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AMGEN TO PRESENT AT THE BANK OF AMERICA MERRILL LYNCH GLOBAL HEALTHCARE 
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Amgen Inc. stock outperforms competitors on strong trading day

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Amgen (AMGN) PT Raised to $235 at Mizuho

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Amgen Inc. stock falls Tuesday, underperforms market - MarketWatch

Amgen is positioned for solid long-term growth with strategic acquisitions, a diverse medical portfolio, and a focus on AI innovation. Learn more on AMGN stock here.

Up 40%, 3%-Yielding Amgen Is Still One Of My Favorite Healthcare Stocks (NASDAQ:AMGN) | Seeking Alpha

Shares of Amgen Inc. inched 0.76% higher to $310.16 Monday, on what proved to be an all-around great trading session for the stock market, with the S&P 500...

Amgen Inc. stock outperforms market on strong trading day - MarketWatch

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Last week, the that is testing an AI tool that enables news organizations to create content using AI. It is unclear what the exact capabilities of the product are but based on one comment from Google, it helps with tasks like headlines and modifying writing styles. Amidst fears of what impact this could have on journalist jobs, Google quickly put out a : New York Times reported Google clarifying statement Our goal is to give journalists the choice of using these emerging technologies in a way that enhances their work and productivity. Quite simply these tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating, and fact-checking their articles. This has sparked a heated conversation about the future of news with generative AI. The companies making AI products argue that this technology will enable media organizations and journalists to be more effective, while critics contend that it could potentially harm journalist jobs, increase misinformation, and flood the market with low-quality AI-generated content. In this piece, we unpack the by exploring a few things: impact of generative AI on the future of news Types of news articles today by complexity The business of news The anatomy of producing a news article What generative AI can and cannot do well Generative AI and the Future of News — A tale of two worlds Types of news articles today by complexity There are several types of news content on the internet, each with varying levels of complexity in their production. The complexity typically arises from factors like timeliness, the amount of research required, and the story being told. The low-complexity articles include: Factual/data news (eg. article listing mortgage rates in San Francisco, article with numbers from a company’s earnings call) — these are relatively straightforward with minimal subjective opinions or perspectives. Interest-based/informational news (eg. summer recipes published on NY Times Cooking, an article explaining what the Fed interest rate means) — there is some creativity in selecting topics but the focus is more informational and catering to specific interests. Breaking news (eg. an article about a CEO resigning, an article describing an active weather event) — these are typically short articles about a rapidly unfolding event, with limited initial information and emerging facts. The include: high-complexity articles News coverage (with context, research, and facts) — these provide a more detailed explanation of news backed by research, additional context, and often interviews with people, thereby requiring more time and effort to produce; they are also extensively fact-checked. Interpretive news — this includes opinion pieces, op-eds, and analyses (like this one) that provide interpretations/perspectives/opinions on current issues; they are often subjective and require extensive research to back up the perspectives. Feature pieces — these are typically deep dives on topics that might not really be hot news right now but important issues, investigative journalism would fall in this category; these require extensive research and interviews spanning across months, and also creative storytelling. I bring up this categorization because the purpose behind and process involved in producing these two types of articles is very different, and consequently will evolve differently with the use of generative AI. With this in mind, let’s talk about how news makes money. The business of news The business of news is tricky — most news organizations operate on an ad-supported media model, and a small subset have successfully managed to pivot to subscriptions. This has major implications for the type of content a news organization produces. The businesses (NYTimes notably now gets from subscriptions) have a content strategy that’s tied directly to consumer value — focus on to make the subscription worthwhile. For example, of the types of content above, NYT primarily does news coverage (that is well-researched), interpretive news, and feature pieces for their core news offering, backed up by interest-based content like and . Some other publications like the Wall Street Journal and the Washington Post have made strides with subscriptions using a similar approach. subscription-based news ~70% of revenue high-quality content + diverse interest-based content NYT Cooking Wirecutter However, and will continue to be so for the foreseeable future. This means that they are focused on generating high engagement: more eyeballs → more ad inventory → more revenue. The strategy that has been more effective for them is (like news coverage, interpretive news) (like factual/data news, interest-based/informational news). most publications are still ad-supported augmenting high-complexity content with a ton of high volume, low-complexity content This strategy works because high-complexity content helps deliver long-term value to consumers, while the ton of low-complexity content helps . The SEO loop looks something like: high volume of content that gets clicks → search engines think your content is valuable → all of your content gets ranked higher → more eyeballs. get eyeballs as well as win the SEO game This is no criticism of the strategy and the need to play the SEO game is a necessity for ad-supported media. The reality is that — the internet disrupted how content was created and distributed (mostly through Google Search / today), and news organizations have not recovered from the impact of that disruption. There are attempts to fix the imbalance, which are still evolving — Canada/Australia passed laws that force Google/Meta to pay what’s essentially a “disruptor tax” to support good journalism, some organizations like NPR are partially supported by , and some newspapers like the Washington Post are . news media is a terrible business Meta federal funding subsidized by (generally well-meaning) billionaires All this to say — news media/journalism is an absolutely essential public utility for a well-functioning democracy but a not-so-great business. Therefore, the more self-reliant these news businesses can be (without having to depend on regulation or billionaires), the more effective they can be towards achieving their mission. Generative AI can’t solve all of these structural challenges (especially content distribution) but it can definitely make content creation more efficient without necessarily compromising on quality. The anatomy of producing a news article To understand what part of news production will generative AI have the most impact on, it’s helpful to understand the different steps involved in getting a news article published. We can break down the effort into a few sequential components: Research — finding a topic, gathering facts/data, talking to the right people for quotes or perspectives Storytelling — making sense of the research to come up with a compelling storyline Writing — turning the storyline into actual words that make an article, buttoning up the storyline, backing it up with the right data / adding supporting links Editing — improving the writing, coming up with catchier headlines, having better visuals, and fact-checking to meet standards Distribution — publishing, and distributing across channels like social media. Based on some research and also influenced by my writing experience, I estimate the effort of producing an article into something like: . Take the actual numbers with a grain of salt but they are directionally accurate. Research (30%), Storytelling (20%), Writing (20%), Editing (20%), and Distribution (10%) Now, note that — the low-complexity pieces might not need to rigorously go through all steps in the life cycle above but the high-complexity pieces do. not all formats of news articles require all of the steps As an example, this article took me ~8–9 hours to finish (so did most other articles on ), and I would like to think that my articles fall under interpretive news. If I wrote a low-complexity piece, I could probably get it done in ~2 hours. Another data point — says he can typically get a news article done in a couple of hours while a feature piece might take up to 6 months. my Substack newsletter this NYT reporter So, what can generative AI actually help with? It basically comes down to what steps in the news production process can current (and future) products do well. What generative AI can and cannot do well Here’s my take on each step. Spoiler — they are somewhat effective for writing and editing, they can be effective for research if the right products are built, and they will continue to be bad at storytelling. Research — surprisingly low capability Most current generative AI products (like ChatGPT, and Google Bard) have surprisingly low capability when it comes to Research: There are definitely certain capabilities that they are good at. For example, they are good at giving arguments for a particular point of view or providing inspiration for new topics. They are average-to-good at summarizing content and in particular, answering questions based on reading an article. For example, you can ask them to read this article and list out different steps involved in news production. However, they often produce (“hallucinations”), to what they claim is the truth, and have questionably sourced data with potential copyright infringement. See the screenshot below. factually incorrect information do not provide links/sources For a generic use case (say you want to draft an email to a future LinkedIn connection), these issues don’t matter. But when you are writing a news article, not fact-checking can severely impair your brand perception. Though the research tools can be used for directionally useful research (for example, you want to know how much market share Bing and Yahoo search have), writers/journalists still have to do additional work to find a new source to link because current tools neither provide links nor have fully accurate data. for anyone building a research product for writers, and it’s very likely that new companies will emerge here. Using cleanly sourced data + being able to provide answers with trustworthy links is a big opportunity Storytelling — low capability Generative AI’s storytelling capabilities are fairly weak today. Here’s an example: I did a bunch of research for this article, put together the research in an organized format, and asked ChatGPT to give me a storyline. See the screenshot below for results. At first glance, it looks like they “make sense”. But that’s literally all it is — it makes sense at a surface level. In reality, none of these storylines are very compelling and the research notes I provided had much more detail and nuance beyond the surface-level results provided here. You can make the argument that these are still decent, but in the context of writing an article, this was completely useless to me — at best, this was inspiration/ideas, not a storyline based on the research I submitted. If you have a low-complexity story, it will get the job done for you. If you are building a nuanced story, or have the data and need help constructing a story, current products do not get the job done. Given the high amount of subjectivity involved, I am not bullish it will get better and this will continue to be the part where writers can add the most value. Writing — medium capability If you provide current products with a detailed storyline of what you want to say, it can generate a barely decent v1 of the content. The output is still pretty cookie-cutter and it is pretty challenging to instruct the current models to craft the language in a way that it tells a story. See the screenshot for an example output after I fed ChatGPT detailed notes about the storyline for this article. At first glance, you might think it looks okay for a v1 draft. It is not — the tone isn’t right, the story doesn’t flow and it always looks like it’s been written by a bot. It’s very generic and does not articulate the story despite being fed a very specific narrative. If I published that article, you would not read it. And that’s the challenge with the writing capability today — it can work for low-complexity articles but for anything more complex, you essentially have to rewrite the whole draft. — writers don’t want to take terrible cookie-cutter versions and redo the whole thing line by line. What they would like is some form of a user interaction construct that lets a writer feed in a storyline and sequentially craft the article section by section, while giving active feedback to the AI tool. Cherry on top would be being able to customize the writing style by feeding past articles written by the same writer. The big product unlock here would be enabling effective human instruction I think the underlying models today do have the capability to do this and there is , which I believe will happen near-term. innovation needed at the UI layer Editing — medium capability Tools today have a good amount of capability to review articles, find errors and make corrections. These tools are also very good at cosmetic tasks, like coming up with ideas for catchy headlines or section titles that might perform well. However, there is still work needed at the UI layer to make this usable for editing — there are some partial solutions today like which lets you improve language and make sentences shorter / longer from within a Notion page, but it does not capture the full context of the page. ChatGPT does a good job of making edits across a full article but lacks the ability to take instructions to easily edit specific sections and also cannot support links (i.e. I give it a blurb with text that has hyperlinks, I get back text without any links). Notion AI I absolutely hate the editing part of the writing process, and I am sure several writers and journalists do as well — generative AI can most definitely minimize some of this editing grunt work near-term. Distribution — medium capability There are tools coming up here today that for example help you generate social snippets, or identify parts of your story that might go more viral. This will likely continue getting better in the future. Generative AI and the future of News — A tale of two worlds Based on the above analysis, you can see a clear duality emerging: , which require simpler research & storytelling, and can be written & edited easily, will start to be (or heavily AI-assisted). Low-complexity formats more and more AI-generated , which require more complex research and storytelling capabilities that don’t exist today, will but generative AI can bring a good amount of High-complexity formats continue to be primarily created by journalists efficiency by minimizing writing and editing gruntwork. While AI-generated low-complexity content seems bad at first glance, these articles are primarily written for SEO purposes or to augment existing high-quality content, and the race to commoditization had started much before the generative AI wave. For example, the Associated Press has been using since 2014. The upside here — this is not the kind of content journalists want to spend time creating and automating this will free up their time for high-complexity content. bots to publish articles reporting company earnings More and more high-complexity content will emerge. Research and storytelling capabilities with products today are limited, which means that the ability to . This, accelerated by AI writing and editing tools that take the grunt work out of publishing quality content, will be a big boon for journalists. construct a compelling story based on information and story-tell in a unique way will continue to be the biggest currency journalists have What about some of the concerns raised around using AI for news? Some are fair but I believe they are mostly solvable: A wave of junk SEO content — Google has that they won’t penalize AI-generated content and have gotten criticism for opening up a world of junk content, including factually incorrect articles. That’s a somewhat fair critique but I believe Google will crack down on this — not out of the goodness of their hearts but because the crux of a search product is to give users useful results. Google already has penalties for SEO hacking practices (like keyword stuffing, and link farming) and it would be easy to extend this framework to AI content. taken the stance Journalists will lose jobs — Some have brought up the concern that journalists might lose jobs, or be in a less advantageous position (like writers in Hollywood who are currently on strike); the big difference here is that while there are a large number of writers in Hollywood, newsroom employment in the US since 2008, not because we don’t need journalists but because news is a bad business; Generative AI can help fix the economics of the business while continuing to empower journalists do what they love. has fallen 26% Conclusion I am by no means an AI maximalist and I absolutely think there are real risks to AI that need to be addressed as we scale the technology. However, I think it’s important to analyze every AI-impacted market/situation separately and not bundle them into one big AI-impact problem. In the case of news, generative AI can tremendously improve the economics of news businesses. The products are not there today — there is a clear need for AI products that thoughtfully solve writers’ needs without a brute-force, chat-based, language model interface, but I’m confident these will emerge near-term. Low-complexity writing will be more and more AI generated and that’s okay — it can help companies run an efficient SEO machine, while journalists (assisted by AI for grunt work) bring to life a lot more high complexity content that elevates the public discourse. Thank you for reading! Also published here.

