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	<title>Roberto Iriondo &#8211; Towards AI</title>
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		<title>Transforming Biology with Generative AI: Unveiling GenBio AI&#8217;s State-of-the-art Multiscale Models</title>
		<link>https://towardsai.net/p/news/transforming-biology-with-generative-ai-unveiling-genbio-ais-state-of-the-art-multiscale-models</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Thu, 19 Dec 2024 14:00:54 +0000</pubDate>
				<category><![CDATA[News]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=38943</guid>

					<description><![CDATA[TL;DR: GenBio AI is advancing biology with Generative AI by developing AI-Driven Digital Organisms (AIDO). The AIDO system integrates multiscale foundation models for DNA, RNA, proteins, and cellular systems, allowing researchers to simulate, predict, and program biological outcomes from molecular to systemic levels. These tools aim to transform drug discovery, disease understanding, and personalized medicine, setting the stage for a new era in biological research. Advancing Biology with Generative AI: Inside GenBio AI&#8217;s AI-Driven Digital Organism Biology is entering an era where artificial intelligence is redefining the way we approach research and discovery. Leading this transformation is GenBio AI with its groundbreaking AI-Driven Digital Organism (AIDO), an integrated system of multiscale foundation models that enables researchers to simulate, program, and predict complex biological outcomes. AIDO addresses critical challenges in medicine, biotechnology, and life sciences by unifying insights across molecular, cellular, and systemic levels. Professor Eric Xing, Co-Founder and Chief Scientist of GenBio AI, underscores the ambition behind AIDO: &#8220;GenBio will usher in a new era of medical and life science—through a paradigm shift powered by next-generation Generative AI technology beyond what has already brought us disruptive results such as ChatGPT. Our transformative technology allows biological data of all types and scales to be utilized to distill holistic and comprehensive knowledge of how living systems work. Therefore, multiscale biological complexities are no longer barriers but opportunities for breakthrough insights.&#8221; Moving Beyond Silos with AIDO Traditional biological models often operate in isolation, analyzing narrow datasets like DNA or proteins without integrating broader system interactions. AIDO disrupts this approach by creating a cohesive framework where modular models interact seamlessly, enabling a comprehensive understanding of biology as an interconnected system. Key Features of AIDO: Multitasking Efficiency: Handles up to 300 tasks simultaneously, surpassing the one or two tasks most current systems manage. Interoperable Modules: Models for DNA, RNA, proteins, single cells, and evolutionary data work in concert, addressing the siloed nature of traditional approaches. Comprehensive Data Utilization: Incorporates diverse biological data types, from sequences to structures, providing unprecedented insight into complex systems. By bridging biological scales, AIDO equips researchers with tools to analyze interactions across molecular, cellular, and organismal levels. Breaking Down the AIDO Foundation Models GenBio AI’s first phase of AIDO introduces six foundational models, each designed to tackle specific biological challenges: AIDO-DNA: A 7-billion-parameter model trained on data from 796 species, offering advanced insights into genomic structure and function. AIDO-RNA: The largest model of its kind with 1.6 billion parameters, tailored for RNA structure prediction, genetic regulation, and vaccine design. AIDO-Protein: A computationally efficient model that facilitates exploration of protein functionality, essential for drug discovery. AIDO-Single Cell: Processes entire human transcriptomes without truncation, uncovering complex cellular dynamics with precision. Protein Structure Model: Focuses on three-dimensional protein modeling, uncovering relationships between structure and biological activity. Evolutionary Information Model: Provides insights into molecular evolution, connecting genetic data across species. These models not only excel individually but also operate as an integrated system, making AIDO a comprehensive toolkit for biological research. You can download them on GitHub or Hugging Face. Transformative Applications of AIDO AIDO’s real-world applications are poised to address some of the most pressing challenges in medicine and biotechnology: Accelerating Drug Discovery Traditional drug development is costly and time-intensive, often with high failure rates. AIDO allows researchers to simulate and test millions of potential compounds in hours, drastically reducing both time and costs. Advancing Personalized Medicine Adverse drug reactions remain a leading cause of mortality worldwide. By creating digital patient twins, AIDO supports the design of personalized treatments that reduce risks and improve therapeutic outcomes. Understanding Complex Diseases From cancer to neurodegenerative disorders, many diseases involve systemic interactions. AIDO’s multiscale approach equips researchers to study these mechanisms and identify new pathways for intervention. Global Expertise, Global Impact GenBio AI’s achievements are the result of a collaborative effort among world-renowned scientists and institutions. Headquartered in Palo Alto, with labs in Paris and Abu Dhabi, the company’s team includes experts from Carnegie Mellon University, Stanford, the Weizmann Institute of Science, and MBZUAI. These partnerships have resulted in six peer-reviewed papers presented at NeurIPS, showcasing the rigorous research behind AIDO. Professor Eran Segal of the Weizmann Institute of Science highlights the significance of this work: &#8220;GenBio AI&#8217;s six multiscale foundation models are a leap forward in our ability to understand and predict biological phenomena. We now have the capacity to uncover systemic insights into how organisms function. This is transformative for genomics research, where the ability to simulate and program at multiple scales opens new avenues for precision medicine and disease intervention.&#8221; Professor Fabian Theis of Helmholtz Munich adds: &#8220;GenBio AI&#8217;s achievement in creating scalable state-of-the-art models on multiple scales is a game-changer. This technology not only accelerates our ability to explore cellular dynamics but also bridges the gap between molecular and systems biology, unlocking unprecedented opportunities for disease modeling and therapeutic innovation.&#8221; Explore the Research: Toward AI-Driven Digital Organism: A System of Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale Mixture of Experts Enable Efficient and Effective Protein Understanding and Design Balancing Locality and Reconstruction in Protein Structure Tokenizer Retrieval Augmented Protein Language Models for Protein Structure Prediction A Large-Scale Foundation Model for RNA Function and Structure Prediction Scaling Dense Representations for Single Cell with Transcriptome-Scale Context The Road Ahead The development of AIDO represents just the beginning of GenBio AI’s roadmap. The company envisions deeper integration between foundational models in future phases, expanding the system’s utility for synthetic biology, environmental sustainability, and longevity research. Dr. Le Song, Co-Founder and CTO of GenBio AI, encapsulates the vision: &#8220;What we have built is revolutionary because our integrated system will use these state-of-the-art models to create interactive digital versions of biological systems that can be safely experimented on and precisely modified. This technology lets us program biology the way we program computers, opening up possibilities we&#8217;ve never had before in medicine and biotechnology.&#8221; As AIDO evolves, it promises to reshape how we [&#8230;]]]></description>
		
