Dark Matter: Jimmi Simpson Compares New Series to Westworld, "They're Both 
Touching on Serious Human Conditions"

Jonathan Nolan and Lisa Joy had plans for a fifth and final season of Westworld. If revived, the HBO series could benefit to explore these storylines.

What Westworld Season 5 Needs to Do if the Series Is Saved

The Auguries (Season 4 Episode 1 of 8): Seven years after the demise of Rehoboam, events are set in motion that reunite allies and enemies. Westworld airs on Sky Atlantic HD at 3:35 PM, Wednesday 27 March. Seven years after the demise of Rehoboam, events are set in motion that reunite allies ...

Westworld - Sky Atlantic HD | TV Guide

Passed Pawn (Season 3 Episode 7 of 8): Bernard, Stubbs, and William escape from all the chaos. Westworld airs on Sky Atlantic HD at 7:55 AM, Wednesday 27 March. Bernard, Stubbs, and William escape from all the chaos

Decoherence (Season 3 Episode 6 of 8): Maeve gets one last chance to stop Dolores. Westworld airs on Sky Atlantic HD at 9:00 AM, Tuesday 26 March. Maeve gets one last chance to stop Dolores

The Winter Line (Season 3 Episode 2 of 8): Bernard makes his way back to Westworld. Westworld airs on Sky Atlantic HD at 12:05 PM, Sunday 24 March. Bernard makes his way back to Westworld

Parce Domine (Season 3 Episode 1 of 8): The critical event that marked the end of the last season threatens to consume the outside world. Westworld airs on Sky Atlantic HD at 10:45 AM, Sunday 24 March. The critical event that marked the end of the last season threatens to consume the outside world

Les Ecorches (Season 2 Episode 7 of 10): Maeve is faced with a scene from her past. Westworld airs on Sky Atlantic HD at 11:10 AM, Saturday 23 March. Maeve is faced with a scene from her past

Parce Domine (Season 3 Episode 1 of 8): The critical event that marked the end of the last season threatens to consume the outside world. Westworld airs on Sky Atlantic HD at 7:55 AM, Friday 22 March. The critical event that marked the end of the last season threatens to consume the outside world

The oversized load became wedged under the bridge on westbound I-70 Friday morning and all westbound lanes remained closed for several hours until just after 4 p.m.

Westbound I-70 reopens at C470 after oversized load gets stuck under bridge in Colorado - CBS Colorado

Westbold

westbold

Small Businesses Are Turning to AI for Content Marketing, New Semrush Report Shows

ትናንሽ ንግዶች ለይዘት ግብይት፣ አዲስ የሴምሩሽ ሪፖርት ትርኢቶች ወደ AI እየተቀየሩ ነው።

Малыя прадпрыемствы звяртаюцца да AI для маркетынгу кантэнту, паказвае новая справаздача Semrush

Mala preduzeća se okreću veštačkoj inteligenciji za marketing sadržaja, pokazuje novi Semrush izveštaj

Malé firmy se obracejí na AI pro obsahový marketing, ukazuje nová zpráva Semrush

Las pequeñas empresas recurren a la IA para el marketing de contenidos, según un nuevo informe de Semrush

Semrush の新しいレポートによると、中小企業はコンテンツ マーケティングに AI を活用し始めている

کوچني سوداګرۍ د مینځپانګې بازارموندنې لپاره AI ته مخه کوي ، د نوي سیمرش راپور ښیې

Bizneset e vogla po i drejtohen AI për marketing të përmbajtjes, tregon raporti i ri i Semrush

Kiçi kärhanalar Mazmun marketingi üçin täze AI-e ýüz tutýarlar, Täze Semrush hasabat görkezişleri

Küçük İşletmeler İçerik Pazarlaması İçin Yapay Zeka'ya Yöneliyor, Yeni Semrush Raporu Gösteriyor

Các doanh nghiệp nhỏ đang chuyển sang AI cho tiếp thị nội dung, báo cáo mới của Semrush cho thấy

