{"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":"markdown","source":"<a id=\"section-one\"></a>\n# **What is Generative AI?** ","metadata":{}},{"cell_type":"markdown","source":"![F1.png](attachment:7c672ce5-5fed-4807-881f-98f108f92595.png)\n<h4 align=\"center\">Figure 1: Divisions of AI (Created by Author)</h4>","metadata":{},"attachments":{"7c672ce5-5fed-4807-881f-98f108f92595.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### Welcome to the era of machine learning and deep learning in 2023!<a class=\"anchor\" id=\"Welcome to the era of machine learning and deep learning in 2023!\"></a>\n\nExploring the state of machine learning today and how it affects various fields is crucial as we move forward into the future. Machine learning's potential for growth is very encouraging. Algorithms have advanced significantly, becoming simpler, more approachable to understand, and more potent than before. Machine learning is dramatically altering our lives, from the emergence of deep learning and generative AI to ground-breaking uses in healthcare, finance, transportation, and retail. Let's embark on a journey through the impact, learnings, newest trends, predictions, and issues to uncover the huge potential of machine learning in the present and the future.<br>\n\n\n\nOver the past few years, deep learning has developed, and its algorithms are now widely used in a variety of industries. It utilizes artificial intelligence neural networks (ANNs) to carry out intricate computations on vast volumes of data, basing its operation on the structure and operation of the human brain. Generative AI, also referred to as creative AI or art AI is one of these potent toolkits that makes use of deep neural networks. It recognizes patterns and structures within the existing data to produce unique and original content that may include text, visual, audio, code, 3D models, or other types of data. For training, generative AI models can use a variety of learning strategies, such as unsupervised or semi-supervised learning. As a result, businesses are now able to quickly build foundational models using a vast amount of unlabeled data. The idea of foundation models is not new; well-known CNNs like Resnet-50 were initially introduced in 2015. DeepMind's Alpha Code (GoogleLab), ChatGPT, GPT-4, DALL-E, Google Bard, Bing AI, Github Copilot, Synthesia, MidJourney, Jasper, and Stable Diffusion are some of the top generative AI technologies. And ChatGPT evolved into OpenAI's most widely used product to date, launching ChatGPT Plus, a pilot paid subscription, in March 2023.<br>\n\n\n\nThe vast majority of well-liked generative AI technologies focus on the creation of images. Text input is used by generative AI systems as the foundation for creating graphics in two, three, and even four dimensions. It can produce incredibly accurate portraits of people. You may be surprised to learn that generative AI cannot draw hands, despite its achievements. And it is the most challenging setback in AI. Making avatars is one of the most popular applications of AI picture production and TikTok is among the most well-liked platforms for sharing examples of generative AI. You can even become a superhero, a royal king or queen, or a mermaid with the use of generative AI technologies. The AI-generated QR code, which replaces the outdated black and white QR code with visually attractive ones, is another new trend. Hugging Face has made a free AI QR code generator available for this purpose. Investment in generative AI has already reached over two billion dollars, up 425 percent from 2020. Additionally, generative AI is widely regarded as the most advanced technology over the next 50 years, with many scientists and experts even referring to it as an age-reversing technology!<br>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# **Generative AI's Evolution** ","metadata":{}},{"cell_type":"markdown","source":"![a01869ed636c744aa01869ed636c744af2.png](attachment:deb714e9-2ff4-4af4-ae52-5ae063a62422.png)\n<h4 align=\"center\">Figure 2: Generative AI's Evolution (Created by Author)</h4>","metadata":{},"attachments":{"deb714e9-2ff4-4af4-ae52-5ae063a62422.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# **Key requirements of Generative AI** ","metadata":{}},{"cell_type":"markdown","source":"\nThe following essential conditions must be met by generative models to be widely used in practical applications:<br>\n* High-generation quality is needed for numerous applications, particularly those that deal directly with those using it.<br>\n* A strong generative model should be able to effectively record variations without compromising generation performance if the training data includes a complicated or significant amount of variability.<br>\n* Easy and quick generation is necessary for many interactive applications, including real-time photo editing.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# **Key concepts of Generative AI** ","metadata":{}},{"cell_type":"markdown","source":"1.\t**GAN** - Generative Adversarial Network pits two neural networks against one another in the context of a zero-sum game. GANs are made to create new, synthetic data that closely resembles the distribution of current data. Here, the two neural networks are a generator and a discriminator. The Generator attempts to deceive the Discriminator by creating artificial samples of data (such as an image, audio, etc.). On the other hand, the Discriminator tries to tell the difference between genuine and fraudulent samples.\n2.\t**VAE** - Variational Auto-Encoders is a subclass of deep generative networks that share the same encoder (inference) and decoder (generative) components as the traditional auto-encoder. It is an autoencoder whose distribution of encodings is regulated during training to guarantee that its latent space has favorable attributes allowing us to produce some new information.\n3.\t**LLM** - Large Language Models are artificial intelligence systems that are based on transformer architecture; built to comprehend and produce human language. In order to grasp the patterns and laws of language, these models are trained to function by learning the statistical correlations between words and phrases in a huge corpus of text.\n4.\t**NLP** - Natural Language Processing is a branch of artificial intelligence that integrates computational linguistics with statistical, machine learning, and deep learning models. With these technologies, computers can completely \"understand\" what is said or written, including the speaker's or writer's intents and feelings, and interpret human language in the form of text or audio data.\n5.\t**RNN** - Recurrent neural networks are a particular type of artificial neural network that are mostly utilized in NLP and speech recognition. It operates on the idea of preserving a layer's output and using that information to anticipate that layer's output from the input. RNN's Hidden state, which retains some details about a sequence, is its primary and most significant characteristic.\n6.\t**Autoencoders** – It is an unsupervised neural network that first learns how to minimize and encode information before teaching itself how to decode the compressed and encoded data and rebuild it into a form that is as close to the original input as practical. By developing the ability to disregard data noise, it minimizes the dimensions of the data.\n7.\t**Autoregressive models** – In order to increase the likelihood of training data, autoregressive models provide an attainable explicit density model. This makes it simple to estimate the likelihood of data observation and to provide an evaluation measure for the generative model using these approaches. It uses a regression model to estimate the value of the following time step after learning from a large number of timed steps and measurements from earlier activities.\n8.\t**Diffusion models** - The diffusion model is a type of generative model that learns to recover the data by reversing the noising process after first erasing the training data by adding Gaussian noise one at a time. The Diffusion Model may be used to produce data after training by simply subjecting randomly sampled noise to the mastered denoising procedure, yielding innovative and varied high-resolution pictures that are similar to the initial data.\n9.\t**Transformer models** - Transformer models are a subset of deep learning models that are frequently used in NLP and other generative AI applications. They are trained to learn the connections between the words in a phrase or line of text. They accomplish this learning by employing a technique known as self-attention, which enables the model to evaluate the relative weights of various words in a sequence according to their context. These models provide the essential benefit of processing input sequences concurrently, outperforming RNNs for many NLP applications.\n10.\t**Data augmentation** - Modifying or \"augmenting\" a dataset with new data is known as data augmentation. The incorporation of this supplementary data, which may be anything from photographs to text, helps machine learning algorithms perform better. By making modified replicas of an existing dataset using previously collected data, it artificially expands the training set. The dataset may be slightly modified, or new data points may be produced using deep learning.\n11.\t**Flow-based models** - Flow-based models define an invertible transformation between the input and output areas to directly represent the data distribution. They provide effective density estimates in addition to data production. To describe complicated data distributions, they use normalizing fluxes, a series of invertible transformations. These changes make it possible to compute likelihoods and sample data quickly.\n12.\t**DeepDream** - With the help of a convolutional neural network, the potent computer vision system DeepDream can identify and improve certain patterns in photos. DeepDream over-interprets and intensifies the patterns it notices in a picture, much like a toddler watching clouds and attempting to make sense of random shapes and formations.\n13.\t**Transfer learning** - Transfer learning is a machine learning (ML) technique that leverages a trained model created for one job to complete another that is similar but unrelated. Transfer learning accelerates training and lowers the cost of creating a new model from scratch. Computer vision, natural language processing, sentiment analysis, and natural language creation are just a few of the generative AI applications that might benefit from transfer learning. \n14.\t**GPT** - Generative Pre-trained Transformers are a class of neural network models that make use of the transformer architecture. They are sometimes referred to as GPT. They are all-purpose language models capable of writing code, summarizing text, extracting information from documents, and producing original content. Applications with GPT models may produce text and material (including photos, music, and more) that is human-like and can converse with users.\n15.