Podcasts about fast forward labs

Welcome to the ongoing mini-series The Orthogonal Bet. Hosted by ⁠Samuel Arbesman⁠, a Complexity Scientist, Author, and Scientist in Residence at Lux Capital. In this episode, he speaks with Hilary Mason, co-founder and CEO of Hidden Door, a startup creating a platform for interactive storytelling experiences within works of fiction. Hilary has also worked in machine learning and data science, having built a machine learning R&D company called Fast Forward Labs, which she sold to Cloudera. She was the chief scientist at Bitly and even a computer science professor. Samuel wanted to talk to Hilary not only because of her varied experiences but also because she has thought deeply about how to use AI productively—and far from naively—in games and other applications. She believes that artificial intelligence, including the current crop of generative AI, should be incorporated thoughtfully into software, rather than used without careful examination of its strengths and weaknesses. Additionally, Samuel, who often considers non-traditional research organizations, was eager to get Hilary's thoughts on this space, given her experience building such an organization. Produced by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Christopher Gates⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠George Ko⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ & Suno

EPISODE 1933: In this special KEEN ON show recorded at the DLD conference in Munich, Andrew talks to the Founder & CEO of Hidden Door, Hilary Mason, who peers behind the hidden door of AI, Gaming and StorytellingHilary Mason is the Founder & CEO of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel Partners. She co-founded HackNY, and she is a member of NYC Resistor.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.

EPISODE 1595: In this KEEN ON show, Andrew talks to Hilary Mason, co-founder and CEO of Hidden Door, about how Open Source AI might both democratize Big Tech and empower writers in the creation of role playing games Hilary Mason is the co-founder and CEO of Hidden Door, a role-playing AI platform. She was previously the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel as well as the Chief Scientist at bitly. She also co-founded of HackNY, co-host DataGotham, and is a member of NYCResistor. Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hilary Mason, CEO of Hidden Door and data scientist in residence at Accel Partners, talks with World of DaaS host Auren Hoffman. Hilary previously co-founded Fast Forward Labs, which was acquired by Cloudera, and served as the Chief Scientist at bit.ly. Auren and Hilary explore how data science has progressed in the past decade, the role of data science in an organization, and data ethics.World of DaaS is brought to you by SafeGraph. For more episodes, visit safegraph.com/podcasts You can find Auren on Twitter at @auren

In this episode, we’re joined by Santiago Giraldo, Director Of Product Marketing for Data Engineering & Machine Learning at Cloudera. In our conversation, we discuss Cloudera’s talks at TWIMLcon, as well as their various research efforts from their Fast Forward Labs arm. The complete show notes for this episode can be found at twimlai.com/sponsorseries.

After building revenue-generating algorithms into the marketing system at Ralph Lauren, Jessica Graves launched Sefleuria to tailor data science research to business outcomes in retail & luxury. She has a background commercializing machine learning technologies at Fast Forward Labs (acquired by Cloudera) and Thread Genius (acquired by Sotheby's). Her computing background began with supporting environmental science research at the University of Chicago, including a patented alternative energy solution currently scaling in Europe as Electrochaea. Following a career ranging from design at Oscar de la Renta to statistical computing at Alvanon for brands like Burberry, she speaks on Machine Intelligence & Creativity on global stages. Her movement artist practice under Internet Jessica includes performances at global black box theaters, museums, TEDx events, & concert halls; artist residencies in the UK; and electronic music performance as seen on Resident Advisor. Her academic impact includes guest lectures & research collaboration with Royal College of Art, ESCP, and King's College London. She has been featured in Texte Zur Kunst, Vogue Business, Sourcing Journal, Drapers and LOVE.Episode Linkshttps://www.sefleuria.com/http://www.electrochaea.com/

When you think of Cloudera, the billion-dollar software company that's virtually a household name, you probably think of a cloud-based, new technology data warehousing company. Sure, but did you know that Cloudera is currently a challenger in the 2017 Gartner Magic Quadrant for Data Management Analytic Solutions against the likes of Oracle, Teradata, IBM and Microsoft? In this episode, Jon Prial talks to Tom Reilly, Cloudera's CEO, along with Hilary Mason, one of the top data scientists in the world, whose company, Fast Forward Labs, Cloudera recently acquired. Together they discuss machine learning from both an executive and technical perspective. You'll hear about: -- Where the market is heading in terms of machine learning adoption -- The types of challenges companies face with machine learning -- The future of machine learning -- Data curation

