Growth tech stocks, especially some of the more speculative ones, have benefited greatly from a more robust market environment as investor enthusiasm is soaring. Just look at <strong>Upstart</strong> (NASDAQ: UPST). The artificial intelligence (AI)-based lending business has seen its shares skyrocket 85% in ...

Warning: This Skyrocketing Stock Has a Hidden Risk

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Japanese Recycling Upstart Tests Waters With SPAC Debut on NYSE

Recently, Zacks.com users have been paying close attention to <strong>Upstart</strong> Holdings, Inc. (UPST). This makes it worthwhile to examine what the stock has in store.

Upstart Holdings, Inc. (UPST) is Attracting Investor Attention: Here is What You Should Know

Pineapple Financial, LendingTree, and Bit Brother gain on positive news, while Hut 8 Corp, SAI.TECH Global, and Heritage Insurance Holdings post losses.

Upstart among financials gainers, Hut 8 Corp in losers | Seeking Alpha

Upstart Holdings Inc (UPST) is up Wednesday morning, with the stock increasing 0.67% in pre-market trading to 33.26. UPST's short-term technical score of 73 indicates that the stock has tr

Should You Sell Upstart Holdings Inc (UPST) Stock Wednesday Morning?

India is far from having structures that are on the list of highest skyscrapers. The most famous Indian building to make the headlines was The World Towers by Lodha.

An upstart from Mumbai dares to go for India’s tallest building

‘We are a catastrophe for them,’ says Venture Global’s Mike Sabel after oil majors allege $18bn windfall

US gas upstart hits back at BP and Shell in export dispute

Former Division III college player Katie McCarthy has designs on building the next generation’s go-to womens golf apparel brand.

Golf Apparel Upstart Kilo Tango Invades Pro Shop Shelves

Upstart Holdings Inc (UPST) stock is higher by 34.55% over the past week and gets a Bullish rating from InvestorsObserver Sentiment Indicator.

How Will the Market React to Upstart Holdings Inc (UPST) Stock Getting a Bullish Rating

Interest rates may be moving in a better direction.