\t**Fine-tuning** - A machine learning model is fine-tuned to improve its performance on a particular task or dataset by changing its hyperparameters or pre-trained weights. When compared to the first pre-training, fine-tuning takes far less data and computing effort. Larger models sometimes perform worse than well-tuned models do.\n16.\t**Zero-shot learning** - A machine learning paradigm known as \"zero-shot learning\" uses semantic data or connections between known and new classes, which is frequently represented as attribute vectors or knowledge graphs, to categorize or recognize novel categories or instances without the need for training examples.\n17.\t**Hallucination** - The primary reason for hallucination is that the LLM employs its internal \"knowledge\" (what it has been developed on), which is irrelevant to the user inquiry. This results in the LLM producing the incorrect output.\n18.\t**Prompt engineering** - The secret to endless universes is AI prompt engineering, which employs prompts to acquire the desired outcome from an AI tool. For the text we want the model to produce, the prompt gives context. The prompts we design might be anything from straightforward instructions to intricate texts. \n19.\t**Foundation models** - A foundation model is a system based on deep learning that has been trained using very sizable data sets downloaded from the internet. Because they contain hundreds of billions of hyperparameters that have been trained using hundreds of terabytes of data, these models can cost millions of dollars to develop.\n20.\t**BERT** - Bidirectional Encoder Representations from Transformers is a deep learning algorithm created by Google AI Research that makes use of unsupervised learning to better comprehend natural language inquiries. The model learns bidirectional representations of text data using a transformer architecture, which enables it to comprehend the context of words inside a phrase or paragraph.\n21.\t**Perceptron** - Perceptrons are a component of artificial neural networks that are utilized for a variety of classification applications. Input values (Input nodes), weights and bias, net sum, and an activation function are the four essential factors that make up this single-layer neural network.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-three\"></a>\n# **Generative AI Tools** ","metadata":{}},{"cell_type":"markdown","source":"Below are 10 different tools that can be used for different output generation. \n\n\n|**Text**|**Visuals**|**Audio**|**Code**|**3D Models**|\n|--------|-----------|---------|--------|-------------|\n|**<a href=\"https://www.jasper.ai/\">Jasper</a>**|**<a href=\"https://www.fotor.com/features/ai-image-generator/\">Fotor</a>**|**<a href=\"https://lovo.ai/?lmref=aQMezw\">Lovo.ai</a>**|**<a href=\"https://openai.com/blog/chatgpt\">ChatGPT</a>**|**<a href=\"https://www.move.ai/\">Move.ai</a>**|\n|**<a href=\"https://www.copy.ai/\">Copy.ai</a>**|**<a href=\"https://openai.com/dall-e-2\">DALL-E 2</a>**|**<a href=\"https://synthesys.io/\">Synthesys</a>**|**<a href=\"https://github.com/features/copilot\">GitHub Copilot</a>**|**<a href=\"https://www.lumirithmic.com/\">Lumirithmic</a>**|\n|**<a href=\"https://www.notion.so/product/ai\">Notion AI</a>**|**<a href=\"https://www.midjourney.com/home/?callbackUrl=%2Fapp%2F\">Midjourney</a>**|**<a href=\"https://murf.ai/?lmref=oW5IkA\">Murf</a>**|**<a href=\"https://www.tabnine.com/\">TabNine</a>**|**<a href=\"https://www.elevate3d.ai/\">Elevate3D</a>**|\n|**<a href=\"https://www.grammarly.com/grammarlygo\">GrammarlyGO</a>**|**<a href=\"https://beta.dreamstudio.ai/generate\">Dream Studio</a>**|**<a href=\"https://voiceover.speechify.com/sign-in?returnTo=https%253A%252F%252Fvoiceover.speechify.com%252Fdashboard\">Voice Over by Speechify</a>**|**<a href=\"https://replit.com/site/ghostwriter\">Replit Ghostwriter</a>**|**<a href=\"https://nv-tlabs.github.io/GET3D/\">Get3D</a>**|\n|**<a href=\"https://writesonic.com/\">Writesonic</a>**|**<a href=\"https://starryai.com/\">StarryAI</a>**|**<a href=\"https://www.altered.ai/?fpr=uniteai\">Altered Studio</a>**|**<a href=\"https://mutable.ai/\">MutableAI</a>**|**<a href=\"https://3dfy.ai/\">3DFY.ai</a>**|\n|**<a href=\"https://www.sudowrite.com/\">Sudowrite</a>**|**<a href=\"https://pictory.ai/?ref=uniteai\">Pictory</a>**|**<a href=\"https://www.listnr.tech/?gr_pk=9bE1&gr_uid=5xqA\">Listnr</a>**|**<a href=\"https://www.seek.ai/\">Seek</a>**|**<a href=\"https://www.sloyd.ai/\">Sloyd.ai</a>**|\n|**<a href=\"https://www.frase.io/\">Frase</a>**|**<a href=\"https://www.synthesia.io/?via=uniteai\">Synthesia</a>**|**<a href=\"https://www.respeecher.com/\">Respeecher</a>**|**<a href=\"https://www.ai2sql.io/\">AI2SQL</a>**|**<a href=\"https://lumalabs.ai/\">Luma AI</a>**|\n|**<a href=\"https://www.hypotenuse.ai/\">Hypotenuse AI</a>**|**<a href=\"https://app.heygen.com/guest/home?sid=rewardful&via=uniteai\">HeyGen</a>**|**<a href=\"https://play.ht/?via=uniteai\">Play.ht</a>**|**<a href=\"https://enzyme.so/\">Enzyme</a>**|**<a href=\"https://masterpiecestudio.com/blog/3d-generative-ai\">Masterpiece Studio</a>**|\n|**<a href=\"https://paragraphai.com/\">ParagraphAI</a>**|**<a href=\"https://www.colossyan.com/?via=uniteai\">Colossyan</a>**|**<a href=\"https://speechelo.com/\">Speechelo</a>**|**<a href=\"https://durable.co/ai-website-builder\">Durable</a>**|**<a href=\"https://dreamfusion3d.github.io/\">Google DreamFusion</a>**|\n|**<a href=\"https://chibi.ai/\">Chibi</a>**|**<a href=\"https://www.deepbrain.io/aistudios?via=antoine\">Deepbrain</a>**|**<a href=\"https://www.shutterstock.com/discover/ampermusic?irclickid=yArT2Nx0hxyPRNwRP7QjnXbsUkFxrU2t4yPo0M0&irgwc=1&utm_campaign=Securities.io&utm_content=42119&utm_medium=Affiliate&utm_source=2459529&utm_term=\">Amper Music</a>**|**<a href=\"https://stenography.dev/\">Stenography</a>**|**<a href=\"https://3d-avatar-diffusion.microsoft.com/#/\">RODIN</a>**|\n\n<h4 align=\"center\">Table 1: Generative AI Tools (Created by Author)</h4>","metadata":{}},{"cell_type":"markdown","source":"# **Generative AI learnings in the past two years**","metadata":{}},{"cell_type":"markdown","source":"The Machine learning community has taken great strides in researching and testing generative AI during the last few years. The ML community has obtained invaluable observations via intensive study and experimentation that have impacted our knowledge of generative AI and its possible applications.\n\n* Generative AI depends primarily on the training data it is given, any biases or constraints in the data may be mirrored in the results that are produced. To ensure fairness, minimize biases, and prevent the amplification of already-existing social inequities, the ML community has acknowledged the significance of varied and representative training data.\n\n* The generative AI community places a strong emphasis on the value of ongoing experimentation and teamwork. The discipline has advanced significantly thanks to the dynamic process of exploration, feedback, and information exchange, which has also sparked more discoveries.\n\n* The ML community is becoming more and more conscious of the ethical issues surrounding the usage of generative AI as it advances. Deepfakes, false information, and possible abuse of AI-generated material are just a few of the issues that have sparked conversations and initiatives to create ethical standards and protections. \n\n* Researchers have studied generative modeling strategies that include numerous models cooperating to provide consistent and varied information. In order to provide more varied outputs, collective generation approaches like ensemble methods and mixture models have been used.\n\n* Generative AI's capacity to unleash creativity is one of the most important lessons the ML community has learned from working with it. In the past several years, generative AI has demonstrated its ability to create fresh and distinctive material, presenting new opportunities for artistic expression and fostering creative solutions in industries like design, music, and the arts.\n\n* In generative models, the application of attention techniques, such as self-attention, is expanding and gaining popularity. Model quality and coherence are increased as a result of attention processes, which allow models to recognize long-range relationships in the data.\n\n* A growing area of focus is user-centric design and dynamic interfaces. Users may direct and influence the output of generative models in accordance with their preferences by using techniques like conditional generation, style transfer, and immersive fine-tuning.\n\n* It is still difficult to make generative models consistently and securely generate outputs given a variety of inputs and circumstances. Instead of producing unrealistic or absurd results, the ML community has concentrated on enhancing generative models' resilience and generalization abilities to provide high-quality outputs.\n\n* Few-shot and one-shot learning approaches have advanced as a result of the search for data productivity and efficient use of data in generative models. Researchers have investigated strategies for training generative models with less data while generating good results. To help generative models learn fast from a small number of instances, strategies including transfer learning, meta-learning, and using additional sources have been researched by the ML community.\n\n* Researchers have investigated methods for interpolating in generative models' latent space. This makes it possible for seamless transitions and mixing between various produced outputs, enabling the development of fresh and ongoing variants.\n\n* The training and performance of generative models have benefited from the increased use of reinforcement learning and self-supervised learning. With the use of these methodologies, models may be trained using various paradigms that allow them to learn from samples they have created themselves or interact with the environment to increase the quality of their creation.\n\n* The capacity of generative models to transfer a style, a trait, or an attribute from one area of expertise to another has been a lucrative field of research. By facilitating seamless synthesis and modification of material across many styles and fields, this learning has cleared the path for improvements in integrating representations across domains.\n\n* Using generative models in conjunction with user feedback loops has been shown to be effective in raising the standard and level of user satisfaction with produced outputs. The potential for AI and humans to work together has been shown through iterative procedures incorporating human input and fine-tuning.\n\n* Large-scale datasets like GPT-3, which are used for pretraining generative models, have shown to be an effective starting point for many downstream applications. Transfer learning enables the models to make use of the pretraining phase's knowledge and experience to enhance performance on certain tasks with fewer datasets.