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By Jackson Elliott, Contributor Follow | Wednesday, October 21, 2020 Jordan Peterson speaks in a video to his supporters that was posted to YouTube on Oct. 19, 2020. | Jordan Peterson/YouTube

Jordan Peterson spoke of God's grace and mercy in his first public communication with his fans on Monday after a year spent largely out of public view as he sought treatment for an addiction to a prescribed medication.

Peterson posted theeight-minute video on his YouTube channel and in it, his cheeks appear more hollow than in his last video. He also spoke more slowly than usual but remained clear and coherent. His voice filled with emotion when he spoke about his friends and family supporting him.

My extended family and friends went above and beyond the call of duty in my estimation, he said in the video. Im certainly not convinced that I would have the character to provide to any one of them what they provided to me. That was a humbling lesson.

In his online lectures, Peterson has shown many people that the Bible contains relevant truths on living a meaningful life. With over 3 million YouTube subscribers from around the world, the soft-spoken psychology professor from Toronto, Canada, is one of the most influential intellectuals in public life today. Commenters on his videos often thank him for pointing them toward God.

Peterson said he plans to write a series of lectures on the book of Exodus and a video series devoted to the book of Proverbs.

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Youve all heard, no doubt, that wisdom is proverbial or theres such a thing as proverbial wisdom. That phrase stems from the book of Proverbs, he said. I think the analysis of those will prove of benefit to me and perhaps to those of you who are inclined to watch or listen to my analysis.

Petersons approach to the Bible doesnt concern itself as much with whether Bible stories are literally true as it does with whether they are metaphorically true. Although he has refused to say whether he believes Jesus rose bodily from the dead, Peterson often tells listeners that goodness overcoming death is an important concept that gives people meaning to live by. The facts dont matter to him so much as the story.

Peterson himself is famously reticent to answer whether he believes in a personal God or is a Christian. When asked by interviewers whether hes a Christian, he said that as a Westerner he was conditioned and influenced by Christian moral teaching. On other occasions, he said he cannot say he believes in God because, he contends, anyone who truly believed in God would live a morally perfect life, and he doesnt.

I try to act as if God exists, because God only knows what youd be if you truly believed, he said in an interview with Prager University in May 2019.

Despite Petersons past statements about his belief in God, he declared that With Gods grace and mercy, Ill be starting to create original material once again.

Peterson visited hospitals around the world searching for a cure after he developed a physical addiction to the medicine he was taking to fix a severe autoimmune reaction to food. The dosage he was prescribed was reportedly increased to help his anxiety after his wife was diagnosed with kidney cancer in 2019. The medicine also had the unusual effect of doing the opposite of what it normally does, he said.

While fighting the addiction and autoimmune disorders in the hospital, Peterson experienced terrible withdrawal symptoms, including a restlessness so severe that he wanted to kill himself.

Ive been suffering from impaired health, he said in the video. Severely impaired health, as a consequence of benzodiazepine use for anxiety, or more accurately from a combination of using that medication and then ceasing its use once I realized it was dangerous.

Peterson ended his video by thanking his fans and YouTube subscribers for their continued support.

Thank you very much, and thank you very much is probably sufficient, he said.

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Jordan Peterson tells fans he's thankful for 'Gods grace ...

Jordan Peterson is often called a rock star. It is a title he flatly rejects.

I am not a performance artist, states the celebrated clinical psychologist, I dont have fans, I have people who are listening carefully to what I am saying.

Petersons universal appeal is undeniable. His worldwide lecture tours routinely sell out and his bestseller 12 Rules for Life has been translated into more than 30 languages. Nearly three million followers subscribe to his YouTube channel, his lectures count a staggering 145 million views, and his podcast has been downloaded over 55,000,000 times.

The Toronto professor skyrocketed to fame in 2016, when he fiercely objected to Canadas C-16 bill, which mandated the use of transgender pronouns. Peterson became the traditionalists hero and his name soon became synonymous with the anti-PC movement.

But Petersons narrative does not concern politics or current events. His search is for eternal values virtues and themes that are common across all human experience, across all time.