Why Upstart Stock Gained 11% in November @themotleyfool #stocks $UPST

Upstart Holdings

upstart

Disclaimer: This article does not offer financial advice, do your own research when using one of the presented products. Use these projects and services at your own risk. Googling for “decentralised finance adoption” provides you with tons of articles talking about the shift towards decentralised finance (DeFi). An article published by discusses how the grew from a niche market to mainstream finance. Cointelegraph DeFi movement The article presents an interesting statistic about the total value locked in DeFi markets. Over the course of one year, the total value of locked Ether rose from $317 million to over $1 billion. Therefore, we can say that 2020 will see an even bigger shift towards DeFi solutions. Many projects offer multi-collateralised and uncollateralised . However, there is still some feeling of uncertainty about these products as the crypto price volatility is still a tricky challenge to tackle in combination with the scalability of these applications. The majority of the DeFi solutions provide lending products. lending products Nevertheless, we’ve seen the rise of many successful DeFi projects, including: Compound: Allows to earn a yield on non-yielding digital assets dYdX: Focus on borrowing and lending for margin trading YouHodler: Lending provider which offers Multi HODL Synthetix: Decentralised synthetic assets MakerDAO: CDP for stable coin stabilisation Dharma: Lending without a centralised bank So, how has DeFi sparked the search for alternative financial products? Search for Stable Financial Products There is a search for new financial products that offer stable returns. Currently, traditional savings accounts are even facing negative interest. This means you have to pay the bank to store your money. Because of this, a lot of young people are looking for new, interesting alternatives to grow their capital and counteract inflation. Therefore, the boomed at the right moment, providing the ability for users to borrow money and earn stable returns. This explains the fast adoption of DeFi products during these current times of financial uncertainty. DeFi industry Akiva Lai, chief product officer at auditing platform , highlights the significant growth of the space and why the financial uncertainty has boosted adoption: Maxonrow DeFi “It’s pretty astounding, to be honest. $1 billion in locked DeFi value may seem minuscule compared to legacy finance, but we need to examine it for what it could be — not what it currently is. Paired with the ballooning growth of exchange products, from derivatives to staking services, and it’s only natural that more users will tap DeFi products in the never-ending search for yield amid an uncertain economic backdrop of negative interest rates and slow growth.” From this whole economy of lending providers, has developed the protocol . The protocol helps you with obtaining the highest yield. Therefore, we can say that iearn.finance is a lending aggregator that helps you with finding the best deals without having to care about the underlying details. Andre Cronje iearn.finance Let’s learn how Andre Cronje has designed iearn.finance and how you can use it? This article is dedicated to iearn.finance’s product and explains how the product works. To clarify things, iearn.finance does not make any profits. It’s a free, open-source protocol. The team behind iearn.finance does not intend to monetize it and they have revoked admin keys so the product can be 100% non-custodial . Q1: Explain what a lending aggregator is? A lending aggregator simply aggregates the lending services other projects offer into one easy-to-use tool. iearn offers a protocol that looks for lending platforms with the highest yield. In addition to finding the highest yield, the protocol rebalances every week to find the highest yield. Q2: Why consider using iearn.finance for maximising profits? allows you to deposit , USDC, USDT, and TUSD. This means only stable coins can be used for lending money. Therefore, the platform reduces risk as there’s barely volatility associated with those stable coins. Iearn.finance DAI Next, iearn.finance aggregates the following lending platforms to maximise your profits: Compound (disabled) Fulcrum dYdX Aave DDEX Currently, the annual percentage yield (APY) is on average 20%. These statistics can be found on . For those unaware what the APY means, here’s a short explanation: iearn’s website In contrast to APR, the APY of a loan includes the amount earned from compounding. A good example explaining the difference between APR and APY can be found at . ValuePenguin.com In short, iearn.finance helps you to earn a stable interest for your inactive cryptocurrencies. Q3: What are the risks of using iearn.finance? When reviewing a platform, it’s fair to also take a look at the risks. Let’s review both internal and external risks. External risks We can say the risks of losing money are minimised by using stable coins as you don’t have to deal with crypto volatility. However, a stablecoin project can stop its operations or be hacked although the chance this happens is very low. Also, there is a small risk something goes wrong with the lending providers. In short, you have to trust the lending providers that they implement the right security measures and manage the product accordingly. Internal risks Andre Cronje, creator of iearn.finance, explains that the internal risks for iearn include: Smart contract failure. Failure of any software components. However, the team has revoked their admin credentials, so there is no risk associated with custodial services. Iean.finance is a non-custodial product and the project lives as an open-source protocol that can be reviewed by anyone. As Andre Cronje loves to say: Low risk, high yield, stable rewards. Providing youth with new, low-risk investment options. Q4: What about the technicals? Here are some cool, technical facts. Cool #1: On-chain investment decisions The cool part of iearn.finance is that a smart contract takes care of making investment decisions. Moreover, it means that it makes these investment decisions on-chain. This means the contract has to find creative solutions to find out about the interest rate a particular project offers. To give an example, the Compound project provides a smart contract that sets the interest rate. From this constant, the APR can be calculated. You can find an example of how iearn did this in the following . Medium article Cool #2: Automatic rebalancing From another article , we can learn that the smart contract automatically rebalances every time someone interacts with the contract. published by iearn Next, it finds the highest rate and moves the underlying funds there. The full details of this mechanism can again be found in the article. Cool #3: What if the liquidity pool is too large? What happens when your liquidity pool is so large, that when you move to take advantage of a lending providers’ yield, you wipe out the yield? It turned out that a simple call to calculate the APR is not sufficient. The team behind iearn.finance has built an oracle that helps to calculate the APR and takes into account the pool size to optimize the yield. This means the oracle calculates how the pool size affects the APR and where to best put the maximum amount of funds to gain the highest aggregate yield. ( ) source If you want to read more about this scenario, then Andre has published a much more comprehensive in which he expands on this topic. article Want to learn more about iearn? Visit or visit their in case you have further questions. Remember, the product is still in beta, only use it at your own risk. iearn.finance platform Telegram channel Happy lending!