\n\n* In order to provide outputs that are more detailed and descriptive, multimodal and hybrid generative models have been designed to produce material that spans many modalities. This has uses in speech synthesis, text-to-image conversion, and picture captioning, among others.\n\n* Due to their intricate architectural designs, generative models' interpretability and explainability continue to be difficult to achieve. In order to help consumers believe and comprehend the outputs produced by generative models, the machine-learning community has been focusing on building approaches to clarify and comprehend the decision-making procedures of these models.\n\n* Generative AI has found use in a variety of fields, including design, healthcare, and entertainment. The ML community has seen firsthand how generative AI has transformed conventional methods, automated chores, and encouraged creativity, boosting economic development and bettering people's lives.\n\n* Collaborations involving generative AI artists, researchers, and subject matter specialists have advanced our knowledge of generative AI. Innovative applications and imaginative investigation in a variety of domains have been made possible by the fusion of technical proficiency with domain knowledge.\n\nIt's crucial to keep in mind that generative AI is constantly expanding and new knowledge is always being discovered as research advances.<br>\n*Let’s now look into the impact of generative AI.*\n","metadata":{}},{"cell_type":"markdown","source":"# **Impact of Generative AI**","metadata":{}},{"cell_type":"markdown","source":"In recent years, generative AI has had a significant influence on society. Generative AI has revolutionized several fields by harnessing the potential of machine learning and neural networks. \n\n* Generative AI has made it possible to provide users with personalized user experiences by adapting content, recommendations, and goods to their tastes. This degree of customization transforms how companies connect with their consumers by enhancing user satisfaction increasing client loyalty, and fostering engagement and entertainment.\n\n* Automating tedious, time-consuming operations is essential for increasing productivity and efficiency. Generative AI is useful in this situation. With the use of automation, organizations may better allocate resources, reorganize workflows, lessen human error, and provide services more quickly and accurately.\n\n* Creative sectors have undergone a transformation thanks to generative AI, which has pushed the limits of human creativity. Today, generative models are used by designers and artists to create original and distinctive material, opening up new avenues for artistic expression and extending the boundaries of human artistic potential.\n\n* Businesses may use data to their advantage by identifying patterns, insights, and forecasts using generative AI. Analytics powered by AI aid businesses in decision-making based on data, process improvement, and trend detection.\n\n* By removing obstacles and ensuring equality, generative AI helps to advance accessibility and inclusion. Systems for language translation driven by AI allow for cross-cultural and linguistic communication. Additionally, AI-generated audio explanations and subtitles for material improve accessibility for people with vision or hearing impairments.\n\n* The healthcare sector has benefited greatly from generative AI, which has revolutionized medical investigation, diagnosis, and therapy. AI models help with medical imaging, drug development, illness diagnosis, and personalized treatment planning by analyzing enormous volumes of medical data; this improves patient outcomes and lowers healthcare costs.\n\n* By maximizing resource use, minimizing waste, and allowing improved energy management, generative AI supports environmental sustainability. AI-driven production models reduce waste, maximize efficiency, and optimize energy use. Additionally, generative AI helps with route optimization and congestion reduction in the transportation sector, promoting sustainable development in the future.","metadata":{}},{"cell_type":"markdown","source":"# **Visualizing the Global Impact of AI**","metadata":{}},{"cell_type":"markdown","source":"### **1. How innovation boosts employment growth while creating new jobs?**<br>\nAlmost, 60% of today's workforce operates in professions that weren't around in 1940. This suggests that the development of new jobs driven by technology accounts for more than 85% of the increase in employment over the past 80 years.<br>\nClerical and administrative work, management, and professional work are among the jobs that have emerged and are rapidly growing. Whereas, agriculture, technicians, and transportation still have a long way to go.<br>\nInnovations in the field of IT gave rise to new professions including web designers, software programmers, and digital marketers. The increase in overall revenue had adverse effects on the demand for employees in the service sector like education, health care, and food services, which further increased the demand for labor.<br>\n<br>\n\n![g2.png](attachment:44049088-5609-4495-bc5f-4d97046cbb14.png)\n<h4 align=\"center\">Figure 3: How innovation boosts employment growth while creating new jobs? (Created by Author)</h4>\n<br>\n<br>\n\n\n### **2. Percentage of what the NLP community members think about AI.**<br>\nAmerican researchers conducted a poll on specialists in natural language processing to find out what they assumed about AI. Even though over 90% said that AI's overall effects in the past and future have been positive, they aren't discounting its potential or its dangers. While 36% believe AI might result in a nuclear-level disaster, 73% anticipate that it will soon bring about dramatic changes in society. Artificial General Intelligence (AGI) is viewed as a serious issue by 58% of experts, and another 58% believe that recent advancements are accelerating the development of AGI.<br>\n<br>\n\n![g3.png](attachment:7dfe9102-d5f9-47a6-b3d9-50379324f683.png)\n<h4 align=\"center\">Figure 4: <h4 align=\"center\">Percentage of what the NLP community members think about AI (Created by Author)</h4>\n<br>\n<br>\n\n\n### **3. Global views on AI ranging from favorable to unfavorable.**<br>\nIn a study done by the international research organization <a href=\"https://www.ipsos.com/en-us/ai-making-world-more-nervous\">IPSOS</a>, 78% of Chinese participants said that there are more advantages to employing artificial intelligence than disadvantages. Only 35% of Americans believe AI has a net advantage, with France having the lowest perception at 31%. According to IPSOS, males are generally more supportive of AI than women. China, Saudi Arabia, India, Peru, and Malaysia are the top five nations with favorable reviews. However, France, Canada, the Netherlands, the United States, and Australia are the top five nations with unfavorable reviews.<br>\n<br>\n\n![g1.png](attachment:d9d8feb1-476d-46bb-a60f-b41a64b25fe1.png)\n<h4 align=\"center\">Figure 5: <h4 align=\"center\">Global views on AI ranging from favorable to unfavorable (Created by Author)</h4>\n<br>\n<br>\n  \n\n### **4. AI has the potential to partially replace few professions.**<br>\nA <a href=\"https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html\">Goldman Sachs analysis</a> claims that 300 million full-time jobs in the US and Europe might be replaced by AI or one-fourth of all work duties.<br>\n9% to 47% of employment might be eliminated by AI, according to University of Oxford research, with technical, legal, and administrative work being most at risk.<br>\n16% of American occupations are predicted to be eliminated by AI by 2025, particularly those that require monotonous, or low-skill labor.<br>\nAlthough there is still much to learn regarding how generative AI can impact the global economy and community, and it will take some time for the consequences to manifest, there are strong indications that they might be significant.<br>\n<br>\n\n![g5.png](attachment:1ed0125e-842e-45b6-a8b9-fb2018dc72fd.png)\n<h4 align=\"center\">Figure 6: <h4 align=\"center\">AI has the potential to partially replace few professions (Created by Author)</h4>\n<br>\n<br>\n\n\n### **5. Several laws associated with AI have started to catch up.**<br>\nOnly one law relating to AI was approved in 2016 compared to 37 in 2022 across 127 nations, according to <a href=\"https://hai.stanford.edu/news/2023-state-ai-14-charts\">Human-Centered Artificial Intelligence</a>. A total of 123 laws have been approved by nations since 2016, most of them in the last few years. Spain ranked second with five regulations, and the Philippines came in third with four. The United States topped the list with nine laws.<br>\n<br>\n\n![g4.png](attachment:40f285f5-519f-4164-b8ac-0cf159e591dc.png)\n<h4 align=\"center\">Figure 7: <h4 align=\"center\">Several laws associated with AI have started to catch up (Created by Author)</h4>\n<br>\n<br>\n\n\n### **6. CO2 emissions of different machine learning models.**<br>\nGiven their enormous environmental effect, generative AI models—in particular LLMs like GPT-3—have raised serious concerns. Stanford University's <a href=\"https://aiindex.stanford.edu/report/\">AI Index Report 2023</a> states that in 2022, GPT-3 produced 500 times as much carbon dioxide as a round-trip flight between New York and San Francisco. Similar emissions from other AI models, such as ChatGPT, are also mentioned in the paper. The International Energy Agency estimates that approximately 1% of the world's electrical consumption and greenhouse gas emissions are attributed to data centers, while the global IT industry is responsible for 1.8% to 3.9% of all emissions.<br>\n<br>\n    \n![g6.png](attachment:bcc7d2e2-397a-44ee-9121-65dee6da533f.png)\n<h4 align=\"center\">Figure 8: <h4 align=\"center\">CO2 emissions of different machine learning models (Created by Author)</h4>\n<br>\n<br>\n\n\n### **7. AI-related disputes and occurrences from 2016 to 2023.**<br>\nThe archive for AI, Algorithmic, and Automation Incidents and Controversies (<a href=\"https://www.aiaaic.org/aiaaic-repository\">AIAAIC</a>) reports that the number of reported concerns is nine times more in 2022 than it was in 2016. That can be attributed to both a rise in AI usage and a rising consciousness of its improper application. The graph only depicts the number of incidents till July 2023, but by year's end, it is expected that this figure would have increased thrice. The <a href=\"https://incidentdatabase.ai/\">AI Incident Database</a>, which also allows any user to report AI-related issues online, was used to compile the occurrences up to July 2023.<br>\n<br>\n\n![g7.png](attachment:8e2c65fb-67bf-43b9-bdec-f57911dec76c.png)\n<h4 align=\"center\">Figure 9: <h4 align=\"center\">AI-related disputes and occurrences from 2016 to 2023 (Created by Author)</h4>\n<br>\n<br>\n\n\n### **8. Total Global Investments in AI from 2016 to 2022.