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Moses was wandering around with the Israelites forever in the desert, Peterson tells the attentive audience. Theyre going left and going right and worshiping idols and having a hell of a time... getting rebellious, and Moses goes up on the mountain and he has this tremendous revelation, sort of, in the sight of God, and it illuminates him and he comes down with the law. Through mediating and trying to keep the peace, Moses considered what principles of peace would satisfy the people. Through Gods intervention he presented the Ten Commandments to the people to say, Look, this is already basically what were doing but now its codified. Thats all a historical process thats condensed into a single story, says Peterson. But obviously that happened, because we have written law that emerged from the bottom up.

LAW IS also touched on through the first chapters of Genesis, along with the idea that both male and female were made in the image of God.

The notion that every single human being regardless of their peculiarities, strangenesses, sins, crimes and all of that has something Divine in them that needs to be regarded with respect, plays an integral role... in the creation of habitable order out of chaos. Its an idea that Peterson believes sits at the base of our legal system. We see how the archetypal Adam and Eve story represents a situation we are always in. Just like Adam and Eve, we humans live in a walled garden, explains Peterson, but there is always a snake. The garden is a place of paradise, warmth, love and sustenance, but its also the place where something can pop up at any moment and knock you out of it. Through Abraham, the father of nations who was ordered by God to sacrifice his son Isaac, we consider what sacrifice is. We realize how without sacrifice, modern civilization would not have come into being. It is our ability to envision ourselves in the future and the need to make a sacrifice in the present that allowed us to progress and thrive.

We follow Cain and Abels dramatic tale as they lead two different life paths. Abel pleases God while Cain becomes resentful and murderous. Through Peterson we see how Cains torment grows. Gods rejection of his sacrifices means that his attempts to give up something valuable in the present to ensure prosperity in the future are insufficient, and in consequence, he fails to prosper.

Every line is a passage to our past, loaded with illuminating insight into human psyche, behavior, evolution and even the origin of the text itself. The story of the Mesopotamian deity Marduk, for example, sheds light on what the Hebrew words tohu vavohu typically translated as unformed and void actually mean. Marduk, who had eyes all the way around his head, fought a deity called Tiamat. We need to know that, explains Peterson, because the word Tiamat is associated with the word tehom. Tehom is the chaos that God makes order out of at the beginning of time in Genesis. Petersons exploration of biblical stories is a journey filled with enlightenment and wonder.

More than 21 million people have tuned in and listened to Petersons gripping journey into the mysterious tales. We see the values and virtues upon which our entire civilization is founded, and the repercussions of neglecting them. We realize that values such as responsibility, humility, sacrifice, striving and courage have lasted for a reason, how they enabled the construction of our magnificent civilization, and the danger posed to our very existence if we lose them.

The idea is to see if theres something at the bottom of this amazing civilization that weve managed to structure, and that I think is in peril, says Peterson. Maybe if we understand it a little bit better we wont be so prone just to throw the damn thing away.

Originally posted here:
Jordan Peterson turns to Genesis for lessons on civilization in peril - The Jerusalem Post

Ernest Clines Ready Player One was a jukebox musical for trash culture of the 80s, a vision of a near-future dystopia where things were so bad, Zaxxon and Twisted Sister were classic works of art meriting serious scholarly attention. Life is certainly imitating art, at least as far as the dystopian nature of 2020 is concerned. So who wouldnt want to read a sequel?

Ready Player Two picks up in medias res after the events of the first book. Our hero, teen gamer Wade Watts, and his motley group of Internet friends have solved the mysterious riddles of one James Halliday, inventor of the OASIS, which is some sort of futuristic extra-cool Second Life for our environmentally-ravaged future. By virtue of finding the many Easter Eggs Halliday has hidden around the Internet and learning the minutiae of the movies and video games he loved, Wade has inherited his company and become the richest and most famous man in the world. Everything is swell! Except that Halliday has left Wade one more thing: an advanced neural interface which allows the user full sensory overload instead of the crummy old immersive VR experiences of yore.

I was awestruck by the perfect replication of all that interlinked sensory input, Wade enthuses, munching on a virtual apple, his olfactory system in kinetic overdrive. These were subtle, nuanced sensations that could never be re-created or simulated by a pair of haptic gloves.