On Building the Upstart DeFi Aggregator iearn.finance with Andre Cronje [Interview]

Digitally native vertical brands are betting that subscription led bundling can counter Amazon led unbundling New York Subway is a strange world, there are many things that one might be amazed by and ads are probably last on the list. But one can’t escape noticing them and recently I have seen so many DNVB (Digitally Native Vertical Brand — sorry, this is a doozy, but is a good article on them) ads that I had to take time to make sense of them. here The one ad that really got me thinking was ; they sell toothbrush on subscription, adopting the razor blade model, with the toothbrush head replacing the razor blade. My first instinct was why would anyone need this and why are so many upstarts foolishly trying to replicate . No doubt that CPG brands have been selling us overpriced products with very limited innovation for decades and they need to get disrupted, but why would I want to subscribe to a toothbrush! quip Dollar Shave Club’s success Though I picked on quip, quick research will tell you that subscription model is all rage with DNVBs ( , , , etc.) The interesting thing is that these brands don’t sell on Amazon! Contrast this to my where I posited that Amazon marketplace is the AWS of brands which will (and has) led to an explosion of new brands, born on Amazon and selling to Amazon customers. However, these DNVBs are going for something else. They have an alternate vision of retail, one where they can co-exist with Amazon, owning categories and customers. They are betting on a brand led future of retail. MeUndies Harry’s Ritual recent post This vision is audacious and true success can only be guaranteed if these new brands can build (retain) pricing power over a large enough base of customers over a long period of time. Building a brand led future of retail with subscriptions The most important thing to appreciate around pricing power is that a brand can have it only if customers are coming to it directly and it has built defensibility against the hundreds of competitors that can challenge its portfolio of products, one by one. , it is search based and customers go about filling their cart one product type (detergent, deodorant, etc.) at a time. This exposes every individual product to head-on competition with tens of similar products, leading to an unsustainable race. Selling on Amazon is inherently unbundled Alternate models of online retail: Bundling driven by DNVBs owning categories and unbundling driven by Amazon DNVBs will build pricing power only if they manage to create a future where the bundled model on the left (above) can exist i.e. customers visit A.com (instead of Amazon.com) for buying all the products that they need for that category. DNVBs are hoping that help them get to that world. Let’s see how. subscription is the silver bullet Subscription is a good test of customer need Buying a subscription by definition means that a customer is agreeing to have a continued relationship with the brand; it is a privilege that the customer gives the brand. This gives subscription brands a better customer touchpoint than brands which model themselves around a one-time purchase. For sure, it’s harder to get a customer to buy into a subscription but simple trial plans (e.g. ) can help with that. Not all categories are suited for subscription but it is a model worth exploring for categories where product use is more frequent or a behavior around frequency can be shaped but no fundamental R&D is required to create great products. Harry’s Thinking “subscription first” pushes brands to design products and marketing that fits into the subscription model and see if customers are willing to give them that privilege to have the continued touchpoint. It’s a leading signal of a category’s readiness for disruption. Subscription buys time Customers get bored easily, novelty of products fades quickly, and there is always something that comes along that promises to change their lives. This means that customers are constantly prone to churning. So, one anchor product is good but brands need to keep it exciting for customers by making tweaks to the anchor product and introducing more ancillary products that make it worth it for the customer to continue the relationship with the brand. Doing this is not easy. With subscription, brands buy time to get to the point when they have more to offer to customers . This time window might be a few months, but it is still better than not having any. without having to reacquire them Once DNVBs have solved enough for customers in a given category, they have likely given customers a reason to come to them directly, thereby proving success in the bundled model of retail. jobs to be done Subscription enables building a product portfolio in a cost effective way It’s well known that CAC has been rising steadily, requiring new brands to need more and more VC money to build a sustainable business. Given that (unlike tech), it is important for brands to figure out how to grow with less cash for the endeavor to make sense both for the founder and the VCs. CPG is not a winner takes all market generally The secret sauce for CPGs historically has been that they have been able to cross-promote new products to their large customer base to ensure that CAC for any new product is under check. These products then sell on Amazon in mass (though at decreasing margins). Owing to an established touchpoint with the customer, subscription gives DNVBs a similar cross-promotion channel, allowing them to build a portfolio of products without spending exorbitant amounts of money and then selling them to a smaller set of customers but at better margins. Subscription makes distribution more cash efficient The need for high levels of working capital is one of the biggest issues faced by startups selling physical products. Inventory management costs and buyer/ supplier payment terms are a big part of that. By allowing better demand prediction and shorter payment terms, subscription allows them to be more cash efficient. Any cash conserved can then be put into product innovation or branding. Overall, subscription is a great selling (distribution) model to explore and comes with many inherent advantages. Expect to see more of it with upstart DNVBs in commoditized categories with frequent product use. Online commerce is especially suited for experimentation around this model and, given the right incentives, there is always scope to shape customer behavior.