Victor Dibia is a Research Engineer with Cloudera’s Fast Forward Labs. On today’s podcast, Wes and Victor talk about the realities of building machine learning in the browser. The two discuss the capabilities, limitations, process, and realities around using TensorFlow.js. The two wrap discussing techniques like Model distillation that may enable machine learning models to be deployed in smaller footprints like serverless. - While there are limitations in running machine learning processes in a resource constrained environment like the browser, there are tools like TensorFlow.js that make it worthwhile. One powerful use case is the ability to protect the privacy of a user base while still making recommendations. TensorFlow.js takes advantage of the WebGL library for its more computational intense operations. - TensorFlow.js enables workflows for training and scoring models (doing inference) purely online, by importing a model built offline with more tradition Python tools, and a hybrid approach that builds offline and finetunes online. To build an offline model, you can build a model with TensorFlow Python (perhaps using a GPU cluster). The model can be exported into the TensorFlow SaveModel Format (or the Keras Model Format) and then converted with TensorFlow.js into the TensorFlow Web Model Format. At that point, the can be directly imported into your JavaScript. - TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models and was made available by the Google AI team. It can give developers a quick jumpstart into using trained models. - Model compression promises to make models small enough to run in places we couldn’t run models before. Model distillation is a process where a smaller model is trained to replicate the behavior of a larger one. In one case, BERT (a library almost 500MB in size) was distilled to about 7MB (almost 60x compression). More on this: Quick scan our curated show notes on InfoQ https://bit.ly/32rWnab You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq Subscribe: www.youtube.com/infoq Like InfoQ on Facebook: bit.ly/2jmlyG8 Follow on Twitter: twitter.com/InfoQ Follow on LinkedIn: www.linkedin.com/company/infoq Check the landing page on InfoQ: https://bit.ly/32rWnab

This episode features special guest Hilary Mason, Data Scientist in Residence at Accel Partners, co-founder of hackNY.org, and founder of Fast Forward Labs, which is now part of Cloudera. Hilary joins CosmiQ’s Ryan Lewis and Nick Weir as they explore lessons learned and recommendations on executing artificial intelligence (AI) beyond research teams. The discussion explores how machine learning technologies can be applied at an enterprise level for corporations that are looking to move beyond basic prototypes and into real product development that shows results.

I caught up with Hilary Mason, GM of Machine Learning at Cloudera and former founder of Fast Forward Labs. We cover how to: - Generate ideas for Machine Learning Research - Hold a good brainstorm - Break into tech as a Data Scientist - Give an engaging and successful tech talk I also asked Hilary about the evolving role of Machine Learning and Data Science, and what she wants to build in the future!

In this episode of DataFramed, Hugo speaks with Cassie Kozyrkov, Chief Decision Scientist at Google Cloud. Cassie and Hugo will be talking about data science, decision making and decision intelligence, which Cassie thinks of as data science plus plus, augmented with the social and managerial sciences. They’ll talk about the different and evolving models for how the fruits of data science work can be used to inform robust decision making, along with pros and cons of all the models for embedding data scientists in organizations relative to the decision function. They’ll tackle head on why so many organizations fail at using data to robustly inform decision making, along with best practices for working with data, such as not verifying your results on the data that inspired your models. As Cassie says, “Split your damn data”.Links from the showFROM THE INTERVIEWCassie on Twitter Is data science a bubble? (By Cassie Kozyrkov, Hackernoon)Incompetence, delegation, and population (By Cassie Kozyrkov, Hackernoon)Populations — You’re doing it wrong (By Cassie Kozyrkov, Hackernoon)What on earth is data science? (By Cassie Kozyrkov, Hackernoon)FROM THE SEGMENTSProbability Distributions and their Stories (with Justin Bois at ~19:45)Justin's Website at CaltechProbability distributions and their stories (By Justin Bois)Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs ~43:45)Sebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMulti-Task Learning for NLP, also by Sebastian RuderGANs for Fake Celebrity Images (Karras et al, Nvidia)Adversarial Multi-Task Learning for Text Classification (Liu et al., arXiv.org)Original music and sounds by The Sticks.