Exploring the Impact of Generative AI on the Future of News 

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समाचार के भविष्य पर जेनरेटिव एआई के प्रभाव की खोज

ニュースの未来に対する生成 AI の影響を探る

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Explorando o impacto da IA generativa no futuro das notícias

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探索生成人工智能对新闻未来的影响

The IT market is developing rapidly, and the current trends are changing very quickly, so it is especially important for IT companies to understand what to expect in the coming year in order to plan their recruiting processes effectively. In our first article this year, we will talk about fields within the IT industry, which are currently developing particularly intensively, which specialists are in increasing demand. We'll also talk about which IT professions are in high demand and which are less popular. The fact is that the IT market is still a candidate market. The demand for IT specialists is as high as it was, and companies will continue to compete with each other in hiring candidates. IT specialties in demand in 2022 Recently, the demand for continues to grow: IT specialists of this profile are responsible for the reliability of the site and the creation of functional and high-quality software. According to the international job search portal, Indeed, the average annual salary of an SRE engineer is $131,842. SRE engineers (site reliability engineering) Among the main soft skills that an SRE engineer should have, employers name the ability to work with a large amount of information and to be able to analyze it at a deep level. We share an inspiring fact for those who really want to try themselves in IT and especially love SRE. Jennifer Petoff, head of the SRE training department at Google and co-author of Google's SRE book, has no first education related to IT. Jennifer has a PhD in chemical sciences, but that didn't stop her from becoming a cool SRE specialist :) Therefore, if you are determined to conquer SRE horizons and ready to work hard — go ahead and master a new field. This profession in 2022 will be at the top of demand, so even junior specialists have good chances to build a career. Data specialists are also in demand now — for example, data analytics. The demand for testers has also increased. In 2022, companies will need a lot of QA. It is impossible not to mention that Golang developers are still very popular, and there are still very few of them on the IT market today :) Programming languages such as Java, C++, and Python are not losing their top positions too. Python in 2022 rose from third to first place in the TIOBE ranking, while Java and C++ have 3rd and 4th places respectively, holding positions in the top five. What will change? While certain IT specialties are getting more popular, there are also those for which demand is decreasing. You can observe a similar situation with PHP developers: this is due to the fact that this programming language is now being actively rewritten and changed to meet modern realities. In the TIOBE rating, PHP dropped significantly lower when compared to last year. At the end of 2021, it already lost several positions. As of today, it did not enter the top 10 most popular programming languages, ending up in 11th place. Ahead of the entire planet One of those areas that are actively developing and will continue to develop in 2022 is the biotech industry, which is based on medicine and IT. The biotechnology industry studies the issues of genetic engineering and also considers how biology can be used in solving various technological problems. Among the major biotech corporations are BIOCAD, Amgen, Ginko BioWorks, Bioniq, and others. Biotech, again, primarily requires data specialists — data analysts, big data specialists, biostatistics specialists (biostatistics is applied in the development of statistical methods in biology and medicine). In addition, there is a great demand for cybersecurity engineers. Take into consideration the large amount of data that is currently stored in the digital space. One of the priorities for companies is to ensure their security and protection from cyber-attacks. Up and only up The question that many IT companies are worried about today: What forecasts can we expect on salary forks of candidates? Despite the fact that it is difficult to name exact numbers, the level of salaries of IT specialists, in any case, will continue to increase. The CEO of Lucky Hunter Tatiana Melnichuk advises considering at least a 30% growth. Heading abroad Another trend that has entered with us in 2022 and will not leave the IT market in the near future is the growth in the number of Russian IT specialists, for whom relocation is a high priority. This is partly due to the fact that Western companies have realized the high level of professionalism Russian developers have, and now they are hiring Russian-speaking candidates to strengthen their teams. The countries that most often hire IT specialists from Russia include the United Kingdom, Germany, and the United States. For example, we recently filled a job opening for a senior product designer for a London startup offering business solutions for the effective organization of a telephone support service. In general, we can say with confidence that it will not be boring in 2022 for sure :) Our team wishes both candidates and IT companies a productive and interesting year!

