more effective way to train machines for uncertain, real-world situations (Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own. Adam Zewe | MIT News Office. Publication Date: May 31, 2023) [14]\n- Using AI, scientists find a drug that could combat drug-resistant infections (The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings. Anne Trafton | MIT News Office. Publication Date: May 25, 2023) [15]\n- Probabilistic AI that knows how well it’s working (It’s more important than ever for artificial intelligence to estimate how accurately it is explaining data. Working together, researchers from MIT and the University of California at Berkeley have developed a new method for building sophisticated AI inference algorithms that simultaneously generate collections of probable explanations for data and accurately estimate the quality of these explanations. The new method is based on a mathematical approach called sequential Monte Carlo (SMC). Rachel Paiste | Department of Brain and Cognitive Sciences. Publication Date: May 25, 2023) [16]\nThe past two years have seen significant development of Generative AI. It has been a sheer pleasure to\"\"\"\n\"\"\"\nsee generative AI take over the internet place instantly and on a large scale. It was a revolutionary phase in the history of app development as ChatGPT garnered more than 10 million users in a span of 10 months – a record no app has ever achieved before. People have started to see the ease and efficiency generative AI can bring to their workspace and creative endeavors.\nAccording to a report from McKinsey, in the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.[17]\n\"\"\"\n\n# THOUGHTS\n\"\"\"\nThe future of Generative AI is uncertain and sometimes dreaded too, but it’s highly dependent on how we use it. After all, Gen AI is also a tool in our toolbox that we use to better ourselves and uplift humanity as a whole.\nI truly hope that the path to your calling is simplified and more fun with Generative AI. HAPPY LEARNING! HAPPY CREATING!\n\"\"\"\n","metadata":{"execution":{"iopub.status.busy":"2023-07-05T23:14:00.792792Z","iopub.execute_input":"2023-07-05T23:14:00.794274Z","iopub.status.idle":"2023-07-05T23:14:00.834412Z","shell.execute_reply.started":"2023-07-05T23:14:00.794231Z","shell.execute_reply":"2023-07-05T23:14:00.831881Z"},"trusted":true},"execution_count":null,"outputs":[]}]}