**<br>\nGlobal investments (minority stakes, mergers and acquisitions, public offers, and private investments) fell for the first time in a decade, down roughly a third from 276.14 billion dollars in 2021 to 189.6 billion dollars in 2022, but the total has still climbed 13-fold over the previous ten years. The Nuance Communications deal, in which Microsoft purchased the computer software business for 19.7 billion dollars, was the largest investment of the year. Government expenditure is growing, at least in the United States, per the study, which is good news for AI research.<br>\n<br>\n\n![g8.png](attachment:b16e0781-ebab-46bd-8149-13c69a382cc3.png)\n<h4 align=\"center\">Figure 10: <h4 align=\"center\">Total Global Investments in AI from 2016 to 2022 (Created by Author)</h4>\n<br>\n<br>\n\n\n### **9. Global perspectives on how AI will affect careers.**<br>\nAround 22,816 people under the age of 75 from 31 countries except India, were interviewed online between May 26 – June 9, 2023. 57% of workers believe AI will alter the way they perform their existing jobs, while 36% believe it will replace them entirely. With variances of up to 50 points, the percentages of workers anticipating each sort of change are high in Southeast Asia and low in Northern Europe. They are also significantly greater among young workers than the rest of the workforce.<br>\n<br>\n\n![g9.png](attachment:fcd70f25-b945-4fac-9cb7-e024a3677125.png)\n<h4 align=\"center\">Figure 11: <h4 align=\"center\">Global perspectives on how AI will affect careers (Created by Author)</h4>\n<br>\n<br>\n\n\n### **10. Public views on how the usage of AI will change the following fields.**<br>\nOver half of people worldwide anticipate that greater use of AI will expand their productivity and entertainment options. Only thirty percent of people, or slightly more, believe it will boost the economy of their nation, their work, and their health. 38% of people believe it will worsen the job market than improve it. Once more, optimism about AI is substantially stronger in low-income countries than in high-income ones, and among young, college-educated adults than among older, less-educated adults. While there is much more to discover and learn about how generative AI might affect the world's economy and society, and it is going to take a while for the effects to become apparent, there are strong signs that they could have potential.<br>\n<br>\n\n![g10.png](attachment:302d60ac-1b57-4a10-a932-1be52c79313c.png)\n<h4 align=\"center\">Figure 12: <h4 align=\"center\">Public views on how the usage of AI will change the following fields (Created by Author)</h4>\n<br>\n","metadata":{},"attachments":{"44049088-5609-4495-bc5f-4d97046cbb14.png":{"image/png":"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AkAACAABkADuL4CgZRx6OQkEgADA8CrAWVIgDxAAAgCjE4C0qHVEAFQBwOg5gK4lMgiABAAGJwD5CyWPCAABgMkJYIIAEAACsDcBIAAEgAAsrgIgAASAAEgACIAqAN2QOQAEQAIAEgACQABAAkAACACsTgAXBIAAgCrAQwQgzsfGZUrHU4EAEAAJwPAhQNC+SPFyoAqAAEgARgtAyC6Ol4IEgABIACYL4NDxwRAAAiAB9E8AwaGRQFUA4ovhGAEgABLA3QUQtJ7Qx0AxAci7xgQBIAASQM+qAEJIbi6uDaCSABAAAug95X7byG5mSAJQXAdw7Hi8EAmAKoBJSDrH3sY5gI4zsCABkABMQnrW7I1IAGoC+OJLs5gDQACDYiPdnYV1CaDrrXknEgACMAhH4diQAFQFQAJAAD2fuZ/Jj82+pwlgQwJAAAhAAxoFQAKgCoAADE8A55Ya+eVUN8YcAAkAAZicAI7yJTJGrAMgASAAEkCjAMRZfp1VTQCiaF5yTwJAAAigjwmga4mMmgAObefNdTzBHAACQAD9SgAdXeOsJoCD5FEZx95WAS4kAARgawLovDYqCeAilwkJgCoAAuhVAhBaBXDuOGmYAyABIACDE8Cpo2uQABAAAjA4AfRVACQABEACMCsBsA4AAYBZCYB7ARAAAiABMAdAFQAB9CQBXKyYA2AdAAmABEACIAEggOFwaF9yTxWAOQAEYHj3b1tyV7+bjgRAAkAARnOSLrilCkACQAA9vGofmzkVyi113MDHOgASAFWAvtF+2lzO2pp6253MAZAASAADSu2Tg04BHJkDIAEggJ4RdJzNSkz0CYB1AKwDQAD3mACQ74L6BbXlrvl9Hptd+X0CIAGQABCARvwkjDNHfFqc1/luqien/XU+G+dRCYAqAAkAASiQLr2KKBOKCeDwVMhe5/Wft3mRAEgAVAH6SBF78TTNomiqnADkuyCxIQFQBSABDFwAUy+ujoiYeytXNQFsNXYN1gGQABDAAxCZN6+/j5Z5qZoAfjxGACQAEgAC0IUbeul1JjBfOqoJ4AcJgDkABDBsAfj5mwCCmwiGnwAuVlQBWAeAALQKwP0kABIACQAB2CyA2U7KahWG4VbytfrI7n6NVW1tpY2t1BrTuJVhqPeD/XVju3s2pnsr+3iS7braMmAOQPwVbsuXIq7OxoT2xu7ywdyHNfT+b+6yy1xD9ljHDhtKFWDlXQv25WJRKv9bIf2f4sNXV0u3r/ts8tvx+asPpnUruxp7+2h/3ZbCVna2JuT7X+9WKm5I12b++fpaU1pPseEsBEy80P3zDQCsosyjbOxPl17CvgCwDpFE9b0AXhiwLwAsNIATLqM8YQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABgJMIR7AQAW/t/0vBKwf/dmO/rs8k4TQt9lktH2j6Z6xf69liZzrTdxS0KX98d4Tq3smpM36MqtB5LnSfZQAUwjzxNBhDpwvOWiabG6oecLEZ6NrKMPU/XVrr1B4s1fTA3qxvz9TQ2qrYy0vRQCK1bGVRbGYV+/46l1pNsmN0/jfPo6/tTJPOqhfT6XKJoLvQcmijMUz3HeRx7eazpcUkiuz58aZlqamwZ5o6eY+ksrp9sFfRtK4Pw2lg+6t2x1HmSDZJgVZk5XH7ZALNlNY7w4ygJ/CzSYgCnfs+hEKLQcEETq+ojCVHlUA0fbBblM7e61mrpG9P6uiiqkZOGrXRXUeYHVZ7TYQCtW5l44cidLbQYQOux1HmSDTT+e/XrRN0vjwKqcUSUpVGd/usfNRggqx9w6iYLL/r6kKJc5FX8LFeRF5df3WP/kHctu7LiOrSAwACSAIkARQj+/zOv7SRQ+5yebFZ0dUrFpHcPmi4Te/m17Bxe7l7odQnbaHe+0GFYQhcsKuVQr3yGyhZAgKJSql5vDZ/CVgIBypxlO/ellewjnzPdJMIBH44AW6jynzgC+M73VmJaM8K5cQgjqwzHxxjWqzmEuHq9iG20ulv6OfAPW0FoaqbVyO8pgACFpIxxjfIh+KRtBRCgyFmSvktFoqSSfeQzmulIn7UAAnTpXqIiCGD5jM1+5ktP8Bx0tS2FfFgl6tDX6uUiCLCI8vn++PtStwffy77KIEAZKenkGEXI7LtdlUEAt6gyZ+mCIEBJJfvIx3XpI/bb+jwLGOi/O9RbN0EtBQLHOZi9UqxGFo90dLdydbwOaPWIDMK4/OfUwCGAp9SpYYgKaCWQlDkJpyzaiSkiJYU3ol1k9lm4c4O+/6DJsZQ5y1EQoKiSfeJThXTO1TqT2372EarNK7daJXWAhACIZSg3rdM85H8xkJm11i+nuiMBGOjfXOIwFJNyNvCdTm+OXzXqX5DyXGohmREC5E4bVOw8q+3WBfQsUwxQRMk+92l3KQJSTmQc50VPjkdaKQQk6vWGAHC8LrGi8tNkDBJMVNvVl6wmHUrcnFKm/Pcm5bBrshC8FVik/FdUSkV6Nf9EAOCp12CSzZc4yzEhAK5kn2n60TPWaxeWs9Zm6l9PkyqO/Y30UksgAAWOuuZbzugXLczSQKoz/dSt+yblDU5FDWQfXHoKvi9iG29SSscdSo17HziRLYIAFJiYbrNtoSKn78ohANMcllxLAM+SOY4pBoCV7DNj/+wZx0geYQcUGyy/eyL/h/Qv2n0BBLCGkd2F4JRSlcPmFObOt5nqeDiHJSYhfqmqhG38kBJktib+D/0kHAGEmBjJRP8cAigSdIu9EvAs6cPPFwLASvaJz5tn7JdV/Bor5a/7KsL/UTZocyNAeIim8TZmJZ1xtoxh5xchRzPYXUi2zPeEU5PRpPtXt4ZtA2Pv/pQS+2nnSt6/d1JpIwQwSDVLLd1q+9PL94Kl/AMBdphtQwhQJFenzNfcCPB/vHH8n3nePeOl4evvE6Ho8VV/e341P/QazSSV65bL4eIZPVqw4zQ7VjUKIEC7R64I6SDbhsf0sKCU9AYRrZnYwpSF6v/VKrUILuuML1BKNS6ufUMAD5VMj5OZf2UQ4Fz28BMBvuz52zP23pMTf6A72fITEsyP+Wzke+T/T+C8sGUklhZkGiTe1uQ/zcO3DXEkrsr5kTUbrjIlpExc2D7Rr151eB5kq2PIjiHHYb5Ekrk6dSEAZLI6vawAArBahNBdCLB/EQ240pwh/u0ZmfjxcIrvHQF2bx7roMp6MnfGsGUMGiNnUUYh4rXp1B8yk+rUKHUmFZ3PbHBgCIZKSacoxnohE8IlsungLjiyHdiWPCmx3GJxGEcAMlN52VwCAWb68OrV6IQA+vwe+ye1Yx35D894Ln5+OBX9hgAd1MxKetJPnRC0F6wz3uj5BwI8T/2jTVTZv54bGAG0vDIBl7Lfoo32W4IQhEs0JyrsnP2hRXmh3ixt5BMJAmDtly0phj6kGQiVJul7uQSgcXTlm2L/ap2aQp4xfc3zDQHctENomhCgWjujdxDo07twBGjt3J797XCflEp/vI+Z/4QluJT1cg5VJNwOMVZ5XrJrKXerhjvegamxgxZ0GhICWKg1sXSLoIjuT0YArGU35hRuDF85AtQX8owRmid2+WX4P7fVNppiCaOhAlRtt0IIwD6HrdTSi4aXelIqfTeMnXItcWG4lIryB8dGK4WsajOIzSrJccjwjVUcmID1/2NacqJiHMwMofBE7L9eccKu69L4C4UCuav4RU+7FLOLV+zuFkKAwY93xbh3DiKz1lvsjJeRNKadQnfaVoN5jaUjk08vQKWM1iUSmm0DxSQA4EiCgnZKtwNIv9qNyXFSq7swwgDgxP7xlokESilTOrfFf+EqoGEvhgC930JXCAHobNc3BEBdttmbttZZ0sdZDmc4FP8nBOB9Z+DcfrNln9NA1F+O/enHJQQYhdX1eBVYlDIhwCBMIGgVWCQmmfShBg0dqBLd2GV8l/7AIwD6ZpIyqflL53/aUgjgApOJTEYADOcHctqlEIAklJpEKvM8X3rcUIZTrTojAO/vfEjZ63WM9V12jI1GKgnHblwzbVVCAGyxaJYyIcCrHbHA5NBmr0d9IYCCYrnJSSFWspxzw9iEkT9IYm6jOhazfef8z1sMANX/zlX++zplATOSGfdxwQOKAKkznrec8JIDCE3OqTNB3oA2n1wXg1eXJ1iuRQyPY/8gKDfcc7uwlBkBsEeNEgwO+4UAmLtmhjOvq2ko0IFOU/Z/Gt/KP4utO/woy1824fxmBMAY0Eva8lAHeJKtmbrJVfOaEeBhz+jqjIcUZzPhAQpMeP9s1DkQAcjLVuwJq5B6dnWAmpwsWcy5yiBAlLIEAlCsE+G3BAK04xzYUNOEApRmZv4PKVfN9ddp/LYOwLll3EsIAFZm8v4Z34EIwHPIQ1REQYCnPaPcGafYJHfG9x1kxl6ywQSUYedeVh6/XtJc23PIzHaPI8AtJYQAFWc5JFje/gEjQCy6xCknp42ZHFZ+WUfFKMDupeQtBR+T++tOW/KyCQHw0myuyM4pTH7uNHLITrkeVH1OnfH27ox7BZbZ/XQjABYzzrImjXBkte3hwQUUrd/1jQBgAfZNSkKAp2k2h3FN7HOm1HLYMb1w9MX6lqecCgTrlZUpKYa4/fuMP1oX1/zSjOewoxHAtT5E+W3EQj0K2ZsrsIWCidQZJyDR50tVG0Ym7nlj9Bltg4AEIqC0R1qUmMevwQaUUtnzq9eARtpvUqrx8Q9zISNAtvsBirKPKb6nKbGbnMAkbaz9WgSYxc2qJiKAUjigxCJivaJpY7WmCKDVesI6E3dnvFtXcM1pZDm9om04bGhs2L1KCMDbO+L49eNgdvZLpZKsta4gNuu5+OUsI+X4FwJAz7WfhkwWRQA2++QNBAG+7hoAVbkl7Z3G9zz08+6d4txfj828Ph5Ab+ctMHmYNHlPE3d+AI8mIYBkjwYDk8RyYtvgfQdQ05gi67PQunTFXGJ5D8uqsfRLBjjitG4JKYsiwNUx6beuCALklalu+zr+j0xTppydYgAsZ4xr7KczL495+rKGf5TwRK0xXqYUDU7NaO/OOBSY3CwnqZKDCnhoLvqVQACKboJf5twBwEyN6Vd+iRkELGVrd/pi5RBgzOWIfuMaRVUKAZT6ugEAyqLMesdRDdhNDd55xnqsNEvaO9nFJT3swr6DPajWeu/aEn2x10+W02uYF5QzboVcXwABzjVXSfh6LOuhXu5o+HYdFV8GSnlGJcsIENDyC0//5fEry9tOHguqDq75l2E5fOaz8BhLU2SZbVLA63CAQOLtOBI3A8pAZQGF7ot0xv9gORV4DmmaSB1gBj/bkqN3+Jfd1NrBo6w40oi95xsJEgLMcPlFFhz2PH5F3n98vIYlpoO6/l4EGGrNc9SJVYU+ooA9U24s9LI2bcJQzrMXO6xfamwzptFzrK0VQICCLKc2LxKKDPQFnMDwN/0CHoxpdfLT8YrCInhOViasacg9xPKL4jvF18jmfLKw9q5yxFzzSxFg4IWdOehGEOBM1ym6ZP/GgwBgMzwXSBjbq+fMwSzaGS/Ichp2o3mAoJ8S4IGi+hx6zfDSrpvLgU/ZtHmBgNo7dGXPVX6RemfcVDWGh9Rpa4JVqpImB8By+GgAuG5RHJDRzHqV21hJARuh3IIKyPWneaawvUSFh7kEGdvd/II74wVZTiNz9nSt7mU7wDeTgul1natF30WBTmRgEHzivDCbvxhY5/gxZFbFKadBP2RORmJIXJmqXgDL4VOf83yPiFv93DMyF/YUBdzY/uEQlNmsZP5OGtqPdTl7fpeLRKPs2Ho8/0fIUY3lWE7DXilhnuuxn9Db/2Sw9tAxJBkDtMxCOT5MCrWlCkj5yQFi054PYNlswSEzfvXZ1/q3sbuqIwmpypHDWGKX6ye6/+0HAiABaESAI42hjCt8n/UeJtvGsPYxlmT+/5hX2NQBGLNrJjPXTEsqxXKyhs204YbppOHrl/NWTLNbD15mVUU4Z46Nt9rgpU5r4uafZpswLPmz/BILxL+tD4+J0jCai0wQvnD7h6oCf8QynbGEAKyA3u4lblNL9nAA2yyVnUY1iC+LkfqIrOyTW7pkUhJmOUU3NKf84ZjXDr5/PU1KnxI6BTDLSQFdZCY/nsBol9wFZv6F7yncwaac/i6/DIuJtwH/TrwEABfL9H/sXVvPrCqT9oBeIKhAlBij//9nflQB9mEmmYlVK59vKxd7X61Ov7Y8VBXP4fQU8S8vqdbFpOafDwF4XsD8Vteic5TuWAjh5vKIFZA0MhEqzzGfhMhyih+mX2+d6D3Zgy07nQ7O9RvPj1k0FtkTp79Q69OLIGPaKXXOzjR+EWUlhuolDBOGQCT4q6uZ2rbl0Ix2IjcUwAAMB9vkep7rFHAS0rSpsagxdg6/mzEz7ZgFZaxO2niyABW3x/45dmNBAJ5HnxCA8hF7rB/i+dxMwFF21HBSNpGZxcYGhGGlaIxmInT8rQnAehxhlJuxaj68J/xCfWm+Jj4Y8yrJHxIQIJ7ZJP6/MLUxjHsMEGBi+aysq+NAAFHVHzUAcbfCtl9MJNlKSf9r2URmdbz66w8ewN3O/+5TmnkaTQN66uQ+tXb0l+a7VBs2DhiBLgCFbTRmbHSexDp740EAFjQJ8J1/RkX4xG6SeDKmXxCIMcQfs5z10MB8Y5k2hncBYnp5xi9YGkaN8650q9XtJoCd+pJlnH8Po+cv7n+OY4NziU02BVP8vNhiMl5EADuThibCutXUXAgg3OFG3renmznoh+VxAVCA4p7asoNhL0gSoHOisolRseYHlvHLew0AZMzbyX95xdmY+6eSj0I168u4KZZjssNmQABhljF27KhNwgC50ytOxMKT50AAO4kDAYSZ19NdRcrCPRBA+pV4Morosg9vWdIoU3ZrUqzRxy9AIYQo0Xps79j6F6iMG1tWBGgPH5VyuMpfGQ3o8YvREQAIDmlmDyR0kmwivHiq7xUPAoT6/4UA9TgS+uxPBBCG7tuLBKDwjq2dHVdi/hebYg1vc2P80k0RgFWa+eoCroZyq3YSrokTAhCptpBkmdpOsduN9LQgckHEyTM5GbP0qK4JCLD2xrdUmm1CgNn0q2ZIxgolwLDiX0i65UiKNcGiWDN6LOvyfQ5ws8UqzbwsAnTxdgJVo4AA1AuomGXL8vwh/wf3v5w6YjJmNS9RQJxkbSQwqXvVpiBBLmd8yJmhm+yxKdbi6w/FoKjWEYImx/v5//BKM6+KAPWYhhEZ1+MOzwAAGZ9JREFUARg2Lo9lAiqTcP9vijybhDYHuXVC2FF7Q6hymqSMBQSQbtEryzB34Hj6XIq19Pg9UhJbvISVt8r/xL+WU5p5XQSA+91EGiUjwL5qNOpkQoBynkus/0kE9FcwYarU6T7Os21kQIFMrGf5NcPbxsB1eGOqdYr4mgEzLDTAY+9uJwCqcY7NKM38QoBlKC6FANnmsV9ILmd2yVbdNAR4czkdsf8vCQT072BC8s9XJTKc1TTrpf/5OxBlYTI9Mw7FGv57kA+BzPR2/P96xO3OJ838frCXSlN7O/hFTeP/aFfLfgFxfECA8exLI99cTjEep3GEw5EvmDCtKd2sI9OGcWN0iiYMB4LpzqVXqS2Mb2OI6+34/2n/M0ozr7oi/4ep+c+WAhXqCORpPsuXy6kHuxMKzYYtmDAtl3V6tm3P35iAiPsr76Mn/QhiCwUYEwLEQAjsupq+cjfz/zn2P5s086qrAgXB1DAhwFGn97SWkd3llDGYMAJA2vV2mVRN+FKRL2mP27WG5P8RRYQsCBCeulbeRjUnReX8p/e/qNmkmddbQ2IjpwsxFgSw2TNiJ3lG/C8up+VlgglTpxPLk3U+/cVCtTSVyB6ymuVwCb+lGiqXawDamLkClyQhxNYUjRn9dK8BQNoLwsGrwiPNvNwK58/avfN/AgKQw0TKJVWKu6dUAMwup0XBF0yYNy/SppuJYJlSHhI92yoGgn2TbJLSVQfRyxGdpUCbxKbn/HsIELrPWCyySDMv1/vD3zi8839ET/6tO3UEUJC4hKwup81gd8EUTAgEgtb3DWaK57CTs2CZzZv2mYFGCFf28VNCy+R36sWka53ssTzUww0BALaEWvku/q/6N3Lyf/DGKXxQqJXEQFQ58bicpnIdjUkKlsi+rK5bu27VUXB3em2hTfcO16LX8N/T8Cu3VEfEn7D2H+Ypp49AsJfYxUrPTPiru+O393/6G5n4P8J6tLMBkq32syYWjoLF5TR1xtonBzFyZJ+Y2rmXe0CBUOd0sbA4/2HRD+5Yp4dMu8eqZshyBOnXma5ZMwGVSgH34H1xVwT49exzwcf/yczYLjbuHC6HIp2TNAdwSEmFCMtmE/RgwnLO5rosdbEox8XYsELjZE+bCWYuQp3FTYM2G+W3rNR8DP2E1T9+DN4eAVj4P1Yv8WQMnb+I1JFTZ9knCJFdTovkZY15AgyEHZP5Pys9SigCSsRfUjCJGDQiAHq5gp8DLTQhdTlQKQk3jlrbongQ4AdXJ8Mxwcb/wXcuHBekDnv3H2RrBpdT6Ix9WSqd1CzEdbQjPcvcPiWvFMScEzlGmgSogOZVaaL9cuutDA3dvBdQ1s1DUTwI8HtLwgxL8dz+i8H6NZ+MlPP6K/GbxeW09mmc2GvanhXvkWT9+U32bs8bnfYFNelsakf0Jkherud+z6QaqBZA3HLWJjROaCxY3BoB9G9OQErfavylORDAgkwqn7cjzf3jAwFYXE4HH+32e00js/fhm1VL5v+cnkxW89t8tFHhu1Ur1Wa7XFQf3Ulqc9bLVZjYIk3wl+H+n/qCSeT4lxHgN1kQm9JTkyCfjnJwZ5eZsTTCpPhCAOKv936A7SOpmRWhNHZN2Pjg/NVM+rTK6dNWLhIxyanpqx6SP9H59ivON3EcgfvfaFc860fX6zSsJAPKgQdlFu4Q9eeMCJCdcQGXPLUzNkrBngde+OIXSmf8iQD7uni6Czh0JEQEwKmGQJVDLFJM+wDAz65j6mwXjhYH3OLA+Beceyi0PQgmZEOAlzMuZGMSbXYhtgeTkjtss0fKX9l8WcueFzkrs+VBh68LGgKEPs5aX4LKAUkct73/vwkAJJsjR4T5N8/odg4nI41m49B/jgkB3p1xo7L9NCwJdABN6qZmsDXt67GYS6cZqUK9nkBEtwuhlwP5oA7PqokJWAHLR/lslF9dJjt1GBrN7vCMhhog1Ma0PIsy2pGzIACbMy5clwDE2YWHGPoVMkV9+JAjtIuU42wp9Cv4BU36v3f+riEA91ihonXxAprGaHt5RtNd44V1quVCADZn3DilgwkHdcqWvxhbwESkAALFwfeSyJcs0JA0Pqmochif/f+Tq+ndajbw2FClKBVtZvflGU1CAMgSmfULAQxxn/E44+I8LFmbsSAAZ8hUDF7bQDWpNdmvpnNmTFhJVTk867Iredj3yWWHyAD49IwmIUCjWrWDijAhgCMS7XicccP+h6p6n7H/pyMAc8hUil4UwMFuGVr2euSxcn/WVVd4oRUk4miL7HiSMz40/p+e0RQEsNrXqeRGBCDvjYbBGTft/wAh8XIjHN8kyh57yFSOXy4aq0hwIjbZPQjw+/U/WnaKiUvn/O0ZDQhwUm2XryPClyM7gDTo38Lgi5ntAw+jM6soYsJ/EDJ1IADtQ1DBqaoHAX69AViA/T/psd65yDafntH1ePZlPO4jrda0YxZVDsvUMCCAiAgwzMeolPTU/kXIFAcCZC23jve5DwL84OocHF027NO4/z0x5UEMPtskfnhGn36pp1bFm/p+6Un2i7s/7DWYaoB+1j3HXv03IVMMCNDrpReijA1cPc7ls19+bW0x3mQIex/2P07vyLV/8p/m8YyuYm4HtNsky45NQZkNyX3g2aOoAWxQA7Q8+/9fhUxB1jEJAMJTsrF8w5fkXvl/Nzn/Y+Z0UXs9osHLQJTGVssylQb9p8me0WK3Q4Pn2LqLfW2pmj0sswNCwcV4R9+64LHLNDH5RyFTKFQmNYZJwTncLv7vRud/F5u9Ft9mqWj6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"},"d9d8feb1-476d-46bb-a60f-b41a64b25fe1.png":{"image/png":"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**Industry-Specific