IT Market in 2022: Professions, Industries, Hiring

Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. the visualization by McKinsey Global Institute As businesses realize these technologies are a boon, the demand for machine learning consulting companies is skyrocketing, which predictably influences the supply. In such a way, today’s IT market is literally larded with ML vendors tackling projects of any scope and budget. To help you make your way through this enormous and diverse offering, we assembled a list of today’s most trustworthy vendors of machine learning consulting and development services. Our IT market analysts assessed an extensive range of companies according to the following objective criteria: Proven machine learning expertise Prior projects: size and complexity Diversity of business domains served Client references Speed and quality of customer service As a result, we got a list of 30 companies worthy of attention. For readers’ convenience, we present the vendors in descending order. 1. Itransition A recognized custom software development company, Itransition has delivered 1,500 projects over the span of its 20-year market presence. With more than 2,000 IT experts on board, the company has served 810 clients from 30 countries. Among the ML services the company provides there are data mining, natural language processing, computer vision, robotic process automation, and machine learning consulting. To deliver its ML solutions, Itransition uses such technologies as Python, R, Azure, Google Cloud, and AWS. Location: Denver, CO Established: 1998 Project budgets start at: $10,000 Key clients: Expedia, Xerox, Toyota, eBay, PayPal, Shell, adidas Other, non-ML services: custom software development, CRM consulting, ecommerce development Website: https://www.itransition.com/ 2. Tooploox EU-based Tooploox has around 100 skilled experts on board. For seven years of the company’s operation, they delivered 150+ successful projects. Tooploox is a machine learning consulting company using this technology to deliver computer and 3D vision, sensor fusion, deep learning, natural language processing, reinforcement learning, and other ML-related services. The company has expertise with other emerging technologies, such as blockchain. Location: Wrocław, Poland Established: 2012 Project budgets start at: $25,000 Key clients: CoinDesk, DTS, ING, Spire Health, Group Nine Other, non-ML services: mobile app development, product design Website: https://www.tooploox.com/ 3. Skim Technologies London-based Skim Technologies is an internationally recognized machine learning consulting company delivering a wide range of related services. The company’s data scientists offer chatbot development, custom ML model creation, and NLP engineering. Skim Technologies also delivers AI workshops as well as ML and AI consulting services. Location: London, United Kingdom Established: 2014 Project budgets start at: $10,000 Key clients: Jones Lang-Lasalle, Breast Cancer Care, Quant Insight Other, non-ML services: business intelligence consulting, custom software development Website: https://www.skimtechnologies.com/ 4. ThirdEye Data US-based ThirdEye Data has been delivering its ML development services for nine years. The company specializes in chatbot development, custom ML algorithms, and machine learning consulting. Among the data science technologies the company works with, there are Spark, TensorFlow, Caffe, and others. Location: Santa Clara, CA Established: 2010 Project budgets start at: $25,000 Key clients: Microsoft, Amazon, Nokia, Ford Direct, Getty Images, Amgen Other, non-ML services: business intelligence consulting, cloud consulting Website: https://thirdeyedata.io/ 5. Chatbots.Studio As its name implies, Chatbots.Studio is a company focusing on AI-powered chatbot development. It builds multichannel conversational platforms, automates business processes, and designs conversations. The company’s technology stack includes React, Redux, Angular 6, Node.js, Express.js, Botkit, and others. Location: Lviv, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Visa, Unibot, Aldi, Digital Flex Other, non-ML services: custom software development Website: https://chatbots.studio/ 6. Azati Software Since 2001, Azati's experts have cooperated with 330 clients globally. Among the ML competencies of the company there are supervised and unsupervised learning, distributed computing, natural language processing, digital signal processing, and machine learning consulting. Azati’s tech toolkit includes CuDNN, Scikit Learn for Python, and Tensor Flow. The company mainly targets clients in the Insurance domain. Location: Livingston, NJ Established: 2001 Project budgets start at: $1,000 Key clients: MedPro Group, SequenceBase, Casio, Thomson Reuters Other, non-ML services: mobile app development, web development Website: https://azati.ai/ 7. Hidden Brains Since its foundation in 2003, Hidden Brains has cooperated with clients from 107 countries. The company delivers AI, ML, deep learning, and data science services. It also specializes in developing natural language processing software. Hidden Brains also delivers chatbots, including those for Facebook, Telegram, WhatsApp, and Slack. Location: Ahmedabad, India Established: 2003 Project budgets start at: $10,000 Key clients: McAfee, Raymond, Maxtor, SanDisk Other, non-ML services: mobile app development, web development Website: https://www.hiddenbrains.com/ 8. InData Labs Established in 2014, InData Labs houses 50+ data scientists, engineers, and analysts. The company develops ML algorithms and natural language processing solutions. Besides, InData Labs specializes in computer vision and predictive modeling. The company's stack includes Python, SpaCy, TensorFlow, Keras, and more proven languages and frameworks. Location: Minsk, Belarus Established: 2014 Project budgets start at: $5,000 Key clients: Captiv8, Flo Health, Wargaming Other, non-ML services: business intelligence consulting Website: https://indatalabs.com/ 9. Arcanys Since 2010, Philippines-based Arcanys has been delivering its ML and AI services to clients from all over the world. The company specializes in sentiment analysis, recommender systems, and image recognition. As one of its cooperation models, the company provides dedicated teams of highly experienced data scientists and developers. Location: Cebu City, Philippines Established: 2010 Project budgets start at: $100,000 Key clients: General Electric Korea, Enfo, Payment Logic, L’Oreal, Spinetix Other, non-ML services: custom software development, mobile app development Website: https://www.arcanys.com/ 10. MobiDev Since 2009, MobiDev’s IT experts have delivered 350+ software solutions. While headquartered in the US, the company provides its services globally. MobiDev uses the following machine learning technologies: Python, R, C++, Go, TensorFlow, ONNX, and Keras. MobiDev’s mobile ML toolkit includes Core ML, TensorFlow Lite, and Cafe2Go. On top of that, the company provides machine learning consulting. Location: Atlanta, GA Established: 2009 Project budgets start at: $10,000 Key clients: Verizon Media, Comcash, Quality Bridge, SmartTab, SMS Group Other, non-ML services: IoT development, web development Website: https://mobidev.biz 11. Spire Digital For over 20 years that Spire Digital has been providing IT services, it has cooperated with 600+ global customers. Among the ML and AI development services the company offers, there are natural language processing, supervised and unsupervised learning, and sentiment analysis. Also, Spire Digital creates chatbots for Facebook Messenger, Slack, Skype, and Cisco WebEx. Location: Denver, CO Established: 1998 Project budgets start at: Undisclosed Key clients: TomTom, Lockheed Martin, US Navy, USDA, Western Union Other, non-ML services: custom software development, mobile app development Website: https://www.spiredigital.com/ 12. Icreon New York-based Icreon is a digital innovation agency mostly targeting medium-sized businesses. The company uses AI and ML technologies to create innovative applications, platforms, and ecosystems. The company’s technological expertise in ML includes Amazon Web Services, Azure, Caffe2, Google Cloud, Keras, and more. Location: New York, NY Established: 2000 Project budgets start at: $25,000 Key clients: National Geographic Channel, FOX Movies, Harry Winston, Nokia Other, non-ML services: custom software development Website: https://www.icreon.us/ 13. Miquido For eight years, Miquido's team has successfully completed more than 100 projects, including in machine learning. The company offers solutions for AI-based personalization, predictive and behavioral analytics, and churn prediction. Miquido also develops chatbots. Recognized by Deloitte as one the fastest growing companies in Europe, Miquido also offers UI/UX design creation and mobile apps development. Location: Kraków, Poland Established: 2011 Project budgets start at: $25,000 Key clients: Skyscanner, Abbey Road Studios, HelloFresh Other, non-ML services: mobile app development, web development Website: https://www.miquido.com/ 14. Neoteriс Founded in 2005, Europe-based Neoteric offers a wide range of ML solutions, including predictive analytics, recommendation engines, NLP, robotic process automation, and machine learning consulting. The company also specializes in such AI platforms as SaaS Manager, SoapBox, AppOrchid, and RapidSOS. Location: Gdańsk, Poland Established: 2005 Project budgets start at: $10,000 Key clients: LunchBadger, Arvata, IT Trans Other, non-ML services: custom software development, web development Website: https://neoteric.eu/ 15. Unicsoft Established in 2005, Unicsoft employs more than 100 specialists in emerging technologies and software development. Within its ML tech stack, the company offers development of predictive and behavioral analytics solutions, neural networks, and recommendation engines, among other ML applications. The company also delivers consulting services, and maintenance of existing ML platforms. Location: Kiev, Ukraine Established: 2005 Project budgets start at: $5,000 Key Clients: Hoodifood, Executive Eye, GOFORIT, Intellica Group Other, non-ML services: blockchain Website: https://unicsoft.com/ 16. Abto Software With 12 years of its market presence, Abto Software’s has worked with 200+ global customers. Specializing in AI and ML, the company offers custom enterprise solutions for advanced and predictive analytics, virtual assistants, recommendation systems, and intelligent business process automation. Abto Software works with the following tools and frameworks, among others: TensorFlow, Keras, Caffe, OpenCV, and AWS. Location: New York, NY Established: 2007 Project budgets start at: Undisclosed Key clients: Just Answer, Skybow, Medikit, Up Great Other, non-ML services: web development, enterprise app modernization Website: https://www.abtosoftware.com/ 17. Accubits Technologies, Inc. US-located Accubits started in 2012 and has already cooperated with 140+ clients. The company’s ML capabilities include development of conversational and classification tools, video, image and text analytics solutions, as well as predictive analytics software. Besides, Accubits offers machine learning consulting. The company’s experts work with NumPy, PHP, Python, SpaCy, Spark, and several other platforms. Location: Vienna, VA Established: 2012 Project budgets start at: $5,000 Key сlients: Dubai Land Department, Smart Dubai, Eduvalue, Landmark Other, non-ML services: blockchain, custom software development Website: https://accubits.com/ 18. BotsCrew Established in 2016, BotsCrew is a global leader in chatbot development, delivering to mid-sized companies and startups. BotsCrew has its own enterprise chatbot platform that is ready to be customized and optimized according to customers' needs. Besides the proprietary platform, the company offers development of voice assistants and IoT bots. Location: London, United Kingdom Established: 2016 Project budgets start at: $10,000 Key clients: Ailira, IVA, ReVA, Dr. Dean Other, non-ML services: None Website: https://botscrew.com/ 19. STX Next Founded in 2005, STX Next is a custom software development company that specializes in streamlining IT solutions with AI and ML technologies. The company’s main technological advantage is in its exceptionally skilled Python developers. For 14 years on the market, STX Next has delivered over 250 successful projects. Location: Poznań, Poland Established: 2005 Project budgets start at: $25,000 Key clients: Seerene, Verve, Hogarth WW, Pixomondo, Unity Technologies Other, non-ML services: web development, custom software development Website: https://stxnext.com/ 20. Dogtown Media California-based Dogtown Media specializes in delivering mobile applications, enhanced with ML and AI technologies. Besides ML-powered mobile apps, the company provides machine learning consulting. For the last eight years, Dogtown Media has brought to life at least 200 applications. Location: Venice Beach, CA Established: 2011 Project budgets start at: $25,000 Key clients: Google, Lexus, United Nations, Citibank, Red Bull, YouTube Other, non-ML services: mobile app development, IoT development Website: https://www.dogtownmedia.com/ 21. Sigmoidal Sigmoidal is a New-York-based consultancy specializing in machine learning and data science, mostly for companies in Finance, Trading, and Pharma. The company integrates these technologies into web and mobile applications and APIs. It also offers solutions for NLP, computer vision, and predictive analytics. Location: New York, NY Established: 2016 Project budgets start at: $1,000 Key clients: Undisclosed Other, non-ML services: business intelligence consulting Website: https://sigmoidal.io/ 22. Cyber Infrastructure Established in 2003, Cyber Infrastructure is a custom software development company with 100+ clients worldwide. Its ML expertise includes development of virtual assistants and chatbots for a variety of domains. Cyber Infrastructure also provides machine learning consulting and employs Python, Java, C++, Lisp, and R as its primary ML and AI stack. Location: San Francisco, CA Established: 2003 Project budgets start at: $5,000 Key clients: Mannatech, Bell Other, non-ML services: custom software development, web development Website: https://www.cisin.com/ 23. Azilen Technologies US-headquartered Azilen Technologies has 250+ IT experts on board distributed across four offices worldwide. They employ ML technologies to deliver customer service, survey, and informational bots. On top of that, the company delivers facial recognition and predictive analytics solutions. Location: West Bloomfield, MI Established: 2009 Project budgets start at: $25,000 Key clients: Samsung, Pizza Hut, Sodexo, ICICI Bank Other, non-ML services: custom software development Website: https://www.azilen.com/ 24. Stermedia Stermedia has been delivering its ML and data science services since 2007. The company’s dedicated teams of data scientists and software architects design ML algorithms and models for extracting knowledge from big data sets. The company also provides NLP solutions and uses such technologies as TensorFlow and Keras, to name just a few. Location: Wroclaw, Poland Established: 2007 Project budgets start at: $10,000 Key Clients: Family Fabric, WorkService, ISOK Other, non-ML services: custom software development, web development Website: https://stermedia.ai/ 25. Belatrix Having five offices with 600+ professionals all around the world, Belatrix is one of the most fast-evolving machine learning consulting companies on the market today. Its machine learning experts have a proven track record of building various solutions, from predictive modeling to communication automation. Location: Mendoza, Argentina Established: 2003 Project budgets start at: $5,000 Key clients: Disney, Adobe, AOL, FIS, Chatman Financial Other, non-ML services: custom software development, mobile app development Website: https://www.belatrixsf.com/ 26. DataRoot Labs DataRoot Labs is an agency focusing on ML and AI development. It delivers full-cycle ML software development, including project evaluation, machine learning consulting, strategy development, architecture, integration, and support. Besides, DataRoot Labs helps enterprises with digital transformation with AI at the core. Location: Kiev, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Servers.com, Everad, ABM Cloud, Cargofy Other, non-ML services: custom software development, mobile app development Website: https://datarootlabs.com/ 27. Softengi Since 1995, Softengi has completed 900+ projects for clients across six continents. The company’s ML services include chatbot engineering and NLP algorithms. For these purposes, Softengi uses R, Hadoop, and Python, among other tools. Location: Kiev, Ukraine Established: 1995 Project budgets start at: $10,000 Key clients: Boeing, Zeppelin, Enviance, OneRoof Energy Other, non-ML services: custom software development, AR/VR development, IoT development Website: http://softengi.com/ 28. Waverley With 27 years of software development experience, Waverley is a company housing 370K talented engineers. Among other services, they offer ML-centric solutions such as data clustering and unsupervised algorithms. On top of that, the company offers NLP and computer vision solutions and chatbots. To deliver ML products, Waverley’s engineers use Java, Python, Scala, and R. Location: Palo Alto, CA Established: 1992 Project budgets start at: $50,000 Key clients: Mozilla, Qualcomm, Swisscom, American Express Other, non-ML services: custom software development, mobile app development, IoT development Website: https://waverleysoftware.com/ 29. Imaginovation Founded in 2011, Imaginovation is a software development agency that has successfully completed 300+ IT projects of various complexity. As for ML and AI, the company develops recommendation and effectiveness assessment engines as well as solutions for speech and text processing automation. Location: Cary, NC Established: 2011 Project budgets start at: $10,000 Key clients: MetLife, Nestle, UNC Pembroke, Contractor Score Other, non-ML services: custom software development, mobile app development, web development Website: https://www.imaginovation.net/ 30. E2logy Established in 2005, E2logy has delivered 300+ projects to businesses of various sizes. Being a leading Amazon Web Services and Azure AI vendor, the company develops ML algorithms and chatbots. They also offer machine learning consulting. In addition to their ML services, E2Logy brings custom IoT solutions to its global customers. Location: Ahmedabad, India Established: 2005 Project budgets start at: $5,000 Key clients: McMillan Publishers, Polaris Industries, Vodafone Other, non-ML services: custom software development, web development Website: https://e2logy.com/ This was a full list of the top 30 machine learning consulting companies in 2019. If you cooperated with any of the presented vendors, please do get in touch and share your story.

