Gavin Christopher Newsom, the 40th governor of California, has put his signature on the California Senate Bill 362 (SB 362), also known as the California Delete Act. Thanks to this historic law, SB 362 will make it easy for Californians to demand the right to delete any information that data brokers have with them.
Citizens of California, through a free, easy-to-use, single-page online portal, can request the California Privacy Protection Agency (CPPA) to delete any private information data brokers have with them, also preventing tracking people online.
The bill defines data brokers as businesses that don’t directly relate to ordinary consumers but collect information about them through online and offline sources and sell those data. Data brokers collect sensitive information pertaining to healthcare, geolocation, spending habits, and employment status.
Josh Becker, who first introduced the bill back in April 2023, said, “Data brokers possess thousands of data points on each and every one of us, and they currently sell reproductive healthcare, geolocation and purchasing data to the highest bidder. The Delete Act protects our most sensitive information.”
In today’s day and age, our digital footprints contain information of great significance to us. Consequently, the right to have a voice regarding the use of our information synchronizes with our right to privacy, and the California law-making agency, through the California Delete Act, has addressed the impending obvious.
Climate change is one of the lingering issues of the 21st Century. There are, of course, different narratives to it. On one side, some experts and politicians, primarily right-wing, call it propaganda and a hoax; on the other side, climate change and global warming might be the reason for humanity’s end.
Open AI CEO Sam Altman happens to be on the latter side of the debate. The 38-year-old entrepreneur is one of the poster boys of the AI revolutions, thanks to his brainchild, ChatGPT, which disrupted not only the AI market but the overall socio-cultural landscape.
Sam Altman foresees the next decade will witness a flurry of “breathtaking scientific discoveries.” Among them, solar geoengineering will be one of the more critical tools to combat climate change and global warming.
the scientific discoveries of the coming few decades will be breathtaking
Introducing space mirrors or spraying reflective particles to deflect sun rays is one of the hypotheses to prevent Earth from overheating, according to the Open AI CEO.
Although geoengineering is still a proto-concept, it has its pros and cons, significantly because it will alter the natural ecosystem, thus creating a counterintuitive situation.
i wish the world were studying solar geoengineering more.
clearly have misgivings about it, but it's so relatively cheap that i think some country is just going to do it if/when the climate crisis gets bad enough as a temporary patch.
However, geoengineering happens to be the only plausible solution to global warming as of now, and to avoid a summer like 2023, which, according to scientists at NASA, was the hottest summer recorded since global records began in 1880, we must begin to understand the possibilities that geoengineering has to offer.
The ninth annual Samsung Developer Conference was held at the Moscone Centre in San Francisco on October 5, 2023. The event was marked by Samsung’s announcement of One UI 6 and the multinational conglomerate’s emphasis on user data privacy, sustainability, and a connected ecosystem.
The One UI 6.0 update that went with the Slogan “Enabling your Galaxy, your way” validates Samsung’s plans to release the OS update by the end of this year after running it on a beta program for a while.
The new One UI 6.0 features contain a restructured Quick Panel, which helps users navigate better because of its easy use. A new typeface, One UI Sans, is also introduced, ensuring better readability of digital screens. The new One UI 6.0 features Samsung Studio, allowing users to edit multi-layered video.
The realm of a secure, connected ecosystem has been paramount for the South Korean electronic giants. Consequently, the concept of a secure and connected smart home is closer than ever as Samsung announced the SmartThings Home API, allowing developers to create SmartThings-based apps easily. Additionally, Samsung is expanding the SmartThings Hub to include sound bars and smart TVs, broadening its product compatibility.
To be announced: 🤳 One UI 6 💡 SmartThings & Bixby-related stuff 🧠 AI-related stuff ⚙️ Others in the Software Department (Watch, Health, Foldables, etc.)
Then, the second-generation Smart Tag 2 was announced, a device that helps users find lost items through Bluetooth Low Energy (BLE) connectivity. The portable Smart Tag 2 has a battery life of upto 700 days with an IP67 rating for water and dust resistance. The device can also trace lost items through rain, dust, and underground.
Samsung Galaxy Smart Tag 2 Tracker has launched globally.