Applications**","metadata":{}},{"cell_type":"markdown","source":"|**Education**|**Fashion & Design**|**Finance & Investment**|**Gaming & Entertainment**|**Healthcare**|**Language & Communication**|**Manufacturing**|**Marketing & Advertising**|**Travel & Transportation**|\n|-------------|--------------------|------------------------|--------------------------|--------------|----------------------------|-----------------|---------------------------|---------------------------|\n|Personalized lessons|Clothing Design|Fraud detection|Procedural content generation|Creating realistic copies of medical records for research|Real-Time translation|Product design and Optimization|Targeted advertising|Itinerary generation|\n|Course design|Turning sketches into color images|Risk management|Player behavior analysis|Disease detection|Transcription services|Process planning and Automation|Dynamic content generation|Identity verification|\n|Content creation for courses|Generating representative fashion models|Trading|AI behavior and Personality for NPCs|Drug discovery and development|Creative writing|Predictive maintenance|AI-generated Ad|Destination recommendations|\n|Lesson plan generation|Trend forecasting|Data privacy protection|Realistic characters|Virtual screening|Text generation|AI-enabled supply chain monitoring|AI-powered social media content|AI-generated virtual guides|\n|Intelligent tutoring systems|Color palette generation|Investment research|User interface design|Side-effect prediction|Grammar and Plagiarism checking|Demand forecasting|Content curation|Virtual reality experiences|\n|Virtual classrooms|Virtual fitting|Personalized marketing|Game testing|Medical imaging|Paraphrasing tools|Human-Robot collaboration|AI-driven customer analytics|Language translation|\n|Simulations labs|Consumer Behavior Analysis|Virtual customer support|Interactive fiction|Clinical trial design|Language practice and Exercises|Cost analysis and estimation|Marketing performance analysis|Food and Dining recommendations|\n|Restoring old learning materials|Bespoke Tailoring|Compliance monitoring|Game levels and Maps|Virtual medical assistants|Speech synthesis|Energy efficiency and Sustainability|AI-driven testing and Experimentation|AI-generated travel articles and Blogs|\n|Plagiarism detection|Personalized Styling|Market prediction|AI-generated narratives|Personalized medicines|Image captioning|Quality control and Inspection|Voice and Conversational marketing|Fraud detection|\n|Research assistance|Sustainable Material Selection|Transaction monitoring|Quest and Mission generation|Clinical documentation|Multimodal communication|Yield optimization|Influencer identification|Public transportation assistance|\n\n<h4 align=\"center\">Table 2: Industry-Specific Applications (Created by Author)</h4>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-three\"></a>\n# **Recent developments and news** ","metadata":{}},{"cell_type":"markdown","source":"### **2021**<br>\n\n* The Covid-19 epidemic had a favorable effect on the market as a result of the rise in demand for generative AI among businesses to better serve their customers' needs and create unique experiences, including customized audio playlists, customized news sources, and customized product recommendations, which are fueling the market's expansion.\n\n* In 2021, the market for generative AI was estimated to be worth 7.9 billion USD. By 2030, it is expected to be worth 110.8 billion USD, growing at a Compound Annual Growth Rate of 34.3%.\n\n* In order to enable more grounded representation learning, the unique visual and language representational training technique known as ALBEF (Align Before Fuse) was introduced that aligns picture and textual representations before fusing them.\n\n* On January 5, 2021, OpenAI unveiled DALL-E, an AI system that can generate pictures from text captions. Using a 12 billion variable model trained on word-image pairings, it can produce original and imaginative visuals that correspond to the provided text. \n\n* On January 5, 2021, OpenAI also published CLIP, an AI system that is capable of learning via any natural language supervision. By training from text-image combinations on the internet, it can accomplish a variety of visual tasks.\n\n* On January 5th, 2021, OpenAI released CLIP (Contrastive Language-Image Pretraining), their first multimodal product. Similar to GPT-2 and GPT-3's zero-shot capabilities, it swiftly learns visual ideas using natural language supervision and can be used with any visual classification benchmark.\n\n* SXIQ was purchased by IBM in November 2021 with the goal of supporting IBM's hybrid cloud and AI strategy by empowering companies to upgrade and enhance complex crucial applications across many platforms and cloud services.\n\n* SEER is a self-supervised computer vision model which contains a billion parameters and can learn from any unrelated collections of online photos, was released by Meta AI.\n\n* Vertex AI, which takes around 80% fewer blocks of code to build a model than competing platforms, was made generally available by Google in May during the Google I/O event.\n\n* Initially designed for text-based jobs, the ViT (Vision Transformer) is a visual representation that is built on the same architecture as transformers. The \"image patches\" used by this model to analyze pictures are then used to forecast the class labels associated with every patch.\n\n* GitHub Copilot, an AI pair programmer for developing better code, was introduced by OpenAI and Microsoft on October 29, 2021.<br>\n<br>\n\n\n### **2022**<br>\n\n* In order to improve, coordinate, and enhance purchasing tasks, the General Services Administration (GSA) in the United States started using generative AI and machine learning in August 2022.\n\n* Additionally, OpenAI utilized GPT-3 as the foundation for the April 2022 release of the DALL-E 2 AI system.\n\n* A Vision-Language Pre-training (VLP) architecture called BLIP (Bootstrapping Language-Image Pre-training) provides cutting-edge performance on a variety of vision-language tasks, such as image captioning, image-text retrieval, and visual question answering (VQA).\n\n* On July 11, 2022, Shutterstock increased its agreement with OpenAI to develop generative AI tools for creating content. Customers of Shutterstock will now have access to OpenAI's generative models for producing unique photos, videos, and audio snippets as a result of the cooperation.\n\n* In 2022, Google DeepMind unveiled Alpha Code, a powerful AI tool for writing computer code. With self-supervised training and an encoder-decoder architecture, it can compete at the level of a human in solving programming challenges.\n\n* OPT (Open pre-trained transformers) is a collection of decoder-only pre-trained transformers, released on May 3rd, 2022, with parameters ranging from 125M to 175B. OPT is sometimes challenging to duplicate without sizable investment and is sometimes not accessible through APIs. When compared to GPT-3, OPT-175B is proven to be equivalent, despite having been produced with just a one-seventh of its ecological impact.\n\n* On July 12, 2022, OpenAI launched MidJourney, an AI program that can alter visuals in response to written commands.\n\n* Merkur Insurance and Mostly AI established cooperation in November 2022 to employ artificial intelligence to create synthetic data for innovations based on data.\n\n* Sparrow, a conversation agent developed by DeepMind on September 2022, lessens the likelihood of giving risky or improper responses. Sparrow converses with users, responds to their questions, and uses Google to look for additional information online to strengthen its arguments.\n\n* Shutterstock and OpenAI collaborated to release an image generator using DALL-E 2 from OpenAI. (Although the Shutterstock-OpenAI agreement first surfaced in 2021, the image maker didn't really debut until the latter half of 2022.) In addition to OpenAI, Shutterstock has created license arrangements with companies like Meta, Nvidia, LG, and others in order to create generative AI tools and models for text, photos, and 3D models.\n\n* Rephrase.ai revealed that the company has funded USD 10.6 million in a round in September 2022, an investment round led by Red Venture, Silver Lake, and 8VC.<br>\n<br>\n\n\n### **2023**<br>\n\n* By merging the capabilities of PaLM-2 and its generative audio model AudioLM, Google has unveiled AudioPaLM, a new multimodal language model for speech understanding and generation.\n\n* Version 5.2 of Midjourney's AI-driven image synthesis model has been released, and it now features a new \"zoom-out\" function that allows for preserving a central synthesized image while automatically building up a bigger environment around it, emulating zooming out with a camera lens.\n\n* A new AI model from Stability AI called SDXL 0.9 can produce more attractive and intricately composed graphics in response to text-based instructions than earlier models.\n\n* Google unveiled LaMDA in March, which is a conversational AI model that can have more in-depth and genuine interactions with users. The first generative AI native search engine was Neeva, while Google caught up with their Bard chatbot.\n\n* Microsoft has released new \"plug-ins\" for Microsoft 365 Copilot, which is a generative AI service that helps in productivity and creativity. In the future, plug-ins might be used to improve operations like processing reimbursement requests or streamlining a business' supply chain.\n\n* RoboCat, a system developed on top of Gato by DeepMind, and released in June 2023, is a new robotics controller that can learn a new task in as short as 100 steps.