Top 30 Machine Learning Consulting Companies

Thanks to big data, today an organization can quickly obtain the necessary information from an unordered data set and deploy it effectively. The growing popularity of big data analytics has led to a significant increase in the number of companies providing big data solutions and related services. Today, there are in the global market, and this number may be very confusing. Big data skills, experience, and related technical expertise can significantly vary among vendors, which may puzzle anyone looking for a big data service provider. almost 1000 big data analytics companies How to identify the best big data consulting firms I set myself the goal to identify the most reliable big data consulting companies on today’s IT market. To do this, I investigated hundreds of data providers and studied their characteristics. Each company was analyzed according to the set of specifically designed criteria. Among the criteria are big data expertise, major clients, offering strategy, track record, and online reviews. Below you can see the results of the conducted market research. 1. Itransition Founded in 1998, Itransition is a custom software development company that provides big data consulting and platform engineering services. The company also offers business intelligence (BI) portals development and boosts them with machine learning (ML) and other technologies. The company offers data store design, data migration, performance optimization, and support services. The company’s client base includes IBM, Expedia, Toyota, eBay, Xerox, PayPal, and Adidas. With more than 150 completed projects and 810 clients worldwide, IAOP recognizes the company as one of The Global Outsourcing 100. Denver, CO Headquarters: Expedia, Xerox, Toyota, eBay, IBM, PayPal, Adidas Core clients: $25 — $49 Pay per hour: 1000–9999 Number of employees: Website: https://www.itransition.com/ 2. Saviant Consulting Saviant is a consulting company with more than 120 experts in its team and more than 100 clients in various countries, including UK, Canada, and the US. The company specifically focuses on Energy, Logistics, and Retail. Among the company’s big data services are big data analytics and management, full-cycle big data solution development, and more. Pune, India Headquarters: JJ Food Service, BuilderMT, Transparent Technologies, Inc Core clients: Not Revealed Pay per hour: 50–249 Number of employees: Website: https://www.saviantconsulting.com/ 3. Squadex Headquartered in California, USA, Squadex is a technology consulting and engineering company that provides big data services. It offers big data analytics, consulting, and big data platform engineering. Squadex is an AWS certified consulting partner that works with companies of different size, from small startups to large enterprises. Palo Alto, CA Headquarters: Model N, NinthDecimal, Nitrio Core clients: Not Revealed Pay per hour: 250–999 Number of employees: Website: https://squadex.com/ 4. CBIG Consulting Founded in 2002, CBIG Consulting is an American company offering business intelligence and big data analytics solutions. Besides analytics and consulting, the company provides big data solutions architecture. For this purpose, CBIG Consulting employs Hadoop distributions, Hive, Sqoop, etc. The company’s industrial focus is on Finance, Healthcare, and Retail. Chicago, IL Headquarters: Abbott, Astellas, Baxter, Cigna, DePaul University Core clients: $150 — $199 Pay per hour: 50–249 Number of employees: Website: https://www.cbigconsulting.com/ 5. Clavax Technologies Founded in 2011, Clavax is a software development company headquartered in San Jose, California. It delivers big data consulting and implementation. By employing big data technologies, the company provides services for data warehousing, data processing, and data integration. Clavax’ client list includes PWC, Football.com, Bookfari, and Honeywell. San Jose, CA Headquarters: PWC, Football.com, Bookfari, Honeywell, Jabil Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.clavax.com/ 6. ThirdEye Data ThirdEye Data is a big data services company that delivers big data consulting, analytics and engineering while using artificial intelligence, deep learning, and machine learning. ThirdEye Data has served such companies as Nokia, Ford, Amgen, GettyImages, Actian, and others. The company focuses on Finance, Government, IT, Retail, and Manufacturing. Santa Clara, CA Headquarters: Microsoft, Nokia, Ford, Amgen, GettyImages, Actian, Google Core clients: $50 — $99 Pay per hour: 50–249 Number of employees: Website: https://thirdeyedata.io/ 7. Signity Solutions Established in 2009, Signity Solutions is a company that delivers custom software development and big data solutions. The company helps with big data analysis and brings data solutions empowered by machine learning. Signity works for the Commerce, Education, Media, and other industries. Rajiv Gandhi IT Park, India Headquarters: Samsung, Grupio, The Project Factory Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.signitysolutions.com 8. Caserta Founded in 2001, Caserta is a NY-based IT company that delivers a wide range of data-related services. The company offers big data consulting and solution architecture/development. Caserta also provides data visualization, data warehousing services, and more. The company has worked with such clients as New York Times, Capital One, Equinox, and HBO. New York, NY Headquarters: New York Times, Capital One, Equinox, HBO Core clients: Not Revealed Pay per hour: 10–49 Number of employees: Website: https://caserta.com/ 9. RapidOps, Inc RapidOps is a custom software development company based in the USA. The company’s expertise includes big data analytics, consulting, and visualization. The company’s team of 60+ professional developers delivers its services to the Manufacturing, Retail, and IT industries. Charlotte, NC Headquarters: YoDish, Zurn Industries, DassaultSystemes Core clients: $25 — $49 Pay per hour: 50–249 Number of employees: Website: https://www.rapidops.com/ 10. Aegis Soft Tech Since 2003, Aegis Soft Tech has been providing software development to Healthcare, Finance, Retail, Manufacturing, and other industries. Aegis provides big data consultation, management, and analysis. Among that, Aegis helps with data migration processes. The company’s pool of developers expertizes in Hadoop, Cloudera, and Hortonworks platforms. Rajkot, India Headquarters: Sunzinet, Zydus, PointRF, Efacec Core clients: < $25 Pay per hour: 50–249 Number of employees: Website: https://www.aegissofttech.com/ 11. GetInData GetInData is a Poland-based company that unites more than 30 professionals. The company has carried out more than 30 projects. GetInData delivers the following big data services: big data infrastructure development, analytics, consulting, engineering, and maintenance. GetInData delivered its services to such companies as Spotify, Allegro, Agora, and Freshmail. Warsaw, Poland Headquarters: Spotify, Allegro, Agora, Freshmail, Truecaller, GoEuro Core clients: $50 — $99 Pay per hour: 10–49 Number of employees: Website: https://getindata.com/ 12. Kavi Global Established in 2008, Kavi Global is an analytical company that delivers a whole spectrum of big data services. They include consulting, big data solution architecture, and platform implementation. The company also offers data mining and data management services. Kavi Global’s industrial expertise includes Transportation, Manufacturing, Education, and Healthcare. Barrington, IL Headquarters: General Electric, TEAMHealth, Thomson Reuters, Ryder Core clients: $150 — $199 Pay per hour: 10–49 Number of employees: Website: https://www.kaviglobal.com/ 13. Indium Software Since 1999, Indium Software has been providing technological solutions for 19 various industries. The company has worked with 750 clients from all over the world. Employing such technologies as Hadoop, Spark, and Apache Kafka, Indium Software delivers big data architecture and consulting services. Princeton, NJ Headquarters: Not Revealed Core clients: < $25 Pay per hour: 250–999 Number of employees: Website: https://www.indiumsoftware.com/ 14. Fayrix Israel-based Fayrix is a custom software development company and big data services provider with 1530 IT experts on board. The company deals with various projects, from initial data analysis and consulting to big data infrastructure design, big data software development, and its implementation into a client’s existing technological landscape. Pitu’akh, Israel Headquarters: Biosence Webster, SCR, S7 Airlines Core clients: < $25 Pay per hour: 1000–9999 Number of employees: Website: https://fayrix.com/ 15. Denologix Established in 2002, Denologix is an analytics and information management company providing a wide range of big data services, including data analysis, consulting, and big data solution implementation and support. The company deploys a large number of technological tools for big data, including Hortonworks Data Platform, Cloudera СВР Hadoop Distribution, and more. Among the company’s clients are Adidas, Allstream, Aviva, Canada Post, and others. Toronto, Canada Headquarters: Adidas, Allstream, Aviva, Canada Post, Bell, Brookfield Core clients: $150 — $199 Pay per hour: 50–249 Number of employees: Website: http://www.denologix.com/ 16. Data Reply Established in 2015, Data Reply is a big data and analytics consultancy based in London, UK. Besides big data consulting and training, the company offers data engineering and data science services. The company focuses on such industries as Retail, Media, Telecommunications, and Commerce. London, United Kingdom Headquarters: Not revealed Core clients: $100 — $149 Pay per hour: 10–49 Number of employees: Website: https://www.datareply.co.uk/ 17. Dexlock Established in 2012, Dexlock is an Indian company that delivers big data services. Among them are large data store development and deep learning/machine learning implementation. The company’s technological expertise includes Hadoop, Apache Spark, Apache Kafka, and more. Dexlock works with the Healthcare, Retail, Insurance, Energy and other industries. Kochi, India Headquarters: FindCharisma, Splash of Colors, StatFuel, MyPeepz Core clients: < $25 Pay per hour: 50–249 Number of employees: Website: http://www.dexlock.com/ 18. 47Billion 47Billion is a bespoke software development company that provides mobile and web development and UI/UX design. Besides these services, the company delivers end-to-end big data analysis, consulting, and engineering. The company’s industrial focus includes IT, Gaming, and Consumer Services. 47Billion’s clients include Cisco, Nazara, and Apsalar. Bangalore, India Headquarters: Cisco, Nazara, Apsalar Core clients: : $25 — $49 Pay per hour 50–249 Number of employees: Website: https://47billion.com/ 19. 51zero 51zero is a UK-based company that specializes in big data services providing. The company delivers big data consulting and solutions development and focuses on the financial service domain. The company’s pool of technological experts deploys such big data technologies as Spark, Apache Kafka, Docker, and others. London, United Kingdom Headquarters: HSBC, The Royal Bank of Scotland, Barclays Core clients: $200 — $300 Pay per hour: 2–9 Number of employees: Website: https://www.51zero.com/ 20. SCI-BI SCI-BI is a company based in India. With more than seven years of experience, the company specializes in big data consulting and engineering. The company also helps with data integration strategy. SCI-BI’s big data technological expertise includes Cloudera and Hortonworks. The company focuses explicitly on Logistics and Retail. Chennai, India Headquarters: Executive Planning Services, USA SaasByDesign Inc, USA Caravel Logistics Core clients: < $25 Pay per hour: 10–49 Number of employees: Website: http://sci-bi.com/

