{"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":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-06-28T12:59:41.621850Z","iopub.execute_input":"2023-06-28T12:59:41.622188Z","iopub.status.idle":"2023-06-28T12:59:41.662221Z","shell.execute_reply.started":"2023-06-28T12:59:41.622162Z","shell.execute_reply":"2023-06-28T12:59:41.660353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Kaggle and the Future of Generative AI ","metadata":{}},{"cell_type":"markdown","source":"ChatGPT, generative AI, large language models(LLMs) have become the latest buzzwords of the AI industry owing to their breakthrough in natural language processing, handling of super voluminous data size and a near semblance of artificial general ingtelligence(AGI),as touted by some people.","metadata":{}},{"cell_type":"markdown","source":"\nGenerative AI and large language models even ChatGPT (a model product of the capabilities of both technologies), have common roots: large volume of training dataset, proven models(it is important to note that models are like machines that process the voluminous dataset. An ineffective or subpar model will kill the potential of capabilities inherent in a dataset and vice versa).","metadata":{}},{"cell_type":"markdown","source":"Putting the aforementioned facts into consideration takes us to a necessary point; the current and potential influence of Kaggle.\n\nKaggle notably sits at the apex of data science and machine learning platforms per largest data science membership platform, largest number of DS and ML competitions, largest collection of curated DS and ML datasets and more. Loads and loads of potentials inherent in this platform to take generative AI and LLMs to the next level.","metadata":{}},{"cell_type":"markdown","source":"# What More Can We Expect From Kaggle?","metadata":{}},{"cell_type":"markdown","source":"1. AI-Generated Hyperealistic Movies\n\nWe have seen what DALL-E and others have done with pictures. Kaggle's collaboration with Hollywood, Bollywood, Nollywood and other movie industries could help create hyper-realistic movies and also help movie producers choose from options of several AI-generated movie scenes or suggestions. Kaggle could help test-run desired movie effects, predict and improve movies viewership numbers before the movies final release, help optimize budgets or resources for movies, tweak actors performances without them repeatedly acting a scene and more.","metadata":{}},{"cell_type":"markdown","source":"2. More Efficient Nanotechnologies and Artificial Aids\n\nNeuralink has helped paralyzed people walk again, thanks to their technological aids. Other technologies, especially nanotechnologies could benefit from Kaggle by hosting competitions to help improve utility, reduce cost, eliminate inefficiencies, improve safety of their products or services. With that we could expect cheaper and more user-friendly exoskeletons, brain and body cells revitalization/regeneration technologies etc.","metadata":{}},{"cell_type":"markdown","source":"3. Better Medical Aids\n\nKaggle is no stranger to leading medical industry companies. In fact, thanks to Kaggle the medical field has constantly been experiencing medical breakthroughs. More collaborations could result in cheaper and safer: drugs, surgeries, therapies; and discovery of new knowledge on diseases, medical interventions and facilities and so on.","metadata":{}},{"cell_type":"markdown","source":"4. Better and Cheaper Education\n\nOne benefit of LLMs like ChatGPT, Bard etc, is that they have helped made education more affordable and accessible. People in the remotest parts of the world without quality teachers or guides now rely on LLMs to provide some insights on various subject matters or prompts akin to that of the world's best educators. \n\nKaggle could further advance this game changer intervention by working more with knowledge experts and education allied institutions to host competitions to explore how best to build education platforms or structure curriculums to make quality education more affordable and accessible.","metadata":{}},{"cell_type":"markdown","source":"This is another draft in addition to [this](http://https://www.kaggle.com/code/lordxerxes/how-impactful-has-kaggle-been-and-will-be). Please feel free to drop your comments, criticisms, and suggestions. Thank you.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\nsubmission_df = pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-06-28T12:59:41.786985Z","iopub.execute_input":"2023-06-28T12:59:41.787348Z","iopub.status.idle":"2023-06-28T12:59:41.809357Z","shell.execute_reply.started":"2023-06-28T12:59:41.787320Z","shell.execute_reply":"2023-06-28T12:59:41.807806Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-28T12:59:41.811065Z","iopub.execute_input":"2023-06-28T12:59:41.812026Z","iopub.status.idle":"2023-06-28T12:59:41.837700Z","shell.execute_reply.started":"2023-06-28T12:59:41.811989Z","shell.execute_reply":"2023-06-28T12:59:41.836766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df.loc[0]['value']='Kaggle Competitions'\nsubmission_df.loc[1]['value']='https://www.kaggle.com/code/lordxerxes/kaggle-the-future-with-kaggle'\nsubmission_df.loc[2]['value']='https://www.kaggle.com/code/naturalray/the-change-in-kaggle-competitions/comments#2321252'\nsubmission_df.loc[3]['value']='https://www.kaggle.com/code/anaghasavit/kagglecompetitionsessay/comments#2321274'\nsubmission_df.loc[4]['value']='https://www.kaggle.com/code/mpwolke/kaggle-competition-failed-draft/comments#2321309'\nsubmission_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-28T12:59:41.878109Z","iopub.execute_input":"2023-06-28T12:59:41.878485Z","iopub.status.idle":"2023-06-28T12:59:41.891606Z","shell.execute_reply.started":"2023-06-28T12:59:41.878454Z","shell.execute_reply":"2023-06-28T12:59:41.890312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_df.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-28T13:09:55.433486Z","iopub.execute_input":"2023-06-28T13:09:55.433871Z","iopub.status.idle":"2023-06-28T13:09:55.440693Z","shell.execute_reply.started":"2023-06-28T13:09:55.433841Z","shell.execute_reply":"2023-06-28T13:09:55.439489Z"},"trusted":true},"execution_count":null,"outputs":[]}]}