		
		
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		<title>Exploring Futuristic Visions of Peru Through AI Image Generation</title>
		<link>https://towardsai.net/p/generative-ai/exploring-futuristic-visions-of-peru-through-ai-image-generation</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Fri, 09 Aug 2024 07:56:32 +0000</pubDate>
				<category><![CDATA[Generative AI]]></category>
		<category><![CDATA[Towards AI - Medium]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=34774</guid>

					<description><![CDATA[Author(s): Roberto Iriondo Originally published on Towards AI. Source: DALL-E By Roberto Iriondo This blog post explores the process of generating futuristic yet realistic scenery images of Peru using AI tools. We discuss the inspirations behind each prompt, the challenges faced in creating these images, and the insights gained from the experience. The post delves into the details of ten images, showcasing the blend of Peru’s natural beauty with sci-fi elements to create unique visual interpretations and the insights gained from the process. In recent years, generative AI has opened up new possibilities for creating stunning visual content, offering a blend of creativity and technology that is changing how we perceive art and design. In this post, we dive into the fascinating world of AI image generation, focusing on a series of futuristic yet realistic images of Peru created using AI models. These images were crafted with a focus on integrating the natural beauty of Peru’s landscapes with futuristic, sci-fi elements. Disclaimer: This post has been created with the help of generative AI. Including DALL-E, Gemini, OpenAI, and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us Generative AI has revolutionized visual content creation by combining advanced algorithms with artistic creativity. This&#8230; Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI]]></description>
		
		
		
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		<title>Jais: A Major Leap Forward in Arabic-English Large Language Models</title>
		<link>https://towardsai.net/p/news/jais-a-major-leap-forward-in-arabic-english-large-language-models</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Wed, 30 Aug 2023 14:15:13 +0000</pubDate>
				<category><![CDATA[News]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=27744</guid>