Semrush 新报告显示，小型企业正在利用人工智能进行内容营销

So it was a fine evening; I was at my work desk with my coffee, which had just been delivered by a Dasher. I was endlessly scrolling through my X account, taking it slowly sip by sip, enjoying the aroma of the seeds. And that’s when I stumbled upon this viral meme. It made me question my reality like Westworld bot. As a person in a creative profession (at least when I do copywriting), I was just staring into the abyss of my overflowing sink. The dishes have piled up high like the despair in my soul, a veritable Mount Everest of ceramic, metal, and leftover spaghetti. The glint of the silverware was mocking my ambitions, the greasy pots, and pans were whispering my impending doom. And that’s when I realized, like Madris Lidaka said, the creative battle with AI was raging on, and I might be losing. Fast. The Problem: The Never-Ending Cycle Let’s get real. Adulting is hard. Like…real hard. But, Adulting with a creative soul? Damn near impossible. Particularly the relentless Sisyphean task of washing dishes. Just when you think you've caught up, the sink laughs in your face and fills up again with one evil laugh. It’s a never-ending cycle, a black hole of mundane tasks that sucks away your precious creative energy. The Hero's Journey (Or Not) Unlike Frodo from LOTR, who had the singular, epic quest of destroying the One Ring, we creative souls face a far more insidious foe. These greasy plates and stained coffee mugs might as well be indestructible horcruxes, anchoring us to a life of drudgery. Each dish scrubbed feels like another step away from our next masterpiece. The hero’s journey? More like a tragic tale of defeat. Because it’s a CYCLE! Stats Time! Did you know that, according to the Bureau of Labor Statistics, the average American spends approximately 1 hour and 27 minutes per day on household chores, with a significant chunk devoted to dishwashing? That’s over 9 hours a week, 468 hours a year. Imagine the novels, songs, and artworks lost to the abyss of dirty dishes. Even if we get into “super-procrastination, “ I still think we can get more work done. The Dark Side of the Dishes Channel your inner Jedi. Feel the Force flow through you. Now, imagine it being choked by a pile of dirty spoons. The lightsaber of your creativity flickers and dims as you scrub away at stubborn stains. The Dark Side isn’t some far-off galactic evil; it’s right here, in your kitchen sink. So You Think You Can Be Creative? Wash Those Dishes First... (Or Maybe Not) Ernest Hemingway didn’t pen “The Old Man and the Sea” while elbow-deep in dishwater. Jane Austen didn’t craft “Pride and Prejudice” between cycles of washing and drying. Of Course, I know they didn’t live during the digital age. But even if they did, what are the chances that we’d have gotten their masterpieces? Yet here we are, torn from our keyboards and sketchpads, reduced to the banal existence of Home Ec students. The muse of creativity isn’t a fan of dish soap, it seems. No, I am not going to attach photos of my room and trigger your OCD. No, I didn’t mean it LITERALLY! We need a hero, and we need it now. Yeah, I know about AI-based dishwashers. They are the knights in shining armor. These machines are the brave knights ready to slay the dragon of dirty dishes. No more pruney fingers, no more lost hours. Just gleaming plates and the sweet, sweet taste of creative freedom. But I wasn’t just talking about dishwashers, had I? My point is to look at the bigger picture. It’s a universal struggle, a relatable cry for help from creative people everywhere. Imagine a world where AI handles the mundane. You wake up, pour yourself a cup of coffee, and head straight to your writing desk. No dishes to distract you, no chores to drain your creativity. In this utopia, you’re free to craft your next novel, compose your next symphony, or paint your next masterpiece. It sounds like something right out of the old cartoon show Richie Rich. Damn, they got everything predicted. The AI tech in your house can be the unsung hero, working tirelessly behind the scenes. However, our creative juice will be overflowing and we could have more masterpieces. So What is the Solution? The Moral of the Story is to Embrace AI, Unleash Creativity AI isn’t here to replace you; it’s here to collaborate. Think of AI technology as a tool to empower your creativity, freeing you from the clutches of dirty dishes. There is a thin line where the intersection of technology and creativity lies. It should be a partnership, a symbiotic relationship where both humans and machines thrive. On the other hand, even if we get Generative AI to do the work for us, I believe that assessing the output would be challenging as creativity is often person-specific or even situation-specific. Here is a research paper from the Journal of Business and Psychology that fully addresses this ambiguity. So, the next time you find yourself drowning in a sea of dirty dishes and feeling the weight of the mundane threatening to crush your creative spirit, remember: there's hope. This isn't just about conquering the sink; it's about a future where technology empowers our creativity. It's about reclaiming those precious stolen hours and letting your creative spirit truly soar. So, fire up your imagination, unleash your artistic talents, and let AI handle the mundane. The world awaits your next masterpiece, one at a time. So it was a fine evening; I was at my work desk with my coffee, which had just been delivered by a Dasher. I was endlessly scrolling through my X account , taking it slowly sip by sip, enjoying the aroma of the seeds. X account And that’s when I stumbled upon this viral meme . It made me question my reality like Westworld bot. viral meme As a person in a creative profession (at least when I do copywriting), I was just staring into the abyss of my overflowing sink. The dishes have piled up high like the despair in my soul, a veritable Mount Everest of ceramic, metal, and leftover spaghetti. The glint of the silverware was mocking my ambitions, the greasy pots, and pans were whispering my impending doom. And that’s when I realized, like Madris Lidaka said, the creative battle with AI was raging on, and I might be losing. Fast. Madris Lidaka The Problem: The Never-Ending Cycle Let’s get real. Adulting is hard. Like…real hard. But, Adulting with a creative soul? Damn near impossible. Particularly the relentless Sisyphean task of washing dishes. Just when you think you've caught up, the sink laughs in your face and fills up again with one evil laugh. It’s a never-ending cycle, a black hole of mundane tasks that sucks away your precious creative energy. The Hero's Journey (Or Not) Unlike Frodo from LOTR, who had the singular, epic quest of destroying the One Ring, we creative souls face a far more insidious foe. These greasy plates and stained coffee mugs might as well be indestructible horcruxes, anchoring us to a life of drudgery. Each dish scrubbed feels like another step away from our next masterpiece. The hero’s journey? More like a tragic tale of defeat. Because it’s a CYCLE! Stats Time! Did you know that, according to the Bureau of Labor Statistics , the average American spends approximately 1 hour and 27 minutes per day on household chores, with a significant chunk devoted to dishwashing? That’s over 9 hours a week, 468 hours a year. Imagine the novels, songs, and artworks lost to the abyss of dirty dishes. Even if we get into “super-procrastination, “ I still think we can get more work done. Bureau of Labor Statistics The Dark Side of the Dishes Channel your inner Jedi. Feel the Force flow through you. Now, imagine it being choked by a pile of dirty spoons. The lightsaber of your creativity flickers and dims as you scrub away at stubborn stains. The Dark Side isn’t some far-off galactic evil; it’s right here, in your kitchen sink. So You Think You Can Be Creative? Wash Those Dishes First... (Or Maybe Not) Ernest Hemingway didn’t pen “The Old Man and the Sea” while elbow-deep in dishwater. Jane Austen didn’t craft “Pride and Prejudice” between cycles of washing and drying. Of Course, I know they didn’t live during the digital age. But even if they did, what are the chances that we’d have gotten their masterpieces? “The Old Man and the Sea” “The Old Man and the Sea” “Pride and Prejudice” “Pride and Prejudice” Yet here we are, torn from our keyboards and sketchpads, reduced to the banal existence of Home Ec students. The muse of creativity isn’t a fan of dish soap, it seems. No, I am not going to attach photos of my room and trigger your OCD. No, I didn’t mean it LITERALLY! No, I didn’t mean it LITERALLY! We need a hero, and we need it now. Yeah, I know about AI-based dishwashers. They are the knights in shining armor. These machines are the brave knights ready to slay the dragon of dirty dishes. No more pruney fingers, no more lost hours. Just gleaming plates and the sweet, sweet taste of creative freedom. But I wasn’t just talking about dishwashers, had I? But I wasn’t just talking about dishwashers, had I? My point is to look at the bigger picture. It’s a universal struggle, a relatable cry for help from creative people everywhere. Imagine a world where AI handles the mundane. You wake up, pour yourself a cup of coffee, and head straight to your writing desk. No dishes to distract you, no chores to drain your creativity. In this utopia, you’re free to craft your next novel, compose your next symphony, or paint your next masterpiece. It sounds like something right out of the old cartoon show Richie Rich. Damn, they got everything predicted. The AI tech in your house can be the unsung hero, working tirelessly behind the scenes. However, our creative juice will be overflowing and we could have more masterpieces. So What is the Solution? So What is the Solution? The Moral of the Story is to Embrace AI, Unleash Creativity AI isn’t here to replace you; it’s here to collaborate. Think of AI technology as a tool to empower your creativity, freeing you from the clutches of dirty dishes. There is a thin line where the intersection of technology and creativity lies. It should be a partnership, a symbiotic relationship where both humans and machines thrive. intersection of technology and creativity On the other hand, even if we get Generative AI to do the work for us, I believe that assessing the output would be challenging as creativity is often person-specific or even situation-specific. Here is a research paper from the Journal of Business and Psycholog y that fully addresses this ambiguity. Journal of Business and Psycholog So, the next time you find yourself drowning in a sea of dirty dishes and feeling the weight of the mundane threatening to crush your creative spirit, remember: there's hope. This isn't just about conquering the sink; it's about a future where technology empowers our creativity. It's about reclaiming those precious stolen hours and letting your creative spirit truly soar. So, fire up your imagination, unleash your artistic talents, and let AI handle the mundane. The world awaits your next masterpiece, one at a time.