– Bluetooth – UWB 2- in – 1 – IP67 rating – Up to 700 days of battery life – Color Options : Black or white
Samsung’s plan to enhance its voice assistant, Bixby, is also underway. First launched back in 2017, the virtual assistant will integrate with SmartThings. With more intuitive command control, Bixby will identify which command best suits a device in a multi-device environment.
Samsung also announced that Tizen is expanding its presence on various devices, including home appliances featuring a 7-inch display. Additionally, it is enhancing user experiences through on-device artificial intelligence (AI) and the Home AI Edge Hub. The Linux-based operating system in the past has successfully powered digital screens, including TVs, monitors, and home appliances, and its further mutation is seen as a game changer.
Last but not least, Samsung showcased its improved health solutions that leverage the strength of connectivity. With Samsung Health and Samsung Food, users will get personalized nutrition recommendations, food delivery services, and recipe-sharing programs; Samsung is designing an all-new digital health experience.
The UK’s largest news organization, BBC, and other top media organizations like CNN blocked OpenAI’s data scraping. As it evaluates the use of generative AI, BBC laid out principles it plans to follow, including for research and production of journalism, personalized experiences, and archiving.
Rhodri Talfan Davies, BBC Director of Nations, stated, “We believe Gen AI could provide a significant opportunity for the BBC to deepen and amplify our mission, enabling us to deliver more value to our audiences and to society.”
BBC’s three guiding principles are that it will always act in the public’s best interest, prioritize talent and creativity by respecting the rights of artists, and be open and transparent about AI-made output.
In a bid to safely develop generative AI and focus on maintaining trust in the news industry, BBC said it will work with tech companies, other media organizations, and regulators.
Several top news publications like CNN, The New York Times, and the Australian Broadcasting Corporation (ABC) blocked Microsoft-backed OpenAI in August from accessing their content to train its AI models.
OpenAI’s web crawler—GPTBot—may scan web pages to improve its AI models. BBC has blocked web crawlers from OpenAI and Common Crawl, meaning these organizations can’t use content from the publication to train their AI models.
Davies emphasized that this move aims to safeguard the interests of the license fee payers and that unauthorized use of BBC data for training AI models isn’t in the public interest.
Additionally, BBC is examining the broader implications of Gen AI on the media industry, including the proliferation of misinformation and the potential effects on website traffic patterns. Davies stated, “In the next few months, we will start a number of projects that explore the use of Gen AI in both what we make and how we work – taking a targeted approach in order to better understand both the opportunities and risks.”
In collaboration with 33 academic labs, Deepmind has collectively gathered data from 22 diverse robot types to establish the Open X-Embodiment dataset and the RT-X model. This marks a significant advancement in the era of robotics, aligning with the goal of training robots for a wide range of tasks.
Deepmind’s recent initiative has resulted in the creation of an enormous dataset known as the Open X-Embodiment dataset. This dataset comprises data gathered from distinct robot types, and these robots have successfully executed 500 different skills and completed over 150,000 different tasks across more than a million episodes.
In this work, the team mentioned when a single model is trained using data from different robot types, and it performs much better across a variety of robots compared to models trained separately for each robot type.
The image below has some examples from the Open X-Embodiment dataset showcasing over 500 skills and 150,000 tasks performed by robots.
RT-X model, a general-purpose robotics model, is built on two of Deepmind’s transformer models. The first one, RT-1-X, is trained on RT-1, a multi-task model designed to tokenize robot inputs and translate them into actions, including motor commands, camera images, or task instructions. This approach enhances real-time control capabilities, making it suitable for real-world robotic control on a large scale.
The other, RT-2-X, is trained using RT-2, a vision-language-action (VLA) model that leverages data from both web sources and robotics. This model excels in translating acquired knowledge into generalized instructions that can be further applied for controlling robotics in various scenarios.
The team has open-sourced this dataset and the trained models, allowing other researchers to further develop and expand upon this work.
The Australian online graphic design and multimedia company—Canva—released Magic Studio to celebrate its 10th anniversary. Designed in collaboration with Runway AI, the company claims that Magic Studio is the “world’s most comprehensive AI-design platform.”
