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Captricity

captricity

A look at 3rd-party AI tools and services, uncovering easy wins, and where to invest in an automated world Artificial Intelligence has become a bit of a divisive topic. While some industries have oriented their entire business around the technology, others are struggling to develop a strategy. The good news is that adopting AI doesn’t need to be this . dramatic An entire ecosystem of applications and services have cropped up to bridge the gap between the business processes of today and the intelligent ones of tomorrow. Adopting AI-enabled solutions can help companies streamline operations and eliminate repetitive tasks. From growing sales with intelligent customer engagement to reducing opex with automated IT operations, there are new tools available to support every department within a business. Here are some tools you can integrate today, at whatever stage or opinion you have on AI. Cross-Functional Solutions Where there is data, there is an opportunity for intelligent systems (AI) — to do something. A number of companies have built cross-functional solutions to meet the data opportunity across the board. The drawback is that these solutions do require collaboration (not plug and play) to develop models that are best suited for your use case, but the impact can be dramatic. : API for human automation. Apres : Builds augmented intelligence software for healthcare, commerce and financial markets. CognitiveScale : Cloud software that transforms handwritten paper forms into digital data. Captricity : Simplifies and accelerates your ability to use AI to unlock the value in your data. Machinify Natural Language Processing (NLP) technology for Splunk enables you to ask “plain English” queries of your machine data. Insight Engines : Maximizing the impact of data through advanced natural language generation (Advanced NLG) for the enterprise. Narrative Science : Human Resources HR is operationally intensive, rigidly structured and often slow to adopt new technologies. Recruiters alone spend over 60% of their time reading through resumes — time better spent with candidates. HR presents a clear opportunity for AI to improve the efficiency of the role: Actively pursue all credible candidates in the market at any given time, all the time — matched against fit criteria tailored toward your business. Optimized Recruiting: The opportunity to deal with employees as people, rather than functions and creating tailored milestones and objectives. Personalized Career Development: The ability to analyze messaging, both in the market and internally, and adjusting accordingly. Employee Sentiment Analysis: Packages tailored to the implicit needs of your employees, such as fitness trackers and programs for health insurance. Tailored Benefits: Uses machine learning to help organizations hire smarter by analyzing a person’s publicly available online presence. FAMA : Your team’s A.I. recruiter. Mya : AI-based anonymous career development platform allowing users to both know their worth and get concrete offers in real time (previously Workey). Gloat : AI powered employee retention tool. UnderstandBetter : Reduce corporate travel spend by rewarding employees for saving. Rocketrip : Cloud HR service that provides an integrated and video enabled digital platform for all recruitment needs. Peoplise : Talent Relationship Marketing (TRM) cloud platform connecting people to their ideal job. Phenom People : Predictive hiring platform that informs you who to hire before you meet them Koru : Helps businesses better understand their employees. Humanyze : Customer Service Advances in Natural Language Processing (NLP) have created new opportunities for companies to engage with customers. From chatbots to voice assistants, AI can now simulate human interaction, triage the required service and route the solution accordingly. This has streamlined customer service centers to personalize customer service and enable more efficient interactions between vendors and consumers. Service personalized to the tune of all available data on the client and intelligent virtual assistants as an interface layer. Differentiated customer experience: Streamline inquiry capture and resolution process, proactive support, and account/client management. Smarter operations: Data analysis on a continuous basis — uncovering new revenue streams and decrease churn. Intelligent insights: Brings practical applications of deep learning & artificial intelligence to customer service operations of large companies. Digital Genius : Supplies artificial intelligence to augment and replace live chat customer support agents at eCommerce companies. Eloquent Labs : Automates customer care conversations. Kylie.ai : Virtual assistant solutions. Interactions : Offers artificial intelligence powered customer service. AlphaBlues : utomated customer support. True AI : A Advanced AI customer service across multiple channels including chat, email, SMS, Facebook Messenger and Twitter. Hello Vera : AI for customer service. HelpScout : Social media customer service. Sparkcentral : AI voice platform for connected devices. Snips : Sales & Marketing SAAS has entrenched the value of sales and marketing data over the last decade, making historic records commonplace and an ideal for applying AI. Conversion-focused tools promise to make a significant impact on the top and bottom line: Automated outbound strategy targeting highest value leads, and delivering qualified prospects at the right time. Optimized account development: AI systems to support the salesforce with relevant insights, market movement and continuous training/coaching. Improved salesforce productivity: Personalized customer engagement, predictive marketing and messaging. Intelligent customer engagement: AI generated content and distribution, predictive marketing to increase retention, real-time competitive intelligence. Automated marketing: People-based intelligence through reorganizing the information on the web based on the people who use it. Node : AI powered sales intelligence to build relationships with your buyers and grow your sales. Nudge : AI software for marketing and sales enablement. Conversica : Records, transcribes, and analyzes sales conversations in real-time, highlighting important moments and revealing why you win and lose deals. Chorus : Provides users with a platform to maximize sales leads with analytics programs and innovative organization tools. FunnelBeam : Helps inside sales reps win more with an AI that learns from how your best reps communicate effectively with their customers. Nitrio : AI-powered analyst for marketers. Aiden : Automatically capture all sales activity to drive intelligent sales management and marketing accountability. People.ai : Predictive Marketing Platform for enterprise enables insight-driven customer engagement to win and retain customers. Mintigo : shows you the best way to communicate with any prospect, customer, or coworker based on their unique personality. Crystal : Virtual assistant that schedules your meetings. Clara : Marketing automation AI that delivers a personalized experience to each customer. Retention Science : AI generated language that resonates the most with any audience, segment or individual. Persado : AI powered marketing insights to create compelling content. Unmetric : Social strategy and analytics firm that provides Convey.AI semantic machine learning technology and Conversus App. Converseon : Legal Legal departments commit the majority of their resources to operational overhead — document parsing, research and discovery. Various AI tools have been developed to support these time-consuming tasks, often more effectively than their human counterparts. Augmenting existing legal teams with AI can provide an enormous benefit/ boost to productivity. AI assistant that provides rapid analysis and summarization of legal briefs. Legal research: AI able to identify relevant briefs and automatically generate next steps and other research tasks. Contract diligence: Documentation review and prioritization, automated story builder, instant translation. Discovery: Artificially intelligent attorney to help power through legal research. ROSS : Casetext’s CARA, automates key legal research tasks to analyze litigation documents, and uses that information to algorithmically query the law. Casetext : Platform for legal search, analytics and visualization. Ravel: Legal legal document review and discovery platform. Everlaw: Legal technology company providing research and analytics tools to turn unstructured case law into structured data. Judicata : Makes in-depth contract reviews accessible to everyone. LawGeex : IT Operations Increasingly complex IT operations are changing infrastructure attack surface area, continuously exposing organizations to new threat vectors. There is a real need for tools that offer the necessary intelligence and speed to keep up with the growing security threats companies face today. Beyond being able to take over manual tasks prone to human error, AI will be enable real-time threat detection and most importantly, prevent threats before they cause damage. Anomaly detection and prevention, real-time monitoring, autonomous remediation. Security: AI system that mimics how a user investigates, monitors, and troubleshoots events. Cognitive Insights: RPA tools to replace manual tasks and provide a real-time view of process workflows, and self-learning topology analysis. Streamlined Operations: Enable NLP bots that talk to a system to identify and resolve performance issues, monitor digital performance of IT infrastructure. Improved Customer Experience: : Root cause analysis software company acquired by Splunk. Rocana : Advanced monitoring platform for modern applications. SignalFx : Human-centered that helps people and machines work together to find clarity in chaos, and accelerate innovation. Moogsoft technology : AI-powered log analysis solution that monitors all your IT stack in real-time to predict issues before they affect the business and help you solve them faster. LoomSystems : Teaching machines to think, reason and communicate, acquired by Microsoft. Maluuba : Multi-turn, easy-to-use, and easy-to-author chatbot. Kitt.AI : AI-driven threat detection and prevention solution. Mistnet security : Enterprise immune system technology for cyber security. DarkTrace : Combines humans and machines into an AI system that detects cyber threats at scale and in real time. PatternEx Conclusion Building an AI muscle in-house is not the right strategy for every company (more here). That said, taking advantage of AI is necessary for all companies to keep up in any market. If the competition is running more efficiently, you are already a step behind. AI will affect every industry, everywhere. Whether or not you see this change happening today, it is important to start planning for it. Being ahead of the curve here is worth it. Disclosure: Juniper Networks is an investor in one of these companies.