					<description><![CDATA[Source: MBZUAI A groundbreaking collaborative effort by Inception, MBZUAI, and Cerebras New York, NY — August 30, 2023: In a collaborative endeavor, Inception, a G42 company, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the world’s first graduate-level AI research university, and Cerebras have developed Jais — a 13-billion parameter generative pre-trained transformer (GPT) model specialized in Arabic and English language processing tasks. The model was engineered on the Condor Galaxy 1 (CG-1) platform, a high-capacity AI supercomputer co-developed by G42 and Cerebras. CG-1 offers multi-exaFLOP computational capabilities and serves as the training infrastructure for Jais. This development has practical implications for G42’s ongoing partnership with Condor Galaxy. The model will be accessible through a dedicated live chat interface and is also slated for inclusion in the Hugging Face model repository. Jais aim to cater to the significant user base of Arabic speakers, estimated to be over 400 million, thereby addressing a gap in the availability of advanced language models for this demographic. Source: Cerebras, G42, MBZUAI Source: Cerebras, G42, MBZUAI Why an Arabic Large Language Model (LLM)? The development of Jais addresses a longstanding gap in the field of AI by focusing on Arabic, a language spoken by over 400 million people in 25 countries. While many companies discuss the concept of “democratizing AI,” the Jais initiative moves beyond rhetoric by providing a substantive, data-driven solution for the Arab-speaking world. By open-sourcing the model under an Apache 2.0 license, we aim to catalyze the growth of an Arabic language AI ecosystem. The Jais project is expected to serve as a model for other languages that are underrepresented in AI, thereby setting a new standard for linguistic inclusivity. Source: Cerebras, G42 Use Cases: Government Ministries: Deployments have already been announced by the UAE Ministry of Foreign Affairs and the UAE Ministry of Industry and Advanced Technology. Healthcare: The Department of Health — Abu Dhabi plans to use Jais for a range of applications, potentially including data analysis and patient interactions. Energy Sector: Abu Dhabi National Oil Company (ADNOC) has committed to implementing Jais in their operations, where it could be used for tasks ranging from predictive maintenance to data analytics. Aviation: Etihad Airways plans to deploy Jais for various applications, possibly including customer service and logistical optimizations. Financial Services: Jais has potential applications in automating customer inquiries, risk assessment, and data analysis in the banking and insurance sectors. Environmental Analysis: Jais can be used to analyze large sets of environmental data, helping to predict trends and identify areas requiring intervention. Education: Educational programs can employ Jais to develop intelligent tutoring systems, automated grading, and even interactive, language-based educational games. Natural Language Interfaces: Jais can be a key component in building more intuitive and responsive voice-activated or text-based interfaces for a range of software applications. Customer Service: Chatbots powered by Jais can handle customer queries with higher accuracy and context awareness, improving user experience. These varied use cases underscore Jais’ flexibility and adaptability, making it a robust solution for a wide range of applications in both the public and private sectors. Source: Cerebras, G42, MBZUAI Performance Metrics Jais sets new performance standards in Arabic language tasks, surpassing all known open-source monolingual and multilingual models. While specific metrics will be released post-launch, preliminary evaluations indicate leading scores in areas such as text summarization, translation, and sentiment analysis. In the realm of English language tasks, Jais demonstrates a competitive edge, scoring within the 95th percentile when compared to existing models such as LLaMa 2, despite operating on 30% fewer English language tokens. Source: Cerebras, G42, MBZUAI  Technical Specifications Jais employs a novel bilingual vocabulary that decreases the average number of tokens per word by approximately 15%, improving both computational efficiency and latency. Advanced techniques like ALiBi positional encodings and SwiGLU activation functions are integrated and adopted from other cutting-edge models like LLaMA. Data Considerations The model was trained on a diverse dataset comprising Arabic, English, and source code text, which is crucial given that high-quality Arabic data is sparse, constituting just 3% of the dataset. An innovative preprocessing pipeline was implemented to optimize data quality, utilizing heuristics-driven methods for data filtering and normalization. Legal and Ethical Concerns Data privacy and intellectual property considerations are integral to Jais’ development. The model’s operational framework incorporates the guidelines and regulations concerning data privacy and complies with global intellectual property laws. Strategic Context The initiative aligns with the UAE’s broader goals of fostering sovereign AI capabilities, without relying solely on externally developed solutions. It addresses the unique complexities of the Arabic language, such as its various dialects and unique writing system, while also offering avenues for future development in other Semitic languages. Why is Jais a Significant Development? Jais distinguishes itself through its specialized architecture designed to understand better the nuances of the Arabic language, including its writing style and word order. This specialization results in responses that are both more accurate and contextually relevant, allowing it to outperform existing models that contain Arabic text as only a minor part of their training data. MBZUAI President and University Professor Eric Xing said, ”Developing such a high-caliber Arabic LLM demanded cutting-edge AI research in addition to an in-depth and nuanced understanding of the Arabic language, its diversity and heritage, and the growing importance of LLMs across all echelons of society. Thanks to our research and partnerships with Inception and other top regional and global organizations, MBZUAI will continue pioneering LLMs that are efficient, effective, and accurate.” Jais also enables faster customization and easier fine-tuning for domain-specific Arabic use cases, thereby reducing both the time and cost associated with deploying AI solutions. This is particularly important in a context where data ownership and sovereignty are concerns; the project’s UAE-based origin alleviates some of these issues. In quantitative terms, Jais sets new performance benchmarks for Arabic language tasks. Special attention to preprocessing has resulted in a model that better supports the intricacies of Arabic, marking a significant step forward for AI applications in the Arab world. By combining technical sophistication with open-source accessibility, Jais offers an Arabic LLM that stands [&#8230;]]]></description>
		
		
		
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		<title>Best GPUs for AI and Deep Learning</title>
		<link>https://towardsai.net/p/machine-learning/best-gpus-for-ai-and-deep-learning</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Fri, 21 Jul 2023 17:14:12 +0000</pubDate>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[computer vision]]></category>
		<category><![CDATA[Data Science]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Towards AI - Medium]]></category>
		<guid isPermaLink="false">https://towardsai.net/p/artificial-intelligence/best-gpus-for-ai-and-deep-learning</guid>

					<description><![CDATA[Originally published on Towards AI. Source: Unsplash When delving into AI and deep learning, choosing the right GPU for your AI rig can make a significant difference. Choosing the right GPU gives you the flexibility to tackle advanced tasks and the opportunity to upgrade your machine as per your evolving needs. In this piece, we’ll take a look at some of the latest consumer-facing GPUs where you’ll be able to choose and equip your machine with the latest models, such as the GeForce RTX 4070 Ti, RTX 4080, and RTX 4090, which offer unprecedented power and efficiency for AI tasks. The revolution of AI has&#8230; Read the full blog for free on Medium. &#160; Join thousands of data leaders in the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI]]></description>
		
		
		
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		<title>Unraveling the Magic of Generative AI: The Ultimate FAQ Extravaganza! ✨</title>
		<link>https://towardsai.net/p/generative-ai/unraveling-the-magic-of-generative-ai-the-ultimate-faq-extravaganza-%e2%9c%a8</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Mon, 20 Mar 2023 13:16:23 +0000</pubDate>
				<category><![CDATA[Generative AI]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=15782</guid>