Runway co-founder and CEO Cristóbal Valenzuela’s post on X (formerly Twitter) reads: “Excited to partner with Canva. Great things are coming.”
Apart from several features that were launched back in March, including Magic Eraser, Magic Edit, and Magic Design, which use text-to-image generative AI, Canva added a host of new features. Magic Studio includes newly added tools such as Magic Morph, Magic Expand, Magic Grab, Magic Switch, and Magic Animate. These new tools are a mix of Canva’s in-house AI and as well as that of partners such as OpenAI and Google.
Alongside its new AI suite, Canva also launched Canva Shield, a suite of advanced trust, safety, and privacy tools leveraging artificial intelligence. It includes features such as customizable AI privacy settings, robust content moderation systems, and AI indemnification for eligible enterprise users.
Canva’s co-founder and Chief Product Officer, Cameron Adams, stated, “We believe that AI has incredible potential to supercharge the 99% of office workers who don’t have design training or access to professional design tools.”
Canva has announced that it has set aside $200 million for the next three years to pay designers who consent to have their content used to train the company’s AI models. Anyone participating in the Creator Campaign Program will receive an initial bonus and monthly payments for as long as their content is being used.
Canva’s current embrace of AI has seen massive success, with 65 million new monthly active users in the last year and a near-doubling of its paying subscribers to 16 million. The new Magic Studio features will be restricted to paid Canva users, while free users can get a limited taste of a few of them.
In the digital era, healthcare is seeing a transformative shift, influenced by technology’s rapid rise and the evolving needs of patients. That shift is accelerating at a dizzying rate, to the extent it can be difficult to keep track of the constant evolutions impacting the sector.
With a world of online medical resources literally just a click away, patients are becoming more informed, challenging the conventional healthcare frameworks but also leading to a landscape where patients might feel as if they know more than they actually do.
As the NHS grapples with balancing contemporary patient requirements and ensuring top-notch healthcare, the landscape is poised for change.
Patients and Online Medical Information
The digital realm offers a vast expanse of medical information, making ‘Dr. Google’ an attractive first point of contact for many. Recent data indicates that a significant portion of the UK’s younger demographic rely on online sources for medical insights, often even before consulting a medical professional.
The motivations? Expediency and instantaneous access to information. However, there are inherent risks. The quality and authenticity of information can vary, and reliance on unchecked sources can sometimes lead to medical negligence, which can result in problematic and costly medical negligence claims.
Public’s Satisfaction with the NHS
The NHS stands as a beacon of public health services in the UK and is famous all over the world for various reasons. It’s a socialised system that offers healthcare to all who live and work in the UK with very few exceptions.
However, as with any expansive system, there are challenges. According to the British Social Attitudes survey, public satisfaction rates have fluctuated over the years, reflecting broader changes in societal expectations and healthcare provision.
Role of AI in Transforming Healthcare Services
Artificial intelligence is making waves in healthcare, bringing about a paradigm shift. Its integration promises improved efficiency, heightened accuracy, and better accessibility. For instance, the UK government has allocated significant funds to roll out AI technologies across the NHS, ensuring patients reap the benefits of cutting-edge care.
However, with this technological boon come ethical dilemmas. Questions about patient data privacy, the impersonal aspect of AI-driven diagnoses, and the potential for mistakes necessitate thorough evaluation.
Bright Horizons: Positive Trends in Healthcare
The silver lining of all this? The convergence of patient empowerment and AI is ushering in notable advancements. Patients, armed with information, are actively participating in their health journeys. Simultaneously, AI tools are enhancing diagnostic precision, treatment plans, and patient care, pointing towards a hopeful horizon for healthcare in the UK.
To sum it up, as healthcare in the UK ventures into the digital age, the integration of patient needs, and technological innovations promises a future where quality care is both accessible and efficient. While challenges exist, the potential for positive transformation is immense.
The techgiant Meta is advancing AI development by integrating ChatGPT-like chatbots into several social media platforms, including WhatsApp, Messenger, and Instagram. This integration aims to enhance communication channels and interactions by making them more creative, expressive, and personalized.