AI, Please Wash My Dishes, Let Me Write: A Desperate Plea for Creative Freedom

এআই, দয়া করে আমার থালা-বাসন ধুয়ে ফেলুন, আমাকে লিখতে দিন: সৃজনশীল স্বাধীনতার জন্য একটি মরিয়া আবেদন

KI, bitte wasche mein Geschirr, lass mich schreiben: Ein verzweifelter Appell für kreative Freiheit

AI, por favor lava mis platos, déjame escribir: una súplica desesperada por la libertad creativa

AI, s'il vous plaît, faites ma vaisselle, laissez-moi écrire : un plaidoyer désespéré pour la liberté de création

एआई, कृपया मेरे बर्तन धोएँ, मुझे लिखने दें: रचनात्मक स्वतंत्रता के लिए एक हताश अपील

AIよ、皿を洗って、私に文章を書かせてください: 創造の自由を求める切実な嘆願

AI, 설거지를 해주세요, 글을 쓰겠습니다: 창작의 자유를 위한 간절한 호소

AI, por favor, lave minha louça, deixe-me escrever: um apelo desesperado por liberdade criativa

ИИ, пожалуйста, помойте мою посуду, позвольте мне написать: отчаянный призыв к творческой свободе

AI, Lütfen Bulaşıklarımı Yıka, Bırak Yazayım: Yaratıcı Özgürlük İçin Umutsuz Bir Dilekçe

AI, Hãy rửa bát cho tôi, để tôi viết: Lời cầu xin tuyệt vọng cho sự tự do sáng tạo

人工智能，请帮我洗碗，让我写作：对创作自由的迫切请求

A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s, when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating. The good news? That’s been figured out (thanks to retrieval-augmented generation, or RAG; more on that below). Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “hype cycle.” Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful, but RAG is the right choice if there’s any concern for privacy, security, or speed. Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft, Google, and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot (over 1 million developers use it), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. warn I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s , when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. GenAI applications reminds me of e-commerce in the late 1990s The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating . The good news? That’s been figured out (thanks to retrieval-augmented generation , or RAG; more on that below). hallucinating retrieval-augmented generation Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “ hype cycle .” announced in January reportedly hype cycle Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. large language model RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful , but RAG is the right choice if there’s any concern for privacy, security, or speed. There are times when this method can be useful Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft , Google , and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. Microsoft Google Oracle report results The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot ( over 1 million developers use it ), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. over 1 million developers use it Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. Learn more about how DataStax enables customers to get their GenAI apps to production . DataStax enables customers to get their GenAI apps to production