Bolt-on AI: Is this the AI shortcut we’ve been looking for?

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

I wrote a Now I want to dig a bit more into that. short piece some time ago on how AI is going to affect the insurance industry. I. A bit of background The insurance sector is one of the most old-fashioned and resistant-to-change space, and this is why AI will have a greater impact on that with respect to more receptive industries. The collection of data of new types (i.e., unstructured data such as reports, images, contracts, etc.) and the use of new algorithms are disrupting the sector in several ways. Traditionally, an insurance company followed this type of process: Identifying pool of customers whom might be risk-assessed; Targeting those customers and assessing the risk for each class; Selling differently priced policies spreading the risks over the pool of customers; Try to retain those customers as long as possible offering lower price for longer contracts. This is a really simplistic representation of the insurance business in the last fifty years, and I am aware that insurance experts might disagree with me in many different ways. There are a couple of further features to be pointed out: first of all, insurance has historically been , which means that brokers and agents were essential to tracking new customers and to even retain old ones. In addition, it is an industry which is by definition rich of data because they collected anything they could, but is also one of the less advanced because either many of those data are unstructured or semi-structured, or the model used are quite old and simple. sold not bought Most of those data were easy to obtain because they were required to correctly price the coverage, while additional complimentary data were provided only by good customers who had incentives in providing as much data as possible to get a cheaper policy. Of course, this works the other way for bad customers, and this is a perspective on the phenomenon of “ ” (i.e., bad customers are going to ask an insurance because they feel they will need it). adverse selection The adverse selection issue is though only one of the intrinsic challenges of the sector: , high level of attempts, and are other features any incumbents should take care of. It is interesting to notice though that some of those are also specific barriers to entry for startups: they might attract indeed people who normally can get affordable insurance with a bigger competitor (adverse selection) and they usually have the capabilities for breaking down the risk complexity but not to support the funding need for risk coverages (so they need to work with incumbents rather than trying to replace them). strong regulation fraud complexity In spite of those problems, in the last decade, we noticed a new trend emerging. Insurances, in the effort of trying to reduce problems, they started offering . This occurred either through a questionnaire (asking the customer for further data in exchange for a lower price) or through devices (healthy devices, black boxes, etc.). The real issue though has been the engagement side of this proposal, because of the opposite nature of information, rewards, and human nature. The rewards offered were indeed either temporary or provided only once and people got lazy very quickly, while the information stream needed to be constant. moral hazard premium discounts to their final customers in order to get extra information directly indirectly The following step has been the introduction of apps to let customers monitor by themselves their own data and behavior, sometimes even given away for free the device itself. Leaving the customer with full power on his data had though an inverse effect, because people did not have the motivation in tracking down their improvements, and they got upset at the same time because they felt they were not getting the most out of that opportunity. Regardless of the specific innovative way in which insurers engaged customers, the process used in the insurance business did not change much in the past century. and dominated the sector setting the rules to be followed in internal workflows, but this is slowly changing with intelligent automation systems. We are actually migrating from rule-based decision systems to statistical learning and eventually machine learning. Expert systems knowledge engineering Image Credit: http://www.creativityatwork.com/2014/06/10/busy-innovate/ II. So how can AI help the insurance industry? AI is helping (or disrupting, depending on how you see the matter) the sector in different ways. First of all, it can help problem which has been just mentioned. The abundance of data can be used indeed to refine the customers’ segmentation and provide personalized offers based on personal features. It also helps in through smart automatization or RPA (robotic process automation). increasing the customer engagement and retention reducing the costs Second, AI is making people , and it is driving them toward better behaviors. more aware of the risks as well as habits Furthermore, the better pricing and risk assessment that AI is introducing analyzing more granular data will make some people (i.e., too risky to be fairly priced and covered) as well as to turn back some previously uninsurable people into insurable customers again. The governments or central regulatory agencies should then start thinking about a “ ” in which they intervene subsidizing the cost of relevant insurances (e.g., basic health coverage) in order to “ . uninsurable pricing/risk threshold guarantee the uninsurables” Finally, it might be useful to think in terms of what an insurable risk is in order to see how AI can help with that. According to Jin Park (Assistant Professor at IWU), an insurable risk is identifiable through the following five conditions: Large number of similar exposure units (mutuality); Accidental and unintentional loss (not predictable and independent from the insured customers); Determinable and measurable loss; Calculable chance of (not catastrophic/systemic) loss; Economically feasible premium. AI is going to affect all those features: with a better and more detailed customer profiling, we won’t need indeed to have such a large base of insured units. It will turn some frequent events into accidental (e.g., affecting drivers’ behavior it will reduce the basic accidents into rare events) and it will improve our ability to forecast and compute both the probability and magnitude potential losses even in those cases too hard to be managed before. All the previous improvements will make many more premium under budgets, and therefore the conclusion is that AI will “ ” the threshold of what we consider nowadays an insurable risk, and it will make then more risks insurable. lower Image Credit: iStock III. Who are the sector innovators? There are plenty of startups out there working at the intersection of AI and insurance, and it essential to look at least at some of them to understand the future direction of the industry, as well as the kind of improvements AI is having in the insurtech space. An interesting thing to notice is that most of the innovation is happening in the UK rather than other countries, in all the segments proposed below. skims the valid claims from the ones that deserve further validations; instead is trying to automatize