{"id":33298,"date":"2023-09-06T03:00:00","date_gmt":"2023-09-06T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33298"},"modified":"2023-09-14T19:19:19","modified_gmt":"2023-09-15T02:19:19","slug":"insidebigdata-ai-news-briefs-9-8-2023","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/","title":{"rendered":"insideBIGDATA AI News Briefs \u2013 9\/8\/2023"},"content":{"rendered":"\n<p>Welcome insideBIGDATA AI News Briefs, our timely new feature bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. We\u2019re working tirelessly to dig up the most timely and curious tidbits underlying the day\u2019s most popular technologies. We know this field is advancing rapidly and we want to bring you a regular resource to keep you informed and state-of-the-art. Enjoy!<\/p>\n\n\n\n<p><a href=\"https:\/\/nocamels.com\/2023\/09\/startup-behind-worlds-most-advanced-generative-ai-raises-155m\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI21 Labs<\/a>, a GenAI tech startup is gaining adoption for&nbsp;enterprise AI infrastructure&nbsp;and used by eBay, Monday.com, Carrefour and Ubisoft. The Israeli company that develops advanced AI technologies to solve complex problems across various industries has raised $155 million.<\/p>\n\n\n\n<p>A few days ago,&nbsp;Gartner&nbsp;released the <a href=\"https:\/\/www.gartner.com\/en\/articles\/what-s-new-in-the-2023-gartner-hype-cycle-for-emerging-technologies\" target=\"_blank\" rel=\"noreferrer noopener\">2023&nbsp;Hype Cycle<\/a> for emerging technologies as shown below. No surprises here. <\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"858\" height=\"715\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Gartner_2023_HypeCycle.png\" alt=\"\" class=\"wp-image-33300\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Gartner_2023_HypeCycle.png 858w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Gartner_2023_HypeCycle-300x250.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Gartner_2023_HypeCycle-150x125.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Gartner_2023_HypeCycle-768x640.png 768w\" sizes=\"(max-width: 858px) 100vw, 858px\" \/><\/figure><\/div>\n\n\n<p>Supercharge AI training and inference workloads with <a href=\"https:\/\/www.latitude.sh\/accelerate\" target=\"_blank\" rel=\"noreferrer noopener\">Latitude.sh Accelerate<\/a>. Using NVIDIA H100 GPUs, Latitude Accelerate speeds up AI and machine learning tasks, making both training and running models faster and more efficient. With dedicated instances, 32-cores\/GPU and hourly billing, Accelerate offers unmatched performance and flexibility,&nbsp;all at the best cost per GPU on the market.<\/p>\n\n\n\n<p><a href=\"https:\/\/github.com\/Pythagora-io\/gpt-pilot\" target=\"_blank\" rel=\"noreferrer noopener\">GPT Pilot<\/a> is a research project to see how can GPT-4 be utilized to generate fully working, production-ready, apps.&nbsp;The main idea is that AI can write most of the code for an app (maybe 95%) but for the rest 5%, a developer is and will be needed until we get full AGI. Here are the steps GPT Pilot takes to create an app:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"700\" height=\"214\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/GPT-Pilot_image.png\" alt=\"\" class=\"wp-image-33304\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/GPT-Pilot_image.png 700w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/GPT-Pilot_image-300x92.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/GPT-Pilot_image-150x46.png 150w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/github.com\/neulab\/prompt2model\" target=\"_blank\" rel=\"noreferrer noopener\">Prompt2Model<\/a> &#8211; Generate Deployable Models from Instructions &#8211; Open-source project which allows developers to train deployable, special-purpose NLP models using natural language task descriptions. The method combines dataset retrieval, LLM-based dataset generation, and supervised fine-tuning.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"573\" height=\"519\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Prompt2Model_image.png\" alt=\"\" class=\"wp-image-33306\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Prompt2Model_image.png 573w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Prompt2Model_image-300x272.