Meta is rolling out a beta version of Meta AI, an advanced conversational assistant available on all three platforms, with plans to expand its availability to Ray-Ban Meta smart glasses and Quest 3. This AI assistant will provide real-time information and rapidly generate photorealistic images for text inputs, allowing users to quickly share them in groups or with friends.
What’s more, Meta also announced the introduction of innovative AI stickers. These stickers enable users to effortlessly create customized stickers for their chats and stories. The technology behind these stickers is Llama 2 and Meta’s image generation model, Emu, which will transform text prompts into multiple unique, high-quality stickers within seconds. This advancement reflects Meta’s ongoing commitment to enhancing user experiences across their platforms.
The integration of chatbots into WhatsApp expands the scope of the application. It will allow users to interact with tasks like shipment tracking, ordering, scheduling appointments, and receiving real-time updates. It will also offer substantial benefits for various businesses by automating routine tasks such as addressing common queries or handling simple transactions. This automation will enable companies to allocate their human resources to more intricate operations and provide personalized assistance to customers when needed.
Similarly, in Facebook Messenger, these chatbots can be integrated to promote a range of interactive functions. They can swiftly respond to inquiries, provide customized product recommendations, and facilitate seamless transactions. In addition, when integrated into group chats, these chatbots will enable collaborative decision-making, event planning, and personalized suggestions. This range of features will offer convenience to both customers and businesses while boosting customer satisfaction.
The integration of ChatGPT-like chatbots into Meta’s messaging platforms marks the start of a transformative era. As AI technology advances, these chatbots will evolve with enhanced natural language understanding and contextual awareness, ultimately improving user interactions. While chatbots bolster user experience through conversational capabilities, there are noteworthy challenges, including data privacy and security.
Are you fascinated by the power of artificial intelligence to create unique and realistic content? Look no further! In this article, we present a curated list of the top generative AI courses that will ignite your creativity and expand your skills. Generative AI skills have become increasingly important in today’s rapidly evolving technological landscape. The potential for generative AI to revolutionize many different businesses and creative fields is enormous as artificial intelligence technology develops. Design, entertainment, and marketing advances may result from the ability to create fresh, realistic content, such as images, music, and text.
We will talk about some of the top generative AI courses in this article that you may take online to increase your technical knowledge. Whether you’re an engineer, a professional coder or just someone who is curious about the potential of artificial intelligence technology, these courses will offer you an exploration and thorough understanding of generative AI. From understanding the principles of generative models to creating breathtaking artwork and lifelike literature, these courses will equip you with the abilities and information required to fully realize the potential of generative AI.
Top Generative AI Courses
Here are some of the best generative AI courses available online that can take your technical skills to the next level.
ChatGPT Prompt Engineering for Developers
Offered by DeepLearning.AI in collaboration with OpenAI, this course reflects the latest understanding of best practices for using prompts for the latest LLM models. This course, ChatGPT Prompt Engineering for Developers, teaches students how to create new, robust apps quickly using a large language model (LLM). This short course of 1-hour duration is taught by Isa Fulford from OpenAI and Andrew Ng from DeepLearning.AI. The course will describe how LLM APIs can be used in applications for a variety of tasks, including summarizing, inferring, transforming, and expanding. The limited-time free version of ChatGPT Prompt Engineering for Developers is user-friendly for beginners. Only a fundamental knowledge of Python is required.
The Generative Adversarial Networks (GANs) Specialization is offered by DeepLearning AI on Coursera. It offers a fascinating introduction to image generation using GANs, outlining a journey from basic concepts to complex techniques using a simple methodology. Additionally, it discusses social aspects such as privacy protection, bias in ML, and how to detect it. Students will be able to develop a thorough theoretical basis and acquire practical GAN experience. In addition, they will assess a number of advanced GANs and train their own model in PyTorch. This Specialization is suitable for levels of learners, even those without prior familiarity with advanced math and machine learning research.