					<description><![CDATA[Source: Unsplash  Top of the most common questions in generative AI answered TL;DR: Buckle up for an exciting ride through the world of Generative AI! In this comprehensive FAQ, we’ve tackled the burning questions that explore the ins and outs of these powerful AI models, their thrilling applications, and the challenges they bring. Get ready to dive deep into how generative AI models can fuel creativity, transform industries, and spark innovation while navigating ethical concerns and hurdles to ensure a responsible and awe-inspiring future! Disclaimer: This article uses Cohere for text generation. Table of Contents What is generative AI? How does generative AI differ from other types of AI? What are the most popular generative AI models? What is the history and evolution of generative AI? How do neural networks contribute to generative AI? What are the primary applications of generative AI? How does natural language processing (NLP) relate to generative AI? What is the role of unsupervised learning in generative AI? How do transformers work in generative AI models? What is the difference between Cohere, GPT-3 and GPT-4? How are generative AI models trained? What are some of the challenges faced during generative AI model training? How do generative AI models generate creative content? What is the concept of fine-tuning in generative AI models? How do generative AI models maintain context over long sequences? How can we control the output of generative AI models? How do generative AI models handle multiple languages? What are some ethical concerns surrounding generative AI? How can generative AI models be made more robust and reliable? What are the limitations of generative AI? How can we evaluate the quality of generated content from generative AI models? How can we mitigate biases in generative AI models? How can generative AI models be used in fields like healthcare, finance, or education? Can generative AI models be used for real-time applications? How can we ensure the security and privacy of generative AI models? How can we make generative AI models more energy-efficient? Can generative AI models be used for reinforcement learning? What is the role of generative AI models in the field of robotics? How can generative AI models contribute to the field of art and design? Can generative AI models be used for anomaly detection? Generative AI has been making waves in the technology landscape, transforming various industries and giving rise to a plethora of innovative applications. During my journey in generative AI, I’ve encountered numerous questions and misconceptions about this groundbreaking technology. This FAQ aims to provide clear, concise answers to the most common questions, helping readers grasp the fundamentals, understand the technology’s capabilities, and identify its potential impact on our lives. In this blog, we will explore the top most common questions related to generative AI, covering topics such as its history, neural networks, natural language processing, training, applications, ethical concerns, and the future of the technology. By understanding the answers to these questions, you’ll gain a solid foundation to further explore the world of generative AI and its remarkable potential. So let’s dive in and begin our journey into the fascinating realm of generative AI!  Get started generating, summarizing, and classifying content with Cohere!   Generative AI FAQ What is generative AI? Generative AI is a subset of artificial intelligence that focuses on creating new content or data by learning patterns and structures from existing data. By leveraging advanced algorithms, generative AI models can generate text, images, music, and more, with minimal human intervention. These models can mimic human-like creativity and adapt to a wide range of tasks, from composing poetry to designing new products. How does generative AI differ from other types of AI? While most AI systems focus on processing and analyzing data to make decisions or predictions, generative AI goes a step further by creating entirely new data based on the patterns it has learned. Traditional AI models, such as classification or regression algorithms, solve specific problems by finding correlations in the data. In contrast, generative AI aims to understand the underlying structure and generate novel content that resembles the original data in terms of style, structure, or theme. What are the most popular generative AI models? Some of the most popular generative AI models include: Generative Adversarial Networks (GANs): A pair of neural networks trained together, with one generating fake data and the other trying to distinguish between real and fake data. GANs have been widely used for generating realistic images, enhancing image resolution, and synthesizing new data. Variational Autoencoders (VAEs): A type of autoencoder that learns to generate new data by approximating the probability distribution of the input data. VAEs are commonly used for image generation, data compression, and denoising tasks. Transformer-based models: These models, such as Cohere’s models, GPT-3 and GPT-4, use the transformer architecture to process and generate sequences of data. They have been particularly successful in natural language processing tasks, such as text generation, translation, and summarization. What is the history and evolution of generative AI? The history of generative AI can be traced back to the early days of AI research in the 1950s and 1960s when researchers started exploring algorithms for generating content, such as computer-generated poetry and music. The field evolved gradually, with the development of neural networks in the 1980s and 1990s, leading to the emergence of more sophisticated generative models like autoencoders and recurrent neural networks (RNNs). The breakthrough moment for generative AI came with the introduction of Generative Adversarial Networks (GANs) in 2014 by Ian Goodfellow and his team. GANs sparked a surge of interest in generative models and their applications. The introduction of transformer-based models, such as Cohere’s models, GPT-2, GPT-3 and GPT-4, further revolutionized the field, particularly in natural language processing and text generation. How do neural networks contribute to generative AI? Neural networks are the backbone of many generative AI models. These networks consist of interconnected nodes or neurons organized in layers, mimicking the structure of the human brain. Neural networks can learn complex patterns, structures, and dependencies in the [&#8230;]]]></description>
		
		
		
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		<title>Best AI Art Generators Using Generative AI</title>
		<link>https://towardsai.net/p/generative-ai/best-ai-art-generators-using-generative-ai</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Wed, 15 Mar 2023 18:04:39 +0000</pubDate>
				<category><![CDATA[Generative AI]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=15500</guid>