experts task for insurances; has a specific focus on car claims; optimizes internal business processes, as well as does; offers instead mobile claim management solutions; and finally aims to help insurers to deliver on-premise smoothing the claiming flow. Claim processing: Shift Technology Tractable ControlExpert Cognotekt Snapsheet Motionscloud RightIndem : is an automated insurance agent who helps you buying any insurance coverage you might want; is a virtual assistant that offers customer care services; identifies which leads intend to purchase, while is a personal health assistant that analyzes symptoms and produces pieces of advice. instead uses EMR (medical records) to assist the patient as it was a virtual doctor, and gives medical advice taking care of tight budget constraints. is another personal insurance agent who works as a comparator for car insurances. Virtual Agents & Chatbots Spixii Cognicor Conversica Your.MD MedWhat Babylon Insurify What today is called simply chatbot is going to be renamed in a few years There are already few examples of companies toward that goal: is indeed an intelligent comparator which identifies gaps in coverage for the customer and looks for the best solution that fits customer’s needs and characteristics, while implements real-time video analytics to keep drivers safe(r). More generally, robo-insurers will be a quite wide class of agents who will end up providing different services, all of them with the final goal of helping the clients to undertake risk-mitigating actions and only cover the real (residual) risks. robo-insurer. Risk Genius PolicyGenius Drive Spotter : is probably the most successful insurtech company out there, with the final goal of making insurance simple and accessible to everyone through a great UX. Similar to Oscar is somehow , while is a tool that helps customers in understanding their own needs and facilitates in one place all the insurance coverages in place, in a similar fashion to . instead creates personalized offers and relevant products based on customer’s characteristics. uses machine learning to convert handwritten files into structured data, and this can be used to better understand the final customer. Finally, ranks the service of insurers to the benefit of the client. Customers engagement Oscar Stride Health Brolly Knip Adtelligence Captricity ValChoice : connected cars and telematics is a pretty big area itself (see to know more about it), but it would be worthy to point out the work that , , and are doing in capturing driving behaviors and habits as well as computing fuel efficiency. works similarly, although it uses smartphone data and mobile devices habits. is trying to revolutionizing the UI/UX within vehicles, displaying information in such a way that the driver does not get distracted. uses vision technology to provide real-time feedbacks to the driver. Telematics CBinsights article Greenroad Vnomics Telogis Cambridge Mobile Telematics Navdy Lytx : AI can be (and actually is) used to spot out hidden correlations to granularly segment customers and risks in a more efficient way. Even though it might in theory possible to identify some algos that could perform better than others (see the work for fraud detection), data always come first, at least for the next close future. Many companies operate in the space, as for instance that provides predictive algorithms and data products for property and casualty and life insurances through the analysis of public data (e.g., social media data). created a machine learning risk management platform, while uses unstructured data to optimize the underwriting and claims process. collects data from wearables and formulates health scores for a better risk assessment, while uses computer vision techniques on geospatial data to improve the level of detail on current houses conditions. creates a more accurate representation of the medical datasets to be used for underwriting purposes by insurances, similarly to that offers also apps products. provides also low-cost insurances, while uses AI to assess the risk of policy applications. A more accurate underwriting can even translate into covering events that are today quite risky (e.g., as and , and are doing for weather risk management). Underwriting Wipro did Carpe Data Atidot Tyche Big Cloud Analytics Cape Analytics Dreamquark FitSense Melody Health Insurance Uvamo MeteoProtect Praedicat Finally, on a side, it is worthy to point out to pure technological enablers as , which offers a management tool to the insurance providers to manage effectively and timely new products launched; , a cloud-based platform for insurance brokers; and finally, is instead an e-commerce provider for product insurances. Instanda Insly SimpleInsurance : , , and are peer-to-peer insurance startups focusing respectively on property and casualty insurance the first two, and car insurance the latter one. P2P insurance Lemonade Friendsurance Guevara : these are companies in the insurance sector that are driven by . offers real-time AML for bitcoin specifically, while is a permanent immutable ledger for diamond certification. is instead a stealth-mode company working on the standardization of smart contracts. provides a P2P supplementary unemployment insurance product, while provides micro-insurance policies on the blockchain. covers multiple parties with insurance cover at short notice and for varying periods, and finally, is another P2P insurance platform run on the blockchain. Insurchain & Smart Contracts blockchain technology Elliptic Everledger Luther Systems Dynamis Saldo.mx SafeShare Teambrella : this class of startups put in customers’ hand the entire insurance buying process. is probably the best example of this new class of players and it allows to ensure things by simply taking a picture of them. is quite similar but with a focus on car insurance, and on travel policies, and is another example of on-demand insurance. But this class does not include only the proper on-demand business model, but also insurance startups which provide products that vary by location, time, use, or customer. In other words, pay-per-mile business model ( ), micro-insurance policies ( ), or eventually Insurance-as-a-service models ( ). Insurance on-demand Trov Cuvva Sure Airsurety Back me up Metromile Neosurance Digital Risks IV. Concluding Thoughts which constitute the insurance profit structure: premium earned and the investment income from one hand, and underwriting cost and claim expenses from the other. AI is and will be able to improve the cost structure, increasing at the same time the competitiveness and enlarging the customer base accessible to insurers, while optimizing internal processes and enhancing the transparency and robustness of the compliance flow. Yan identifies four elements The greatest challenge I still see in insurance is the which might prevent insurance to adopt early AI solutions, although this won’t probably have a long life given the incredible pressure to innovate the insurance providers are undergoing through. cultural mindset — — Follow me on Medium Look at my other articles on AI and Machine Learning: Artificial Intelligence Classification Matrix What you are too afraid to ask about Artificial Intelligence (Part I): Machine Learning Who is best positioned to invest in Artificial Intelligence? A descriptive analysis