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It's quite possible that the threats allegedly posed by AI are nothing more than campfire scaremongering (C) Illustration created with Bing. What have we learned about ourselves thanks to Artificial Intelligence? What can we learn about ourselves through interaction with ? Let's try to find, if not the answers to these questions, at least the directions in which to look for them. Artificial Intelligence What have we already learned about ourselves thanks to AI? Do not hurry to turn on the snobbery and accuse the authors of an inaccurate statement of the question. We know that true AI has not yet been created, and the widespread use of the term artificial intelligence in relation to chatbots based on Large Linguistic Models (LLM) is nothing more than a commercial moniker. By the way, here is an interesting test: Would you bet... say half of all you own, that our contention above is true? We sure wouldn't. Nor would we bet that a real AI will be exactly created and not, say, self-generated from "ingredients" that are already available. After all, the only intelligence known to us so far, according to the majority of biologists, was self-generated. Yes, we are aware that the religious have other opinions on this. But if we get away from the position taken by science, we will surely soar to the heights of philosophy, from which the topic of this article will seem utterly insignificant. So, it is better to stay with our two feet planted on solid ground. So, let us return to our starting point. Even the most superficial study of the issue shows that despite the fact that there is no real AI yet, it is already interacting with us, at least as a mirror or a lantern illuminating the dark corners of the closet in our soul. By soul, we mean the whole complex of our mind, psyche, consciousness, and subconsciousness. Cowardice as a strategy The main thing we've learned, thanks to AI, is that humans have turned into terrible cowards. In fact, we have never been cowards like we are now. Not even back in the days when we could counter all of the world’s threats with simple stone spears and axes. And that's surprising because, generally speaking, our species is characterized by curiosity over fear. Otherwise, we would not have departed the African Savanna and scattered across the planet many thousands of years ago. We will not engage here in pathos, enumerating the risky ventures and great accomplishments of our species, such as Leif Ericsson's voyage or the moon landing. Let's just say that throughout history, humans have implemented progressive innovations quickly and without much thought about the risks, even if these innovations involved risk. Let's take one example - bronze. The oldest bronze alloys contained arsenic, the vapors from which had a negative effect on the health of the masters who made metal products. Do you think that our distant ancestors were not aware of this? They were. It is no coincidence that Hephaestus, the divine blacksmith and patron of craftsmen in ancient Greece, was born with deformed legs and was sickly and frail. Observant Greeks perfectly saw what problems, including hereditary issues, faced foundry workers and blacksmiths working with bronze. But this knowledge did not stop progress or return people to the Stone Age. Humanity just invented other, safer alloys. Throughout history, much of humanity’s progress looked the same as the development of bronze. The invention of technology - the use of technology along with gathering information about its risks and pitfalls gained from practical experience - moves in parallel with the improvement of technology and the development of better technology. This was the case with sailing ships and windmills, steamships and steam locomotives, automobiles and airplanes, nuclear power plants, and spacecraft. But with AI, we suddenly broke from this pattern. Simply put, we chickened out. Without even seriously approaching the creation of true AI, we have already started to be afraid of it. Already, a huge number of fantastic works of fiction have been created in the “AI attacks humanity” genre. Among them are many real masterpieces which have almost turned the idea of an inevitable war between people and intelligent machines into an axiom. Once again, consider this point. Full-fledged AI has not yet become a reality, but we are already afraid of it! We've scared ourselves! We have infected ourselves with Frankenstein syndrome, turning it into a kind of epidemic. It looks especially strange that real gurus of technical progress, who know what risk is and know how to take it, often oppose the development of AI. Even "sober heads" who do not believe that AI poses a purely physical threat to humanity talk about the economic and psychological threats that may affect humanity. What if smart machines and AI systems put people out of work? What will happen if people find it more interesting to communicate with Artificial Intelligence and robots than with other people? In essence, all these people are suggesting that humanity should choose cowardice as a basic strategy in this matter. Some would say that putting the brakes on AI development until we have calculated all the risks and set all the limits and boundaries is not cowardice but prudent caution. But let's face it. When considering a system as complex as Artificial Intelligence, it is impossible to calculate and anticipate all the risks. If a full-fledged, self-aware AI does emerge, it is highly likely to break through any red