					<description><![CDATA[Source: Unsplash  Unleash Your Inner Artist with the Best AI Art Generators Using Generative AI! TL;DR: If you’re tired of the same old art and looking for something new and exciting, look no further than AI art generators! Using the power of generative AI, you can create stunning and unique art in just a few clicks. This post highlights the best free AI art generators available today, from the popular Deep Dream Generator and Artbreeder to the cutting-edge Urza’s AI and MidJourney. Each generator offers its own unique features and capabilities, allowing you to explore the vast potential of generative AI and push the boundaries of what’s possible in art. So why not unleash your creativity and see what these amazing tools can do? Whether you’re an artist, designer, or just someone looking for a fun and easy way to create something new, these AI art generators are sure to inspire and delight! Disclaimer: This article uses Cohere for text generation. Are you looking for a new way to unleash your creativity and create stunning, one-of-a-kind art? Look no further than AI art generators! With the power of generative AI, you can create art that’s truly unique and exciting, without having to spend hours drawing or painting. In this post, we’ll explore the best free AI art generators available today, from the popular Deep Dream Generator and Artbreeder to the cutting-edge Urza’s AI and MidJourney. Each generator offers its own unique features and capabilities, allowing you to explore the vast potential of generative AI and push the boundaries of what’s possible in art. So let’s dive in and see what these amazing tools can do! With the help of generative AI, these art generators are able to create one-of-a-kind pieces of art in just a few clicks, making them an exciting tool for artists, designers, and enthusiasts alike. Whether you’re looking to experiment with new technologies or simply want to create beautiful and unique art without spending hours drawing or painting, these AI art generators are sure to provide a wealth of creative opportunities and inspiration. In this post, we’ll explore the top AI art generators available today, highlighting their unique features and capabilities to help you find the ones that best suit your artistic goals and vision. These are the Top AI Art Generators That Use Generative AI! Urza’s AI MidJourney Deep Dream Generator ArtBreeder GANBreeder NeuralStyle.art RunwayML DALL·E Prisma Pikazo AI Painter StyleMyPic Artisto X Degrees of Separation Each of these art generators offers unique features and capabilities, so be sure to explore and experiment to find the ones that best suit your artistic goals and vision. Whether you’re an established artist or simply looking to experiment with new technologies, these AI art generators can provide a wealth of creative opportunities and inspiration. Let’s dive into the full list of the best art AI generators! Urza’s AI Get ready to have your mind blown by Urza’s AI, a website that uses artificial intelligence (AI) to generate playable Magic the Gathering cards. With the help of language AI and text-to-image AI, Urza’s AI can create hundreds of thousands of Magic cards in just a few days. The team behind Urza’s AI used finetuning to customize their language model and improve the quality of the card information generated. They also used the Wombo Art API to generate stunning card images that match the theme of the generated text. And the best part? You don’t need to be an AI expert to use Urza’s AI, just enter a card name and let the AI do the rest. With over 38,000 visitors within the first four days of its launch, Urza’s AI is proof that the future of AI development is accessible to all. So what are you waiting for? Try it out and unleash your creativity! Source:MidJourney MidJourney Midjourney is a cool AI program that creates images from text descriptions, like OpenAI’s DALL-E and Stable Diffusion. It’s led by David Holz, who co-founded Leap Motion, and the company is already profitable, according to Holz. Users make art using Discord bot commands, which is super convenient. The program is only accessible through Discord, but Midjourney is working on a web interface too. Artists can use Midjourney to show clients prototypes of their ideas before starting work. The advertising industry also loves AI tools like Midjourney because it helps them make original content quickly. The program has been used by The Economist, Corriere della Sera, and even featured in a segment on Last Week Tonight with John Oliver. However, some artists have accused Midjourney of devaluing original work because it uses artists’ copyrighted works as part of its training set. Midjourney’s terms of service have a DMCA takedown policy, which means artists can request their work to be removed if they believe copyright infringement has occurred. In 2022, a Midjourney-generated image won first place in a digital art competition, causing controversy among other digital artists. In December 2022, Midjourney was used to create illustrations for a children’s book, which drew criticism from some artists because the program was trained off of artists’ work without their consent. Source: Deep Dream Generator Deep Dream Generator Deep Dream Generator is an online tool that uses generative AI to turn your photos into trippy, dreamlike images. Just upload your photo and choose a style, and Deep Dream Generator’s algorithm will create a unique, surreal version of your picture. The tool uses a technique called convolutional neural networks, which are commonly used in image recognition tasks. With Deep Dream Generator, you can explore the weird and wonderful world of AI-generated art. It’s like a kaleidoscope for your photos, with a touch of AI magic. So go ahead, upload your favorite picture and see where the AI takes you! Source: Artbreeder Artbreeder Artbreeder is a web-based application that uses generative artificial intelligence to create unique and often stunning images by combining different art styles, genres, and themes. It allows users to remix and evolve existing art pieces or [&#8230;]]]></description>
		
		
		
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		<title>Top AI Conferences in 2023</title>
		<link>https://towardsai.net/p/artificial-intelligence/top-ai-conferences-in-2023</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Mon, 20 Feb 2023 00:09:08 +0000</pubDate>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[AI Conferences]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=15377</guid>