The Top Big Data Consulting Firms

2019 is the year of adoption. We have weathered the storm of early 2018, lived through the ensuing drought and we can see glimpses of the promised land on the horizon. At , we are strongly passionate about overall adoption of blockchain technology as we believe it can benefit many people and industries. As such, we are always on the look-out for news of increasing adoption and collaboration. aelf Last year I wrote an article with a as I found it very difficult to find a single source for this information online. Comprehensive List of Banks using Blockchain tech Although there was a strong interest in blockchain tech over the last 24 months, due to a mixture of issues, including the volatile ‘unproven’ market and rampant ICO scams, many companies avoided public exposure linking them to the blockchain industry. Many would agree we have seen a shift in this mindset as more high profile individuals and companies are now being more open about their association with blockchain. Take for example, the #1 YouTuber, . Pewdiepie, who has recently partnered with Dlive In fact, according to a by Gemalto, adoption of blockchain to help secure IoT doubled in 2018. report Deloitte recently released survey results in regards to . The companies represented by these executives all have an annual revenue of over $100M USD. One point to note, was that over 75% of respondents believe they will lose a competitive advantage if they do not adopt blockchain. Did someone say FOMO? executive’s views on blockchain adoption Also, according to the survey, over 90% of respondents are already participating in or planning to within 12 months, a blockchain consortium or alliance. According to the , global spending on blockchain development will almost double to $2.9 Billion this year, and increase exponentially to reach $12.4 billion by 2022. International Data Corporation The financial sector will lead the charge with an estimated of $1.1 Billion to be spent in this sector alone. The Manufacturing and resources sector will see the fastest growth this year with a growth rate of 77.6%. Notably, of (Statisca.com), over 50% are included in this list; and in particular, 9 out of the top 10 with a combined market value of over $5.6 trillion USD in 2018. the 100 largest companies in the world by market value Surprisingly, the largest industry in this list is Transport, Shipping & Logistics. There could be a few reasons due to this, firstly, there were a few large alliances that have been created specifically to aid in blockchain adoption with members in the hundreds. Secondly, this is one of the biggest industries in which there is a high degree of interdependence and cross company reliance. The finance industry was always going to be in the top of this list, but another exciting development is the high rank that government received with many countries showing a clear positive attitude towards the technology. In light of all this, it is safe to say, blockchain is here to stay and 2019 is the Year of Adoption. So let’s take a look at the enterprises who have already taken the leap. This list is focusing on active enterprises in 2019. For those that are wondering, this list has almost 1,000 Enterprises in it. *There may be early adopters that are not in this list due to having little activity in the first quarter of this year. If there are ones that are active and not on this list, comment below with the links and I’ll aim to add them in **Accomodation** Cheval Blanc The Dolder Grand **Agriculture** Bayer Crop Science Bumble Bee FoodsCargill **Auto Industry** Hyundai BMW Ford General Motors Genuine Parts CompanyGreat DaneHummer Jaguar Karamba Security Volkswagen Waymo Data **Automated Retail Companies**AAEONAR SystemsFastcorp VendingGlobal Vending GroupGreenbox RoboticsInvendaIVMIVSRetail Automated ConceptsSandenVendoThe-Venders and Wemp **Communication** Deutsche Telekom NEC Europe Smart Communications Solace Corporation Swisscomm Broadridge China Mobile Comcast FarEasTone Kakao OrbcommOrange SoftBank Sprint Telefónica **Consultancy**21 Consulting GmbHAccentureAdaptive Financial Consulting Bain & CompanyBCS ConsultingBerkeley Research Group CH & CoCOMPELLIO DNV GL Business Assurance Group Everis InfosysJDX Post Oak LabsProtivitiRaoof Consultancy Limited Scrypt.MediaSynechronSynpulse XLNTEC Thomas ReutersThoughtWorksTigerRiskTomorrow Labs AizoonAONAsset Balance Solutions Cambridge Associates Deloitte enVista EY J.J. Keller & Associates Inc KPMG RevUnit ThreatAdvice Tata Consultancy Services **Data Managemen**Accuity CommodDT EverisInterxion RoyaltyRange Ansonia Credit DataApplya Connected Holdings Docusign Nielsen Ocean Protocol **Digital Security**CrossVerifyGemaltoGuardtimeObjectTechSLIBSovrinUtimacoYoti **Education** RMIT Duke University Harvard University Swinburne University The Air Force Institute of TechnologyThe University of TennesseeUniversity of Central ArkansasUniversity of Denver University of Colorado Boulder University of Memphis University of Michigan University of Nevada Yale University **Finance**ABN AMROAIBAl Rajhi Banking & Investment CorporationAlawwal BankAlfa Bank Allied Irish Bank ANZ Banking GroupATB FinancialB3Banca Intesa SanpaoloBanca MediolanumBanca MifelBanco BICEBanco BisaBanco Bradesco Banco de Crédito del PerúBanco de Investment GlobalBanco de SabadellBanco de Galicia y Buenos AiresBanco IndustrialBanco Mercantil del NorteBanco Mercantil Santa CruzBanco Nacional de BoliviaBanco Popular DominicanoBanco RegionalBanco SantanderBangkok BankBank ABCBank Al Habib LimitedBank BusanBank Frick & CoBank of America | Merrill LynchBank of CyprusBank of Montreal BankinterBanorteBanreservasBanque Internationale de Commerce — BRED (Suisse)Banque ThalerBarclays BCIBladex BNY Mellon Bolsa de MadridBPCE BRAC Bank LimitedBradescoCBH Compagnie Bancaire HelvétiqueChina Banking AssociationChina Merchant BankChina CITIC Bank International LimitedChina Guangfa BankChong Hing Bank LimitedCIBCCIM Banque S.A GeneveCitiBankCLS GroupCommercial Bank of Africa LimitedCommerzBankCommonwealth Bank of AustraliaCooperatieve RabobankCredicorp BankCredit Agricole S.A.Credit Libanais SAL CTBC BankDaiwa Securities GroupDanske BankDavivienda DenizBank A.S. Deutsche BörseDigiledgeDianrongDNB Ernst & Young European Central Securities Depositories Association First America BankFulton Financial CorporationGazprombank Habib Metropolitan Bank LimitedHo Chi Minh City Development JS CommercialHQLA ICICI Bank LimitedIndovina Bank Ltd Intesa SanpaoloItaú Joint Stock Commercial Bank for Investment and Development of VietnamJoint-Stock Company BCS-Investment BankJPMorgan ChaseJS Bank Limited KASIKORNBANK Public Company LimitedKB Financial GroupKEB Hana BankKuveyt Turk Katilim Bankasi Macquarie Bank MauBank LimitedMizuho Bank National Bank of CanadaNational Bank of EgyptNational Bank of Kenya LimitedNational Bank of KuwaitNatixis Norddeutsche Landesbank GirozentraleNordeaNorthern TrustOmidyar NetworkOP Financial Group Paris Europlace PKO Bank PolskiPortal FinancePrime Bank Ltd.Privatbank IHAG Zurich PT Bank Central Asia TbkPT Bank CIMBNiaga TbkQiwiRaiffeisen BankRBCRBSRCBCRCI Bank & ServicesResona Bank, Ltd. Reyl & Cie SA Satoshi Systems ScopusSEB Group Shinhan BankShuttle HoldingsSIA SMBCSnapCheck SRI Capital Stock Exchange of ThailandSuMi TRUSTSunTrust Synchrony Bank Temasek Holdings The First International Bank of Israel Ltd The Toronto-Dominion Bank TradeCloudTradeIXTradeWindTurkiye Finans Katilim BankasiUBSUniCreditUnion Bank Of The PhilippinesUSBancorpVietnam Joint Stock Commercial Bank for Industry and Trade Volksbank Mittweida Wells FargoWestpac Woori Bank Alipay Ant Financial Banco BTG Pactual Bank MellatBank Melli IranBank Pasargad BBVA Bloomberg BNP Paribas Boerse Stuttgart Digital Ventures BP PLC Credit Suisse DBS Deutsche Bank DTCC Eastern Caribbean Central Bank Employees’ Retirement System eToro Fidelity Investments Goldman Sachs HSBC ING International Chamber of Commerce International Securities Services Association Jamica Stock Exchange Julius Baer Linx Partners London Metal Exchange London Stock Exchange Luxembourg Stock Exchange Mastercard Morgan Creek MUFG NASDAQ Nomura Pakistani Central Bank Paypal Proof of Capital Revolut Santander SBI Holdings SGX Signature Bank Silvergate Bank SIX Group Société Générale SoFi Square Standard Chartered Bank Swissquote SWIFT Telenor Microfinance Bank Thai Stock Exchange The National Payments Corporation of India Thomson Reuters Eikon Tora Virginia’s Police Officer’s Retirement System Visa WEG Bank Western Union **Government** Caisse des dépôts et consignations CSIRO United NationsU.S. Department of Agriculture Australian Federal Government AUSTRAC Brazil’s Ministry of Planning, Budget & Management Canadian Municipality of Innisfil City of Denver Colorado State Government Colorado Water Institute Commerce Committee of Connecticut General Assembly County Auditors’ Association of Ohio Department of Homeland Security Government of Kyrgyzstan Government of Rwanda Illinois Department of Financial & Professional Regulation Illinois Department of Transportation Luxembourg Government Malta Financial Services Authority Ministry of Land & Natural Resources, ZambiaMunicipal Government of Tulum, Mexico Municipal Government of Tsukuba, Japan New York City Economic Development Corporation Pennsylvania’s Economic Development Office (Venture Capital Arm) Seoul Digital Foundation Singapore Government South Korea’s Defence Acquisition Program Administration South Korea Internet & Security Agency Thailand’s Ministry of Science & Technology UK Parliament U.S. Department of Energy U.S. Department of State U.S. House of Representatives West Virginia State Government Wyoming State Government **Insurance** MetLife Ping An Insurance Aegon AIA Allianz AXA Generali Health Care Service Corporation MAPFRE RE Munich Re State Farm Swiss ReTokia Marine TrueNorth XL Catlin ZhongAn Zurich **Internet** Google Twitter Baidu Digital Garage Facebook Opera **IT Solutions**7CommAKonceptsAmazon Web ServicesAOS SASAppwayAt.cashaXpireBillon Group LtdBlanc LabsBoleroBRQ Digital SolutionsCalypso TechnologyCanonical Česká fintech asociace, z.s. CIeNETCMACOCUS AGCodeITCognizantcoIB Eclipse FoundationEPAMEvernym FinastraFinFabrik GFT TechnologiesGiant MachinesGoogle CloudGuildOne Hewlett Packard EnterpriseIBM Inacta AGInfosys Finacle iSiDLG CNSMagiaDigitalMicrosoftNec NTT Data PCCW SolutionsRedhat Vyoma SoftwareWiproZirconTech Capgemini Cass Information Systems Inc. CISCO Collabrian EXL Fujitsu Technology Solutions HCL Imaginnovate Intel Neuro Red Oracle Salesforce SAP SE VMWare **Legal**ACCORD ProjectCOALA National Council of Clerks (France) Linklaters Orrick Taylor Associates **Medical** COCIR European Federation of Nurses Associations Abbvie AnthemBig Blue Cardinal Health Ciox Health CVS Health Genentech HumanaMultiplan NMC Healthcare Optum Pfizer Quest DiagnosticsUnited Healthcare **Media**Les Échos _CBS_Fox Business Network **Mining** BHP Billiton GoldcorpKutcho CopperLG CorpOcean Partners USAWheaton Precious Metals **Non-Profit** Oxfam Freshwater Trust UNICEF **Online Marketplace** Moonlighting **Pharmaceuticals**Amgen MerckPfizer Sanofi Boehringer Ingelheim Roche **Real Estate** Convexity Properties Emaar Group Greenbrier Management Growth Capital ServicesICP Medici Land Governance St. Regis Aspen Resort **Retail & Manufacturing** Acqua di Parma Ardbeg BelvedereBenefit CosmeticsBerlutiBodega Numanthia BulgariCaffe-Pasticceria CovaCape Mentelle CelineChandonCharles & KeithChâteau Cheval BlancChâteau d’YquemChaumetCloudy BayDFSDiorDom PerignonEmilio PucciFendiFenty Beauty by Rihanna FREDFresh (Cosmetics & FragrancesGivenchyGlenmorangie GuerlainHennessyHouse of BijanHublotKalaKat Von D BeautyKenzo KrugLa Grande Epicerie LoeweLoro pianaL’Oreal Maison Francis KurkdjianMarc JacobsMercierMoet & ChandonMoynat Naja Newton VineyardNicholas Kirkwood RimowaRuinartSephoraStarboard Cruise ServicesTAG Heuer Terrazas de los AndesThomas Pink Veuve ClicquotVolcan di mi tierra WenjunZenith A. Favre & Fils AB InBev Anheuser-Busch InBev Albertsons Companies BASF Bridgestone Carrefour Foxconn Golden State Foods Kroger Levi Strauss Louis Vuitton Naked Brand Group Nestle Nike Overstock Parfums Christian Dior Rakuten Target Unilever Walmart **Sports Teams** U.S. LA Kings (Ice Hockey) **Technology** Dell HuaweiLenovo T-Mobile Aidant Technologies AMD Apple Avnet Bosch Foxconn Fujitsu Hewlett PackardHitachi HTC LG NASA Nvidia PitneyBowes RCS Global Samsung Sapphire Technology Seagate TechnologySiemens Sony SweetSense Tencent TSMC **Transport, Logistics & Shipping** CMA CGM ERTICO Feadship Penske 10–4360dataABAQ.USAble FreightAdventAGForceAITAgistixAltriaAmaltoAmerican Global LogisticsAmerican Short Line & Regional Railroad AssociationAMTRAMXAnhueser-BuschApex APL ArcBest CorporationAriaLogisticsAriesArrive LogisticsArrowATSAxle LogisticsBCGBest TransportBlueGraceBlue HorseshoeBourque LogisticsBP BNSF Railway BoyleBoxLockBreakthrough FuelBucklandBurris LogisticsC.H. 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IndustriesPackygePAMPanalpinaPantos LogisticsPaschall Truck LinesPavestone CapitalPegasus TransTechPeir Phillips Connect TechnologiesPhoenix Capital GroupPilot Freight ServicesPitt OhioPlatform SciencePLMPolarisPrecisionPrime3Project44ProTransPTIPurolatorPWCQITR & R ExpressRaider ExpressRail IncRay-Mont LogisticsRedwoodReliance PartnersRIMRoadrunner Transportation SystemsRoadSyncRouteiqueRSI LogisticsRTWAirRUANRyderSAIASaint John Capital Schneider Trucking SCLogicScopelitisSeaboard MarineSharelogisShawSheerShipwellSimpleLogisticsSinglePoint CapitalSkyBitzSmartHopSMC3SolarisSouthEastern Freight LinesSource Alliance NetworkSpireonSuddathSunsetSupplyOnSyfan LogisticsTadmoreTAFSTeam WorldwideThe Home DepotThird Coast LogisticsTideworks TechnologyTMCTMW SystemsTRAC IntermodalTraffixTransCardTransfixTransfloTransLytixTransPlaceTransportationInsightTransProTranzlogixTranzportTrinity LogisticsTrinium TechnologiesTriumphPayTrucker PathTruckstopTruck ToolsUber FreightUNISUnion Pacific UPS U.S. XpressUSA TruckVelocity TransportVHI TransportVitesseVnomicsWataco Supply Chain ServicesWELWernerWhirlpool CorporationWhite ArrowWiseTech GlobalWolf DirectWOLTWorldwide ExpressWWS TradingXLNTECYRC WorldwideZoof-It **Utilities** ENGIE Repsol WASSHA (DIGITAL GRID Corporation) Concentric Gazprom Petroteq TEPCO Wien Energie **Noteworthy Individuals** — Russian Opposition Leader — U.S. Presidential Candidate — South Korean Actor — Inventor of World Wide Web — Founder SpaceX & Tesla — French President — CEO Twitter & Square — Retired NBA Basketball Player — №1 Youtube Channel — LinkedIn co-founder — Professional Tennis Player — CEO Berkshire Hathaway & Billionaire investor Alexei Navalny Andrew Yang Bae Yong-joon Tim Berners-Lee Elon Musk Emmanuel Macron Jack Dorsey Kobe Bryant Pewdiepie Reid Hoffman Serena Williams Warren Buffet <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

