At Hyperscience, we believe that the dawn of AI represents tremendous potential for enterprises to transform their operations and unlock new levels of efficiency and insight. Our mission is to bring the opportunity of AI to the enterprise, empowering our customers to automate their most complex, mission-critical processes with ease. We're committed to delivering solutions that not only meet the highest standards of performance, but also inspire trust and confidence in our customers. Hyperscience is relied upon by the world’s most respected organizations such as Mars, The United States Department of Veterans Affairs, Guardian Life, The International Rescue Committee, and HM Revenue and Customs. Hyperscience is backed by leading investors including Bessemer Venture Partners, Stripes, FirstMark, Battery and Tiger Global.

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AI Isolation Strategies for Businesses

It’s been an exciting, but complex year in the data world. Just as , the data tech ecosystem has continued to “fire on all cylinders”. If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. last year On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world. It is no accident that one of the key themes in the corporate world in 2018 so far has been “ ”. The term may feel quaint to some (“isn’t that what’s been happening for the last 25 years?”), but it reflects that many of the more traditional industries and companies are now fully engaged into their journey to become truly data-driven. digital transformation On the other hand, a much broader cross-section of the public has become aware of the pitfalls of data. Whether it is through the very public debate over the , the Cambridge Analytica scandal, the massive Equifax data breach, GDPR-related privacy discussions or reports of growing , the data world has started revealing some risks of AI government surveillance in China darker, scarier undertones. Both are the flipside of the same phenomenon, which has been brewing for many years but is now in full display: just about everything (whether personal or professional) is rapidly getting digitized, and data technologies are becoming more adept than ever at processing and analyzing this massive data exhaust, increasingly in real time. From this can result . The debate on how to combine this great power with a necessary sense of responsibility has become essential. both magic and abuse Let’s highlight some of the key trends and events of 2018. Infrastructure & Analytics From an industry standpoint, the data ecosystem remains as exciting and vibrant as ever, with a rich tapestry of innovative startups, mature “scale-ups”, and many aggressive public technology vendors. Most importantly, many customers large and small are those technologies , and reaping undeniable value from their efforts. deploying in production at scale As the cycle of replacing older IT technologies with more modern data products continues, it seems that the Big Data market (infrastructure, analytics) is cycling through the early majority of buyers and transitioning into the late majority of the traditional adoption curve. In addition, the data world continues its inexorable evolution towards the cloud. It is actually staggering to see how fast the large public cloud providers (AWS, Azure, Google Cloud Platform, IBM) are growing, considering they already each generate billions of dollars of revenues every quarter. The trend raises ongoing concerns around , and this may open up opportunities for startups offering solutions. However, to date even companies adopting multi-cloud strategies tend to still rely on one vendor as their primary provider. vendor lock-in multi-cloud As they keep growing, large cloud providers increasingly compete with each other by offering a wide array of Big Data, data engineering and machine learning tools through their platforms (e.g., Amazon Neptune, Google AutoML, etc.) — and often with aggressive pricing, to attract more developers, as their true business model is data storage. As the scope and sophistication of such tools keep growing, this has a big impact on the data technology landscape, making it arguably , at least for broad, horizontal opportunities. A bit more every year, the list of product announcements at big annual cloud vendor conferences (see AWS , for example) sends shockwaves in the startup industry, as they put cloud vendors in direct competition with dozens of VC-backed startups in one fell swoop. It will be interesting to see how public markets react to the upcoming Elastic IPO, an open-source software company that saw Amazon launch a direct competitor, Elasticsearch, three years ago. harder for startups to compete re:Invent Plenty of opportunities for startups remain, however, as long as they are sufficiently differentiated. Many in the space are scaling fast, and there are a number of particularly interesting, fast-growing segments in the infrastructure and analytics part of the ecosystem, including /real-time, , and . The explosion