Artificial Intelligence Verticals (I): Insurance

Diving into the intricacies of crypto onboarding and the vision behind one of the industry's most impactful startups, Ishan Pandey welcomes Szymon Sypniewicz, the co-founder of Ramp Network, for our "Behind the Startup" series. From discussions about bridging the fiat-crypto divide to addressing regulatory challenges, this interview provides a comprehensive look into Ramp's mission to democratize crypto access and its ongoing efforts to make the crypto world accessible and user-friendly for all. Beyond Exchanges: Szymon Sypniewicz Discusses Ramp Network's Unique Vision for the Future of Cryptocurrency Ishan Pandey: Greetings, Szymon Sypniewicz. It's a privilege to feature you in our "Behind the Startup" series. Can you shed light on your professional journey and what catalyzed your decision to co-found Ramp Network? Thank you, Ishan, for having me. My journey began in Warsaw during my university years where I met my co-founder, Przemek Kowalczyk. We both had diverse academic backgrounds - I studied law while Przemek delved into physics. Our shared interest in the potential of cryptocurrency brought us together at a meeting of the Polish libertarian party. From the outset, we saw cryptocurrency not just as a financial tool, but as a means of social change and a way to increase an individuals’ agency. Szymon Sypniewicz: Our initial foray into the crypto world involved working on several projects and startups, most of which didn't take off as we hoped. However, these experiences were invaluable as they paved the way for the inception of Ramp. The core idea behind Ramp was to bridge the gap between users operating in the fiat world and the applications native to the crypto world. We envisioned a platform that would act as a gateway, or a "ramp", facilitating seamless interactions between these two realms. Our goal was to ensure that cryptocurrencies could function in a truly decentralized manner. As we developed Ramp, we faced skepticism and questions about why we didn't simply create another crypto exchange. But our vision was clear - we wanted to offer something fundamentally different. We aimed to provide a platform that would enable crypto wallets, exchanges, NFT marketplaces, and gaming companies to integrate crypto payment infrastructure seamlessly. This would allow users to purchase crypto assets without the need to switch between multiple apps. Looking back, our journey has been filled with challenges that have taught us invaluable lessons which made each milestone reached that much more satisfying. From our humble beginnings in a windowless office in Warsaw to the challenging environment for crypto in recent months, our commitment to our vision has never wavered. We've always believed in the transformative power of blockchain and the potential of decentralized systems and as we move forward, we remain dedicated to making the crypto world more accessible and user-friendly for everyone. Ishan Pandey: Ramp Network stands as a beacon for democratizing crypto access. In an ecosystem riddled with technical hurdles, how has Ramp ensured an intuitive user experience while simplifying integration processes for enterprises? At Ramp, our mission is clear: make crypto as easy to use as any other online service. We've built a full-stack payment infrastructure that allows brands and partners to seamlessly and swiftly integrate crypto-enabled services using the Ramp SDK. In fact, integration can be as quick as a few hours. Szymon Sypniewicz: For users, we aim for a one-click experience. No more jumping through hoops or navigating complex interfaces. For developers, we envision a world where adding crypto to their apps is as simple as copy-and-paste, or even drag-and-drop. Our partnerships with hundreds of developers, including the likes of Metamask, Opera Browser, Dapper Labs, Aave, Argent, and Trust Wallet, have been instrumental in refining our approach. Through these collaborations, we've been able to tailor our solutions to the real-world needs of the crypto community. : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program We are stripping away the complexities for both end-users and enterprises. Our goal is to facilitate and support broader crypto adoption through simplicity and efficiency. We won't stop until we achieve it. Ishan Pandey: The Ramp SDK is a pivotal element of your service suite. Can you expound on its multifaceted capabilities and how it fortifies businesses in seamlessly embedding crypto on/off-ramp solutions? Ramp’s SDK is designed to empower businesses, enabling them to effortlessly integrate crypto on/off-ramp solutions into their platforms. Ramp is not just about making transactions easy for the end-users, for developers our SDK is a dream come true. We have refined the integration process, making it more simple and efficient. With Ramp you are able to embed a robust crypto solution with just a few lines of code. Szymon Sypniewicz: And the best part? Our partnerships with top-tier industry leaders such as Metamask, Ledger and Brave means that users can effortlessly buy crypto directly within their favorite apps, sending it right to their non-custodial wallets. It's all about creating a seamless, direct route to the crypto universe, without the usual roadblocks. Ishan Pandey: With Ramp's footprint spanning over 150 countries, how do you believe this extensive coverage accelerates the universal embracement of cryptocurrencies? Being in 150 countries is our way of tapping into the global heartbeat of the crypto movement. Web3 products burst onto the scene with a global mindset from the get-go. When a web3 developer dreams up a product, they're not just thinking of their backyard. They are  picturing a user base that spans continents and are looking for partners like Ramp to help them realize that global ambition. Szymon Sypniewicz: At Ramp we think global and act local, for us supporting over 40 fiat currencies isn't just a number; it's our commitment to that global vision. It ensures that a user in Berlin or Buenos Aires can dive into crypto using their local currency. In line with our goal to make web 3 easily accessible for everyone, we have gone that extra mile to integrate local payment methods. We want to make sure your entry into crypto feels familiar and is straightforward. Catering to this worldwide ambition is exhilarating and is definitely challenging. But it's also what drives us, making sure that the global essence of web3 is not just a vision but a lived reality. Ishan Pandey: In an era where security breaches are rife, how does Ramp Network ensure robust security amidst the intricacies inherent to cyberattacks and a theft-prone environment? At Ramp, we've taken a different route to most. Instead of holding onto users' assets, we've championed the self-custodial approach. When users buy crypto through our platform, it's sent directly to their self-custodial wallets. This means there's no central pot of funds for hackers to target and users always maintain control over their assets. Szymon Sypniewicz: Our partnerships with leading self-custody solutions like Metamask, Ledger, and Brave Browser ensure that users have top-tier security without sacrificing user experience. Plus, with our focus on