\n\n* \"In April 2023, the European Union proposed <a href=\"https://www.reuters.com/technology/eu-lawmakers-committee-reaches-deal-artificial-intelligence-act-2023-04-27/\">new copyright regulations for generative AI</a> that would require companies to disclose any copyrighted material used to develop these tools.\" (Hughes, 2023)\n\n* A generative AI collectibles campaign that was jointly developed by Alethea AI and Polygon in January 2023 enables training, quick development, and trade of AI characters as NFTs on Polygon.\n\n* Nvidia launched innovative metaverse technologies in January 2023 for businesses, including omniverse portals and a suite of creative AI tools.\n\n* In January 2023, Nvidia debuted a new generative AI model that can produce synthetic proteins for applications in medicine and other sectors in collaboration with the pharmaceutical business Evozyne. Nvidia's BioNeMo architecture serves as the foundation for this novel protein transformer variational auto-encoder (ProT-VAE).\n\n* Chinese tech businesses began working to create their own AI world in January 2023 with official support. Through IDEA, a research facility owned and supported by the Chinese Communist Party, local governments in China are making independent investments in a number of initiatives.\n\n* Grammarly, a writing assistant powered by artificial intelligence with headquarters in the United States, announced the release of GrammarlyGo in March 2023. This new feature enables users to write, edit, and customize content.\n\n* AWS, a U.S based provider of software service management, introduced Amazon Bedrock and a number of generative AI services in April 2023. These services can be used to generate and summarize text, create chatbots, and categorize photos based on a given prompt.\n\n* American healthcare software provider Epic Systems and Microsoft Corp., a technology business, worked in April 2023 to integrate AI and LLMs tools into Epic's electronic medical records software. Through this cooperation, healthcare practitioners will be assisted in boosting productivity while lowering administrative expenses.\n\n* On July 12, 2023, Kakao Brain, an AI division of the South Korean internet company Kakao, released Karlo 2.0, an AI-powered application that can generate graphics based on prompts. Using a GAN with 1.5 billion variables, it can produce realistic and varied pictures from natural language descriptions or drawings.<br>\n<br>\n\n\n### **News**<br>\n\n* GPT-4, Open AI's most recent text-generation model, has been made generally available through its API.\n\n* As the initial buzz wears off, ChatGPT experiences a 9.7% traffic decline and a 5.7% decline in unique visitors in June.\n\n* The world's interest may have been piqued by ChatGPT. However, the Google-owned research facility DeepMind asserts that its upcoming LLM; Gemini, would be on par with or perhaps superior to OpenAI's.\n\n* Developers will be able to sell AI models created using OpenAI's AI technology on a marketplace that will soon be available.\n\n* The market for generative AI was worth 10.3 billion US dollars, and it is projected to increase at a compound annual growth rate of 32.2% to reach 54 billion US dollars in five years.\n\n* To stop ChatGPT and other generative AI tools from being used and to create alternatives, the South Korean steel company POSCO collaborated with Samsung Electronics and LG Electronics.\n\n* In the generative AI space, six businesses—OpenAI, Hugging Face, Lightricks, Jasper, Glean, and Stability AI—have attained unicorn status and are each valued at over 1 billion dollars.\n\n* The MBUX infotainment system from Mercedes-Benz will now include OpenAI's ChatGPT language model. This will improve user experience by enabling more organic and sophisticated interactions between drivers and passengers and the system.\n\n* Hugging Face has approximately 200K models and 36K datasets, making it one of the GitHub open-source projects with the quickest growth.\n\n* \"The more powerful the AI, the more energy it takes. If chatbots become as popular as search engines, the energy costs of deploying the AIs could add up. The good news is that AI can run on renewable energy by bringing the computation to where green energy is more abundant.\" (Kate Saenko, associate professor of computer science at Boston University, wrote in an article at <a href=\"https://theconversation.com/is-generative-ai-bad-for-the-environment-a-computer-scientist-explains-the-carbon-footprint-of-chatgpt-and-its-cousins-204096\">The Conversation</a>, 2023.)","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-four\"></a>\n# **Technology predictions** ","metadata":{}},{"cell_type":"markdown","source":"* The demand for prompt engineers will rise as organizations increasingly accept and use AI tools, which is being marked as the future of work.\n\n* A survey found that in 2022, 63% of marketers used AI for email marketing, 58% for advertising, and 57% for data analysis. We might anticipate an increase in those figures in 2023 as AI technology becomes more widely used.\n\n* Content produced by AI will soon become the new norm. Natural language processing (NLP) enables AI to produce content more quickly, accurately, and consistently than humans.\n\n* As massive data sources for audio machine learning, music streaming services like Spotify or Apple Music will surely lead to more AI-generated music. To make it easier for artists to create new music, Spotify has already developed certain AI-based tools, such as the open-source audio-to-MIDI software Basic Pitch.\n\n* Robots with synthetic muscles are capable of carrying greater weight and producing a greater amount of mechanical power than those powered by human muscles.\n\n* The transition from smartphones to wearable augmented reality (AR) glasses is expected to begin before 2026 and significantly increase speed once the implementation of 5G is finished.\n\n* With an ever-expanding data pool at their disposal, hackers will be able to create highly tailored spear phishing lures, ranging from free and open-source material like job openings to sensitive information disclosed in data breaches.\n\n* By the end of 2023, 66% of organizations will have already acquired at least two cloud providers. By doing this, businesses won't become overly entwined with a single environment.\n\n* The industry's adoption of cloud-native technologies and innovation are anticipated to be further boosted by recent breakthroughs in edge computing and 5G.\n\n* API-based options are what email security will look like in the future, as opposed to secure email gateways (SEGs), which isolate email security from internal networks.\n\n* By 2025, generative AI models are expected to be the source of at least 30% of all recently discovered materials and drugs, according to Gartner.\n\n* Augmented intelligence, which is the next AI trend for 2023, is aimed at improving cognitive productivity through the combination of automated machines and humans. The banking and insurance services, healthcare, travel, and business industries will all see a significant increase in this AI trend.\n\n* By using touchless fingerprinting to conduct remote biometric identity verification in 2023, organizations using current biometric database technology will be able to safely perform operations like opening banking accounts and verifying transactions.\n\n* Security keys and credentials that are FIDO2-compliant relieve end users from privacy obligations, demonstrating that a password-less era is within reach.\n\n* By 2025, three-fifths of businesses are expected to have a <a href=\"https://www.cisco.com/c/en_in/products/security/what-is-sase-secure-access-service-edge.html#:~:text=Secure%20access%20service%20edge%20(SASE)\">SASE</a> strategy in place, according to Gartner. <br>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-five\"></a>\n# **Challenges** ","metadata":{}},{"cell_type":"markdown","source":"* Generative AI models may consist of billions of parameters, and their training methods require quick and effective pipelines of information. A sizable amount of financial investment, technical know-how, and computing infrastructure are required to construct and maintain generative models.\n\n* Conversations must occur quickly and precisely for collaborative use cases like chatbots, AI voice assistants, or customer service applications. Diffusion models' slow sampling rates have come to light as a result of their growing popularity and the high-quality samples they can provide.\n\n* To create artificial data for various use cases, generative AI models are frequently utilized. Still, not all data can be used to train AI models, even though enormous amounts of data are produced daily around the globe. To function, generative models need reliable, unbiased information.\n\n* Getting commercial authorization to use pre-existing datasets or to create new evidence-based datasets to train generative models is a challenge for many organizations. This procedure is crucial for preventing concerns about intellectual property violations.\n\n* Regarding the use of creative AI tools, legal and ethical issues can arise. One of these abilities is the ability to quickly produce \"deepfakes,\" which are computer-generated images or movies that appear genuine but are fake or misleading. ChatGPT is already causing problems for universities. They are particularly having trouble with generated degree work since it is simpler to duplicate. Midjourney and Dall-E have faced similar criticism from artists who view them as either a form of stealing or just \"not art.\" \n\n* Machine learning, statistics, and computer science are just a few of the highly specialized fields that must be thoroughly understood to work in the field of generative AI. This may make it difficult for a small number of businesses to design and execute unique generative AI solutions, which may slow down the use of generative AI and hike compliance costs. Additionally, there is a significant demand for skilled individuals in the field of generative AI, but only a few are available.\n\n* The next difficulty is assuring generative AI's social and human effect, including preserving trust and openness, encouraging inclusiveness and diversity, and promoting ethical and advantageous use cases.\n\n* Making sure that generative AI is used in a way that respects intellectual property rights, protects security and privacy, and steers clear of dangerous or immoral use cases is another challenge.