{"id":33355,"date":"2023-09-13T03:00:00","date_gmt":"2023-09-13T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33355"},"modified":"2023-09-12T14:54:18","modified_gmt":"2023-09-12T21:54:18","slug":"insidebigdata-ai-news-briefs-9-13-2023","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/","title":{"rendered":"insideBIGDATA AI News Briefs \u2013 9\/13\/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:\/\/www.intel.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Intel<\/a> Shows Competitive AI Inference Performance with MLPerf Inference Benchmark Results &#8211; impressive competitive AI gains with three products \u2013 Habana Gaudi2 accelerators, 4th Gen Xeon Scalable processors and Intel Xeon CPU Max Series. These results build on the MLPerf Training 3.0 GPT-3 June benchmark results that validated Gaudi2 as the ONLY viable alternative to H100 and the<a href=\"https:\/\/huggingface.co\/blog\/bridgetower\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;Hugging Face performance benchmarks<\/a>&nbsp;that show Gaudi2 can outperform Nvidia\u2019s H100 on a vision language AI model.<\/p>\n\n\n\n<p>A few key takeaways from the results:<\/p>\n\n\n\n<ul>\n<li>Gaudi2 delivers compelling performance vs. Nvidia\u2019s H100, with H100 showing a slight advantage of 1.09x (server) and 1.28x (offline) performance relative to Gaudi2.<\/li>\n\n\n\n<li>Gaudi2 outperforms Nvidia\u2019s A100 by 2.4x (server) and 2x (offline).<\/li>\n\n\n\n<li>The Gaudi2 submission employed FP8 and reached 99.9% accuracy on this new data type.&nbsp;<\/li>\n\n\n\n<li>For the GPT-J 100-word summarization task of a news article of approximately 1,000 to 1,500 words, 4th Gen Intel Xeon processors summarized two paragraphs per second in offline mode and one paragraph per second in real-time server mode.&nbsp;&nbsp;<\/li>\n\n\n\n<li>For GPT-J, the Intel Xeon CPU Max Series, which provides up to 64 gigabytes (GB) of high-bandwidth memory, was the only CPU able to achieve 99.9% accuracy.<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/sima.ai\/breaking-new-ground-sima-ais-unprecedented-advances-in-mlperf-benchmarks\/\" target=\"_blank\" rel=\"noreferrer noopener\">MLPerf results<\/a> show that SiMa.ai, delivering ML at the embedded edge, outperformed NVIDIA in the Closed Edge power category. With frames per second per watt as the defacto performance standard for edge AI and ML, these results demonstrate SiMa.ai\u2019s pushbutton approach drives continued leadership in unrivaled power efficiency that does not compromise performance.&nbsp;<\/p>\n\n\n\n<p>Tidio did a&nbsp;<a href=\"https:\/\/www.tidio.com\/blog\/ai-hallucinations\/\" target=\"_blank\" rel=\"noreferrer noopener\">study<\/a> on AI hallucinations&nbsp;and what people think of them.&nbsp;Here are some of our findings:<\/p>\n\n\n\n<ul>\n<li>About&nbsp;<strong>96%<\/strong>&nbsp;of internet users&nbsp;know of AI hallucinations, and around&nbsp;86% have personally experienced them<\/li>\n\n\n\n<li>Around&nbsp;<strong>93%<\/strong>&nbsp;are convinced that&nbsp;AI hallucinations can harm the users<\/li>\n\n\n\n<li>Only&nbsp;27% blame users who write prompts&nbsp;for AI hallucinations, while&nbsp;22% believe it\u2019s the fault of governments&nbsp;who want to push their agenda<\/li>\n\n\n\n<li>About&nbsp;48%&nbsp;of people&nbsp;would like to see improved user education about AI&nbsp;to fight AI hallucinations, while&nbsp;47% would vote for stronger regulations&nbsp;and guidelines for developers<\/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=\"465\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Tidio_image.png\" alt=\"\" class=\"wp-image-33358\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Tidio_image.png 700w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Tidio_image-300x199.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/09\/Tidio_image-150x100.png 150w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/figure><\/div>\n\n\n<p>RNDGen, the state-of-the-art <a href=\"https:\/\/www.rndgen.com\/data-generator\/\" target=\"_blank\" rel=\"noreferrer noopener\">random data generator<\/a> designed to meet the diverse needs of developers, testers, data analytics, and data scientists. RNDGen is not just another data generator. It&#8217;s a comprehensive tool that offers over 100 types of dummy data templates, It was created by the company for internal use. Allowing users to generate large amounts of randomized synthetic test data seamlessly. JSON, CSV, SQL, XML, and Excel formats are supported. <\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/abs\/2308.16898v2\" target=\"_blank\" rel=\"noreferrer