providing a comprehensive exchange infrastructure we've baked in robust security measures at every step, from compliance to payment processing. Ishan Pandey: The regulatory environment for crypto remains in flux. What preemptive strategies and adaptations has Ramp Network incorporated to stay abreast of evolving regulations? Szymon Sypniewicz: Our approach to navigating the dynamic regulatory environment is both strategic and technologically driven. We recognize the importance of adhering to the highest standards, especially in an industry where trust is essential to its long term success. Being registered with the UK's Financial Conduct Authority (FCA) and FinCEN in the US is not just a testament to our commitment but also a reflection of our ability to meet the rigorous demands of two of the world's strictest regulatory bodies. These registrations are more than just badges, they represent our dedication to ensuring that our operations are transparent, secure, and trustworthy. Our technological edge plays a pivotal role in this. We've integrated advanced financial crime compliance platforms to enhance our oversight capabilities. By leveraging cutting-edge technology, we can proactively detect potential financial crimes, ensuring a safer and more reliable environment for our users. This integration is a clear example of how we employ technology to not only meet but exceed regulatory requirements. Furthermore, our recent FCA approval has enabled us to roll out our off-ramp solution globally. This expansion is not just about reaching more users; it's about providing a seamless, safe and compliant service to users across different jurisdictions. It underscores our commitment to making crypto more accessible while ensuring that we operate within the bounds of local and international regulations. Looking ahead, we're not resting on our laurels. We are in the advanced stages of securing additional licenses which will further expand our regulatory footprint. This proactive approach ensures that as the crypto industry evolves, Ramp Network remains at the forefront setting standards and building trust. Ishan Pandey: Striking a balance between user-centricity and blockchain's decentralized ethos is a tall order. How does Ramp Network navigate this dichotomy? At Ramp Network, we've always believed that the true potential of blockchain can only be realized when it's accessible and user-friendly. While we champion the decentralized ethos, we're acutely aware of the challenges it can pose from a user experience standpoint. Szymon Sypniewicz: Our approach is tailored to this very challenge. By providing infrastructure that emphasizes non-custodial crypto products, we ensure users maintain control over their assets. But control doesn't have to come at the cost of simplicity. Our platform is designed with ease of use at its core. Through partnerships with leading self-custody solutions, we've been able to offer a seamless and intuitive interface, making transactions straightforward even for those new to the crypto world. Furthermore, our integrations allow users to purchase crypto directly within their apps and receive it in their non-custodial wallets. This bypasses the often cumbersome processes associated with traditional crypto exchanges, streamlining the user journey. In essence, Ramp Network is striving to redefine the balance between decentralization and user-centricity. Our goal is to offer a platform where the principles of blockchain and top-tier UX coexist, making the crypto experience both empowering and effortless. Ishan Pandey: The journey of procuring investments is rarely linear. Could you share some pivotal moments and collaborations that have been instrumental in catapulting Ramp Network's ascent? Ramp's growth has been strategic and deliberate. Key to our ascent has been our licensing in both the UK and US, which underscores our commitment to compliance and trust. This has enabled us to expand globally, offering local payment methods tailored to diverse markets. All of the above have been instrumental in building trust with our investors. Szymon Sypniewicz: Our pioneering efforts in the gaming space have been particularly noteworthy. By forging strong partnerships with industry leaders like Sorare and Axie Infinity, we've positioned ourselves at the forefront of on-ramping in this rapidly growing sector. Our dedication to blockchain technology is evident in our unparalleled coverage of layer 2 solutions, ensuring faster and more efficient transactions for our users. Moreover, our off-ramp capabilities are best in class, allowing users to sell in over 130 countries, further emphasizing our global reach and commitment to accessibility. In short, our focus remains on innovation, global expansion and fostering trust within the crypto community and with the regulatory bodies across the globe have been integral to building a company that many believe in and want to partner with. Ishan Pandey: In what ways can cryptocurrencies unlock untapped markets for SMEs beleaguered by exorbitant cross-border transaction fees? How do you envision businesses capitalizing on crypto transactions to sidestep steep currency conversion and card charges on an international scale? Our long-term vision at Ramp is to enable a seamless global financial ecosystem powered by crypto. By implementing on and off-ramping solutions across the world, we're laying the groundwork for businesses to bypass traditional financial bottlenecks. Imagine a future where a user can effortlessly use a cost-effective onramp in one country, transfer their crypto to another country, and then off-ramp using another affordable method, all in a single and efficient move. Szymon Sypniewicz: This not only sidesteps the exorbitant currency conversion and card charges but also democratizes access to financial systems. It's a future where borders don't dictate financial efficiency, and businesses can truly capitalize on the global nature of crypto to drive their international operations. Ishan Pandey: The blockchain cosmos is in perpetual flux. How does Ramp Network keep its finger on the pulse of this rapid evolution, and what's the roadmap to infuse emergent blockchain trends and technologies into your platform? At the heart of Ramp lies a team of crypto natives, passionate about the space and deeply understanding of its nuances. However, we don't operate in a vacuum, we actively listen to the feedback from millions of our users and insights from our hundreds of partners. This collective intelligence shapes our roadmap and informs our next steps. Szymon Sypniewicz: Recently, we've recognized the importance of Layer 2 solutions in the crypto ecosystem. With the challenges of scalability and high transaction fees on mainnets, L2s offer a promising solution. Guided by our commitment to user-centricity and the feedback loop we've established, our latest focus has been on enabling users to onboard directly to L2s. This not only ensures faster and cheaper transactions but also a smoother user experience. For a deeper dive into our L2 initiatives, I'd recommend checking out our . It's all about staying agile, listening to the community, and continuously innovating to serve them better. recent blog post Don’t forget to like and share the story!