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Prompt2Model_image-150x136.png 150w\" sizes=\"(max-width: 573px) 100vw, 573px\" \/><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/github.com\/nlpxucan\/WizardLM\" target=\"_blank\" rel=\"noreferrer noopener\">WizardLM<\/a> aims to improve large language models (LLMs) by generating complex instruction data using LLMs rather than manual human input. The model uses a method called Evol-Instruct to evolve simpler instructions into more complex ones for fine-tuning. <\/p>\n\n\n\n<p>At last week&#8217;s <a href=\"https:\/\/cbybs-zc1.maillist-manage.in\/click\/1127b8852690a0ed\/1127b88526907969\" target=\"_blank\" rel=\"noreferrer noopener\">Google Cloud Next event<\/a>&nbsp;in San Francisco,&nbsp;Google made a surprise announcement that the company is offering&nbsp;<a href=\"https:\/\/ai.meta.com\/llama\/\" target=\"_blank\" rel=\"noreferrer noopener\">Llama 2<\/a>&nbsp;as well as&nbsp;<a href=\"https:\/\/falconllm.tii.ae\/\" target=\"_blank\" rel=\"noreferrer noopener\">Falcon LLM<\/a>&nbsp;on Google Cloud\u2019s <a href=\"https:\/\/cloud.google.com\/vertex-ai\" target=\"_blank\" rel=\"noreferrer noopener\">Vertex AI<\/a>. This move was not expected since Google was the sole cloud provider that hadn\u2019t partnered with rival institutions to host Llama 2 or any other open-source LLM models before this.&nbsp;It appears that this decision by Google has taken into account enterprises that are looking for more options. Following this trend, after GPT-4,&nbsp;Llama 2 is the most sought-after large language model, considering it is open-sourced and commercially available. In the case of Llama 2, Google said that it is the only cloud provider offering both adapter tuning and RLHF.<\/p>\n\n\n\n<p>Additional info! <a href=\"https:\/\/ai.meta.com\/resources\/models-and-libraries\/seamless-communication\/\" target=\"_blank\" rel=\"noreferrer noopener\">SeamlessM4T<\/a> (Massive Multilingual Multimodal Machine Translation) by Meta is the a multimodal model representing a significant breakthrough in speech-to-speech and speech-to-text translation and transcription. Publicly-released under a CC BY-NC 4.0 license, the model supports nearly 100 languages for input (speech + text), 100 languages for text output and 35 languages (plus English) for speech output. It aims to eliminate reliance on multiple models by unifying capabilities into a single one. It can handle:<\/p>\n\n\n\n<ul>\n<li>101 languages for speech input<\/li>\n\n\n\n<li>96 Languages for text input\/output<\/li>\n\n\n\n<li>35 languages for speech output<\/li>\n<\/ul>\n\n\n\n<p>The model achieves state-of-the-art results by leveraging Fairseq2, the largest open dataset for multimodal translation, and other advancements. It reduces toxicity and bias compared to previous models. This unified model enables multiple tasks without relying on multiple separate models:<\/p>\n\n\n\n<ul>\n<li>Speech-to-speech translation (S2ST)<\/li>\n\n\n\n<li>Speech-to-text translation (S2TT)<\/li>\n\n\n\n<li>Text-to-speech translation (T2ST)<\/li>\n\n\n\n<li>Text-to-text translation (T2TT)<\/li>\n\n\n\n<li>Automatic speech recognition (ASR)<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"700\" height=\"437\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/SeemlessM4T_Meta.png\" alt=\"\" class=\"wp-image-33316\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/SeemlessM4T_Meta.png 700w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/SeemlessM4T_Meta-300x187.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/SeemlessM4T_Meta-150x94.png 150w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/figure><\/div>\n\n\n<p>OpenAI <a href=\"https:\/\/scale.com\/blog\/open-ai-scale-partnership-gpt-3-5-fine-tuning\" target=\"_blank\" rel=\"noreferrer noopener\">partners<\/a> with Scale for GPT-3.5 fine-tuning and advanced data labeling, allowing you to unlock the full potential of GPT by adapting the model to your own data. Companies like Brex are already using the platform to optimize their business and model performance. Scale\u2019s high-quality Data Engine and custom LLM platform will help:<\/p>\n\n\n\n<ul>\n<li>Build custom LLMs that fits your business needs<\/li>\n\n\n\n<li>Create powerful custom models that increase efficiency and reduce costs&nbsp;<\/li>\n\n\n\n<li>Benefit from Scale&#8217;s fine-tuning and data preparation platform<\/li>\n\n\n\n<li>Optimize your AI investment<\/li>\n\n\n\n<li>Make your AI work for you, not the other way around<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/github.com\/getumbrel\/llama-gpt\" target=\"_blank\" rel=\"noreferrer noopener\">LlamaGPT<\/a> is a self-hosted, offline chatbot that offers a private, ChatGPT-like experience.. The project is a culmination of open-source contributions from various developers.<\/p>\n\n\n\n<p><a href=\"https:\/\/blog.allenai.org\/dolma-3-trillion-tokens-open-llm-corpus-9a0ff4b8da64\" target=\"_blank\" rel=\"noreferrer noopener\">AI2 releases the largest open source text dataset for LLM pretraining<\/a> &#8211; Dolma is a 3 trillion-token dataset that sets&nbsp;a new standard for openness in language model research.<\/p>\n\n\n\n<p><a href=\"https:\/\/techcrunch.com\/2023\/08\/24\/hugging-face-raises-235m-from-investors-including-salesforce-and-nvidia\/\" target=\"_blank\" rel=\"noreferrer noopener\">Hugging Face raises $235M series D at $4.5B valuation<\/a> &#8211; The round received contributions from major players including Google, Amazon, NVIDIA, Salesforce, AMD, Intel, IBM, and Qualcomm. The funds will be allocated towards talent acquisition.