noopener\">Transformers as Support Vector Machines<\/a> &#8211; This new research paper establishes an equivalence between the optimization geometry of self-attention in transformers and a hard-margin Support Vector Machine (SVM) problem. This equivalence is used to characterize the implicit bias of 1-layer transformers optimized with gradient descent. The main issue is understanding the optimization landscape and implicit bias of transformers. Specifically, the intent was to understand how the attention layer selects and composes tokens when trained with gradient descent. The proposed solution optimizes the attention layer with vanishing regularization converges in direction to an SVM solution. The concept of &#8220;Attention-SVM&#8221; (Att-SVM) is introduced which separates and selects optimal tokens from each input sequence.<\/p>\n\n\n\n<p><a href=\"https:\/\/time.com\/collection\/time100-ai\/\">AI in TIME<\/a>&nbsp;&#8211; The first-ever&nbsp;<strong>Most Influential People in AI<\/strong> List&nbsp;spotlights leaders, innovators, shapers, and thinkers. <\/p>\n\n\n\n<p><a href=\"https:\/\/techcrunch.com\/2023\/09\/07\/ibm-rolls-out-new-generative-ai-features-and-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">IBM Rolls Out GenAI<\/a>&nbsp;&#8211; The new features and models across its WatsonX data platform include data generation, a privacy and ethics toolkit and more. It will even reveal&nbsp;the data used to train its models.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.reuters.com\/technology\/ai-chip-startup-d-matrix-raises-110-mln-with-backing-microsoft-2023-09-06\/\">d-Matrix NextGen AI Chips<\/a>&nbsp;&#8211; The fast-moving startup designs GenAI-optimized and energy-efficient chips and expects $70M+ in ARR and break-even in 2 years. A Series B round of&nbsp;$110M&nbsp;was just backed by Temasek, Playground Global and Microsoft. <\/p>\n\n\n\n<p>Apple may have showed up late for the AI hype-cycle, but you shouldn&#8217;t discount the company just yet. A history of visionary innovation and a massive distribution advantage means they could soon overcome competitors to become a leader of the pack. Actually, the genesis for Apple\u2019s work on conversational AI&nbsp;<a href=\"https:\/\/www.theinformation.com\/articles\/apple-boosts-spending-to-develop-conversational-ai\">was four years ago<\/a>. Meanwhile, they have been quietly funneling \u201cMillions per day\u201d and the personnel resources of four teams into working on language or image model-based features. Additionally, Apple plans to incorporate LLM\u2019s into Siri to allow users to automate complex tasks using voice commands. They could also solve some of the privacy, cost and speed problems prevalent in today\u2019s LLMs with their \u201cedge AI\u201d approach, with face detection AI models running on iPhones rather than servers.<\/p>\n\n\n\n<p><a href=\"https:\/\/github.com\/KillianLucas\/open-interpreter\" target=\"_blank\" rel=\"noreferrer noopener\">Open Interpreter: Let language models run code on your computer<\/a> &#8211; Open Interpreter is an open-source platform that enables Large Language Models (LLMs)&nbsp;to run code on your local machine. It offers a natural-language interface for a wide range of general tasks such as:<\/p>\n\n\n\n<ul>\n<li>Editing and creating photos, videos, PDFs, and more<\/li>\n\n\n\n<li>Managing a Chrome browser to perform research<\/li>\n\n\n\n<li>Generating, cleaning, and analyzing large data sets<\/li>\n<\/ul>\n\n\n\n<p>Open Interpreter utilizes a&nbsp;function-calling language model&nbsp;supported by OpenAI. Primarily, it employs the robust GPT-4 model, but it also allows for the use of other LLM variants like Code LLaMA or any HuggingFace model.<\/p>\n\n\n\n<p>Llama 2 is now available to run for free on Graphcore IPU using a Paperspace Gradient Notebook &#8211; Llama 2 is the next frontier of open-source Large Language Models (LLMs) developed by Meta. It is going to be a game changer for adoption and commercialization because of its comparable performance with much larger models and its permissive open-source license that allows its use and distribution in commercial applications.&nbsp;You can try Llama 2-7B and Llama 2-13B on IPU <a href=\"https:\/\/www.graphcore.ai\/posts\/llama2-run-metas-open-source-large-language-model-for-free-on-ipus\" target=\"_blank\" rel=\"noreferrer noopener\">at no cost<\/a> via the <a href=\"https:\/\/console.paperspace.com\/github\/graphcore\/Gradient-HuggingFace\" target=\"_blank\" rel=\"noreferrer noopener\">Paperspace free tier environment<\/a>, using a Graphcore IPU-Pod<sub>4<\/sub>&nbsp;system. This is a great way to get started, but to get the performance you need you can scale up to paid IPU-Pod<sub>16<\/sub>&nbsp;systems for faster inference. Also introduced is another powerful and efficient LLM &#8211; <a href=\"https:\/\/www.graphcore.ai\/posts\/fine-tuning-flan-t5-xxl-the-poweful-and-efficient-llm\" target=\"_blank\" rel=\"noreferrer noopener\">Flan-T5<\/a> XXL (and Flan T5-XL, its smaller 3B-parameter relative) fine-tuning for Graphcore IPUs.