Changing the “culture” at a large company is impossibly hard, few get through it. And, it’s little wonder, you’re usually asking them to do completely irrational things. In the context of Google shutting down Google+ and a small write-up of Blockbuster failure fairy tales, we spend time discussion the “if it ain’t broke, don’t fix it” problem of digital transformation. We then talk about Elastic search and their recent IPO, and follow-up with some better commentary on Cloudera and Hortonworks merging - better than we did last week. Hotel breakfast buffet strategies and the Chase Sapphire series of cards. Oh, and before that Matt and Coté spend a good 10 to 15 minutes talking about hotel breakfast buffet strategies. Also, it’s episode #150 - yay us! Our first episode was on May 27th, 2014, where Coté’s lamp played a prominent role, and we did video (https://www.youtube.com/watch?v=4S0_PzuYJJE&index=58&list=PLk_5VqpWEtiWnQ7od08nzkB32oT4gnDiP). Relevant to your interests Chase Sapphire Reserve (https://creditcards.chase.com/a1/sapphire/reserve), and others in the Sapphire line (https://www.chase.com/personal/credit-cards/sapphire-on-location). AAdvantage Executive card (https://secure.fly.aa.com/citi/direct-exec?anchorLocation=DirectURL&title=citiexecutive). SpringOne Platform videos (https://www.youtube.com/playlist?list=PLAdzTan_eSPQsR_aqYBQxpYTEQZnjhTN6&disable_polymer=true) are all up. Coté went to Puppetizer 2018 Amsterdam. They’re really into being “a portfolio company” (https://www.instagram.com/p/BovoMzaCxsJ/?taken-by=bushwald) now. Lots of stacks presented (http://cote.coffee/2018/10/10/thats-some-stack.html); much discussion on managing Puppet itself. A very well run event. See also Register (https://www.theregister.co.uk/2018/10/09/puppet_data_exhaust/) coverage of their SF event (https://www.theregister.co.uk/2018/10/09/puppet_data_exhaust/). Google is shutting down Google+ following massive data exposure (https://www.engadget.com/2018/10/08/google-shutting-down-google-plus/) - “90 percent of Google+ user sessions last for less than five seconds.” be like google prd mgmt desertion effect other enterprise props? legacy services OpenOffice watch (https://www.theregister.co.uk/2018/10/10/apache_open_office_not_dead/) - ‘Back in 2015, Red Hat developer Christian Schaller called OpenOffice "all but dead."’ Austin Ernest says make sure you don’t cargo cult The SRE (https://theagileadmin.com/2018/10/02/sre-the-biggest-lie-since-kanban/). The Demise of Blockbuster, and Other Failure Fairy Tales (https://medium.com/s/story/how-blockbuster-kodak-and-xerox-really-failed-its-not-what-you-think-e0a8c12e863d) - Strategy is hard, execution at the middle-management later is harder. Put yet another way, company executives have a lot less power than you’d think. Related: WTF is “culture” (https://twitter.com/cote/status/1050246624881061889)? This week in IPOs: Elastic has a party, Solarwinds figuring one out (https://www.channele2e.com/business/finance/solarwinds-ipo-plan-update/). Elastic (https://www.cnbc.com/2018/10/05/elastic-estc-ipo-stock-makes-debut-on-nyse.html): “The stock closed at $70 per share, representing a 94.4 percent rise.” Close of market on Oct. 10th (https://finance.yahoo.com/quote/ESTC?p=ESTC): $62.50 per share. 451 on Elastic revenue, Scott Denne (https://blogs.the451group.com/techdeals/ipo/elastic-adds-spring-to-the-fall-ipo-market/): “The developer of open source search software for IT log analysis, security analytics and other applications nearly doubled its top line in its fiscal year (ending April 30) to $160m, up from $88m a year earlier, while increasing the share of subscription revenue in its mix.” More: “Judging by Elastic’s offering, the [Q3] dry spell had little impact on investor appetites, setting up a favorable environment for Anaplan and SolarWinds as both look to price this month.” 