Okay, so Proust this is not. In the hands of a more agile writer, there might be ripe potential for satire here; Halliday is, quite literally, a deus ex machina figure, constantly one step ahead of his billions of devoted OASIS minions. If it werent for Clines obvious affection for the sheer hubris of creating a virtual world in ones image, Halliday would seem like a mustache-twirling digital archvillain, a Bezos writ large.

Thats the overwhelming issue with the Ready Player Whatever universe: at no point does Cline question the wisdom of an all-encompassing monoculture that screeches to a halt around 1988, while technology evolves at hyperspeed around it. Ah, the good old days, he sighs, and writes another chapter about fucking Donkey Kong or whatever. Hes the Gamemaster Anthony of genre fiction, a clunky stylist content to wallop the reader over the head with a never-ending barrage of Remember when?s.

In Ready Player One, the main antagonists were the corporate suits of Innovative Online Industries, which was sort of a combination of a for-profit online university and an internment camp. By the end of the first chapter of Ready Player Two, our heroes have managed a hostile takeover of IOI and transformed themselves into an unstoppable megacorporation with a global monopoly on the worlds most popular entertainment, education, and communications platform, as well as releasing all of IOIs indentured servants and, presumably, creating a massive labor crisis. But they finally manage to pay off the national debt and donate hundreds of billions of dollars to solve world poverty, or something. So thats nice!

Their efforts to ditch this crappy planet and terraform the nearest habitable rock eventually fall by the wayside when an evil sentient AI springs the murderous CEO of IOI out of prison and forces our heroes to go on a lengthy fetch quest through VR time and space to retrieve the seven pieces ofoh, who cares. At this point you already know whether or not Ready Player Two is the book for you. It is not the book for me.

Cline had some legitimately good ideas the first time around. Theres potential in interrogating the nature of escapism in times of social upheaval. This time, though, instead of character development, hes chosen to double down on lengthy, dull descriptions of battle scenes and minutely-detailed virtual worlds. A complicated boss fight against Princeyes, Princeis crassly opportunistic even by the standards of posthumous tributes to Prince. A climactic showdown in Middle-Earth is as monotonous and impenetrable as The Silmarillion itself.

Add to that some of the most excruciating sex scenes in recent fiction and youve got the stuff of nightmares. We lost our virginity to each other three days after that first kiss, Wade reminisces. Then we spent the rest of that week sneaking off to make the beast with two backs at every opportunity. Like Depeche Mode, we just couldnt get enough. Oh, brother. Luckily hours of futuristic VR porn have cured him of that pesky bout of transphobia, and his own dalliance with omnipotence has provided him with valuable insight as to the human condition.

Cline muses, in full Jordan Peterson gets an Oculus Quest mode: Human beings were never meant to participate in a worldwide social network comprised [sic] of billions of people. We were designed by evolution to be hunter-gatherers, with the mental capacity to interact and socialize with the other members of our tribea tribe made up of a few hundred other people at most. Like so much of Clines writing, this is cheap introspection disguised as trenchant insight.

Maybe the freshman seminar-level Big Ideas of this book will make more sense when Steven Spielberg or whoever inevitably turns it into another expensive action-movie pastiche. The Ready Player One movie grossed nearly $600 million, and an adaptation of Ready Player Two cant be far behind. This book is criticism-proof; the people who ate it up the first time are just going to gorge on it again. They didnt even bother to send out advance copies for review.

When I finished reading it, I felt physically drained, exhausted after living in Ernest Clines head for nearly 400 pages of Animotion and Van Hagar, John Hughes movies and bad video games. I needed a break from the constant clanging drone of coin-op nostalgia. I stumbled outside to get some fresh air. Like Depeche Mode, I enjoyed the silence.

Read more here:
Ready Player Two Book Review - Book and Film Globe

Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.

Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.

In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.

Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands. Websites recommend products and movies and songs based on what we bought, watched, or listened to before. Robots vacuum our floors while we do . . . something better with our time. Spam detectors stop unwanted emails from reaching our inboxes. Medical image analysis systems help doctors spot tumors they might have missed. And the first self-driving cars are hitting the road.