&nbsp;<\/p>\n\n\n\n<p>Tencent releases AI model for businesses as competition in China heats up&nbsp;&#8211; Chinese tech giant&nbsp;Tencent&nbsp; launched its AI model \u201cHunyuan\u201d for business use. The news comes days after&nbsp;Baidu&nbsp;revealed a slew of&nbsp;<a href=\"https:\/\/www.cnbc.com\/2023\/09\/05\/baidu-launches-more-ai-apps-after-ernie-chatbot-gets-public-approval.html\" target=\"_blank\" rel=\"noreferrer noopener\">AI-powered applications<\/a>&nbsp;on Tuesday in the wake of more supportive regulation. Tencent has said it was&nbsp;<a href=\"https:\/\/www.cnbc.com\/2023\/08\/16\/tencent-tcehy-earnings-report-q2-2023.html\" target=\"_blank\" rel=\"noreferrer noopener\">internally testing its Hunyuan AI model<\/a>&nbsp;on advertising and fintech. <\/p>\n\n\n\n<p id=\"speakable-summary\">OpenAI will host a developer conference \u2014 its first ever \u2014 on November 6, the company&nbsp;<a href=\"https:\/\/openai.com\/blog\/announcing-openai-devday\" target=\"_blank\" rel=\"noreferrer noopener\">announced<\/a>. At the one-day OpenAI DevDay event, which will feature a keynote address and breakout sessions led by members of OpenAI\u2019s technical staff, OpenAI said in a blog post that it\u2019ll preview \u201cnew tools and exchange ideas\u201d \u2014 but left the rest to the imagination.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.technologyreview.com\/2023\/09\/11\/1079244\/what-to-know-congress-ai-insight-forum-meeting\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI Insight Forums<\/a>\u00a0&#8211;<strong>\u00a0<\/strong>The US Congress is heading back into session, and they are hitting the ground running on AI. We\u2019re going to be hearing a lot about various plans and positions on AI regulation in the coming weeks, kicking off with Senate Majority Leader Chuck Schumer\u2019s\u00a0<a href=\"https:\/\/www.democrats.senate.gov\/newsroom\/press-releases\/majority-leader-schumer-floor-remarks-on-the-senates-first-ai-insight-forum-to-take-place-next-week\">first AI Insight Forum<\/a>\u00a0on Wednesday. This and planned future forums will\u00a0bring together some of the top people in AI to discuss the risks and opportunities\u00a0posed by advances in this technology and how Congress might write legislation to address them.\u00a0The first of nine 6-hour\u00a0<strong>congressional forums\u00a0<\/strong>on Wednesday will host Sundar Pichai,\u00a0Mark Zuckerberg, Sam Altman, Elon Musk, Satya Nadella, Jensen Huang and others to define the US\u2019 position on AI. <\/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":[526,1369,115,182,180,210,67,268,56,84,1],"tags":[437,133,264,277,95],"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\/13\/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\/13\/insidebigdata-ai-news-briefs-9-13-2023\/\" \/>\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\/13\/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\/13\/insidebigdata-ai-news-briefs-9-13-2023\/\" \/>\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=\"2023-09-13T10:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-12T21:54:18+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=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/\",\"name\":\"insideBIGDATA AI News Briefs \u2013 9\/13\/2023 - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-09-13T10:00:00+00:00\",\"dateModified\":\"2023-09-12T21:54:18+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/09\/13\/insidebigdata-ai-news-briefs-9-13-2023\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"insideBIGDATA AI News Briefs \u2013 9\/13\/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\/13\/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\/13\/insidebigdata-ai-news-briefs-9-13-2023\/","og_locale":"en_US","og_type":"article","og_title":"insideBIGDATA AI News Briefs \u2013 9\/13\/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-8FZ","jetpack-related-posts":[{"id":32923,"url":"https:\/\/insidebigdata.com\/2023\/07\/27\/insidebigdata-ai-news-briefs-7-27-2023\/","url_meta":{"origin":33355,"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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