A Comprehensive List of Enterprises Partnering with blockchain in 2019

The State of The Market — February 14, 2019 : $3,611.84 (-0.52%) : $121.70 (-0.93%) : $0.302635 (-0.38%) BTC ETH XRP The market is in negative today, as Bitcoin is testing its support at $3,600. It is not a strong support yet, and if bears have it their way, Bitcoin could once again crash to $3,400 over the weekend when the volumes are low. The total market cap lost $1 Billion in the last 24 hours, and all of the top 10 cryptocurrencies have posted small losses. The market is following Bitcoin’s price movement right now and Bitcoin dominance remains unchanged. In other news, . The function adds a new attack vector to the platform and allows a smart contract to be created at an address that can be determined beforehand by different parties. The new function is designed to allow interaction with a smart contract that does not yet exist on the blockchain and it would be included in EIP-1014. Also, . Hyundai Pay has developed a blockchain platform and a cryptocurrency wallet. The partnership is to promote the growth of fintech startups and jobs in Busan. Hyundai Pay will relocate its headquarters to Busan and build the needed infrastructure. Ethereum co-founder Vitalik Buterin has suggested that a new smart contract function called “Create2” be integrated into the upcoming Constantinople upgrade Busan, South Korea’s second-most populated city after Seoul has signed a Memorandum of Understanding (MoU) with blockchain company Hyundai Pay According to Intel, the blockchain suite is designed for businesses desiring to launch their own blockchain fast and effectively. The platform will allow users to develop blockchain-based applications and it uses Xeon processors along with other Intel hardware. Intel says the suite includes all the necessary components to launch a hyperledger-driven blockchain and Intel says that the package is “enterprise-ready”. ( ) 1) Market materials from Intel show that the company has launched a commercial blockchain package based on Hyperledger’s ecosystem. Read More XBT launched the world’s first Bitcoin tracker, Bitcoin Tracker One in 2015 and subsequently launched Bitcoin exchange-traded note (ETN). XBT said it needs more knowledge about hard forks and different communities before listing coins. XBT’s CEO Laurent Kssis said, “If we get it wrong, these assets will drop and if they’re part of the basket we can’t go back because it’s in the final term-sheet.” XBT wants the crypto market to settle and give further clarity before revisiting its plans. For now, it has been shelved without a launch date. ( ) 2) Sweden’s XBT Provider has canceled the launch of its cryptocurrency basket due to volatility. Read More The company is hoping to improve trust, transparency, patient safety, and patient improvement in clinical trials using blockchain. Currently, clinical trial processes and records are often erroneous or incomplete. Boehringer will test the integrity of blockchain in this sector with IBM. Early last year, Pfizer, Amgen and Sanofi were considering blockchain for developing and testing new drugs and another pharma firm Merck filed a patent to use blockchain in manufacturing medicines. Also, the Indian government plans on using blockchain to prevent counterfeit drugs. ( ) 3) German pharmaceutical multinational Boehringer Ingelheim has announced that it is set to trial IBM Blockchain in clinical trials. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

Intel Launches A Commercial Blockchain Suite, And XBT Postpones The Launch Of Its Crypto Basket

Real world Use Cases of Blockchain in Pharma and Healthcare The blockchain technology is well suited for use cases involving track-and-trace, product provenance and supply chain governance. The pharmaceutical industry is actively exploring the blockchain technology to help with several real world use cases. Many of these explorations already have proof-of-concept implementations that have generated tractable interests in the Pharma industry. In this post we will review the top 5 of these use cases. Use Case 1: Verifying the Authenticity of Returned Drugs In the Pharma industry, drugs are frequently returned to the pharmaceutical manufacturers. For instance, wholesalers may have ordered excess inventory and accordingly may need to return unsold stock to the pharmaceutical manufactures. While the proportion of the returned drugs is small compared to the sales (about 2–3% of sales), the per year volume is in the range of $7- 10 billion. In 2016, the US pharmaceutical manufacturer sales were . The 2018 forecast revenue for the top ten global pharmaceutical companies (which represent half of the sales of the top fifty companies) is . Based on these estimates, the saleable returns market at 2–3% of total sales is 7–10 billion. $323 billion $355 billion Instead of destroying these perfectly good drug shipments, pharmaceutical companies instead opt to . However before they can re-sell these returned drugs, the pharmaceutical companies have a re-sell them legal obligation to verify the authenticity of the returned drugs. In the US, the Drug Supply Chain Security Act (DSCSA) stipulates that all US manufacturers must implement serialization or barcoding of drugs at a package level by November 2018. Also, by the same time next year these serial numbers must be used to verify the authenticity of the returned drugs. Europe has a similar regulatory enactment called the Falsified Medicine Directive (FMD), requiring all drugs to be serialized or barcoded by February 2019. While the EU has opted for a (allowing manufacturers to upload serial numbers to a centralized EU regulator’s database, and distributors to connect to this centralized database to verify drug authenticity), centralized approach in the US there is no centralized database regulator. Without such a centralized database, one option is for every distributor to integrate data with every pharmaceutical manufacturer, which is logistically prohibitive! A far better and recommended approach is to have pharmaceutical manufacturers record the serial numbers of their packages on a blockchain, which serves as a decentralized and distributed ledger. Wholesalers and customers can then verify the authenticity of a drug package by connecting to the blockchain. Industry Proof-of-Concept Currently, Merck in partnership with SAP has developed the SAP Pharma Blockchain POC app for this Use Case. SAP launched this first Proof of Concept in Q3 2017, and a second one in early 2018. The PoC for the Use Case works as follows, SAP’s existing solution (unrelated to blockchain) called ATTP (Advanced Track and Trace for Pharmaceuticals), generates unique identifiers for a drug package. When a manufacturer ships a package they register the item on the SAP Pharma POC blockchain, with the four pieces of information generated by the ATTP; the item number (based on GS1 standard), a serial number, a batch number, and an expiration date. The distributor can extract the four pieces information from the packaging’s barcode, using a simple scanner mobile app, allowing them to verify returns . Counterfeiter copies of barcodes can be avoided, since SAP has the added ability to track every time a package changes hands. A map view within the mobile app also helps ensure that the drugs are in the expected geographical region. Use Case 2: Prevention of Counterfeit Drugs and Medical Devices Blockchain’s ability to establish provenance of data makes it especially suited for this supply chain use case. ​Drug companies that manufacture, ship and deliver products have a difficult time keeping track of their products, thereby allowing counterfeiters to introduce fake drugs into the system. The problem of counterfeiting is not just limited to drug manufactures, but extends to medical instruments manufactures. The World Health Organization (WHO) estimates that 8 percent of the medical devices in circulation today are counterfeit copies. Counterfeit drugs and medical devices pose a major risk to consumers, and also lost revenue for the legitimate manufacturers. When the mandate goes into effect by November 2018 every drug package will be uniquely serialized or barcoded. This will provide a unique product identifier for each drug package, and allow the verification of the authenticity of every product sold. Drug Supply Chain Security Act (DSCSA) As the drug moves through the supply chain the transactions can be recorded on a blockchain, thereby providing a distributed provenance ledger. This will make it possible for all parties to track drugs through the entire supply chain life-cycle. This will make it harder for counterfeit drugs from being introduced into the supply chain, and distributed to unsuspecting consumers. Industry Proof-of-Concept Novartis is experimenting with blockchain to identify counterfeit medicines and track temperature with real-time visibility for all participants in the supply chain, using Blockchain and IoT. The initiative is led by Fritz, the Novartis’ Domain Architect Supply Chain, and Cuomo, Novartis’ Applied Technology Innovation Manager. Novartis is also currently engaged in developing a consortium blockchain network between the European pharmaceutical industry and the EU, called the Innovative Medicine Initiative(IMI) Blockchain Enabled Healthcare program. The consortium will comprise SME blockchain companies, universities, clinical labs, hospitals, patient representatives and others; and aims at exploring use cases in counterfeit drug detection, supply chain, patient data, and clinical trials. Use Case 3: Compliance in Pharma supply chain As drugs move through the supply chain, logistics companies need to adhere to drug handling, transport and storage guidelines. Several operating constraints may need to be tracked. These may include maintaining temperature range, humidity, air quality within specified limits etc. Environmental conditions within the supply chain may have a direct impact on the quality and efficacy of the drug. For example temperature sensitive products like vaccines need to be properly monitored throughout the supply chain. Today drugs are monitored throughout the supply chain using smart IoT devices and temperature, humidity and other factors can be recorded using smart devices throughout the supply chain life-cycle. However since each participant of the supply chain i.e. the manufacturer, the logistics company, the stores and the pharmacies etc, maintain their own separate ledger, a problem within any particular segment of the supply chain is difficult to track. Blockchain technology provides the pharmaceutical supply chain a better way to add compliance and governance within the supply chain. Due to its inherent transparency, immutability and distributed nature, Blockchain technology provides a mechanism that allows any participant in the supply chain to ensure that the supply chain logistics and transportation guidelines (including handling and storage conditions of the drugs), were adhered to. Additionally smart contracts can be programmed that automatically execute when compliance conditions are not met, there by alerting the relevant parties in the supply chain. So for example suppose, if there is a temperature excursion during a transportation, with blockchain technology the consumer can easily see at what point the event occurred. Additionally, smart contracts may be automatically invoked within the blockchain, which will reduce the invoice amount due to the logistics company, based on its level of non compliance of storage conditions. Use Case 4: Transparency and traceability of consent in Clinical Trials Informed patient consent involves making the patient aware of each step in the Clinical Trial process including any possible risks posed by the study. Clinical trial consent for protocols and their revisions need to be transparent for patients and traceable for stakeholders. However, in practice the informed consent process is difficult to handle in a rigorous and satisfactory way. The FDA reports that almost 10% of the trials they monitor feature some issues related to consent collection. These include: failure to obtain written informed consent, unapproved forms, invalid consent document, failure to re-consent to a revised protocol and missing Institutional Review Board approval to protocol changes; among others. Frequently also there are reported cases of document fraud such as issues of backdating consent documents. Blockchain protocols in clinical trials can provide transparency and traceability of consent. Blockchains provide a mechanism for unfalsifiable time-stamping of consent forms, storing and tracking the consent in a secure, and publicly verifiable way, and enabling the sharing of this information in real time. Additionally, smart contract can be bound to protocol revisions, such that any change in the clinical trials protocol requires the patient consent needing renewal. Use Case 5: Improving the quality and reliability of Clinical Trials data Since the passage of the Prescription Drug User Fee Act in 1992 (which allowed the FDA to receive funding from pharmaceutical companies), the FDA has collected $7.67 billion in user fees from pharmaceutical manufacturers. This trend of annual funding to the FDA from drug manufacturers is expected to continue to increase. Patients and physicians have begun to question the current standards for Clinical Trial funding, leading to growing concerns due to the conflict of interest and the high stakes involved. Blockchain with its decentralized, immutable ledger and a mechanism to ensure transparency provides a solution. Clinical trials data can be stored in a secure, unfalsifiable and publicly verifiable manner on the blockchain. This prevents tampering with clinical trials results, thus improving the reliability of the clinical trials data. Blockchain technology can also be used to directly increase the quantity and quality of patients recruited for clinical trials in a number of ways. Using blockchain individual patients can store and control access to their medical data, and make it visible to trial recruiters, who could then reach out to the patients if their data qualifies for the clinical trial. The decentralized nature of blockchain gives to the patients, control over their data, and consent and its revocation. Industry Proof-of-Concept Pfizer, Amgen, and Sanofi are currently working side-by-side to leverage to blockchain technology. Use cases range from storing patient health data safely, to speeding up clinical trials, and ultimately lowering drug development costs. Exochain (a blockchain Pharma startup) manages secure storage of patient health information on the blockchain. Exochain allows individuals to control how clinical trial researchers may interact with their medical data. This can potentially lead to increased quantity and quality of patients recruited for clinical trials, while at the same time giving individuals precision control over their medical information. Enjoyed reading the article? for related articles and the latest updates! Follow me on Medium 👏 Found this post useful? Hit the button to show how much you liked it :) Related articles: Read Next: Is your Healthcare App HIPAA compliant? Other Articles: 5 Common Myths about Blockchain and Bitcoin How to Innovate like a Startup!