\n<br>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-seven\"></a>\n# **Ethical generative AI** ","metadata":{}},{"cell_type":"markdown","source":"The use of generative AI systems, especially those that mimic human artistic ability, has raised ethical questions for certain individuals. Deepfake films and false information produced by generative AI may be difficult to link back to the sources, making it tricky to hold those responsible accountable. This has sparked a larger discussion about ethical AI and the need for regulations to stop data professionals from scraping the internet to create the massive data sets required for training their machine learning models. Currently, the legality of collecting free data from the internet for training purposes through web scraping relies on several criteria, including the regulations and laws of the area, the sort of data being gathered, and the intended use of the data. Machine learning engineers (MLEs) might be obligated to pay site owners for any information they use to train their generative models as the value of reliable data sets continues to rise and data owners become increasingly conscious of the value of the content they provide to data scientists.<br>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-eight\"></a>\n# **How will the future look if generative AI takes the lead?**","metadata":{}},{"cell_type":"markdown","source":"Generative AI's capabilities have already proven to be useful in various contexts, and as the technology develops, so will its possibilities as well as its usage. Progress in generative model research and development fosters innovation across a range of AI-related domains. The potential for generative AI to fundamentally alter many facets of human existence remains enormous as it develops, setting the path for a future characterized by innovation, customization, and increased effectiveness. While some occupations may be replaced by generative AI, according to the technology's proponents, new ones will be created because there will always be a demand for a human-in-the-loop (HITL). Humans are still needed to choose the best generative AI model for the task, assemble and prepare training data, and assess the output of the AI model. Nevertheless, tackling the dangers that generative AI may pose is essential to its continued development. As AI becomes more advanced, maintaining <a href=\"https://www.techrepublic.com/article/data-governance-program/\">data governance</a> and reducing biases will be essential for ensuring that it is utilized ethically. To maximize the advantages of generative AI while minimizing any potential negative effects on the workforce, a balance between artificial intelligence and interactions with humans will be essential. Over time, more generative AI application cases are anticipated to surface. We must proceed cautiously, even though generative AI's future will enhance numerous processes specific to particular industries. The potential of generative AI and other methods is still not fully appreciated, despite the abundance of promising examples. It's crucial to use AI carefully and with reasonable expectations for those who choose to do so in their organizations.<br>\n\n<h4>Let’s see what the rest of 2023 and the future have in store for us in the field of AI!</h4><br>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-nine\"></a>\n# **References:** ","metadata":{}},{"cell_type":"markdown","source":"1. Emmanuel Ramos, (2023). *<a href=\"https://www.google.com/url?q=https://www.forbes.com/sites/forbestechcouncil/2023/01/26/unlock-the-potential-of-generative-ai-a-guide-for-tech-leaders/&usg=AOvVaw1ou-gHru5OeV4W0xMy-K0F\">Unlock The Potential Of Generative AI: A Guide For Tech Leaders.</a>* Forbes Technology Council.\n2. S Akash, (2023). *<a href=\"https://www.google.com/url?q=https://www.analyticsinsight.net/what-is-the-state-of-machine-learning-in-2023/&usg=AOvVaw1kFey3cCyMscjFB7BsDnwx\">What is the State of Machine Learning in 2023?</a>* Analytics Insight.\n3. Stuart Bingë, (2023). *<a href=\"https://www.google.com/url?q=https://www.sitepen.com/blog/the-state-of-machine-learning-2023&usg=AOvVaw2vH1snsG4zn64MuVtlSTfJ\">The State of Machine Learning 2023.</a>* sitepen.\n4. Yuri Musienko, (2023). *<a href=\"https://www.google.com/url?q=https://merehead.com/blog/machine-learning-2023/&usg=AOvVaw0LK7_8NKkjtACKVg2cV3ID\">Machine Learning Trends in 2023.</a>* MEREHEAD.\n5. Stephen Weigand, (2022). *<a href=\"https://www.google.com/url?q=https://www.scmagazine.com/feature/emerging-technology/2023-tech-predictions-ai-and-machine-learning-wicome-into-their-own-for-security&usg=AOvVaw1sO1M_H173AcL5vAWqwuH7\">2023 tech predictions: AI and machine learning will come into their own for security.</a>* SCMEDIA.\n6. Geri Mileva, (2023). *<a href=\"https://www.google.com/url?q=https://influencermarketinghub.com/ai-trends/%23:~:text%3DFocusing%2520on%2520democratization%2520of%2520AI,enhanced%2520customer%2520experiences%252C%2520and%2520overall&usg=AOvVaw0I1pYkVVQc82fyuzMD4F6W\">Top 10 AI Trends That Will Transform Businesses in 2023.</a>* Influencer Marketing Hub.\n7. Yulia Gavrilova, (2022). *<a href=\"https://www.google.com/url?q=https://serokell.io/blog/ai-ml-trends&usg=AOvVaw0YKCYdOQ4_Jy3hWL90xr_8\">Machine Learning Trends for 2023.</a>* serokell.io\n8. James Maguire, (2023). *<a href=\"https://www.google.com/url?q=https://www.eweek.com/artificial-intelligence/generative-ai-examples/%23:~:text%3DFor%2520example%252C%2520generative%2520AI%2520can,MRI%252C%2520CT%2520and%2520PET%2520scans.&usg=AOvVaw0llDyQNJsILrYmnlvdx5Gk\">Generative AI Examples.</a>* eWEEK.\n9. Yuri Musienko, (2023). *<a href=\"https://www.google.com/url?q=https://merehead.com/blog/artificial-intelligence-2023/&usg=AOvVaw0i0AjI9hAti37xJSbUuCTy\">BIGGEST ARTIFICIAL INTELLIGENCE (AI) TRENDS IN 2023.</a>* MEREHEAD.\n10. Owen Hughes, (2023). *<a href=\"https://www.google.com/url?q=https://www.techrepublic.com/article/what-is-generative-ai/%23:~:text%3DThe%2520most%2520compelling%2520advantage%2520generative,on%2520more%2520important%2520strategic%2520objectives.&usg=AOvVaw0Gn0eQF_zBD8HRs-qcCMEf\">Generative AI defined: How it works, benefits and dangers.</a>* TechRepublic.\n11. Eray Eliaçik, (2023). *<a href=\"https://www.google.com/url?q=https://dataconomy.com/2023/02/17/what-is-generative-ai-tools-images/%23:~:text%3DThe%2520generative%2520AI%2520market%2520size,bigger%2520in%2520the%2520market%2520daily.&usg=AOvVaw2M9IxfJvOIsaoKbD32OSo6\">Generative AI: The origin of the popular AI tools.</a>* DATACONOMY.\n12. Margaret Rouse, (2023), *<a href=\"https://www.google.com/url?q=https://www.techopedia.com/definition/34633/generative-ai%23:~:text%3DAny%2520time%2520an%2520AI%2520technology,write%2520articles%2520and%2520paint%2520pictures.&usg=AOvVaw0zaNR07-5py1fUUoDnPq9Y\">Generative AI.</a>* techopedia.\n13. *<a href=\"https://www.google.com/url?q=https://www.nvidia.com/en-us/glossary/data-science/generative-ai/%23:~:text%3DGenerative%2520AI%2520models%2520use%2520neural,semi%252Dsupervised%2520learning%2520for%2520training.&usg=AOvVaw1HEpHDkF1PVkPjNA7Czyg0\">Generative AI – What is it and How Does it Work?</a>* NVIDIA.\n14. Jason Nelson, (2023). *<a href=\"https://www.google.com/url?q=https://decrypt.co/142483/whats-environmental-impact-generative-ai-tools&usg=AOvVaw3WDy3Jy0jxA353fSZBlPcY\">What’s the Environmental Impact of Generative AI Tools?</a>* Decrypt.\n15. Richard Waters, (2023). *<a href=\"https://www.google.com/url?q=https://www.ft.com/content/b9bb533e-d2cb-492f-89b9-69b26e64ad3a&usg=AOvVaw2rJ6Lt4aBguPFJsiYK-Bns\">Microsoft launches generative AI tools for developers.</a>* San Francisco. FINANCIAL TIMES.\n16. Shelby Hiter, (2023). *<a href=\"https://www.google.com/url?q=https://www.eweek.com/artificial-intelligence/generative-ai-apps-tools/&usg=AOvVaw06LWIjpL1tZDFoUuGXAh8R\">Top 9 Generative AI Applications and Tools.</a>* eWEEK.\n17. Kate Saenko, (2023). *<a href=\"https://theconversation.com/is-generative-ai-bad-for-the-environment-a-computer-scientist-explains-the-carbon-footprint-of-chatgpt-and-its-cousins-204096\">Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins.</a>* The Conversation.\n18. Supantha Mukherjee, Foo Yun Chee, Martin Coulter, (2023). *<a href=\"https://www.reuters.com/technology/eu-lawmakers-committee-reaches-deal-artificial-intelligence-act-2023-04-27/#:~:text=STOCKHOLM%2C%20April%2027%20(Reuters),comprehensive%20laws%20governing%20the%20technology.\">EU proposes new copyright rules for generative AI.</a>* REUTERS.\n19. *<a href=\"https://www.cisco.com/c/en_in/products/security/what-is-sase-secure-access-service-edge.html#:~:text=Secure%20access%20service%20edge%20(SASE)\">What Is Secure Access Service Edge (SASE)?</a>* CISCO.\n20. Aminu Abdullahi, (2022). *<a href=\"https://www.techrepublic.com/article/data-governance-program/\">What is a data governance program?</a>* TechRepublic.\n21. Margaret Rouse, (2022). *<a href=\"https://www.techopedia.com/definition/33305/synthetic-data\">What is Synthetic Data?</a>* techopedia.\n22. Cem Dilmegani, (2023). *<a href=\"https://research.aimultiple.com/generative-ai-applications/\">Top 70+ Generative AI Applications / Use Cases in 2023.</a>* AIMultiple.\n23. Akash Takyar, *<a href=\"https://www.leewayhertz.com/generative-ai-use-cases-and-applications/\">GENERATIVE AI USE CASES AND APPLICATIONS.</a>* LeewayHetrz.\n24. *<a href=\"https://www.gartner.com/en/topics/generative-ai\">Generative AI: What is it, Tools, Models, Applications, and Use cases.</a>* Gartner.\n25. Prathamesh Ingle, (2023). *<a href=\"https://www.marktechpost.com/2023/06/01/best-ai-3d-generators-in-2023/\">Best AI 3D Generators in 2023.</a>* MARKTECHPOST.\n26. Harry Guinness, (2023). *<a href=\"https://zapier.com/blog/ai-text-generator/\">The top AI text generators in 2023.</a>* zapier.\n27. Alex McFarland, (2023). *<a href=\"https://www.unite.ai/10-best-ai-voice-generators/\">10 Best AI Voice Generators.</a>* UNITE.AI.\n28. Alex McFarland, (2023). *<a href=\"https://www.unite.ai/best-ai-video-generators/\">10 Best AI Video Generators.</a>* UNITE.AI.\n29. Melanie Easton, (2023). *<a href=\"https://www.fotor.com/blog/best-ai-image-generators/\">13 Best AI Image Generators You Can Try in 2023.</a>* fotor.\n30. *<a href=\"https://renaissancerachel.com/best-ai-code-generators/#:~:text=GitHub%20Copilot%20is%20one%20of,and%20it%20supports%20several%20languages.\">14 Best AI Code Generators of 2023.</a>* Renaissance Rachel.\n31. *<a href=\"https://www.mygreatlearning.com/blog/generative-ai-models/\">Generative AI Models.</a>* GreatLearning.\n32. *<a href=\"https://c3.ai/generative-ai-enterprise/generative-ai-terms-and-their-definitions/\">Generative AI Terms and Their Definitions.</a>* C3.ai\n33. Natasha Nel, (2023). *<a href=\"https://www.producthunt.com/stories/the-ultimate-a-z-guide-to-generative-ai-terminology\">The ultimate A-Z guide to generative AI terminology.</a>* producthunt.\n34. Ashley Eusanio, (2023). *<a href=\"https://www.salesforce.com/blog/generative-ai-glossary/\">AI From A to Z: The Generative AI Glossary for Business Leaders.</a>* Salesforce.\n35. Avijeet Biswal, (2023). *<a href=\"https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm\">Top 10 Deep Learning Algorithms You Should Know in 2023.</a>* simplilearn.\n36. *<a href=\"https://aiindex.stanford.edu/report/\">The AI Index Report - Measuring trends in Artificial Intelligence.</a>* Stanford University.\n37. Cornellius Yudha Wijaya, (2023). *<a href=\"https://www.kdnuggets.com/2023/06/2023-ai-index-report-ai-trends-expect-future.html\">2023 AI Index Report: AI Trends We Can Expect in the Future.