We can expect more. As big data keeps getting bigger, as computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms, machine learning will drive greater and greater efficiency in our personal and work lives.

There are four basic steps for building a machine learning application (or model). These are typically performed by data scientists working closely with the business professionals for whom the model is being developed.

Training data is a data set representative of the data the machine learning model will ingest to solve the problem its designed to solve. In some cases, the training data is labeled datatagged to call out features and classifications the model will need to identify. Other data is unlabeled, and the model will need to extract those features and assign classifications on its own.

In either case, the training data needs to be properly preparedrandomized, de-duped, and checked for imbalances or biases that could impact the training. It should also be divided into two subsets: the training subset, which will be used to train the application, and the evaluation subset, used to test and refine it.

Again, an algorithm is a set of statistical processing steps. The type of algorithm depends on the type (labeled or unlabeled) and amount of data in the training data set and on the type of problem to be solved.

Common types of machine learning algorithms for use with labeled data include the following:

Algorithms for use with unlabeled data include the following:

Training the algorithm is an iterative processit involves running variables through the algorithm, comparing the output with the results it should have produced, adjusting weights and biases within the algorithm that might yield a more accurate result, and running the variables again until the algorithm returns the correct result most of the time. The resulting trained, accurate algorithm is the machine learning modelan important distinction to note, because 'algorithm' and 'model' are incorrectly used interchangeably, even by machine learning mavens.

The final step is to use the model with new data and, in the best case, for it to improve in accuracy and effectiveness over time. Where the new data comes from will depend on the problem being solved. For example, a machine learning model designed to identify spam will ingest email messages, whereas a machine learning model that drives a robot vacuum cleaner will ingest data resulting from real-world interaction with moved furniture or new objects in the room.

Machine learningmethods (also called machine learning styles) fall into three primary categories.

Supervised machine learning trains itself on a labeled dataset. That is, the data is labeled with information that the machine learning model is being built to determine and that may even be classified in ways the model is supposed to classify data. For example, a computer vision model designed to identify purebred German Shepherd dogs might be trained on a data set of various labeled dog images.

Supervised machine learning requires less training data than other machine learningmethods and makes training easier because the results of the model can be compared to actual labeled results. But, properly labeled data is expensive to prepare, and there's the danger of overfitting, or creating a model so closely tied and biased to the training data that it doesn't handle variations in new data accurately.

Learn more about supervised learning.

Unsupervised machine learning ingests unlabeled datalots and lots of itand uses algorithms to extract meaningful features needed to label, sort, and classify the data in real-time, without human intervention. Unsupervised learning is less about automating decisions and predictions, and more about identifying patterns and relationships in data that humans would miss. Take spam detection, for examplepeople generate more email than a team of data scientists could ever hope to label or classify in their lifetimes. An unsupervised learning algorithm can analyze huge volumes of emails and uncover the features and patterns that indicate spam (and keep getting better at flagging spam over time).

Learn more about unsupervised learning.

Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled dataset to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.

Reinforcement machine learning is a behavioral machinelearning model that is similar to supervised learning, but the algorithm isnt trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.

The IBM Watson system that won the Jeopardy! challenge in 2011 makes a good example. The system used reinforcement learning to decide whether to attempt an answer (or question, as it were), which square to select on the board, and how much to wagerespecially on daily doubles.

Learn more about reinforcement learning.

Deep learning is a subset of machine learning (all deep learning is machine learning, but not all machine learning is deep learning). Deep learning algorithms define an artificial neural network that is designed to learn the way the human brain learns. Deep learning models require large amounts of data that pass through multiple layers of calculations, applying weights and biases in each successive layer to continually adjust and improve the outcomes.

Deep learning models are typically unsupervised or semi-supervised. Reinforcement learning models can also be deep learning models. Certain types of deep learning modelsincluding convolutional neural networks (CNNs) and recurrent neural networks (RNNs)are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.

See the blog post AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference? for a closer look at how the different concepts relate.

Learn more about deep learning.