451 on Elastic’s product, Nancy Gohring: “One of the most important messages that emerged from ElasticOn is that Elastic is positioning its software to serve as a platform for collecting and analyzing a wide array of machine data that can be used in a variety of use cases. With its recently announced APM UI and the forthcoming Infra UI, as well as the Canvas visualization capabilities, SQL-like querying and advancing machine-learning techniques, the Elastic Stack will be usable as a centralized platform for collecting and analyzing logs, events and metrics by constituents within a business including IT ops, security, executive leadership, product management and others.” So, Elastic is…an OSS (presumably) cheaper Splunk, but for general search not just IT? Or, wait, it is just IT stuff? Solarwinds: Coté hasn’t been able to parse out the Solarwinds deal. The big question is/will be, “so, did it make sense to go private, or could that have done whatever they’re doing by staying public?” Serverless and FaaS, survey shows confusion (https://thenewstack.io/add-it-up-serverless-faas/): “Despite attempts to educate the market, we still believe the word “serverless” connotes many different things, especially for the 79 percent of organizations that plan to adopt serverless architecture but have not planned to use FaaS in the next 18 months.” Coté’s old saw that “serverless” has just come to mean “doing programming on-top of cloud shit.” This is what Pivotal usually means when they say “cloud native,” versus the container kids who mean just “kubernetes,” at broadest, “containers.” Cloudera/Hortonworks follow-up: TPM (https://www.nextplatform.com/2018/10/05/hadoop-needs-to-be-a-business-not-just-a-platform/): “Cloudera has raked in $1.28 billion in revenues in the past six and a half years, while Hortonworks only brought in $808 million. Add in the venture capital of $1.31 billion in venture capital, plus $225 million that Cloudera raised in early 2017 for its IPO and the $100 million that Hortonworks raised in late 2014 from its IPO, and the total pile of cash that has come to the pair is $3.69 billion. Hortonworks still has $86 million of cash and Cloudera still has $440.1 million. But over that same time period, Cloudera has booked cumulative losses of $1.19 billion and Hortonworks has cumulative losses of $979 million, for a total of $2.16 billion. Both separately and together, these companies are burning the wood a lot faster than they can cut it.” TPM’s TAM summary, as suggested by the two companies: “The core market that Hadoop is chasing is comprised of three different segments, according to Cloudera-Hortonworks, and will grow at a compound annual growth rate of 21 percent between 2017 and 2022, from $12.7 billion to $32.3 billion. Within that, cognitive and artificial intelligence workloads represent a $14.3 billion opportunity in 2022, $4.9 billion for advanced and predictive analytics software, and $13.2 billion for dynamic data management systems (what we would call modern storage). In addition to that, the Hadoop platform is also chasing relational and non-relational database management systems and data warehouses, which is another $51 billion opportunity in 2022, for a total TAM of $83 billion. Even a small slice of this, which is what Hadoop currently gets today, could be billions of dollars by then.” Forrester on TAM penetration, Noel Yuhanna (https://go.forrester.com/blogs/cloudera-and-hortonworks-merger-a-win-win-for-all/): “We estimate that [just] 7% of organizations have completely migrated their traditional data warehouses to big data platforms. “ That’s 93% more left, assuming 20% capture for a leader, (shoddy percentage math follows)17 to 18%, I guess? Meanwhile, also from Forrester (https://www.forrester.com/report/Digital+Insights+Are+The+New+Currency+Of+Business/-/E-RES119109): “While 74% of global data and analytics decision makers tell us they will have invested in a big data lake by the end of 2017, we find that many of these are being kept on life support by the technology management shops that drove them.” Also, Forrester on HARK (Hadoop & Spark), Noel Yuhanna & Mike Gualtieri (https://www.forrester.com/report/Now+Tech+HadoopSpark+Platforms+Q3+2018/-/E-RES142699): “Distributed computing software and services that are rooted in open source Apache Hadoop and Apache Spark to store, process, and analyze data to find and use insights to improve customer experiences, create timely business intelligence, optimize business processes, and make decision making smarter and faster.” Like traditional analytics, but bigger and with more ML? 451 (Matt Aslett & James Curtis) (https://clients.451research.com/reportaction/95775/Toc?SearchTerms=Cloudera): “Although there are cross-selling opportunities and the two companies share an underlying open source foundation, there are also significant areas of product overlap and competing functionality, as well as a history of animosity to overcome.” Tamped down TAM: “Another way of looking at this is that the Hadoop market hasn't expanded enough to support the growth targets of two