ChatGPT for Beginners: The Ultimate Use Cases For Everyone
Through this Udemy course, students will learn how to use ChatGPT’s power to automate tasks, make money, and develop their skills. From novice to expert users, this course, ChatGPT for Beginners: The Ultimate Use Cases For Everyone, is created for people and companies of all skill levels. Students will discover how ChatGPT works and how to use it to boost output, cut down on wait times, and streamline processes throughout the course. Additionally, students will gain practical experience utilizing ChatGPT to create realistic content while learning how to set up and customize ChatGPT to suit their individual needs. Apart from an introduction to ChatGPT and its capabilities, this course also includes tips and best practices for effectively using ChatGPT. One can even get advice on how to integrate ChatGPT into their business or personal workflow.
The Fundamentals of ChatGPT
The experts at Digital Partner have developed The Fundamentals of ChatGPT course to help learners take advantage of this important new technology, ChatGPT, as it begins to change the world. The course defines the role of OpenAI in promoting AI technology globally and explains how ChatGPT works step by step, along with highlighting some of the major shortcomings of chatbots. The course presents various case studies and examples of developers interacting with ChatGPT as they test its capabilities, which include writing, mathematics, coding, and more. The course also compares the standard ChatGPT and ChatGPT Plus, which charges a monthly subscription fee to use. Instructors of the course will provide strategies that they can use to develop and customize their own GPT platform.
Building Systems with the ChatGPT API
This one-hour course, taught by Isa Fulford of OpenAI and Andrew Ng of DeepLearning.AI, builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite. In Building Systems With The ChatGPT API, one can learn how to automate complex workflows using chain calls to a large language model. Learners will build chains of prompts that interact with the completions of prior prompts as well as systems where Python code interacts with both completions and new prompts. They will also create a customer service chatbot using all the techniques from this course.
Most importantly, they will learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning.
LangChain for LLM Application Development
LangChain for LLM Application Development, a one-hour course instructed by the creator of LangChain, Harrison Chase, as well as Andrew Ng, will vastly expand the possibilities for leveraging powerful language models, where students can now create incredibly robust applications in a matter of hours. In LangChain for LLM Application Development, learners will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. At the end of the course, they will have a model that can serve as a starting point for their own exploration of diffusion models for applications. Users will learn about calling LLMs, providing prompts, parsing the response, creating sequences of operations, and applying LLMs to their proprietary data and use case requirements.
How Diffusion Models Work
How Diffusion Models Work, a one-hour course by DeepLearning.AI and taught by Sharon Zhou, will expand one’s generative AI capabilities to include building, training, and optimizing diffusion models. In this course, users will gain a deep familiarity with the diffusion process and the models which carry it out. In this course, learners will explore the cutting-edge world of diffusion-based generative AI and create their own diffusion model from scratch. They will gain deep familiarity with the diffusion process and the models driving it, going beyond pre-built models and APIs. This course will help one acquire practical coding skills by working through labs on sampling, training diffusion models, building neural networks for noise prediction, and adding context for personalized image generation.
Introduction to Generative AI
Introduction to Generative AI is a beginner-level microlearning course provided by Google that seeks to define and explain what Generative AI is, which is, in short, a type of artificial intelligence technology that can create many types of material, including text, imagery, audio, and synthetic data. The course will examine the technology’s applications and how they differ from conventional machine learning techniques. It also covers Google Tools to help students develop their own Generative AI apps. The course will explain generative AI model types and their applications of the same. This course is estimated to take approximately 45 minutes to complete. Users can earn a badge when they complete this course.
Introduction to Large Language Models
Offered by Google, Introduction to Large Language Models is an introductory-level microlearning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how one can use prompt tuning to enhance LLM performance. Large Language Models (LLMs) are foundational machine learning models that make use of deep learning algorithms to process and understand natural language. Learners will learn how these models are trained on vast amounts of text data to learn patterns and entity relationships in the language. The course also covers Google tools to help users develop their own generative AI apps. This course is also estimated to take approximately 45 minutes to complete.
Introduction to Responsible AI
Introduction to Responsible AI is an introductory-level microlearning course aimed at explaining what responsible AI is, why it’s important, and how Google implements responsible AI in its products. Responsible AI is the practice of designing, developing, and deploying AI with the purpose of empowering employees and organizations and having an equitable influence on consumers and society. This enables businesses to build trust and confidently scale AI. The course also introduces Google’s 7 AI principles. These principles are: Be socially beneficial, Be built and tested for safety, Avoid creating or reinforcing unfair bias, Be accountable to people, Uphold high standards of scientific excellence, Incorporate privacy design principles, and Be made available for uses that align with these principles.