As noted at the outset, machine learning is everywhere. Here are just a few examples of machine learning you might encounter every day:

IBM Watson Machine Learning supports the machine learning lifecycle end to end. It is available in a range of offerings that let you build machine learning models wherever your data lives and deploy them anywhere in your hybrid multicloud environment.

IBM Watson Machine Learning on IBM Cloud Pak for Data helps enterprise data science and AI teams speed AI development and deployment anywhere, on a cloud native data and AI platform. IBM Watson Machine Learning Cloud, a managed service in the IBM Cloud environment, is the fastest way to move models from experimentation on the desktop to deployment for production workloads. For smaller teams looking to scale machine learning deployments, IBM Watson Machine Learning Server offers simple installation on any private or public cloud.

To get started, sign up for an IBMid and create your IBM Cloud account.

Go here to see the original:
What is Machine Learning? | IBM

Learning Curve

Artificial intelligence has been a hot technology area in recent years and machine learning, a subset of AI, is one of the most important segments of the whole AI arena.

Machine learning is the development of intelligent algorithms and statistical models that improve software through experience without the need to explicitly code those improvements. A predictive analysis application, for example, can become more accurate over time through the use of machine learning.

But machine learning has its challenges. Developing machine-learning models and systems requires a confluence of data science, data engineering and development skills. Obtaining and managing the data needed to develop and train machine-learning models is a significant task. And implementing machine-learning technology within real-world production systems can be a major hurdle.

Heres a look at a dozen startup companies, some that have been around for a few years and some just getting off the ground, that are addressing the challenges associated with machine learning.

AI.Reverie

Top Executive: Daeil Kim, Co-Founder, CEO

Headquarters: New York

AI.Reverie develops AI and machine -earning technology for data generation, data labeling and data enhancement tasks for the advancement of computer vision. The companys simulation platform is used to help acquire, curate and annotate the large amounts of data needed to train computer vision algorithms and improve AI applications.

In October AI.Reverie was named a Gartner Cool Vendor in AI core technologies.

Anodot

Top Executive: David Drai, Co-Founder, CEO

Headquarters: Redwood City, Calif.

Anodots Deep 360 autonomous business monitoring platform uses machine learning to continuously monitor business metrics, detect significant anomalies and help forecast business performance.

Anodots algorithms have a contextual understanding of business metrics, providing real-time alerts that help users cut incident costs by as much as 80 percent.

Anodot has been granted patents for technology and algorithms in such areas as anomaly score, seasonality and correlation. Earlier this year the company raised $35 million in Series C funding, bringing its total funding to $62.5 million.

BigML

Top Executive: Francisco Martin, Co-Founder, CEO

Headquarters: Corvallis, Ore.

BigML offers a comprehensive, managed machine-learning platform for easily building and sharing datasets and data models, and making highly automated, data-driven decisions. The companys programmable, scalable machine -earning platform automates classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery and topic modeling tasks.

The BigML Preferred Partner Program supports referral partners and partners that sell BigML and oversee implementation projects. Partner A1 Digital, for example, has developed a retail application on the BigML platform that helps retailers predict sales cannibalizationwhen promotions or other marketing activity for one product can lead to reduced demand for other products.

StormForge

Top Executive: Matt Provo, Founder, CEO

Headquarters: Cambridge, Mass.

StormForge provides machine learning-based, cloud-native application testing and performance optimization software that helps organizations optimize application performance in Kubernetes.

StormForge was founded under the name Carbon Relay and developed its Red Sky Ops tools that DevOps teams use to manage a large variety of application configurations in Kubernetes, automatically tuning them for optimized performance no matter what IT environment theyre operating in.

This week the company acquired German company Stormforger and its performance testing-as-a-platform technology. The company has rebranded as StormForge and renamed its integrated product the StormForge Platform, a comprehensive system for DevOps and IT professionals that can proactively and automatically test, analyze, configure, optimize and release containerized applications.

In February the company said that it had raised $63 million in a funding round from Insight Partners.

Comet.ML

Top Executive: Gideon Mendels, Co-Founder, CEO

Headquarters: New York

Comet.ML provides a cloud-hosted machine-learning platform for building reliable machine-learning models that help data scientists and AI teams track datasets, code changes, experimentation history and production models.