lines we may draw - both thin and thick. So basically, the whole issue comes down to a simple choice. We either develop technologies that can lead to full-fledged AI, dealing with the risks and problems that arise along the way, or we give up and restrict progress in this direction as much as possible, as, in fact, we did with genetic modification. Once again, we would like to draw your attention to the fact that we are not calling for humanity to ignore risk; assessing and weighing the risks of AI are important. But we believe that this should be done as the technology develops, not by putting the development on pause. We do not know what mankind will choose, but we want to believe that our civilization will not turn into a community of cowards. It is the fear we have of AI that seems to be the biggest threat to humanity at the moment, rather than anything coming from intelligent machines. I want to believe that we will once again find the spirit of curiosity and adventure that made us take the first steps into the uncharted and unknown. Dark Side A less talked-about, but no less important, problem that we realized while thinking about a future with full-fledged AI is the danger of awakening in us those manifestations of human nature that are commonly referred to as "base" or "animal" instincts. All that we have managed, with great difficulty, to hide under a thin layer of societal norms and behaviors. Is there a risk that we will really use AI-equipped robots as disenfranchised slaves in hard and dangerous jobs, as sex toys, and in gladiatorial games? Could the future realities from , Westworld, or A.I. Artificial Intelligence really come true? Yes, and it's bound to happen unless we start doing something about it today. Blade Runner Why are we so sure of this? First of all, because a lot of people don't see anything wrong with the scenarios we have described. Let people "blow off steam" on robots instead of hoarding it all in themselves and taking it out on other people. The opportunities offered by a variety of digital entertainment are often cited as an example. But, most importantly, there is the second reason. Namely, the fact that there are still these mindsets of inhumanity and how people treat each other today. You are aware that slavery, forced sexual exploitation, and other forms of human cruelty still exist on planet Earth, right? Even in countries that are considered civilized. Do you want an example? Please. Two people pummeling each other with their fists in an effort to get their opponent to pass out? You are aware that knockdowns and knockouts in professional boxing, to put it mildly, do not lengthen life or improve its quality? That a knockout surely equals a concussion? And this is just one example of modern "gladiatorial games.” The fact that professional athletes receive substantial salaries does not change the essence of professional sports. At the peak of the popularity of gladiatorial games in Ancient Rome, free citizens sold themselves as gladiators for money and fame. What is there to say about citizens - some Roman emperors even entered the arena! “So what's the problem?" some readers will ask, or, "Why not eliminate the issue of brutal, forced exploitation of people by substituting smart machines? We respond: The problem is that if robots were to be equipped with true AI, they would be at least intellectually comparable to humans. Is it ethical to force intelligent creatures, albeit artificial, to become slaves, engage in prostitution, or serve as "whipping boys" for the amusement of fans of this kind of entertainment? The answer to this question should be provided by lawmakers, preferably before the emergence of a full-fledged AI. Unfortunately, it seems that the parliamentarians of the most technically developed countries of the world have not yet seriously concerned themselves with the problems of the legal basis of relations between humans and Artificial Intelligence. So far, if this issue is discussed, it is discussed by individual lawyers and futurologists as a kind of thought experiment. What is the cause for this reluctance? Is it a failure to realize the importance of the subject or an unwillingness to close the window of opportunity? Whatever the case may be, the fact that politicians ignore this problem is another important piece of the puzzle that makes up the portrait of modern humanity. New formats of communication I do not want to end on a gloomy note, so let's look for something positive in the aforementioned closet of the soul. It's something like a beautiful vintage toy or a record collection with good old jazz or rock and roll. The emergence of modern chatbots based on LLM has shown that people are quite willing to engage normally in communication with AI. It turns out that users don't limit their interaction with chatbots to asking them to find information, write text, or come up with slogans, headlines, and greeting cards. We are happy to communicate with these digital creations on a variety of topics - discussing our hobbies, personal problems, and abstract issues, and even dialoguing in the same manner as we do with people. Moreover, this dialog is often freer and more frank than interactions with people. Based on this, we can easily assume that a generation of people is already forming today for whom fear of AI will seem ridiculous, and the idea of normal partnership with intelligent machines and granting them equality is completely natural. To be continued. The article was created in collaboration with Andriy Tkachenko