Why are so many upstart brands in love with subscriptions

Michael Saylor Wants To Turn MicroStrategy Into a Trillion-Dollar Bitcoin Bank

মাইকেল স্যালর মাইক্রোস্ট্র্যাটেজিকে ট্রিলিয়ন-ডলার বিটকয়েন ব্যাংকে পরিণত করতে চান

Michael Saylor will MicroStrategy in eine Billionen-Dollar-Bitcoin-Bank verwandeln

Michael Saylor quiere convertir a MicroStrategy en un banco de Bitcoin de un billón de dólares

Michael Saylor veut transformer MicroStrategy en une banque Bitcoin d'un milliard de dollars

माइकल सैलर माइक्रोस्ट्रेटजी को एक ट्रिलियन डॉलर के बिटकॉइन बैंक में बदलना चाहते हैं

マイケル・セイラーはマイクロストラテジーを1兆ドルのビットコイン銀行にしたいと考えている

Michael Saylor, MicroStrategy를 1조 달러 규모의 비트코인 은행으로 만들고 싶어

Michael Saylor quer transformar a MicroStrategy em um banco de Bitcoin de um trilhão de dólares

Майкл Сэйлор хочет превратить MicroStrategy в биткоин-банк стоимостью триллион долларов

Michael Saylor, MicroStrategy'yi Trilyon Dolarlık Bir Bitcoin Bankasına Dönüştürmek İstiyor

Michael Saylor muốn biến MicroStrategy thành một ngân hàng Bitcoin trị giá nghìn tỷ đô la

Michael Saylor 希望将 MicroStrategy 打造成一家价值万亿美元的比特币银行

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Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost. This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘Introduction to Generative AI learning path’, and the other is for technical practitioners (more advanced) ‘Generative AI for Developers learning path’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging, from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI. Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge. It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses. Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI - course
Introduction to Large Language Models - course
Introduction to Responsible AI - course
Generative AI Fundamentals - earn a skill badge
Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Responsible AI for Developers - course
Introduction to Image Generation - course
Attention Mechanism - course
Encoder-Decoder Architecture - course
Transformer Models and BERT Model - course
Create Image Captioning Models - course
Introduction to Generative AI Studio - course
Generative AI Explorer: Vertex AI - earn a skill badge
Explore and Evaluate Models Using Model Garden - lab
Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes
Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes
Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes
Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes
Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes
Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes
Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI, taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud, designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. Originally published here. Contributed by: Google Cloud Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost . This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘ Introduction to Generative AI learning path ’, and the other is for technical practitioners (more advanced) ‘ Generative AI for Developers learning path ’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost Introduction to Generative AI learning path Introduction to Generative AI learning path Generative AI for Developers learning path Generative AI for Developers learning path Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging , from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. use cases are far-ranging use cases are far-ranging Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI . Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Introduction to Generative AI Introduction to Generative AI If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge . It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. continue your no-cost learning Generative AI Fundamentals skill badge Generative AI Fundamentals skill badge two hours to complete Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses . Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI Introduction to Generative AI Introduction to Generative AI this path includes no-cost video courses Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Generative AI for Developers Generative AI for Developers Generative AI for Developers Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Get Started with Generative AI Studio Get Started with Generative AI Studio Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design Generative AI with Vertex AI: Prompt Design Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Vertex AI PaLM API: Qwik Start Vertex AI PaLM API: Qwik Start Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Integrate Search in Applications Using Gen AI App Builder Integrate Search in Applications Using Gen AI App Builder Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Prompt Design using PaLM Prompt Design using PaLM Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Explore and Evaluate Models using Model Garden Explore and Evaluate Models using Model Garden Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI , taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. DeepLearning.AI DeepLearning.AI DeepLearning.AI DeepLearning.AI Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud , designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Introduction to AI and Machine Learning on Google Cloud Introduction to AI and Machine Learning on Google Cloud this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here . sign up here sign up here New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here Originally published here . here Contributed by: Google Cloud

Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level

Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike, a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029, the brainpower it needs. The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety. "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike , a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029 , the brainpower it needs. Spike set to reach $148 billion by 2029 The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety . searches for AI in healthcare being orders of magnitude higher than searches for AI safety "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. A version of this article was originally published on Dataconomy and is republished with permission. published on Dataconomy

Spike Aims to Give Healthcare AI a Brain Boost and Outsmart the Tech Giants

For Entrepreneurs, Endurance is more Important than Intellect 

Every four years, the Bitcoin network undergoes a significant change known as "halving,” a process that reduces the rewards for mining new blocks by half. This mechanism is built into Bitcoin’s protocol to control inflation and cap the total supply at 21 million coins. Halving events, therefore, are pivotal, highly anticipated occasions within the cryptocurrency community, often triggering speculation and market volatility. Bitcoin's price trajectory often responds dramatically to halving events. After the first halving on November 28th, 2012, when the mining reward dropped from 50 BTC to 25 BTC, Bitcoin's price skyrocketed from $12 to $1,075 within a year, a stunning 8,858% increase. This surge was coupled with a drop in Bitcoin's inflation rate from 25.75% to 12% by January 2013. The second halving on July 9th, 2016, exhibited a similar pattern. Bitcoin Halving Schedule Many believe that the price of Bitcoin will immediately increase following a halving. While historical trends have shown price increases following halvings, these gains have not always been instantaneous and are influenced by a wide range of factors beyond just the halving event. On the opposing view, many think that the Bitcoin halving is already priced in. While some anticipation of the halving may be factored into Bitcoin's price, the complexity of market dynamics and external factors suggests that the entire impact of the halving might not be fully accounted for in advance. Despite the generally bullish sentiment, recent geopolitical tensions following Iran's drone strikes toward Israel on April 13, 2024, triggered market turbulence. Bitcoin experienced a sharp decline, reaching its lowest level in a month. This event led to a sell-off, shedding about 8% of Bitcoin’s value in a brief period. Historically, though, such geopolitical conflicts have eventually fueled the cryptocurrency market. Currently, Bitcoin is exhibiting a pre-halving retracement characterized by bearish signals and lateral market movements. A technical analysis of the weekly time frame reveals the formation of a Cup and Handle pattern in Bitcoin’s price chart. Traditionally, this pattern can proceed further downward movements. Notably, there is robust support within the $60,000 to $61,000 price range. Should this support level be breached, it is plausible to anticipate a retraction towards the $51,000 mark. In a note to clients last week, Goldman Sachs acknowledged that Bitcoin’s past three halving cycles have resulted in massive price run-ups in the aftermath – though the exact time it took to reach a new all-time high afterward has differed greatly. This cycle looks even more different: Bitcoin already topped its previous cycle’s all-time high above $69,000 in March, one month before the halving even occurred. “Caution should be taken against extrapolating the past cycles and the impact of halving, given the respective prevailing macro conditions,” analysts said. In a Wednesday report, JPMorgan predicted that Bitcoin’s price would not rise following the event and that it would be more likely to resume declines that began earlier this month. The bank’s outlook remains consistent with its relatively bearish forecasts throughout the year, refusing to be swayed by growing optimism around Bitcoin spot ETFs or the halving. The analyst used the price of gold as a reference point, as both assets share a similar investment thesis as a risk-off store of value and inflation hedge. On a volatility-adjusted basis, the bank argued that Bitcoin’s price should only be $45,000, meaning its current market price ($63,700) is significantly overbought. Backing their case is a continuing long bias in Bitcoin futures’s open interest and a lack of venture funding in the crypto industry this year. “The technical picture for bitcoin is rather worrying, as we saw no rebound after the price drop on Friday and Saturday,” added Alex