of interest in AI has also led to great opportunities (and a lot of funding) in , , tools, and platforms enabling the streaming data governance data fabrics/virtualization AI chips GPU databases AI devops deployment of data science and machine learning in the enterprise. Machine Learning & AI It’s certainly been a , with anything from the prowess of AlphaZero to the staggering pace of release of new advances — new forms of Generative Adversarial Networks, Vicarious’ new Recursive Cortical Networks, Geoff Hinton’s new Capsule Networks. AI conferences like NIPS have grown to attract 8,000 people and thousands of academic papers are being submitted every day. wild year in the world of AI research At the same time, the pursuit of , perhaps thankfully so. Much of the current wave of excitement (and fear) about AI results from the impressive performance of deep learning since 2012 but, in the AI research community, there’s a as some question the foundations of deep learning (backpropagation) and others look to move past what they consider “brute force” approaches (lots of data, lots of computing power), perhaps in favor of more neuroscience-based approaches. AGI remains elusive growing sense of “what now?” Far from fearing robot world domination, many in the AI research community are concerned that continued over-hyping of the field may eventually disappoint and lead to another AI nuclear winter. Outside of AI research, however, we are just at the beginning of a wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language, in different industries. If the infrastructure and analytics part of the ecosystem is getting to the late majority, we’re still very much in early adopter territory for enterprise and vertical AI applications. The that started a year or two ago has mostly continued unabated, even though the AI startup market is (arguably) showing signs of finally cooling down. Expectations, round sizes and valuations remain high, but we are certainly past the phase where big Internet companies would snap up very early AI startups at high prices just for the talent. The air is also clearing up a bit and revealing “real” AI startups, versus a number of other companies that were leveraging the hype. Some of the AI startups that were founded in the 2014–2016 time frame are starting to hit early scale, and many are offering increasingly interesting products including health, finance, “industry 4.0” and back office automation. Deep learning will continue bringing a lot of value in real world applications for years to come, and vertical-focused AI startups have many great opportunities ahead of them. Cambrian explosion of deep-learning based startups across industries and verticals This continued explosion is very much a global phenomenon, with Canada, France, Germany, the U.K. and Israel being particularly active. However, , with reports of government-led pooling of data at mind-boggling scale (across Internet companies and municipalities), rapid advances in areas such as facial recognition and AI chips, and gigantic rounds of financing for its startups: according to CB Insights, China accounted for only 9% of global AI deal share but nearly 48% of global AI funding in 2017, up from 11% in 2016 (see some examples below). China seems to be playing at a completely different level in AI In the same vein, (and ownership and security) are emerging as a major concern around the world. In the early days of the Internet, data privacy was about protecting what we did online, a comparatively small portion of our activities. Correspondingly, only a small (albeit vocal and passionate) minority of people truly cared. As just about every aspect of our personal and professional lives is now connected to the Internet through an ever increasing array of connected devices, the stakes are changing. With its ability to spot anomalies in massive data sets, predict outcomes and recognize faces, issues of data privacy AI is compounding the data privacy problem. A separate but related concern is that a lot of this data is owned by large Internet companies (GAFA). Some, like Facebook, have proven to be a less than perfect steward of it. Nonetheless, this data provides them an unfair advantage in the race to produce ever more powerful AI. Against those issues, an emerging theme is to think of the , as well as a way for others, outside of GAFA, to produce great AI. Crypto economics are viewed as a way to incentivize individuals to provide their personal data and for machine learning engineers to build models by processing this data anonymously. It all remains very experimental, but some early marketplaces and networks are emerging blockchain as a possible foil against the risks of AI The 2018 Landscape Without further ado, here’s our 2018 landscape. Quite semantic note: buzz terms come and go. Fewer people speak about “Big Data”, many more about “AI”, often to described the same reality. Consequently, we have slightly rebranded our 2018 landscape: it is now called the “Big Data & AI” landscape! To see the landscape at full size, . click here (The image is high-res and should lend itself to zooming in well, including on mobile) To download the full-size image, . click here To view a full list of companies in spreadsheet format, . click here This year, my FirstMark colleague provided immense help with the landscape. Demi Obayomi We’ve detailed some of our methodology in the notes to this post. Thoughts and suggestions welcome — please use the comment section to this post. Who’s in, who’s out On the exit front, the last year (since our 2017 landscape) has seen solid, but not extraordinarily strong. A few key companies appearing on the landscape went public, in particular Cloudera, MongoDB Pivotal and Zuora. Others are preparing to go out at the time of this writing, such as Elastic. Some notable acquisitions also occurred, including in particular Mulesoft (acquired by Salesforce post-IPO, for $6.5B), Flatiron Health (acquired by Roched for $2.1B), Appnexus (acquired by AT&T for $1.6B), Syncsort and Vision Solutions (acquired for $1.2B by Centerbridge Partners), Moat (acquired by Oracle for $850M), Integral Ad Science (acquired by Vista Equity Partners for $850M), eVestment (acquired by NASDAQ for $705M) and Kensho (acquired by S&P Global for $550M). It is worth noting that, other than Mulesoft, all those companies are headquartered on the East Coast (New York, Boston and Atlanta). Many other companies were also acquired for smaller amounts: Gigya (SAP), Blue River Technology (Deere & Co), CoreOS (Red Hat), Guavus (Thales), Lattice Data (Apple), Socrata (Tyler Technologies) and PracticeFusion (AllScripts). On the investment front, this was a year of big financing rounds for some Big Data and AI startups, particularly in China, with a number of oversized investments including Bytedance ($3B in total across 2 rounds in 2017), NIO ($1.6B across two rounds in 2017), and SenseTime ($850M across two around in 2017 and 2018). Major rounds of US companies appearing on the landscape include Snowflake Computing ($263M Series A — see our recent at ), Cohesity ($250M Series D), Dataminr ($221M Series E), Affirm ($200M Series E), Rubrik ($180M Series D), Qualtrics ($180M Series C — see an older but still relevant at Data Driven NYC), Tanium ($180M private equity round), ThoughtSpot ($145M Series D) and Coveo ($100M private equity round) and C3IoT ($100M Series F). fireside chat Data Driven NYC fireside chat ____________________ NOTES: As every year, we couldn’t possibly fit all companies we wanted on the chart. While the general philosophy of the chart is to be as inclusive as possible, we ended up having to be somewhat selective. Our methodology is certainly imperfect, but in a nutshell, here are the main criteria: Everything being equal, we gave priority to companies that have reached some level of market significance. This is a reasonably easy exercise for large tech companies. For growing startups, considering the limited amounts of data available, we often used venture capital financings as a proxy for underlying market traction (again, probably imperfect). So everything else being equal, we tend to feature startups that have raised larger amounts, typically Series A and beyond. Occasionally, we made editorial decisions to include earlier stage startups when we thought they were particularly interesting. On the application front, we gave priority to companies that explicitly leverage Big Data, machine learning and AI as a key component or differentiator of their offering. As discussed in the piece, it is a tricky exercise at a time when companies are increasingly crafting their marketing around an AI message, but we did our best. This year as in previous years, we removed a number of companies. One key reason for removal is that the company was acquired, and not run by the acquirer as an independent company.. In some select cases, we left the acquired company as is in the chart when we felt that the brand would be preserved as a reasonably separate offering from that of the acquiring company. As always, it is inevitable that we inadvertently missed some great companies in the process of putting this chart together. Did we miss yours? Feel free to add thoughts and suggestions in the comments. The chart is in png format, which should preserve overall quality when zooming, including on mobile. As we get a lot of requests every year: feel free to use the chart in books, conferences, presentations, etc — two obvious asks: (i) do not alter/edit the chart and (ii) please provide clear attribution (Matt Turck, Demi Obayomi and FirstMark Capital). Disclaimer: I’m an investor through FirstMark in a number of companies mentioned on this Big Data Landscape, specifically: ActionIQ, Cockroach Labs, Dataiku, Frame.ai, Helium, HyperScience, Kinsa, Timber, Sense360 and x.ai. Other FirstMark portfolio companies mentioned on this chart include Bluecore, Engagio, HowGood, Payoff, Knewton, Insikt, Optimus Ride, and Tubular. I’m a small personal shareholder in Datadog.