					<description><![CDATA[Source: Image generated by the author with generative AI via Midjourney. Exploring the Top AI Conferences in 2023: A Must-Attend for Researchers and Practitioners Alike TL;DR: Get ahead in the AI game by attending these top conferences in 2023! From NeurIPS to KDD, these events bring together the best and brightest minds in the field to share the latest research, developments, and insights. Whether you’re a researcher or practitioner, don’t miss your chance to stay up-to-date and network with other professionals in the field. Disclaimer: This article uses Cohere for text generation. The world of artificial intelligence (AI) is rapidly advancing with new discoveries and breakthroughs emerging at an unprecedented pace. For researchers and practitioners in the field, staying current and connected is vital, and attending top AI conferences in 2023 can offer unique opportunities for collaboration, inspiration, and professional growth. From NeurIPS to KDD, these conferences bring together leading experts in machine learning, deep learning, natural language processing, and more. Whether you’re an established researcher, an aspiring practitioner, or just passionate about the latest AI developments, these conferences are a must-attend. So join the excitement and start planning your trip to one of these top AI conferences in 2023. NeurIPS NeurIPS (the Conference on Neural Information Processing Systems) is one of the premier AI conferences, and it brings together researchers, practitioners, and industry professionals from around the world. The conference features keynote talks, paper presentations, workshops, and tutorials covering a broad range of topics in machine learning, deep learning, computer vision, natural language processing, and more. NeurIPS 2023 is set to take place in Vancouver, Canada, in December. Location: New Orleans, LA Conference Dates: Dec 10–16, 2023 ICML The International Conference on Machine Learning (ICML) is a leading forum for researchers, practitioners, and industry professionals to share and discuss the latest research and applications in machine learning. The conference features paper presentations, workshops, and tutorials covering a wide range of topics, including deep learning, reinforcement learning, and probabilistic modeling. ICML 2023 is scheduled to take place in Amsterdam, Netherlands, in July. Location: Honolulu, HI Conference Dates: Jul 23–29, 2023 ICLR The International Conference on Learning Representations (ICLR) is a premier conference in the field of deep learning, featuring papers, posters, and invited talks from leading researchers and practitioners. The conference covers a wide range of topics, including computer vision, natural language processing, and reinforcement learning. ICLR 2023 will be held in Sydney, Australia, in April. Location: Kigali, Rwanda Conference Dates: May 1–5, 2023 AISTATS The Conference on Artificial Intelligence and Statistics (AISTATS) brings together researchers in machine learning, statistics, and related fields to discuss the latest advances in AI and its applications. The conference features invited talks, paper presentations, and poster sessions covering a wide range of topics, including Bayesian inference, graphical models, and deep learning. AISTATS 2023 will be held in Barbados in April. Location: Palau de Congressos, Valencia, Spain Conference Dates: Apr 25–27, 2023 AAAI The Association for the Advancement of Artificial Intelligence (AAAI) hosts an annual conference that brings together researchers and practitioners in AI and related fields. The conference features technical presentations, invited talks, and workshops on a wide range of topics, including machine learning, natural language processing, robotics, and AI ethics. AAAI 2023 will be held in Austin, Texas, in February. Location: Washington, DC Conference Dates: February 7–14, 2023 KDD The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is one of the premier conferences in data mining and machine learning, featuring keynote talks, paper presentations, and workshops on a wide range of topics. The conference covers a range of topics, including data mining, machine learning, and artificial intelligence. KDD 2023 will be held in San Diego, California, in August. Location: Long Beach, CA Conference Dates: Aug 6–10, 2023 Attending the top AI conferences in 2023 can offer a wealth of opportunities for researchers and practitioners in the field. These conferences bring together the brightest minds in the industry to share their latest research, insights, and innovations. Whether you’re looking to network with other professionals, learn from leading experts, or simply stay up-to-date with the latest developments, these events are not to be missed. So start planning your trip, mark your calendar, and get ready to be inspired by the cutting-edge research and ideas presented at these top AI conferences in 2023! Don’t miss out on the chance to be a part of the forefront of AI research and development. Support me in this generative AI journey by becoming a member or by buying me a coffee. Follow me on Linkedin or my website to stay tuned on generative AI.]]></description>
		
		
		
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		<title>Generative AI: The Future of Artificial Intelligence (AI)</title>
		<link>https://towardsai.net/p/generative-ai/generative-ai-the-future-of-artificial-intelligence-ai</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Wed, 15 Feb 2023 01:00:02 +0000</pubDate>
				<category><![CDATA[Generative AI]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=15319</guid>