Redefining Decentralization: How Ramp Network Balances User-Centricity with Blockchain's Core Ethos

Redefiniendo la descentralización: cómo Ramp Network equilibra la orientación centrada en el usuario con el espíritu central de Blockchain

Redéfinir la décentralisation : comment le réseau Ramp équilibre l'orientation utilisateur avec la philosophie fondamentale de Blockchain

विकेंद्रीकरण को पुनर्परिभाषित करना: रैम्प नेटवर्क ब्लॉकचेन के मूल लोकाचार के साथ उपयोगकर्ता-केंद्रितता को कैसे संतुलित करता है

分散化の再定義: Ramp Network がユーザー中心性とブロックチェーンの中核精神のバランスを取る方法

Redefinindo a descentralização: como a Ramp Network equilibra a centralização no usuário com o espírito central do Blockchain

Xác định lại sự phân quyền: Cách mạng Ramp cân bằng việc lấy người dùng làm trung tâm với đặc tính cốt lõi của Blockchain

重新定义去中心化：Ramp Network 如何平衡以用户为中心与区块链的核心精神

Dick Sands the Boy Captain by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . IN CAPTIVITY here IN CAPTIVITY. So far from Mrs. Weldon and Jack having succumbed to the hardships to which they had been exposed, they were both alive, and together with Cousin Benedict were now in KazonndÃ©. After the assault upon the ant-hill they had all three been conveyed beyond the encampment to a spot where a rude palanquin was in readiness for Mrs. Weldon and her son. The journey hence to KazonndÃ© was consequently accomplished without much difficulty; Cousin Benedict, who performed it on foot, was allowed to entomologize as much as he pleased upon the road, so that to him the distance was a matter of no concern. The party reached their destination a week sooner than Ibn Hamish's caravan, and the prisoners were lodged in Alvez' quarters. Jack was much better. After leaving the marshy districts he had no return of fever, and as a certain amount of indulgence had been allowed them on their journey, both he and his mother, as far as their health was concerned, might be said to be in a satisfactory condition. Of the rest of her former companions Mrs. Weldon could hear nothing. She had herself been a witness of the escape of Hercules, but of course knew nothing further of his fate; as for Dick Sands, she entertained a sanguine hope that his white skin would protect him from any severe treatment; but for Nan and the other poor negroes, here upon African soil, she feared the very worst. Being entirely shut off from communication with the outer world, she was quite unaware of the arrival of the caravan; even if she had heard the noisy commotion of the market she would not have known what it meant, and she was in ignorance alike of the death of Harris, of the sale of Tom and his companions, of the dreadful end of the king, and of the royal obsequies in which poor Dick had been assigned so melancholy a share. During the journey from the Coanza to KazonndÃ©, Harris and Negoro had held no conversation with her, and since her arrival she had not been allowed to pass the inclosure of the establishment, so that, as far as she knew, she was quite alone, and being in Negoro's power, was in a position from which it seemed only too likely nothing but death could release her. From Cousin Benedict, it is needless to repeat, she could expect no assistance; his own personal pursuits engrossed him, and he had no care nor leisure to bestow upon external circumstances. His first feeling, on being made to understand that he was not in America, was one of deep disappointment that the wonderful things he had seen were no discoveries at all; they were simply African insects common on African soil. This vexation, however, soon passed away, and he began to believe that "the land of the Pharaohs" might possess as much entomological wealth as "the land of the Incas." "Ah," he would exclaim to Mrs. Weldon, heedless that she gave him little or no attention, "this is the country of the manticorÃ¦, and wonderful coleoptera they are, with their long hairy legs, their sharp elytra and their big mandibles; the most remarkable of them all is the tuberous manticora. And isn't this, too, the land of the golden-tipped calosomi? and of the prickly-legged goliaths of Guinea and Gabon? Here, too, we ought to find the spotted anthidia, which lay their eggs in empty snail-shells; and the sacred atenchus, which the old Egyptians used to venerate as divine." "Yes, yes;" he would say at another time, "this is the proper habitat of those death's-head sphinxes which are now so common everywhere; and this is the place for those 'Idias Bigoti,' so formidable to the natives of Senegal. There must be wonderful discoveries to be made here if only those good people will let me." The "good people" referred to were Negoro and Harris, who had restored him much of the liberty of which Dick Sands had found it necessary to deprive him. With freedom to roam and in possession of his tin box, Benedict would have been amongst the most contented of men, had it not been for the loss of his spectacles and magnifying-glass, now buried with the King of KazonndÃ©. Reduced to the necessity of poking every insect almost into his eyes before he could discover its characteristics, he would have sacrificed much to recover or replace his glasses, but as such articles were not to be procured at any price, he contented himself with the permission to go where he pleased within the limits of the palisade. His keepers knew him well enough to be satisfied that he would make no attempt to escape, and as the enclosure was nearly a mile in circumference, containing many shrubs and trees and huts with thatched roofs, besides being intersected by a running stream, it afforded him a very fair scope for his researches, and who should say that he would not discover some novel specimen to which, in the records of entomological science, his own name might be assigned? If thus the domain of Antonio Alvez was sufficient to satisfy Benedict, to little Jack it might well seem immense. But though allowed to ramble over the whole place as he liked, the child rarely cared to leave his mother; he would be continually inquiring about his father, whom he had now so long been expecting to see: he would ask why Nan and Hercules and Dingo had gone away and left him; and perpetually he would be expressing his wonder where Dick could be, and wishing he would come back again. Mrs. Weldon could only hide her tears and answer him by caresses. Nothing, however, transpired to give the least intimation that any of the prisoners were to be treated otherwise than they had been upon the journey from the Coanza. Excepting such as were retained for old Alvez' personal service, all the slaves had been sold, and the storehouses were now [Illustration: He contented himself with the permission to go where he pleased within the limits of the palisade.] full of stuffs and ivory, the stuffs destined to be sent into the central provinces and the ivory to be exported. The establishment was thus no longer crowded as it had been, and Mrs. Weldon and Jack were lodged in a different hut to Cousin Benedict. All three, however, took their meals together and were allowed a sufficient diet of mutton or goats'-flesh, vegetables, manioc, sorghum and native fruits. With the traders' servants they held no communication, but Halima, a young slave who had been told off to attend to Mrs. Weldon, evinced for her new mistress an attachment which, though rough, was evidently sincere. Old Alvez, who occupied the principal house in thedÃ©pÃ´t, was rarely seen; whilst the non-appearance of either Harris or Negoro caused Mrs. Weldon much surprise and perplexity. In the midst of all her troubles, too, she was haunted by the thought of the anxiety her husband must be suffering on her account. Unaware of her having embarked on board the "Pilgrim," at first he would have wondered at steamer after steamer arriving at San Francisco without her. After a while the "Pilgrim" would have been registered amongst the number of missing ships; and it was certain the intelligence would be forwarded to him by his correspondents, that the vessel had sailed from Auckland with his wife and child on board. What was he to imagine? he might refuse to believe that they had perished at sea, but he would never dream of their having been carried to Africa, and would certainly institute a search in no other direction than on the coast of America, or amongst the isles of the Pacific. She had not the faintest hope of her whereabouts being discovered, and involuntarily her thoughts turned to the possibility of making an escape. She might well feel her heart sink within her at the bare idea; even if she should succeed in eluding the vigilance of the watch, there were two hundred miles of dense forest to be traversed before the coast could be reached; nevertheless, it revealed itself to her as her last chance, and failing all else, she resolved to hazard it. But, first of all, she determined, if it were possible, to discover the ultimate design of Negoro. She was not kept long in suspense. On the 6th of June, just a week after the royal funeral, the Portuguese entered the depÃ³t, in which he had not set foot since his return, and made his way straight to the hut in which he knew he should find the prisoner. Benedict was out insect-hunting; Jack, under Halima's charge, was being taken for a walk. Mrs. Weldon was alone. Negoro pushed open the door, and said abruptly,- "Mrs. Weldon, I have come to tell you, that Tom and his lot have been sold for the Ujiji market; Nan died on her way here; and Dick Sands is dead too." Mrs. Weldon uttered a cry of horror. "Yes, Mrs. Weldon," he continued; "he has got what he deserved; he shot Harris, and has been executed for the murder. And here you are alone! mark this! alone and in my power!" What Negoro said was true; Tom, Bat, ActÃ¦on, and Austin had all been sent off that morning on their way to Ujiji. Mrs. Weldon groaned bitterly. Negoro went on. "If I chose, I could still further avenge upon you the ill-treatment I got on board that ship; but it does not suit my purpose to kill you. You and that boy of yours, and that idiot of a fly-catcher, all have a certain value in the market. I mean to sell you." "You dare not!" said Mrs. Weldon firmly; "you know you are making an idle threat; who do you suppose would purchase people of white blood?" "I know a customer who will give me the price I mean to ask," replied Negoro with a brutal grin. She bent down her head; only too well she knew that such things were possible in this horrid land. "Tell me who he is!" she said; "tell the name of the man who ..." "James Weldon," he answered slowly. "My husband!" she cried; "what do you mean?" "I mean what I say. I mean to make your husband buy you back at my price; and if he likes to pay for them, he shall have his son and his cousin too." [Illustration: "I suppose Weldon will not mind coming to fetch you?"] "And when, and how, may I ask, do you propose to manage this?" replied Mrs. Weldon. forcing herself to be calm. "Here, and soon too. I suppose Weldon will not mind coming to fetch you." "He would not hesitate to come; but how could he know we are here?" "I will go to him. I have money that will take me to San Francisco." "What you stole from the 'Pilgrim'?" said Mrs. Weldon. "Just so," replied Negoro; "and I have plenty more I suppose when Weldon hears that you are a prisoner in Central Africa, he will not think much of a hundred thousand dollars." "But how is he to know the truth of your statement?" "I shall take him a letter from you. You shall represent me as your faithful servant, just escaped from the hands of savages." "A letter such as that I will never write; never," said Mrs. Weldon decisively. "What? what? you refuse?" "I refuse." She had all the natural cravings of a woman and a wife, but so thoroughly was she aware of the treachery of the man she had to deal with, that she dreaded lest, as soon as he had touched the ransom, he would dispose of her husband altogether. There was a short silence. "You will write that letter," said Negoro. "Never!" repeated Mrs. Weldon. "Remember your child!" Mrs. Weldon's heart beat violently, but she did not answer a word. "I will give you a week to think over this," hissed out Negoro. Mrs. Weldon was still silent. "A week! I will come again in a week; you will do as I wish, or it will be the worse for you." He gnashed his teeth, turned on his heel, and left the hut. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2005). Dick sands, the boy Captain. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/ebooks/3250.html.images This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