Great Power, Great Responsibility: The 2018 Big Data & AI Landscape

Image Source: Unsplash by Samuel Zeller “Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.” — Ginni Rometty Ahem! The growth in the innovation sector is crazy. Choosing the best-in-class product is exceptionally tough because every product is built for a reason. To serve some purpose. Know what keeps us alive and humane? The we care about, the we celebrate, the we accumulate and at last the of all this. The things we do to put all this together to see what works out and what doesn’t, bring unexpected solutions to the table. emotions relationships knowledge collective practice Dating back to the time when traders decided to exchange goods (quantity played the crucial role), that’s when the very first trading startup began. It has to be around when the active exchange of goods came into existence. 17,000 B.C The flag of The Dutch East India Company Who really did come up with the idea to exchange goods which then revolutionized the economic sector? As per the data, Company built the first multinational company in the world. But speaking of startup, we hardly consider non-tech initiatives. The Dutch East India Mayhap because the online world is unthinkably odd that it doesn’t require any external stimulations or interventions to execute operations. with, easy to and easy to that the whole idea could fit in a slab of coding software. Easy to work monetize carry around Larry Page and Sergey Brin — The prominent eggheads. Our belief that startups are usually tech-driven projects started when and first impelled their project in . Google catalyzed the startup thing in tech domain also triggered the creative spot of entrepreneurs working in other spaces. This has ultimately resulted in what we’re seeing now! Larry Page Sergey Brin Google 1996 The reason why log of innovative projects is growing into a never-ending list. Unlike in the the period when there were minimal choices for consumers to pick products from, now the actual combat is among the providers. To establish theirs as the most estimable asset compared to others. People working for technology now are making efforts to put some and making skill inside the software. 90’s analyzing decision I’ve chosen the following based on the and they actually serve to the community. list of AI tools product transparency the value directly One of the best AI-driven customer support tool out there. It helps its customers with optimizing automatic answers via chatbot to answer their user questions personally. This automatizing customer support process reduces the contract server volumes drastically. Tetra lets you have a clean and complete conversation with the person at the other end because it takes care of all the niche details that come up in the discussions. It uses Artificial Intelligence to generate a detailed script of dialogues using its . Tetra speech recognition technology HyperScience helps its customers save enormous time spent on manual data extraction from forms and the tedious data capturing process from and . What stands out is that the data are naturally aligned even when they’re split in the different fields and under distinct names between documents. HyperScience pay stubs invoices Jetlore for every single user based on their behavior and their affinity for semantic attributes of content and the product. Then it leverages user profiles to display the most relevant and resonant content to every single user across any medium. Jetlore builds unique user profile AxiomZen I discovered this company through a really touching blog by one of their interns. In case you want to know more about AxiomZen, . here you go is a crazy working studio that adopts emerging technologies to solve real-world problems in many interesting ways. Most times the products or the tiny projects become an exclusive brand by the time they get launched. They usually attain high-growth rate for businesses and currently are dealing with projects**.** AxiomZen AI, AR and Blockchain