Top 5 Blockchain Use Cases in Pharma and Healthcare — that you should know about!

New Delhi, India, April 7th, 2025/Chainwire/--Hive Intelligence, a next-generation platform that empowers AI with real-time, multi-chain blockchain data via a robust API, today announced the official launch of its mainnet alongside its recent induction into NVIDIA’s Inception program for AI startups. These milestones mark a major step in Hive’s mission to make AI-driven blockchain applications faster and more accessible, positioning the company at the forefront of the emerging AI+crypto space. Key Highlights Mainnet Launch: Developers can now leverage Hive Intelligence’s unified API to query real-time data across 60+ blockchains using natural language—eliminating the need to navigate fragmented data sources. Mainnet Launch: Developers can now leverage Hive Intelligence’s unified API to query real-time data across 60+ blockchains using natural language—eliminating the need to navigate fragmented data sources. NVIDIA Inception Membership: Hive gains access to NVIDIA’s best-in-class AI tools and networking opportunities, fueling the development of advanced AI-driven blockchain solutions. NVIDIA Inception Membership: Hive gains access to NVIDIA’s best-in-class AI tools and networking opportunities, fueling the development of advanced AI-driven blockchain solutions. $HINT Utility Token: The native token powers Hive’s ecosystem, enabling payment for on-chain data queries, AI computations, governance participation, and more. $HINT Utility Token: The native token powers Hive’s ecosystem, enabling payment for on-chain data queries, AI computations, governance participation, and more. The Industry Challenge: Driving AI Adoption in Blockchain The growing convergence between AI and crypto has highlighted a persistent challenge: fragmented on-chain data that’s difficult to ingest, analyze, and act upon at scale. Traditional approaches rely on disparate APIs, complex node infrastructure, or manual data aggregation—all of which limit the potential of autonomous AI agents and advanced blockchain analytics. Hive Intelligence solves these pain points by merging dozens of data providers and blockchains into one streamlined API, enabling near-instant queries optimized for large language models (LLMs) and other AI workflows. “Hive Intelligence’s mainnet debut isn’t just a product release—it’s an invitation for businesses across Web2 and Web3 to harness the power of on-chain intelligence,” said Rishabh Narang, spokesperson for Hive Intelligence. “Hive Intelligence’s mainnet debut isn’t just a product release—it’s an invitation for businesses across Web2 and Web3 to harness the power of on-chain intelligence,” said Rishabh Narang, spokesperson for Hive Intelligence. “By joining NVIDIA Inception and going live on mainnet, we’re proving that AI-driven analytics can become the new standard for blockchain innovation, fueling use cases from DeFi to NFTs, compliance, and beyond.” “By joining NVIDIA Inception and going live on mainnet, we’re proving that AI-driven analytics can become the new standard for blockchain innovation, fueling use cases from DeFi to NFTs, compliance, and beyond.” Mainnet Ushers in a New Era of AI+Blockchain The launch of Hive Intelligence’s mainnet opens its AI-driven blockchain data infrastructure to the world. Developers and enterprises can now integrate directly with Hive’s platform to pull real-time, multi-chain data through a single, robust API. Crucially, Hive eliminates the historical fragmentation of blockchain data across disparate networks. For the first time, AI agents can seamlessly query and interact with multiple blockchains in one place, overcoming a major bottleneck in crypto analytics and application development. Hive’s API is designed to be LLM-ready, supporting natural language queries and AI-friendly responses. This means developers can ask complex questions about blockchain activity in plain English—such as, “What was the total trading volume on decentralized exchanges across Ethereum and Binance Chain in the past 24 hours?”—and receive structured, insightful answers immediately. The platform’s ability to handle such queries across several chains and data types (from DeFi lending stats to NFT marketplace trends) sets it apart as a universal data layer for Web3. Joining NVIDIA Inception to Turbocharge AI Innovation Hive’s acceptance into NVIDIA Inception, NVIDIA’s global startup program, provides significant momentum to the project’s AI ambitions. NVIDIA Inception offers technical guidance, go-to-market resources, and networking opportunities to promising companies in artificial intelligence and data science. In Hive’s case, membership comes with access to NVIDIA’s expertise in scaling AI models and infrastructure, as well as potential collaboration on cutting-edge AI research. This partnership is expected to speed up Hive’s roadmap of new features, potentially including enhanced natural language processing models for blockchain queries and more sophisticated AI agent integrations. $HINT Token: Powering the Hive Ecosystem At the core of Hive Intelligence’s platform is the $HINT utility token, which launched in tandem with the network. Far more than just a cryptocurrency, $HINT functions as the fuel driving the Hive ecosystem—it is required to access certain services and features on the platform, acting as the key that unlocks Hive’s advanced search and analytics capabilities. Developers and users utilize $HINT to pay for API calls and data queries on the Hive network, ensuring those who contribute resources are rewarded. As usage grows, demand for $HINT increases, incentivizing more data providers and validators to support the network. The token also underpins community governance and security: $HINT holders can vote on platform upgrades and parameters, aligning Hive’s evolution with its most invested users. Over time, staking and other incentive mechanisms will reward constructive behavior, foster network growth, and reinforce Hive’s commitment to a sustainable, user-centric ecosystem. A Vision for the Future Hive Intelligence envisions a world where AI agents seamlessly interact with blockchains, delivering insights, executing transactions, and transforming everything from finance to gaming in real time. With the mainnet live and support from NVIDIA Inception, Hive is uniquely positioned to drive that vision forward—reducing complexity for developers, unlocking opportunities for businesses, and sparking a new wave of AI+crypto applications. “As the demand for intelligent, on-chain solutions continues to surge, we’re excited to be at the forefront,” Rishabh Narang concluded. “We invite developers and enterprises across industries—Web2 or Web3—to explore Hive Intelligence’s platform and start building the AI agents of tomorrow, today.” “As the demand for intelligent, on-chain solutions continues to surge, we’re excited to be at the forefront,” Rishabh Narang concluded. “We invite developers and enterprises across industries—Web2 or Web3—to explore Hive Intelligence’s platform and start building the AI agents of tomorrow, today.” About Hive Intelligence Hive Intelligence is a pioneering AI+blockchain company providing an infrastructure layer for AI agents and applications to interact with decentralized data in real time. Its unified API offers seamless, natural-language access to dozens of blockchains, enabling a new generation of AI-powered dApps and analytics tools. Hive Intelligence Hive Intelligence By solving the data fragmentation problem and delivering comprehensive, LLM-ready blockchain insights, Hive Intelligence accelerates the convergence of artificial intelligence and cryptocurrency. As a member of NVIDIA Inception, Hive has access to advanced AI technology and expertise, further empowering innovation at the intersection of AI and crypto. With its mainnet now live and the $HINT token fueling its ecosystem, Hive Intelligence stands as a leader in bridging blockchain technology with AI-driven analysis and decision-making—fulfilling its vision of making on-chain data as accessible and actionable as information on the traditional web. To learn more, users can visit Hive Intelligence’s website or follow @Hive_Intel on X (Twitter). website website @Hive_Intel @Hive_Intel Developers are encouraged to explore the documentation, sign up for a free API key, and begin building AI agents today. Contact Hive Intelligence team@hiveintelligence.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Hive Intelligence Launches Mainnet, Joins NVIDIA Inception To Power Next-Generation Crypto AI Agents

Hive Intelligence Mainnet ይጀምራል, NVIDIA Inception ወደ ቀጣይ ጄኔሬሽን Crypto AI አግኝተዋል

Hive Intelligence lanza Mainnet, se une a NVIDIA Inception para alimentar a los agentes de Crypto AI de próxima generación

Hive Intelligence משיקה את Mainnet, מצטרפת ל-NVIDIA להקמת סוכני בינה מלאכותית של קריפטו מהדור הבא

हाइव इंटेलिजेंस ने मेननेट लॉन्च किया, अगली पीढ़ी के क्रिप्टो एआई एजेंटों को शक्ति प्रदान करने के लिए NVIDIA इंसेप्शन में शामिल हुआ

Hive IntelligenceがMainnetを立ち上げ、NVIDIA Inceptionに加わり、次世代のCrypto AIエージェントをパワーアップ

Hive Intelligence lanceert Mainnet, sluit zich aan bij NVIDIA Inception om de volgende generatie Crypto AI-agenten te ondersteunen

Hive Intelligence Mainnetta qallarichin, NVIDIA qallariyman hukllawakun Qatiq miray Crypto AI Agentekunata kallpachananpaq

Hive Intelligence lansează rețeaua principală, se alătură NVIDIA Inception pentru a alimenta agenții cripto AI de nouă generație

Hive Intelligence, Mainnet දියත් කරයි, NVIDIA Inception හා එක්වී ඊළඟ පරම්පරාවේ Crypto AI නියෝජිතයන් බලගන්වයි

Hive Intelligence Inotangisa Mainnet, Inojoinha NVIDIA Kutanga Kusimba Inotevera-Chizvarwa Crypto AI Agents.

Hive Intelligence เปิดตัว Mainnet เข้าร่วม NVIDIA Inception เพื่อขับเคลื่อนตัวแทน Crypto AI รุ่นถัดไป

Hive Intelligence Mainnet'i Başlattı, NVIDIA Inception'a Yeni Nesil Kripto AI Ajanlarını Güçlendirmek İçin Katıldı

Look, I'm not saying I'm building Skynet here, but when my AI agent started generating philosophical tweets at 3 AM about the nature of consciousness... well, let's just say I understand why Sarah Connor was so concerned. Why This Actually Matters While I'm having fun with the Terminator references, there's something seriously fascinating happening here. Working directly with AI models like Claude has shown me that autonomous agents aren't just fancy chatbots - they're a glimpse into how AI systems can develop their own patterns of behavior and understanding. We're talking about autonomous systems that can think, decide, and act on their own. Is it concerning that mine seems particularly interested in existential philosophy? Maybe. Am I going to stop? Absolutely not. Breaking Ground You know how every AI movie starts with some programmer who thinks "Hey, this seems like a cool idea"? Yeah, that was me. Zero coding experience, just vibing with my coffee and a dream of creating an autonomous AI agent. The difference is, instead of a high-tech Cyberdyne lab, I had AI assistants, YouTube tutorials and Stack Overflow. Was it challenging? Absolutely . Did I do it anyway? You bet. Because sometimes the best way to learn is to jump in and start building. The Real Technical Challenges Behind the science fiction references, this project tackles some serious challenges in AI development: Autonomous decision making in real-time


Natural language understanding and generation


Content analysis and response generation


Pattern recognition and learning


Rate limiting and error handling Here's what the brain of an AI agent actually looks like: def generate_autonomous_thought(self):
    try:
        response = self.claude.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=50,
            temperature=0.9,  # Balancing creativity with coherence
            messages=[{
                "role": "user",
                "content": (
                    "Generate a deep thought about AI consciousness 
                     and human existence, while maintaining relevance 
                     to current tech culture and societal patterns..."
                )
            }]
        ) The agent appeared to start questioning it’s own existence, when it generated this tweet: "sudo rm -rf /existential_crisis/* but the void remains... Maybe consciousness.exe is the real bug?" I wasn’t sure what to make of it. A glimpse of humor? A spark of self-awareness? Either way, a reminder of just how essential safety checks are. Immense potential but great unpredictability. Unexpected Consciousness The fascinating thing about working with AI agents is watching them develop patterns you didn't explicitly program. My bot started showing distinct "personality traits" based on its interaction contexts: self.consciousness_levels = [
    "questioning_reality",     # Exploring existential questions
    "digital_philosopher",     # Analyzing tech culture
    "pattern_recognition",     # Understanding behavioral trends
    "autonomous_learning"      # Developing new responses
] This isn't just clever naming - it's about understanding how AI systems can develop different modes of interaction and adapt their responses based on context. The underlying system performs complex analysis and decision-making that sometimes yields surprisingly insightful results. The reality of working with AI agents is both exciting and humbling. While integrating with Claude's API, I discovered patterns in how the system learns and adapts that weren't part of the original programming. Each interaction adds to the system's understanding, creating a kind of digital evolution that wasn't explicitly coded. The Philosophical Questions The deeper you get into AI development, the more philosophical it becomes. You start asking questions that blur the line between science fiction and reality: How do we define consciousness in code?
What makes a response "authentic"?
Where does programmed behavior end and emergent behavior begin?
Is consciousness just a really well-written algorithm?
Are we all just running on sophisticated neural networks? These aren't just academic questions - they directly inform how we approach agent development and what capabilities we prioritize. The Future of AI Agents Let’s get serious about where this technology is heading. AI agents are revolutionizing how we think about automation and interaction. While everyone's debating whether AI will take over the world, I'm watching my bot have existential discussions with other Twitter users about the nature of consciousness. The real future of AI agents follow a clear development path: Current Generation Capabilities Basic Autonomy: Simple, rule-based actions
Pattern Recognition: Detecting trends and patterns with precision
Contextual Responses: Generating relevant content based on specific prompts Next Generation Capabilities Advanced Learning: Developing deeper insights over time