IN CAPTIVITY

THE SACRED BEETLE IN CAPTIVITY

The retail industry is undergoing a major paradigm shift. Retailers are now looking at how they can leverage personalization to drive customer centricity in the metaverse era, as they are faced with mounting pressure to evolve and keep up with the ever-changing customer expectations. Retailers have always been in the business of selling products and services, but today’s customers want more than that. They want a personalized experience that is tailored to their individual needs and wants. In order to provide this type of experience, retailers must adopt new strategies that leverage not only personalization at scale but also the metaverse and AI technologies. We have two points that are very important to leverage customer centricity: Integrating the Data Layer with AI. With the data layer in place, retailers can use that to build personalized experiences for their customers. A retailer wanting to leverage personalization needs technology that is capable of understanding, interpreting, and creating individualized customer experiences. The first step retailers must take is integrating their data layer with AI capabilities so they can better understand and interpret customers’ needs and wants as they progress through the information discovery journey in real time. This is even more necessary now that third-party data is a thing of the past and we only have mostly first-party data to go off of. Operating on the Metaverse. Retailers should place themselves in a different world, where they are able to cater to each customer's individual needs and wants, regardless of location or time. This could mean designing a virtual retail environment or leveraging the immersive capabilities of VR and AR technologies. To create personalization at scale, data is the key element and we are now facing a paradigm shift regarding data, as the metaverse is imminent and inevitable. In the current paradigm, people exist, and then companies go after their data. However, in this new paradigm, data will come first, and people will exist only because of their data as an avatar in that metaverse. This will create infinite opportunities for brands to create custom experiences and environments around people's interests, needs, and preferences. The impact will be anything from creating tailored experiences for every individual consumer in a company's database to understanding how our preferences move with context and changing circumstances. We are already seeing this when we shop online. As an example, Nike knows that I am interested in sneaker styles because I've bought shoes from them before. Nike also knows that when I'm shopping for designer shoes, my mood changes and there is a different list of things on my mind that can potentially impact my purchase intent. There is also a lot of concern among marketers about the death of third-party data, but it must be remembered that as we live our lives in a fully-developed metaverse we will generate infinite amounts of data. In addition to that, marketers can use AI-generated insights and automation to generate the most valuable experiences for their potential customers. Key things to have in mind for retailers that want to leverage the full power of personalization at scale and the metaverse: Consumer Centricity: Focus on spaces where consumers engage - in-store, online, and even in the metaverse - and the experiences they want to have with brands. Consumers want and need to be emphasized. Consumers care about and expect brands to have a purpose. Have a measurable purpose plan in place that will deliver real-world impact. Be Digital-First: From smartphone penetration to online usage and e-commerce adoption, consumers across the globe are largely shifting to a digital, mobile-first lifestyle. Marketing with a digital-first approach will be crucial to staying connected to consumers. Gain Attention: In the age of distractions, active attention matters. Making an impact with your advertising and driving consumers to choose your brand over the competition requires a focus on attention as the new metric that matters. The Metaverse can be a huge opportunity, but also a huge challenge in terms of gaining the attention of the users. Target Gen Z: Gen Z will have unprecedented spending power and is more self-focused than previous generations. This can be a good thing because brands will be able to provide them with a personalized service, which strengthens their relationship. Brands are expected to engage with them about their interests and they are always connected, through their phones, to social interactions. We can see that the state of personalization in this era is more than just targeted content. Personalization is about creating an environment that is tailored to each individual user’s needs and preferences, no matter how different they are from each other. The next frontier for personalization is in VR and AR, and the next frontier of retail is consumer-first, the ability to create and interact with 3D worlds in ways that allow them to be tailored to each individual's unique desires. This is the future of personalization: not just how companies conduct their advertising campaigns, but how they market themselves and where the brands live as well.

How Retailers Can Leverage Personalization to Drive Customer Centricity in the Metaverse Era

David Copperfield: Chapter 26 - I Fall Into Captivity

, Professional Scrum Trainer, Gary Pedretti Scrum.org I often see User Experience (UX) Designers struggle to align focus, work, and approach with Scrum Teams building increments of product in iterations of 30 days or fewer. Common concerns and questions I hear include: “Developers aren’t really interested in the user, they just want to build.” “I’m trying to work side-by-side with the team, but I’m always a bit ahead of them, so what should we talk about in our Daily Scrum?” “I don’t work in sort cycles, or tactically. My work is strategic.” “Why don’t developers use my design artifacts and decisions? Why do they never go back and refactor for good design? Why do I feel like I never have any influence?!?!” These challenges often result in tenuous relationships between — or even worse, distrust, separation, and “staggered Sprints” (where completely separate teams pass work to each other that they execute in completely separate blocks of time, which they still call “Sprints” — even though those blocks of time function exactly the same as waterfall phases). UX Designers (UXDs) and Scrum Teams UX and Scrum: Better Together But UX Designers don’t have to work in silos, away from Scrum Teams. They can be stewards of design processes, user-centricity, and hypothesis-driven development across teams. In fact, we can borrow from our experience in integrating other specialities into the Scrum Team — testers, data analysts, and application architects — to provide some initial ideas to try when doing the same with UXDs. I focus on the example of application architects for this post, but realize the attitudes and patterns are very similar in regards to team integration for other skill sets and specialties I mentioned. Early on, I heard things like this from architects: “Developers often aren’t interested in good architecture. They just want to crank out features.” “I need to work ahead of them, prior to feature integration, so I don’t belong on the team.” “My work is strategic.” “Developers just need to do what I tell them to do. And yet, they usually don’t…” Sound familiar? 🙂 Notice the extensive use of “they/their/them” and “I/my” — just as the list above talking about UX challenges. What happened over time is architects realized all of these challenges are interrelated, and need to be addressed holistically, from within the team. Architects turned from order givers to collaborators, building consensus and instilling a sense of “why” behind their decisions across the entire team. That is, developers less likely to be interested in good architecture when you’re working ahead of them, denigrating their work as “tactical,” and not working with them outside of showing up to deliver orders. Or, more importantly, you will get a well-architected application when developers understand the “why” behind good architectural decisions, when architects embed themselves on teams (if only as part-time members), and when architects change their stance from commanders to stewards and mentors of good architecture. It is ultimately a mindset shift towards collaboration and common goals. are Simple but effective things architects did (that UX Designers could also do) to precipitate and demonstrate this mindset shift included: Sitting down with coders to harvest existing, established code and conventions into reusable components and principles Bringing awareness back to higher level concerns — architecture, product vision, user needs — when people get innocently lost in the details “Lunch and Learns” that focused on “whys” and gathering feedback, as opposed to “I’m going to lecture about something…” Sharing work and artifacts early and often, as opposed to holding work before delivering it in a “big bang” fashion In the new by Scrum.org, we talk about this mindset shift and actionable tactics to achieve it over two immersive, experiential days of learning. The PSU training course teaches students how to successfully use UX techniques and Scrum together, giving students the opportunity to practice these techniques with cross-functional teams throughout the class. Professional Scrum with User Experience (PSU) training course UX and Scrum: More to Come! As a person interested in “individuals and interactions over process and tools,” I wanted to start this UX and Scrum conversation with the aspects of bringing design and development closer together. Future posts in this series will discuss more human-centric ideas, such as user-centricity, but also and suggestions to achieve the same goal discussed here: bringing design and development closer together to acknowledge common goals. human process artifact For example — regarding process, how could UX designers actually structure work — i.e., how could it fit into Sprint cadences of one month or less? Regarding artifacts, how could common design system artifacts, such as style guides, not only be built out incrementally, but inform the actual work as soon as possible? We will look at those questions in future blog posts about — stay tuned! UX and Scrum together Resources https://martinfowler.com/ieeeSoftware/whoNeedsArchitect.pdf Originally published at https://www.scrum.org/resources/blog/break-down-silos-enhance-user-centricity-ux-scrum-teams .