</a>* KDnuggets.\n38. Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel, (2023). *<a href=\"https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction\">The economic potential of generative AI: The next productivity frontier.</a>* McKinsey Digital.\n39. Amy Sarah John, (2023). *<a href=\"https://wire19.com/generative-ai-in-industries/\">How generative AI can boost productivity in enterprises and industries.</a>* WIRE19.\n40. *<a href=\"https://www.jpmorgan.com/insights/research/generative-ai\">The Rise of Generative AI.</a>* J.P.Morgan.\n41. Neeru Gupta, (2023). *<a href=\"https://www.labellerr.com/blog/what-is-generative-ai-and-its-impact-on-various-industries/\">What is Generative AI and Its Impact On Various Industries?</a>* LABELLERR.\n42. Jackie Wiles, (2023). *<a href=\"https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of-generative-ai-for-enterprises\">Beyond ChatGPT: The Future of Generative AI for Enterprises.</a>* Gartner.\n43. Deborah Nas, (2023). *<a href=\"https://deborahnas.medium.com/5-sectors-that-will-be-most-affected-by-generative-ai-fd4c23bc3b0\">5 Sectors that will be most affected by Generative AI.</a>* Medium.\n44. Tekla S. Perry, (2023). *<a href=\"https://spectrum.ieee.org/state-of-ai-2023\">10 Graphs That Sum Up the State of AI in 2023.</a>* IEEE Spectrum.\n45. *<a href=\"https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html\">Generative AI could raise global GDP by 7%.</a>* GoldmanSachs.\n46. Matt Carmichael, (2023). *<a href=\"https://www.ipsos.com/en-us/ai-making-world-more-nervous\">AI is making the world more nervous.</a>* Ipsos.\n47. Chris Vallance, (2023). *<a href=\"https://www.bbc.com/news/technology-65102150\">AI could replace equivalent of 300 million jobs - report.</a>* BBC NEWS.\n48. Aaron Mok, Jacob Zinkula, (2023). *<a href=\"https://www.businessinsider.in/policy/economy/news/chatgpt-may-be-coming-for-our-jobs-here-are-the-10-roles-that-ai-is-most-likely-to-replace-/slidelist/97560110.cms\">ChatGPT may be coming for our jobs. Here are the 10 roles that AI is most likely to replace.</a>* BUSINESS INSIDER.\n49. *<a href=\"https://www.ipsos.com/en-us/artificial-intelligence-key-insights-data-and-tables\">Artificial Intelligence: Key insights, data and tables.</a>* Ipsos.\n50. Shana Lynch, (2023). *<a href=\"https://hai.stanford.edu/news/2023-state-ai-14-charts\">2023 State of AI in 14 Charts.</a>* HAI.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-ten\"></a>\n# **Coding:**","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom textblob import TextBlob\nfrom wordcloud import WordCloud\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:10.744065Z","iopub.execute_input":"2023-07-16T17:59:10.744876Z","iopub.status.idle":"2023-07-16T17:59:12.291977Z","shell.execute_reply.started":"2023-07-16T17:59:10.744832Z","shell.execute_reply":"2023-07-16T17:59:12.290788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Dataset courtesy: Arinjay Pathak.","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/generative-ai-tweets/GenerativeAI tweets.csv', parse_dates=['Datetime'])\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:12.293707Z","iopub.execute_input":"2023-07-16T17:59:12.294046Z","iopub.status.idle":"2023-07-16T17:59:13.238122Z","shell.execute_reply.started":"2023-07-16T17:59:12.294019Z","shell.execute_reply":"2023-07-16T17:59:13.237046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>This set of data includes statistics for a single year, from 2022 to 2023.</h4>","metadata":{}},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.240189Z","iopub.execute_input":"2023-07-16T17:59:13.240623Z","iopub.status.idle":"2023-07-16T17:59:13.285439Z","shell.execute_reply.started":"2023-07-16T17:59:13.240584Z","shell.execute_reply":"2023-07-16T17:59:13.284231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>The dataset consists of 56221 number of tweets about Generative AI and has no null values.</h4>","metadata":{}},{"cell_type":"markdown","source":"<h4>We will plot a graph of the frequency of the term \"Generative AI\" in order to identify the trend. This can be accomplished by counting the daily occurrences of tweets and then creating a graph.</h4>","metadata":{}},{"cell_type":"code","source":"daily_counts = df.set_index('Datetime').resample('D')['Tweet Id'].count()\nprint(daily_counts)","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.287502Z","iopub.execute_input":"2023-07-16T17:59:13.287930Z","iopub.status.idle":"2023-07-16T17:59:13.340262Z","shell.execute_reply.started":"2023-07-16T17:59:13.287890Z","shell.execute_reply":"2023-07-16T17:59:13.339288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"max(daily_counts)","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.342909Z","iopub.execute_input":"2023-07-16T17:59:13.343742Z","iopub.status.idle":"2023-07-16T17:59:13.350371Z","shell.execute_reply.started":"2023-07-16T17:59:13.343701Z","shell.execute_reply":"2023-07-16T17:59:13.349072Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>From above, we can observe that a certain day had an average of 1011 tweets.</h4>","metadata":{}},{"cell_type":"code","source":"df['Datetime'].groupby(df.Datetime.dt.to_period(\"M\")).agg('count')","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.352229Z","iopub.execute_input":"2023-07-16T17:59:13.352625Z","iopub.status.idle":"2023-07-16T17:59:13.373786Z","shell.execute_reply.started":"2023-07-16T17:59:13.352590Z","shell.execute_reply":"2023-07-16T17:59:13.372929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Out of all the months, March 2023 has the most tweets due to the introduction of Generative AI tools and announcements like GPT-4, Microsoft Bing, Github Copilot, ChatGPT plugins, UNESCO calling for AI ethics implementation, AI to predict cancer patient survival, Concerns about deepfakes, Canva and Zoom introducing AI features and tools, PyTorch 2.0 release, Ford choosing autonomous driving with Latitude AI, Launching of Snapchats chatbot-My AI, and many more.</h4>","metadata":{}},{"cell_type":"code","source":"plt.plot(daily_counts.index, daily_counts.values)\nplt.xlabel('Date')\nplt.ylabel('Number of tweets')\nplt.title('Usage of Generative AI from 2022-2023')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.374772Z","iopub.execute_input":"2023-07-16T17:59:13.375969Z","iopub.status.idle":"2023-07-16T17:59:13.700960Z","shell.execute_reply.started":"2023-07-16T17:59:13.375931Z","shell.execute_reply":"2023-07-16T17:59:13.699842Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>We can infer from the rising volume of tweets about \"generative AI\" that as more people become aware of it, the average usage of the technology will rise as well. And ChatGPT's influence is primarily accountable for the rise in popularity of Generative AI in 2023.</h4>","metadata":{}},{"cell_type":"markdown","source":"<h4>Let's perform sentiment analysis and visualize the results.</h4>","metadata":{}},{"cell_type":"code","source":"# Defining a function to classify sentiment\ndef get_sentiment(text):\n    blob = TextBlob(text)\n    sentiment = blob.sentiment.polarity\n    if sentiment > 0:\n        return \"Positive\"\n    elif sentiment < 0:\n        return \"Negative\"\n    else:\n        return \"Neutral\"\n\n# To add a new column with the sentiment classification\ndf[\"sentiment\"] = df[\"Text\"].apply(get_sentiment)\n\n# To print the number of tweets in each category\nprint(\"Sentiment Distribution:\\n\", df[\"sentiment\"].value_counts())\n\n# To print 10 tweets from each category\nprint(\"\\nPositive Tweets:\")\nprint(df[df[\"sentiment\"] == \"Positive\"][\"Text\"].head(10))\nprint(\"\\nNegative Tweets:\")\nprint(df[df[\"sentiment\"] == \"Negative\"][\"Text\"].head(10))\nprint(\"\\nNeutral Tweets:\")\nprint(df[df[\"sentiment\"] == \"Neutral\"][\"Text\"].head(10))\n\n# To visualize the distribution of sentiment\nsns.countplot(x=\"sentiment\", data=df)\nplt.savefig('blob.png')","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:13.702303Z","iopub.execute_input":"2023-07-16T17:59:13.702635Z","iopub.status.idle":"2023-07-16T17:59:34.075821Z","shell.execute_reply.started":"2023-07-16T17:59:13.702607Z","shell.execute_reply":"2023-07-16T17:59:34.074805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Positive sentiment about generative AI can be found in 52% of tweets. Neutral and negative tweets made up 39% and 9% of the total, respectively.</h4>","metadata":{}},{"cell_type":"markdown","source":"### **Word Cloud**","metadata":{}},{"cell_type":"code","source":"def show_wordcloud(data, title = None):\n    wordcloud = WordCloud(\n        background_color = 'white',\n        max_words = 200,\n        max_font_size = 40, \n        scale = 3,\n        random_state = 42\n    ).generate(str(data))\n\n    fig = plt.figure(1, figsize = (20, 20))\n    plt.axis('off')\n    if title: \n        fig.suptitle(title, fontsize = 20)\n        fig.subplots_adjust(top = 2.3)\n\n    plt.imshow(wordcloud)\n    plt.show()\n    \n# print wordcloud\nshow_wordcloud(df[\"Text\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:34.077420Z","iopub.execute_input":"2023-07-16T17:59:34.078367Z","iopub.status.idle":"2023-07-16T17:59:34.726492Z","shell.execute_reply.started":"2023-07-16T17:59:34.078333Z","shell.execute_reply":"2023-07-16T17:59:34.725650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h4>Above are the most commonly used words</h4>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-eleven\"></a>\n# **Submission Process**","metadata":{}},{"cell_type":"code","source":"submission=pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/sample_submission.csv\")\nsubmission.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:34.727752Z","iopub.execute_input":"2023-07-16T17:59:34.728624Z","iopub.status.idle":"2023-07-16T17:59:34.743902Z","shell.execute_reply.started":"2023-07-16T17:59:34.728580Z","shell.execute_reply":"2023-07-16T17:59:34.742852Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.loc[0]['value']='Generative AI'\nsubmission.loc[1]['value']='https://www.kaggle.com/code/sanjushasuresh/2023-kaggle-ai-report-generative-ai'\nsubmission.loc[2]['value']='https://www.kaggle.com/code/satyaprakashshukl/2023-kaggle-ai-generative-ai-summary/comments#2309341'\nsubmission.loc[3]['value']='https://www.kaggle.com/code/ahmedfzl/generative-ai-advancements-from-2021-to-2023/comments#2309270'\nsubmission.loc[4]['value']='https://www.kaggle.com/code/manansoam/evolution-of-generative-ai/comments#2327705'\nsubmission.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:34.745367Z","iopub.execute_input":"2023-07-16T17:59:34.745672Z","iopub.status.idle":"2023-07-16T17:59:34.756054Z","shell.execute_reply.started":"2023-07-16T17:59:34.745646Z","shell.execute_reply":"2023-07-16T17:59:34.755055Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2023-07-16T17:59:34.757486Z","iopub.execute_input":"2023-07-16T17:59:34.757802Z","iopub.status.idle":"2023-07-16T17:59:34.770319Z","shell.execute_reply.started":"2023-07-16T17:59:34.757776Z","shell.execute_reply":"2023-07-16T17:59:34.769298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h3>Thank you.</h3>","metadata":{}}]}