<\/p>\n\n\n\n<p><a href=\"https:\/\/huyenchip.com\/2023\/08\/16\/llm-research-open-challenges.html\" target=\"_blank\" rel=\"noreferrer noopener\">Chip Huyen Outlines 10 Open&nbsp;Challenges for&nbsp;LLMs<\/a> &#8211; Chip Huyen, a prominent figure in AI research, shed light on the top 10 challenges facing Large Language Model (LLM) development in her recent blog post that gained significant attention.<\/p>\n\n\n\n<ol>\n<li>Hallucinations: Minimizing AI&#8217;s creation of inaccurate data.<\/li>\n\n\n\n<li>Context Mastery: Enhancing LLMs&#8217; understanding of context.<\/li>\n\n\n\n<li>Data Modalities: Incorporating diverse data types, like text and images.<\/li>\n\n\n\n<li>Efficiency: Boosting LLMs&#8217; speed and affordability.<\/li>\n\n\n\n<li>Architecture Evolution: Innovating beyond current model designs.<\/li>\n\n\n\n<li>Beyond GPUs: Seeking alternatives to dominant AI training hardware.<\/li>\n\n\n\n<li>AI Agents: Crafting LLMs for real-world tasks.<\/li>\n\n\n\n<li>Human Preferences: Refining models based on human feedback.<\/li>\n\n\n\n<li>Chat Interfaces: Streamlining user interactions with LLMs.<\/li>\n\n\n\n<li>Multilingualism: Expanding LLMs to non-English languages.<\/li>\n<\/ol>\n\n\n\n<p>Addressing these challenges is crucial for the next generation of LLMs. As AI becomes an integral part of various sectors, solving these issues will determine its future utility and impact. Chip Huyen&#8217;s insights provide a roadmap for researchers and industry professionals in the AI domain.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn:&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook:&nbsp;<a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Welcome insideBIGDATA AI News Briefs, our timely new feature bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. We\u2019re working tirelessly to dig up the most timely and curious tidbits underlying the day\u2019s most popular technologies. We know this field is advancing rapidly and we want to bring you a regular resource to keep you informed and state-of-the-art. <\/p>\n","protected":false},"author":37,"featured_media":32924,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,1369,115,182,180,67,268,56,1],"tags":[437,324,133,1245,277,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>insideBIGDATA AI News Briefs \u2013 9\/8\/2023 - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"insideBIGDATA AI News Briefs \u2013 9\/8\/2023 - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Welcome insideBIGDATA AI News Briefs, our timely new feature bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. We\u2019re working tirelessly to dig up the most timely and curious tidbits underlying the day\u2019s most popular technologies. We know this field is advancing rapidly and we want to bring you a regular resource to keep you informed and state-of-the-art.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-06T10:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-15T02:19:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/07\/AI-News-Briefs-column-banner.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1100\" \/>\n\t<meta property=\"og:image:height\" content=\"550\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/\",\"name\":\"insideBIGDATA AI News Briefs \u2013 9\/8\/2023 - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-09-06T10:00:00+00:00\",\"dateModified\":\"2023-09-15T02:19:19+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"insideBIGDATA AI News Briefs \u2013 9\/8\/2023\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\",\"name\":\"Daniel Gutierrez\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"caption\":\"Daniel Gutierrez\"},\"description\":\"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"insideBIGDATA AI News Briefs \u2013 9\/8\/2023 - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2023\/09\/06\/insidebigdata-ai-news-briefs-9-8-2023\/","og_locale":"en_US","og_type":"article","og_title":"insideBIGDATA AI News Briefs \u2013 9\/8\/2023 - insideBIGDATA","og_description":"Welcome insideBIGDATA AI News Briefs, our timely new feature bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/07\/AI-News-Briefs-column-banner.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-8F4","jetpack-related-posts":[{"id":32923,"url":"https:\/\/insidebigdata.com\/2023\/07\/27\/insidebigdata-ai-news-briefs-7-27-2023\/","url_meta":{"origin":33298,"position":0},"title":"insideBIGDATA AI News Briefs &#8211; 7\/27\/2023","date":"July 27, 2023","format":false,"excerpt":"Welcome insideBIGDATA AI News Briefs, our podcast channel bringing you the latest industry insights and perspectives surrounding the field of AI including deep learning, large language models, generative AI, and transformers. 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