Break Down Silos! How to Enhance User-Centricity on Scrum Teams

Are you looking to create engaging faceless short videos for platforms like YouTube or TikTok but want to avoid the hassle of complex video editing? This article will walk you through how to automate the entire process using OpenAI, ElevenLabs, and MoviePy. By the end of this tutorial, you'll know how to automatically generate visuals and voiceovers for short videos based on any script. Whether you’re creating educational content, storytelling, or meme videos, this workflow will save you tons of time. Prerequisites Before getting started, you’ll need: API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers).
Basic Python knowledge.
MoviePy and other required Python libraries installed (moviepy, openai, elevenlabs, etc.). Step 1: Setting Up API Keys import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") Start by getting API keys from OpenAI and ElevenLabs. Replace the placeholders in the code with your actual API keys. Step 2: Preparing the Script Your video starts with a story or script. You can replace the story_script variable with the text you want to turn into a video. Here’s an example script about Dogecoin: story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" The script will be split into sentences to match each visual and audio segment. Step 3: Generating Images with OpenAI’s DALL-E For each sentence, we generate a corresponding image using OpenAI’s DALL-E model. def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename This function sends each sentence to DALL-E and saves the generated image. We ensure the generated visuals match the video's theme. Step 4: Generating Voiceovers with ElevenLabs Once we have the visuals, we need voiceovers. ElevenLabs converts each sentence into speech. def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename This function generates an audio file for each sentence. You can select different voice settings to customize the narration style. Step 5: Combining Audio and Video Next, we pair each image with its corresponding voiceover using MoviePy: from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) Each image is displayed for the duration of its audio clip, ensuring synchronization. Step 6: Applying Video Effects To make the video more dynamic, we apply zoom and fade effects to each image. For example, the apply_zoom_in_center effect slowly zooms into the center of the image: def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) Other effects include zooming in from the upper part or zooming out. These effects are applied randomly to each clip to keep the video visually engaging. Step 7: Final Video Assembly We combine all video clips into one seamless video and add background music: final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) Step 8: Adding Captions Captions improve video accessibility and engagement. We use Captacity to automatically add captions based on the audio. captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) Step 9: Adding Background Music To finish the video, background music is added. The volume is reduced so that it doesn't overpower the narration. background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) Conclusion The GitHub repository for this project is available here. By using OpenAI and ElevenLabs, we’ve automated the creation of faceless videos from text. You can now quickly generate YouTube Shorts or TikToks without needing a camera or microphone. This automated process has allowed us to create a Faceless Shorts Video service on our Robopost software, offering content creators a seamless way to produce high-quality videos. Whether you are creating educational videos, short stories, or even meme-style content, this service handles everything from visuals to voiceovers with minimal effort. Now, you can focus on creativity and storytelling while Robopost handles the heavy lifting of video production. Happy creating! Are you looking to create engaging faceless short videos for platforms like YouTube or TikTok but want to avoid the hassle of complex video editing? This article will walk you through how to automate the entire process using OpenAI , ElevenLabs , and MoviePy . OpenAI ElevenLabs MoviePy By the end of this tutorial, you'll know how to automatically generate visuals and voiceovers for short videos based on any script. Whether you’re creating educational content, storytelling, or meme videos, this workflow will save you tons of time. Prerequisites Before getting started, you’ll need: API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers). Basic Python knowledge. MoviePy and other required Python libraries installed (moviepy, openai, elevenlabs, etc.). API keys for both OpenAI (for generating visuals) and ElevenLabs (for voiceovers). API keys Basic Python knowledge. MoviePy and other required Python libraries installed ( moviepy , openai , elevenlabs , etc.). moviepy openai elevenlabs Step 1: Setting Up API Keys import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") import openai
from elevenlabs import ElevenLabs

# Set up your OpenAI and ElevenLabs API keys
openai.api_key = "your_openai_api_key"
elevenlabs_client = ElevenLabs(api_key="your_elevenlabs_api_key") Start by getting API keys from OpenAI and ElevenLabs . Replace the placeholders in the code with your actual API keys. OpenAI ElevenLabs Step 2: Preparing the Script Your video starts with a story or script. You can replace the story_script variable with the text you want to turn into a video. Here’s an example script about Dogecoin : story_script Dogecoin story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" story_script = """
Dogecoin began as a joke in 2013, inspired by the popular 'Doge' meme featuring a Shiba Inu dog. It unexpectedly gained a massive following thanks to its community's charitable initiatives, eventually evolving into a legitimate cryptocurrency with support from Elon Musk.
""" The script will be split into sentences to match each visual and audio segment. Step 3: Generating Images with OpenAI’s DALL-E For each sentence, we generate a corresponding image using OpenAI’s DALL-E model. def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename def generate_image_from_text(sentence, context, idx):
    prompt = f"Generate an image without any text that describes: {sentence}. Context: {context}"
    response = openai.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1792",
        response_format="b64_json"
    )

    image_filename = f"images/image_{idx}.jpg"
    with open(image_filename, "wb") as f:
        f.write(base64.b64decode(response.data[0].b64_json))
    return image_filename This function sends each sentence to DALL-E and saves the generated image. We ensure the generated visuals match the video's theme. Step 4: Generating Voiceovers with ElevenLabs Once we have the visuals, we need voiceovers. ElevenLabs converts each sentence into speech. def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename def generate_audio_from_text(sentence, idx):
    audio = elevenlabs_client.text_to_speech.convert(
        voice_id="pqHfZKP75CvOlQylNhV4",
        model_id="eleven_multilingual_v2",
        text=sentence,
        voice_settings=VoiceSettings(stability=0.2, similarity_boost=0.8)
    )
    audio_filename = f"audio/audio_{idx}.mp3"
    with open(audio_filename, "wb") as f:
        for chunk in audio:
            f.write(chunk)
    return audio_filename This function generates an audio file for each sentence. You can select different voice settings to customize the narration style. Step 5: Combining Audio and Video Next, we pair each image with its corresponding voiceover using MoviePy : MoviePy from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) from moviepy.editor import ImageClip, AudioFileClip

image_clip = ImageClip(image_path, duration=audio_clip.duration)
image_clip = image_clip.set_audio(audio_clip)
video_clips.append(image_clip.set_fps(30)) Each image is displayed for the duration of its audio clip, ensuring synchronization. Step 6: Applying Video Effects To make the video more dynamic, we apply zoom and fade effects to each image. For example, the apply_zoom_in_center effect slowly zooms into the center of the image: apply_zoom_in_center def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) def apply_zoom_in_center(image_clip, duration):
    return image_clip.resize(lambda t: 1 + 0.04 * t) Other effects include zooming in from the upper part or zooming out. These effects are applied randomly to each clip to keep the video visually engaging. Step 7: Final Video Assembly We combine all video clips into one seamless video and add background music: final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) final_video = concatenate_videoclips(video_clips, method="compose")
final_video.write_videofile(output_video_path, codec="libx264", audio_codec="aac", fps=30) Step 8: Adding Captions Captions improve video accessibility and engagement. We use Captacity to automatically add captions based on the audio. Captacity captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) captacity.add_captions(
    video_file=output_video_path,
    output_file="captioned_video.mp4",
    font_size=130,
    font_color="yellow",
    stroke_width=3
) Step 9: Adding Background Music To finish the video, background music is added. The volume is reduced so that it doesn't overpower the narration. background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) background_music = AudioFileClip(music_filename).subclip(0, final_video.duration).volumex(0.2)
narration_audio = final_video.audio.volumex(1.5)
combined_audio = CompositeAudioClip([narration_audio, background_music])
final_video.set_audio(combined_audio) Conclusion The GitHub repository for this project is available here. The GitHub repository for this project is available here. By using OpenAI and ElevenLabs, we’ve automated the creation of faceless videos from text. You can now quickly generate YouTube Shorts or TikToks without needing a camera or microphone. This automated process has allowed us to create a Faceless Shorts Video service on our Robopost software , offering content creators a seamless way to produce high-quality videos. Whether you are creating educational videos, short stories, or even meme-style content, this service handles everything from visuals to voiceovers with minimal effort. Faceless Shorts Video service Faceless Shorts Video service on our Robopost software Robopost software Now, you can focus on creativity and storytelling while Robopost handles the heavy lifting of video production. Happy creating!

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