					<description><![CDATA[Source: Image generated by author via Midjourney Generative AI: The Future of Artificial Intelligence (AI) Creating the Future: How Generative AI is Set to Revolutionize Industries and Transform Society TL;DR: This article explores generative AI and provides an overview of its capabilities and applications. Generative AI involves the use of neural networks to create new content such as images, videos, or text. Its ability to create realistic and novel content has promising applications in fields such as entertainment, design, and medicine. It also raises ethical concerns around issues such as bias and the potential misuse of generated content. Disclaimer: This article uses Cohere for text generation. Generative AI is a fascinating field that has gained a lot of attention in recent years. It involves using machine learning algorithms to generate new data based on existing data. This technology has the potential to transform a wide range of industries, including healthcare, finance, and entertainment. In this article, we will explore what generative AI is, how it is being used today, and what the future holds for this exciting field. What is Generative AI? Generative AI is a subset of artificial intelligence (AI) that involves using algorithms to create new data. This can include anything from generating new images and videos to creating new text or music. The key difference between generative AI and other types of AI is that generative AI is focused on the creation of new data, rather than simply analyzing or processing existing data. Generative AI works by training algorithms on large datasets, which the algorithm can then use to generate new data. For example, a generative AI algorithm could be trained on a large dataset of images, and then use that training to create new, never-before-seen images. This approach has been used to create some incredible works of art, as well as some impressive technological innovations. How Is Generative AI Being Used Today? Generative AI is being used in a wide range of industries today, from entertainment to healthcare. One of the most notable applications of generative AI is in the field of art, where it is being used to create stunning works of art that would be impossible for a human artist to create. In addition, generative AI is being used to create new music and even entire films. Another exciting application of generative AI is in the field of healthcare. Generative AI algorithms can be used to create new drugs, based on existing drugs or other data. This approach has the potential to revolutionize the field of medicine, allowing researchers to discover new treatments and cures faster than ever before. In the finance industry, generative AI is being used to create new financial models and trading algorithms. These algorithms can help traders and investors make more informed decisions, based on a wider range of data. This has the potential to make the financial markets more efficient and more profitable for everyone. What Are the Best Platforms for Generative AI Nowadays?? Cohere and OpenAI are two of the most widely used tools and platforms for generative AI. Cohere, a startup that specializes in natural language processing, has developed a reputation for creating sophisticated applications that can generate natural language with great accuracy. Their technology has been used to create chatbots, automated content generation, and many other natural language processing applications. OpenAI, on the other hand, is an AI research laboratory that was founded in 2015. The organization is dedicated to developing AI technologies that are safe and beneficial for society, with a particular focus on generative AI. OpenAI has created several tools for generative AI, including GPT-3, a powerful autoregressive language model that has received a great deal of attention for its ability to generate coherent and natural-sounding text. Both Cohere and OpenAI have made significant contributions to the field of generative AI, and their platforms and tools are widely used by researchers, developers, and organizations around the world. With the continued growth and development of generative AI, it is likely that we will see even more innovative tools and platforms emerging in the years to come. How to Get Started With Generative AI? Getting started with generative AI can be a daunting task, but it is not as difficult as you might think. The first step is to learn the basics of machine learning and deep learning, which are the technologies that underpin generative AI. There are many resources available online, including free courses and tutorials. Once you have a basic understanding of machine learning, you can start exploring generative AI by experimenting with different algorithms and datasets. There are many open-source libraries and tools available that can help you get started, including Cohere, OpenAI, or AI2Labs. Source: Image generated by author via Midjourney What Is the Future of Generative AI? Looking ahead, the future of generative AI is undoubtedly bright. As technology continues to evolve, we can expect to see even more advanced and sophisticated applications emerging in a wide range of industries. One of the most exciting prospects for the future of generative AI is the development of even more powerful algorithms that are capable of generating more complex and nuanced outputs. This could include everything from virtual reality environments to music and art, and it has the potential to transform the way we experience and interact with technology. Another important trend to watch in the future of generative AI is the growing focus on ethical and responsible AI development. With the potential of AI to impact society in profound ways, it is crucial that we take a responsible approach to its development and use. This includes ensuring that AI is used in ways that benefit society, and that it is designed to be transparent and explainable. Overall, there is no doubt that generative AI will play an increasingly important role in shaping the future of technology and society. As more researchers and developers continue to explore this field, we can expect to see even more exciting and innovative applications emerging in the [&#8230;]]]></description>
		
		
		
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		<title>How Gather AI’s Automated Inventory Management System Helps Businesses</title>
		<link>https://towardsai.net/p/news/how-gather-ai-automated-inventory-management-system-helps-businesses-91ef99ae547b</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Tue, 29 Mar 2022 13:14:37 +0000</pubDate>
				<category><![CDATA[News]]></category>
		<category><![CDATA[inventory management systems]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=11948</guid>

					<description><![CDATA[Gather AI is building the world’s first large-scale and truly autonomous inventory management system — so much that small to large organizations can’t imagine going back to old inventory management methods, saving businesses 15–30x in costs and resources Source: Gather AI Pittsburgh, PA—March 29, 2022: Gather AI, the first truly automated inventory management system that brings near-real-time visibility to warehousing operations, has positively impacted many customers. From retailers having inventory visibility to their whole network without traveling to each site to finding inventory in the dark corners of their distribution centers (DCs). Small-size physical stores all the way to multinational corporations like Walmart and Amazon depend on reliable and accurate inventory management software systems, as these are needed everywhere, and with even more challenging tasks at large-scale retailers, such as the warehouses from the biggest retailers in the Fortune 500. But, even with inventory management software, large organizations still rely on people on forklifts with barcode readers to perform cycle counts, from a significant amount of employees to costly machinery to properly manage large-scale inventories, such as those found in retail, third-party logistics, food distribution, and warehouses in air cargo industries. Most importantly, the visibility of what&#8217;s sitting on the DC floor is delayed by 3-4 months. “We have found $156k of missing inventory in the first 60 days.” &#8211; Director of Supply Chain at one of the largest government retailers in the US Source: Gather AI To solve this significant problem, Gather AI is building the world’s first truly autonomous inventory management platform, freeing logistic-driven organizations from inefficient and manual tasks through intelligent and robust automation. Their enterprise-ready platform provides an up-to-date view of physical inventory and assets, both indoors and out, so companies can make the best decisions in their day-to-day operations. “With the drone, we are shifting some tasks in terms of counting the goods, detecting damages, from the warehouse to the office; where actually from a safety point of view, we avoid our people in the warehouse,” said Guillaume Crozier, SVP Cargo UAE of dnata. This statement dramatically highlights how Gather AI’s inventory management system platform has become an essential part of the safety and efficiency of its customers’ warehousing operations. “The savings we see using Gather’s drone technology, even just within our own ICQA team… as an example, we’re conducting an ROI on a single footwear and apparel account, and for that account alone, we’re projecting an annualized $70-80k savings just for our ICQA resources…in having to count and resolve variances, and that doesn’t even speak to productivity increases and labor savings,” said James Rapoza, VP Business Process Optimization of Barrett Distribution. &#8220;Well, the information that we can obtain from the drone, we can download that into our cargo management system, and it will take those dimensions into play, and it will actually build a ULD (unit load device)&#8230; I think in the future, it&#8217;s going to become a tool everybody wants to use,&#8221; said Kerry Galegher, SVP Cargo of dnata. Source: Gather AI About Gather AI Gather AI is enabling organizations to significantly reduce costs and speed the inventory management process with their enterprise-ready platform and quickly provide an up-to-date view of physical inventory and assets, both indoors and outdoors, so companies, from small to large scale, can make the best decisions on their day-to-day inventory management operations. Gather AI was founded at Carnegie Mellon’s School of Computer Science in 2017 by Co-founder and CEO Dr. Daniel Maturana, Co-founder and CEO Dr. Sankalp Arora, and Co-founder and Chief Systems Officer Geetesh Dubey, and is backed by over $7M in seed funding from Xplorer Capital, XRC, Expa, Dundee, and others, the company is based in Pittsburgh. Is your company in need of an automated inventory management system and ready to step into the future? Request a demo and see how one of America’s most promising AI companies can help you automate your inventory management needs. For more information, visit gather.ai and follow Gather AI on Twitter, Facebook, or Linkedin. Contact Sean Mitchell Gather AI 412–219–7492 sean.mitchell@gather.ai Press release distributed by Towards AI, Inc. on behalf of Gather AI, Inc., on Tuesday, March 29, 2022. Source: Gather AI]]></description>
		