Introduction to Image Generation
Offered by Google, this Introduction to Image Generation course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models have become popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces learners to the theory behind diffusion models and how to train and deploy them on Vertex AI.
Encoder-Decoder Architecture
Encoder-Decoder Architecture by Google Skills Boost gives learners a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as text summarization, machine translation, and question answering. Students will learn about the key components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, they will code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
Attention Mechanism
Attention Mechanism course will introduce learners to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. Attention Mechanism is an attempt to implement the action of selectively concentrating on fewer relevant things while ignoring the others in deep neural networks. One may understand how the attention mechanism functions and how it can be applied to enhance a number of machine learning tasks, such as question-answering text summarization, and machine translation. It should take you about 45 minutes to finish this course.
Transformer Models and BERT Model
Transformer Models and BERT Model, offered by Google, gives an introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model. A neural network called a transformer model follows relationships in sequential input, such as the words in this sentence, to learn context and subsequent meaning, whereas BERT is an open-source ML framework for natural language processing. The self-attention mechanism, for example, and how it is used to construct the BERT model will be thoroughly explained to students as important parts of the Transformer architecture. Additionally, they will become familiar with the various tasks that BERT is capable of performing, including text classification, natural language inference, and question answering. It should take you 45 minutes to complete this course, on average.
Create Image Captioning Models
This short course by Google, Create Image Captioning Models, teaches how to create an image captioning model by using deep learning. Imagine captioning is the process of developing a written summary of a picture. Both computer vision and natural language processing are used to generate the captions. Learners will understand the various parts of an image captioning model, such as the encoder and decoder, as well as how to train and test your model. They will be able to develop their own image captioning models by the conclusion of the course and use them to produce captions for photos.
Introduction to Generative AI Studio
As the name Introduction to Generative AI Studio suggests, this course by Google introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your applications. Generative AI Studio is a Google Cloud console tool for rapidly prototyping and testing generative AI models. Learners will test sample prompts, design their own prompts, and customize foundation models to handle tasks that meet their application’s needs. In this course, they will also learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, learners will have a hands-on lab to apply what they learned and a quiz to test your knowledge.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI provides this course titled Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning. This course, which is a part of their upcoming Machine Learning in Tensorflow Specialisation, will cover TensorFlow, a well-known open-source machine learning framework. The most fundamental theories of deep learning and machine learning are covered in this specialization by Andrew Ng. It also shows you how to apply those principles using TensorFlow so that students may begin creating and using scalable models to solve real-world issues. To gain a deeper grasp of how neural networks operate, this course is recommended.
Last month, a multimodal model called CM3Leon was released by Meta AI, which performs both text-to-image and image-to-text creation tasks. CM3Leon can also understand instructions to edit existing images.
CM3leon is a first-of-its-kind multimodal model that achieves state-of-the-art performance for text-to-image generation despite being trained with five times less compute than previous transformer-based methods.
Yesterday, Meta took to Twitter to reiterate that the model manages to demonstrate cutting-edge performance for text-to-image generation despite having been trained with five times less computing than previously used transformer-based methods.
The transformer model CM3Leon uses a concept called “attention” to evaluate the usefulness of input data like text or graphics. Model training speed can be increased, and models can be more easily parallelized, thanks to “attention” and other architectural peculiarities of transformers. With significant but not insurmountable increases in computation, larger transformers can be easily trained.
According to the blog post by the company, CM3leon is a first-of-its-kind multimodal model that achieves state-of-the-art performance for text-to-image generation despite being trained with five times less computing than previous transformer-based methods. It also has the versatility and effectiveness of autoregressive models while incurring low training costs and inference efficiency.
According to the company, Meta AI used a dataset of millions of licensed images from Shutterstock to train CM3Leon. The most capable of several versions of CM3Leon that Meta has built has 7 billion parameters, which is over twice as many as DALL-E 2.