Launched in 2017, Comet.ML has raised $6.8 million in venture financing, including $4.5 million in April 2020.

Dataiku

Top Executive: Florian Douetteau, Co-Founder, CEO

Headquarters: New York

Dataikus goal with its Dataiku DSS (Data Science Studio) platform is to move AI and machine-learning use beyond lab experiments into widespread use within data-driven businesses. Dataiku DSS is used by data analysts and data scientists for a range of machine-learning, data science and data analysis tasks.

In August Dataiku raised an impressive $100 million in a Series D round of funding, bringing its total financing to $247 million.

Dataikus partner ecosystem includes analytics consultants, service partners, technology partners and VARs.

DotData

Top Executive: Ryohei Fujimaki, Founder, CEO

Headquarters: San Mateo, Calif.

DotData says its DotData Enterprise machine-learning and data science platform is capable of reducing AI and business intelligence development projects from months to days. The companys goal is to make data science processes simple enough that almost anyone, not just data scientists, can benefit from them.

The DotData platform is based on the companys AutoML 2.0 engine that performs full-cycle automation of machine-learning and data science tasks. In July the company debuted DotData Stream, a containerized AI/ML model that enables real-time predictive capabilities.

Eightfold.AI

Top Executive: Ashutosh Garg, Co-Founder, CEO

Headquarters: Mountain View, Calif.

Eightfold.AI develops the Talent Intelligence Platform, a human resource management system that utilizes AI deep learning and machine-learning technology for talent acquisition, management, development, experience and diversity. The Eightfold system, for example, uses AI and ML to better match candidate skills with job requirements and improves employee diversity by reducing unconscious bias.

In late October Eightfold.AI announced a $125 million Series round of financing, putting the startups value at more than $1 billion.

H2O.ai

Top Executive: Sri Ambati, Co-Founder, CEO

Headquarters: Mountain View, Calif.

H2O.ai wants to democratize the use of artificial intelligence for a wide range of users.

The companys H2O open-source AI and machine-learning platform, H2O AI Driverless automatic machine-learning software, H20 MLOps and other tools are used to deploy AI-based applications in financial services, insurance, health care, telecommunications, retail, pharmaceutical and digital marketing.

H2O.ai recently teamed up with data science platform developer KNIME to integrate Driverless AI for AutoMl with KNIME Server for workflow management across the entire data science life cyclefrom data access to optimization and deployment.

Iguazio

Top Executive: Asaf Somekh, Co-Founder, CEO

Headquarters: New York

The Iguazio Data Science Platform for real-time machine learning applications automates and accelerates machine-learning workflow pipelines, helping businesses develop, deploy and manage AI applications at scale that improve business outcomeswhat the company calls MLOps.

In early 2020 Iguazio raised $24 million in new financing, bringing its total funding to $72 million.

OctoML

Top Executive: Luis Ceze, Co-Founder, CEO

Headquarters: Seattle

OctoMLs Software-as-a-Service Octomizer makes it easier for businesses and organizations to put deep learning models into production more quickly on different CPU and GPU hardware, including at the edge and in the cloud.

OctoML was founded by the team that developed the Apache TVM machine-learning compiler stack project at the University of Washingtons Paul G. Allen School of Computer Science & Engineering. OctoMLs Octomizer is based on the TVM stack.

Tecton

Top Executive: Mike Del Balso, Co-Founder, CEO

Headquarters: San Francisco

Tecton just emerged from stealth in April 2020 with its data platform for machine learning that enables data scientists to turn raw data into production-ready machine-learning features. The startups technology is designed to help businesses and organizations harness and refine vast amounts of data into the predictive signals that feed machine-learning models.

The companys three founders: CEO Mike Del Balso, CTO Kevin Stumpf and Engineering Vice President Jeremy Hermann previously worked together at Uber where they developed the companys Michaelangelo machine-learning platform the ride-sharing company used to scale its operations to thousands of production models serving millions of transactions per second, according to Tecton.

The company started with $25 million in seed and Series A funding co-led by Andreessen Horowitz and Sequoia.

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The 12 Coolest Machine-Learning Startups Of 2020 - CRN