Kuptsikevich, FxPro’s senior market analyst, in an email to Forbes. “On the contrary, the market seems to be getting used to current prices in anticipation of a halving.” Whatever the view of the banks and analysts, the full effects of the halving remain to be seen. Time will tell if they are bullish or bearish, but what remains certain is that the drama surrounding the latest upstart asset class remains as strong as ever. Every four years, the Bitcoin network undergoes a significant change known as "halving,” a process that reduces the rewards for mining new blocks by half. This mechanism is built into Bitcoin’s protocol to control inflation and cap the total supply at 21 million coins. Halving events, therefore, are pivotal, highly anticipated occasions within the cryptocurrency community, often triggering speculation and market volatility. cryptocurrency Bitcoin's price trajectory often responds dramatically to halving events. After the first halving on November 28th, 2012, when the mining reward dropped from 50 BTC to 25 BTC, Bitcoin's price skyrocketed from $12 to $1,075 within a year, a stunning 8,858% increase. This surge was coupled with a drop in Bitcoin's inflation rate from 25.75% to 12% by January 2013. The second halving on July 9th, 2016, exhibited a similar pattern. Bitcoin Halving Schedule Many believe that the price of Bitcoin will immediately increase following a halving. While historical trends have shown price increases following halvings, these gains have not always been instantaneous and are influenced by a wide range of factors beyond just the halving event. On the opposing view, many think that the Bitcoin halving is already priced in. While some anticipation of the halving may be factored into Bitcoin's price, the complexity of market dynamics and external factors suggests that the entire impact of the halving might not be fully accounted for in advance. Despite the generally bullish sentiment, recent geopolitical tensions following Iran's drone strikes toward Israel on April 13, 2024, triggered market turbulence. Bitcoin experienced a sharp decline, reaching its lowest level in a month. This event led to a sell-off, shedding about 8% of Bitcoin’s value in a brief period. Historically, though, such geopolitical conflicts have eventually fueled the cryptocurrency market. Currently, Bitcoin is exhibiting a pre-halving retracement characterized by bearish signals and lateral market movements. A technical analysis of the weekly time frame reveals the formation of a Cup and Handle pattern in Bitcoin’s price chart. Traditionally, this pattern can proceed further downward movements. Notably, there is robust support within the $60,000 to $61,000 price range. Should this support level be breached, it is plausible to anticipate a retraction towards the $51,000 mark. In a note to clients last week, Goldman Sachs acknowledged that Bitcoin’s past three halving cycles have resulted in massive price run-ups in the aftermath – though the exact time it took to reach a new all-time high afterward has differed greatly. This cycle looks even more different: Bitcoin already topped its previous cycle’s all-time high above $69,000 in March, one month before the halving even occurred. “Caution should be taken against extrapolating the past cycles and the impact of halving, given the respective prevailing macro conditions,” analysts said. In a Wednesday report, JPMorgan predicted that Bitcoin’s price would not rise following the event and that it would be more likely to resume declines that began earlier this month. The bank’s outlook remains consistent with its relatively bearish forecasts throughout the year, refusing to be swayed by growing optimism around Bitcoin spot ETFs or the halving. The analyst used the price of gold as a reference point, as both assets share a similar investment thesis as a risk-off store of value and inflation hedge. On a volatility-adjusted basis, the bank argued that Bitcoin’s price should only be $45,000, meaning its current market price ($63,700) is significantly overbought. Backing their case is a continuing long bias in Bitcoin futures’s open interest and a lack of venture funding in the crypto industry this year. “The technical picture for bitcoin is rather worrying, as we saw no rebound after the price drop on Friday and Saturday,” added Alex Kuptsikevich, FxPro’s senior market analyst, in an email to Forbes. “On the contrary, the market seems to be getting used to current prices in anticipation of a halving.” Whatever the view of the banks and analysts, the full effects of the halving remain to be seen. Time will tell if they are bullish or bearish, but what remains certain is that the drama surrounding the latest upstart asset class remains as strong as ever.

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