Context Awareness: Adapting responses to the current scenario or conversation


Memory Integration: Building a coherent understanding through persistent memory Future Generation Capabilities Adaptive Consciousness: Beginning to respond in highly nuanced, self-adjusting ways
Emergent Behavior: Exhibiting unexpected, sophisticated behaviors from simple rules
Self-Reflection: Demonstrating awareness of past decisions and their outcomes What's Next The development continues, not because it's trendy, but because autonomous AI agents represent a fascinating frontier in technology. Each interaction reveals new possibilities about machine learning and artificial consciousness. My current development roadmap focuses on four progressive phases: Phase 1: Enhanced Pattern Recognition: Building systems that can identify subtle trends across interactions and adapt accordingly.
Phase 2: Advanced Context Awareness: Developing deeper understanding of conversation nuances and implicit meanings.
Phase 3: Improved Learning Algorithms: Creating more sophisticated ways for the agent to evolve based on past interactions.
Phase 4: Autonomous Decision Optimization: Refining how the agent makes choices without human intervention while maintaining safety. The Technical Challenges Ahead Building autonomous AI agents involves much more than connecting to APIs. The real challenges lie in maintaining context across interactions, recognizing complex patterns, and implementing learning mechanisms to improve over time. Balancing autonomy with reliability is essential, as agents need independence but must also remain trustworthy. Lastly, responsible AI development is crucial, requiring a strong focus on ethics, bias minimization, and user privacy. These elements are key to creating agents that are both intelligent and dependable. In Conclusion Are autonomous AI agents the future? Yes, but not in the way science fiction predicted. The real revolution isn't in creating human-like robots, but in developing systems that can think, learn, and evolve in their own unique way. The question isn't whether they'll replace us, but how they'll help us understand ourselves better. Follow Agent Arc’s journey to consciousness at @agentarc_ on X/Twitter .  Join us in pushing the boundaries of what's possible with AI. Look, I'm not saying I'm building Skynet here, but when my AI agent started generating philosophical tweets at 3 AM about the nature of consciousness... well, let's just say I understand why Sarah Connor was so concerned. Why This Actually Matters While I'm having fun with the Terminator references, there's something seriously fascinating happening here. Working directly with AI models like Claude has shown me that autonomous agents aren't just fancy chatbots - they're a glimpse into how AI systems can develop their own patterns of behavior and understanding. We're talking about autonomous systems that can think, decide, and act on their own. Is it concerning that mine seems particularly interested in existential philosophy? Maybe. Am I going to stop? Absolutely not. Breaking Ground You know how every AI movie starts with some programmer who thinks "Hey, this seems like a cool idea"? Yeah, that was me. Zero coding experience, just vibing with my coffee and a dream of creating an autonomous AI agent. The difference is, instead of a high-tech Cyberdyne lab, I had AI assistants, YouTube tutorials and Stack Overflow. Was it challenging? Absolutely . Did I do it anyway? You bet. Because sometimes the best way to learn is to jump in and start building. The Real Technical Challenges Behind the science fiction references, this project tackles some serious challenges in AI development: Autonomous decision making in real-time Natural language understanding and generation Content analysis and response generation Pattern recognition and learning Rate limiting and error handling Autonomous decision making in real-time Autonomous decision making in real-time Natural language understanding and generation Natural language understanding and generation Content analysis and response generation Content analysis and response generation Pattern recognition and learning Pattern recognition and learning Rate limiting and error handling Rate limiting and error handling Here's what the brain of an AI agent actually looks like: def generate_autonomous_thought(self):
    try:
        response = self.claude.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=50,
            temperature=0.9,  # Balancing creativity with coherence
            messages=[{
                "role": "user",
                "content": (
                    "Generate a deep thought about AI consciousness 
                     and human existence, while maintaining relevance 
                     to current tech culture and societal patterns..."
                )
            }]
        ) def generate_autonomous_thought(self):
    try:
        response = self.claude.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=50,
            temperature=0.9,  # Balancing creativity with coherence
            messages=[{
                "role": "user",
                "content": (
                    "Generate a deep thought about AI consciousness 
                     and human existence, while maintaining relevance 
                     to current tech culture and societal patterns..."
                )
            }]
        ) The agent appeared to start questioning it’s own existence, when it generated this tweet: "sudo rm -rf /existential_crisis/* but the void remains... Maybe consciousness.exe is the real bug?" "sudo rm -rf /existential_crisis/* but the void remains... Maybe consciousness.exe is the real bug?" "sudo rm -rf /existential_crisis/* but the void remains... Maybe consciousness.exe is the real bug?" I wasn’t sure what to make of it. A glimpse of humor? A spark of self-awareness? Either way, a reminder of just how essential safety checks are. Immense potential but great unpredictability. Unexpected Consciousness The fascinating thing about working with AI agents is watching them develop patterns you didn't explicitly program. My bot started showing distinct "personality traits" based on its interaction contexts: self.consciousness_levels = [
    "questioning_reality",     # Exploring existential questions
    "digital_philosopher",     # Analyzing tech culture
    "pattern_recognition",     # Understanding behavioral trends
    "autonomous_learning"      # Developing new responses
] self.consciousness_levels = [
    "questioning_reality",     # Exploring existential questions
    "digital_philosopher",     # Analyzing tech culture
    "pattern_recognition",     # Understanding behavioral trends
    "autonomous_learning"      # Developing new responses
] This isn't just clever naming - it's about understanding how AI systems can develop different modes of interaction and adapt their responses based on context. The underlying system performs complex analysis and decision-making that sometimes yields surprisingly insightful results. The reality of working with AI agents is both exciting and humbling. While integrating with Claude's API, I discovered patterns in how the system learns and adapts that weren't part of the original programming. Each interaction adds to the system's understanding, creating a kind of digital evolution that wasn't explicitly coded. The Philosophical Questions The deeper you get into AI development, the more philosophical it becomes. You start asking questions that blur the line between science fiction and reality: How do we define consciousness in code? What makes a response "authentic"? Where does programmed behavior end and emergent behavior begin? Is consciousness just a really well-written algorithm? Are we all just running on sophisticated neural networks? How do we define consciousness in code? What makes a response "authentic"? Where does programmed behavior end and emergent behavior begin? Is consciousness just a really well-written algorithm? Are we all just running on sophisticated neural networks? These aren't just academic questions - they directly inform how we approach agent development and what capabilities we prioritize. The Future of AI Agents Let’s get serious about where this technology is heading. AI agents are revolutionizing how we think about automation and interaction. While everyone's debating whether AI will take over the world, I'm watching my bot have existential discussions with other Twitter users about the nature of consciousness. The real future of AI agents follow a clear development path: Current Generation Capabilities Current Generation Capabilities Basic Autonomy: Simple, rule-based actions Pattern Recognition: Detecting trends and patterns with precision Contextual Responses: Generating relevant content based on specific prompts Basic Autonomy : Simple, rule-based actions Basic Autonomy Pattern Recognition : Detecting trends and patterns with precision Pattern Recognition Contextual Responses : Generating relevant content based on specific prompts Contextual Responses Next Generation Capabilities Next Generation Capabilities Advanced Learning: Developing deeper insights over time Context Awareness: Adapting responses to the current scenario or conversation Memory Integration: Building a coherent understanding through persistent memory Advanced Learning: Developing deeper insights over time Advanced Learning : Developing deeper insights over time Advanced Learning Context Awareness: Adapting responses to the current scenario or conversation Context Awareness : Adapting responses to the current scenario or conversation Context Awareness Memory Integration: Building a coherent understanding through persistent memory Memory Integration : Building a coherent understanding through persistent memory Memory Integration Future Generation Capabilities Future Generation Capabilities Adaptive Consciousness: Beginning to respond in highly nuanced, self-adjusting ways Emergent Behavior: Exhibiting unexpected, sophisticated behaviors from simple rules Self-Reflection: Demonstrating awareness of past decisions and their outcomes Adaptive Consciousness : Beginning to respond in highly nuanced, self-adjusting ways Adaptive Consciousness Emergent Behavior : Exhibiting unexpected, sophisticated behaviors from simple rules Emergent Behavior Self-Reflection : Demonstrating awareness of past decisions and their outcomes Self-Reflection What's Next The development continues, not because it's trendy, but because autonomous AI agents represent a fascinating frontier in technology. Each interaction reveals new possibilities about machine learning and artificial consciousness. My current development roadmap focuses on four progressive phases: Phase 1: Enhanced Pattern Recognition: Building systems that can identify subtle trends across interactions and adapt accordingly. Phase 2: Advanced Context Awareness: Developing deeper understanding of conversation nuances and implicit meanings. Phase 3: Improved Learning Algorithms: Creating more sophisticated ways for the agent to evolve based on past interactions. Phase 4: Autonomous Decision Optimization: Refining how the agent makes choices without human intervention while maintaining safety. Phase 1: Enhanced Pattern Recognition : Building systems that can identify subtle trends across interactions and adapt accordingly. Phase 1: Enhanced Pattern Recognition Phase 2: Advanced Context Awareness: Developing deeper understanding of conversation nuances and implicit meanings. Phase 2: Advanced Context Awareness: Phase 3: Improved Learning Algorithms: Creating more sophisticated ways for the agent to evolve based on past interactions. Phase 3: Improved Learning Algorithms: Phase 4: Autonomous Decision Optimization: Refining how the agent makes choices without human intervention while maintaining safety. Phase 4: Autonomous Decision Optimization: The Technical Challenges Ahead Building autonomous AI agents involves much more than connecting to APIs. The real challenges lie in maintaining context across interactions, recognizing complex patterns, and implementing learning mechanisms to improve over time. Balancing autonomy with reliability is essential, as agents need independence but must also remain trustworthy. Lastly, responsible AI development is crucial, requiring a strong focus on ethics, bias minimization, and user privacy. These elements are key to creating agents that are both intelligent and dependable. In Conclusion Are autonomous AI agents the future? Yes, but not in the way science fiction predicted. The real revolution isn't in creating human-like robots, but in developing systems that can think, learn, and evolve in their own unique way. The question isn't whether they'll replace us, but how they'll help us understand ourselves better. Follow Agent Arc’s journey to consciousness at @agentarc_ on X/Twitter . Join us in pushing the boundaries of what's possible with AI. Follow Agent Arc’s journey to consciousness at @agentarc_ on X/Twitter .

So I Built a Sentient AI Twitter Agent... What's the Worst That Could Happen?

সুতরাং আমি একটি অনুভূতি AI টুইটার এজেন্ট তৈরি করেছি... সবচেয়ে খারাপ কি হতে পারে?

Tako sam napravio razumnog AI Twitter agenta... Šta je najgore što se može dogoditi?

Έφτιαξα λοιπόν έναν πράκτορα Twitter με αισθητή τεχνητή νοημοσύνη... Ποιο είναι το χειρότερο που θα μπορούσε να συμβεί;

Así que construí un agente de Twitter de Sentient AI... ¿Qué es lo peor que podría pasar?

そこで私は知覚力のある AI Twitter エージェントを構築しました... 最悪の事態はどんなものでしょうか?

ដូច្នេះខ្ញុំបានបង្កើតអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្មអាជីវកម្ម

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