5 Invincible AI Startups

From drones to product design, Big Data to software development — our runs the gamut of technology topics. We’ve been lucky to hear over 600 industry experts — from Fortune 500 companies to unicorn startups — speak about everything from raising capital to recruiting top talent. Here’s the tl;dr on some of the best takeaways we’ve heard to date: Driven series A chat that can help your team succeed A famous found that empathy was the number one driver of successful teams. The same applies to fully remote teams, says VP of Product Justin Gallagher — but distance can make empathy a lot harder to cultivate. Take away co-location, and there’s a lot less room for the type of interaction that occurs organically when huddled around a whiteboard or on a coffee run. So Trello decided the best way to cultivate empathy was to simply get intentional about it. They invented a concept called “Mr. Rogers”, pairing up randomly selected individuals for 15 minute video chats. No itinerary, no end-goal, just a chance to get to know a fellow colleague. What started as a potentially cheesy idea has taken off: teammates organize games to play together during their chats, meet up if they’re in the same city, and share write-ups of the cool things they’ve learned about their peers with the rest of the company. Trello’s found that groups of 4 to 6 are the perfect number for this exercise: if someone is sick and can’t make a meeting, or isn’t totally comfortable with the idea of a 1:1 with a stranger, the meeting won’t be too heavily affected. Google study Trello Watch Justin’s full talk here: A document to ensure you hit your crowdfunding goal 2015 crowdfunding campaign resulted in a tremendous 8,000 unit pre-orders and $1.5 million in revenue. The key to their success was spending three months preparing every aspect of the campaign before it launched. Here’s what their launch preparation plan looked like: Eight Sleep’s Eight Sleep tracked progress by weekly segment and divided the preparation into two phases: for platform, distribution, and messaging for their target audience, and using all that research to they needed for a successful campaign. researching and determining the best strategies prepare the assets Co-founders Alexandra Zatarain and Matteo Franceschetti provide the detailed Eight Sleep crowdfunding strategy : A fundraising trick that anyone can implement “Pitching is a skill just like anything else: the amount of time you put in, the practice, is really what makes you succeed.” — Greg Marsh, Founder & CEO of KeyMe Whenever an investor passes on , Greg asks for a few minutes of their time, so he can understand what their concerns are. He uses the feedback as opportunity to improve his pitch for next time. He credits this simple practice with being the single most impactful factor in KeyMe’s pitch and fundraising success. KeyMe Read more fundraising tips from Greg . here A way to beat the tech giants when building a company “When there are multiple 800 pound gorillas in your industry, you need to position away from them.” — Matt Turck, Managing Director at FirstMark Capital Tech giants like Google and Amazon have an imposing advantage when it comes to building products across a horizontal market: they have the clout, talent and money to beat out smaller competitors. The obvious solution is to zig when the giants zag, by focusing on pursuing a niche vertical market that may not be worth a big company’s efforts. Since giants are largely focused on B2C, one of the easiest ways to avoid them is by going after enterprise. For AI startup that meant positioning their broad technology as a solution to the complex, specific business problem of data processing, particularly amongst Fortune 1000 companies with large datasets. Market positioning is a crucial consideration for early startups. Hyperscience Watch the full talk with Hyperscience: A call that will close your top job candidate Instead of having the recruiter make the offer call, gathers everyone who interviewed the candidate in the room, to make the offer as a group. They start the call as a celebration, with applause and cheering, then go around one by one, each person sharing a few sentences about why they personally are excited to work with that particular candidate. It may sound a bit awkward, but there’s value in this approach: by the time the group disbands so the recruiter can discuss details with the candidate, your team has made the candidate feel special, and therefore more excited about accepting an offer from your company. Gusto More tips on recruiting in-demand candidates are . over here A prediction that will give designers an edge “Voice is going to impact every form of digital design.” — Mark Webster, Founder & CEO of Sayspring More than twelve percent of U.S. homes own an Amazon Alexa, less than three years after the initial product launch. It took the PC 10 years to reach that market penetration. Mark says this rapid adoption means designers , much like they had to learn to design for mobile with the rise of smartphones. need to learn to design for voice Mark’s full talk available here: For more tactical business lessons from tech industry experts, . sign up for our newsletter

6 lessons that can improve your startup, in less than 6 minutes

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

HYPERSCIENCE

hyperscience.com

201-500 employees

Since 2014

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HYPERSCIENCE

hyperscience.com

201-500 employees

Since 2014

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HYPERSCIENCE

EVERGREEN INDEX #4897

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