		
		
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		<title>AI Facts and Myths, an Essay by ML Researchers on the Social Dilemma, And +!</title>
		<link>https://towardsai.net/p/newsletter/ai-facts-and-myths-an-essay-by-ml-researchers-on-the-social-dilemma-and</link>
		
		<dc:creator><![CDATA[Roberto Iriondo]]></dc:creator>
		<pubDate>Wed, 01 Dec 2021 15:43:50 +0000</pubDate>
				<category><![CDATA[Newsletter]]></category>
		<category><![CDATA[Towards AI - Medium]]></category>
		<guid isPermaLink="false">https://towardsai.net/?p=10953</guid>

					<description><![CDATA[Author(s): Towards AI Team Originally published on Towards AI the World&#8217;s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Artificial Intelligence (AI) Newsletter by Towards AI #17 If you have trouble reading this email, see it on a web browser. Hey everyone. I hope you are well. In this issue, we dive into an ML research essay on the social dilemma, some exciting deals to make your AI-holiday shopping even better, the misconceptions of AI and the challenges of natural language generation (NLG), an updated chart of all significant neural networks, and the ML research paper highlight of the month. This issue is brought to you thanks to our friends at Amazon Science: Interested in working at Amazon? Check out the Amazon Science website to learn more about the company’s unique approach to customer-obsessed science, and how it helps attract some of the brightest minds in artificial intelligence, machine learning, and related fields. Find blog posts and research papers from Amazon scientists and academics, including which conferences they’ll be attending, and how to collaborate. View available jobs. Before we get started, I wanted to let you know that we have decided to publish the latest machine learning research every weekday after 8 PM ET. All based on your opinion, it seems that you are even hungrier for more — research in AI, CV, NLP, and others. So stay tuned; we’ll keep you updated with what we decide on doing to keep you up to date. All right, so let’s get to it. An Essay by ML Researchers on “The Social Dilemma” Deja Vu much? Researchers at Carnegie Mellon argue on the blog “When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators” the challenges of implementing the right policies and regulations that balance ethics, the economy, individual rights, and proprietary data. The thorough essay showcases the gray areas between every piece of content published and distributed on the internet and what we can do about it. AI-Holiday Deals Ho ho ho! I hope I don’t bore you too much with these. But! In case you are looking for a new AI rig, we just updated our shopping recommendations for deep learning laptops or AI workstations. So please take a look, and as always, all feedback is welcome — if you do get one (and have browser cookies enabled), you’ll be supporting us, and we genuinely appreciate it. AI Misconceptions and the Challenges of NLG Artificial intelligence’s public point of view can be strongly misguided. This blog post dives on AI facts and myths, highlighting Stanford researcher Abigail See, which showcases some genuinely exciting problems with the public’s understanding of AI, to the challenges of natural language generation, along with what’s in the future for generative models — as more and more become part of our daily lives. Neural Network Topologies We just updated our chart on the types of neural networks and their applications, diving from the perceptron all the way to complex neural networks, such as a Neural Turing Machine (NTM), to generative adversarial networks (GANs), and deep convolutional inverse graphics networks. ML Research Paper Highlight of the Month Wolfgang Konen and Samineh Bagheri from the University of Applied Sciences in Germany published a cool paper called “Final Adaptation Reinforcement Learning for N-Player Games,” which concentrates n-tuple based reinforcement learning algorithms for games, specifically new ones tackling TD-, SARSA-, and Q-learning. All reproducible and open-source code for the paper is available on Github. Anyhow, I am super thankful for your time. If you enjoy the newsletter? Please consider subscribing if you haven’t yet or share it with your friends and colleagues — it is genuinely appreciated. Thank you for joining us! Until next time, Roberto and the team at Towards AI For previous issues, check out our AI newsletter archive. Helping Scale AI &#38; Technology Startups to Enterprises &#124; Towards AI Shop ↓ &#124; Towards AI Join us ↓ &#124; Towards AI Members &#124; The Data-driven Community Where to follow us: [ Facebook ] &#124;[ Twitter ]&#124; [ Instagram ]&#124; [ LinkedIn ] &#124; [ Github ] &#124; [ Google News ] AI Facts and Myths, an Essay by ML Researchers on the Social Dilemma, And +! was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story. &#160; Join thousands of data leaders on the AI newsletter. It’s free, we don’t spam, and we never share your email address. Keep up to date with the latest work in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI]]></description>
		
		
		
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