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The fashion industry is gearing up for a major transformation with the emergence of Web3 technology. In fact, the digital fashion market is expected to be worth by 2028. As the latest wave in fashion, Web3 promises to bring about a seismic shift in the way the industry operates and engages with consumers. With new opportunities for collaboration, creativity, and innovation, Web3 is poised to redefine the fashion landscape in exciting and unprecedented ways. $195 billion As an industry that has always been at the forefront of innovation, from Coco Chanel's revolutionary trousers to Réard's daring bikinis and Westwood's punk-inspired designs, fashion has played a key role in reflecting and shaping cultural and technological changes. Now, the industry is undergoing a transformation on multiple fronts, including the empowerment of women, the integration of artificial intelligence, the drive towards a sustainable economy, and the emergence of Web3 technology. A new industry is driven by a new internet The advent of Web3, also known as the "new internet," has given rise to a collaborative fashion industry where brands, artists and consumers come together to create, collect and trade in both physical and virtual realms. With the emergence of technologies like blockchain, augmented reality, non-fungible tokens (NFTs), generative AI and the metaverse, the industry is now moving towards a new paradigm of blending real and digital fashion experiences rather than treating them as separate entities. This presents exciting new opportunities to innovate and engage with consumers in unique and immersive ways. For instance, a McKinsey analysis found that fashion companies that integrate AI now could see a by 2030 because consumers want things like . 118% cashflow increase hyperpersonalization and connected shopping experiences The convergence of Web3 technology and the fashion industry presents a wealth of unprecedented marketing opportunities for enterprises operating in this ecosystem, including exclusive digital or “phygital” collections that can be earned or purchased and even commercially traded or worn by digital avatars. This innovative approach has been further supported by insights from Web3 consulting agency, , which predicts that retailers will increasingly turn to strategies such as phygitals, loyalty programs and token-gated content as part of their Web3 marketing efforts in 2022. At the same time, companies are leveraging the power of tokenization and digital marketplaces to create new revenue streams and monetize their Web3 initiatives. Moonblock Web3 technology is opening up new avenues for digital artists to collaborate with fashion brands in innovative ways. One prime example of this is the growing trend of non-fungible tokens (NFTs), which has seen leading fashion houses team up with visual artists to create unique digital collections. In addition, some artists are even venturing into the realm of virtual real estate platforms, expanding their fashion lines into new and exciting territory. London-based fashion brand Martina Spetlova, for example, to create a digital collection of her garments in the Decentraland metaverse, blending real-world and virtual fashion to create a brand-new experience. recently partnered with Seamm This approach is creating new opportunities for collaboration and engagement between artists, fashion brands and consumers and is poised to reshape the fashion industry in exciting and unprecedented ways. Worlds Collide: Fashion and Video Games Are Converging One such experience is the emergence of digital fashion in video games. This is already common, with games like The Sims or Animal Crossing dropping items from , Gucci Marc Jacobs and Valentino Snapchat’s Bitmoji avatar platform is also collaborating with major names like to provide digital versions of popular real-world items. In fact, Snap plans to expand this idea even further, selling digital clothing directly. Carhartt, Nike and Ralph Lauren However, it’s also a huge trend in Roblox, Fortnite, Minecraft and other community-centric games. There are entire marketplaces for purchasing in-game skins so players can better express themselves. Fortnite has on this alone and partnered with the likes of and to develop online collections, so it’s clear players are keenly interested in personalizing their digital presence. made billions Balenciaga Moncler Ultimately, we may even see a video game future where a single, totally customized avatar is able to be used across apps and games, becoming a truly unique virtual representation of each player. Digital Fashion is here to stay According to Gerard Lopez, co-founder and CEO of the Lydian Group, "the mainstream adoption of the digital assets space continues to grow thanks to the blend of fashion and technology, which bridges the gap between physical and digital worlds." Indeed, the following business and tech landmarks the increasing investments in fashion tech: support AR as a digital try-on feature. Designer fashion brands creating NFTs. Major retailers accepting online crypto payments. Customizable gaming characters leading to new revenue streams. Metaverse's immersive and social shopping. Digital clothing supporting sustainability by reducing waste. Along with AI, Web3 is the next technological milestone. In fact, Vayner's report What to watch on Web3 how enterprises implemented avant-garde strategies over the past two years in this new digital arena. describes As expected, given its cutting-edge DNA, the fashion industry is leading this revolution.

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