independent publicly traded companies, especially with the cloud providers to contend with.” Cloudera is the winner: “While the deal is being described by the companies as a merger, make no mistake that Cloudera is acquiring Hortonworks. After the transaction closes, Cloudera shareholders will own approximately 60% of the combined company, which will do business as Cloudera, with Hortonworks shareholders owning approximately 40%.” Products, Hortnworks: “Its primary product is the Hortonworks Data Platform (HDP), which consists of core Hadoop and some 20+ open source projects. But in August 2015, the company purchased Onyara, which was based on the Apache NiFi technology, and designed to enable users to collect, process and distribute data.” Products, Cloudera: “To date, Cloudera offers several products and while Hortonworks has adopted a pure 100% open source approach. Cloudera has a hybrid strategy, mixing open source with its proprietary tooling. The company's core offering is the Cloudera Enterprise Data Hub (CDH) – specifically targeted products are provided for data warehousing, operational database, and data science and engineering. Its cloud offering is Altus, a PaaS available on AWS and Azure.” 451 in another report (Agatha Poon) (https://clients.451research.com/reportaction/95135/Toc?SearchTerms=Cloudera), on Cloudera, June 2018: “At present, data analytics tools and offerings are driving regional opportunities with enterprises slowly but clearly moving out from legacy data warehouse platform to a new generation of data analytics platform, which is highly distributed and open standards based, Cloudera says. For machine learning and advanced data analytics, the company believes that data scientists will be the main users and strategic partners to boost future uptake. While data scientists can make use of algorithms to train the model into production data clusters, it could be a time-consuming and complex endeavor. With that in mind, Cloudera has stepped up its game by acquiring applied machine learning research startup Fast Forward Labs in late 2017, deepening its expertise in applying machine learning to practical business problems. The bigger Cloudera says it is committed to researching new techniques to resolve real-world business problems, building codes as well as providing customers with machine learning advisory services leveraging Fast Forward Labs' domain expertise.” Cloudera strategy: “Cloudera's proposition remains largely unchanged: lead machine learning in the enterprise, disrupt the data warehouse market for analytical and operational data workloads, capitalize on cloud adoption and drive innovation for simplification while mitigating data security risk. With cloud being an agent for digital transformation, the company has publicly announced its intent to lead with cloud innovation as part of the future growth strategy at the company level.” Conferences, et. al. Oct 16th - DevOpsDays Paris (https://www.devopsdays.org/events/2018-paris/welcome/) - Coté at a table. Pivotal will have a raffle! Oct 17th - JDriven Managers summit (https://www.jdriven.com/events/) - near Amsterdam - Coté talking. Oct 22nd - Cloud Native tour in Milan, Italy (https://connect.pivotal.io/milan_cloud_native_advocate_22oct.html). Coté and friends: a half day, a summit on Spring, DevOps, and cloud native programming. Free. Oct 31st - Coté speaking at New Relic’s FutureStack Amsterdam (https://web.cvent.com/event/23ce37e7-6077-42f5-8015-4a47a0cee30d/summary). Nov 3rd to Nov 12th - SpringOne Tour (https://springonetour.io/) - all over the earth! Coté will be MC’ing Beijing Nov 3rd, Seoul Nov 8th, Tokyo Nov 6th, and Singapore Nov 12th (https://springonetour.io/2018/singapore). Nov 14th to 16th - Devoxx Belgium (https://devoxx.be/), Antwerp. Coté’s presenting on enterprise architecture (https://dvbe18.confinabox.com/talk/ASN-9274/Rethinking_enterprise_architecture_for_DevOps,_agile,_&_cloud_native_organizations). Dec 12th and 13th - SpringTour Toronto (http://springonetour.io/2018/toronto), Coté. Nonsense Costco sought to provide a streaming service to customers (https://www.axios.com/costco-streaming-service-media-walmart-63c67545-67ef-4725-861f-fb70d285eb69.html). Listener Feedback Jermey is professor at a university in Chicago teaching cloud native and "devops" technologies to undergrads. “The Podcast has been a great benefit to the students. Could I get a few stickers to pass out to them?” SDT news & hype Join us in Slack (http://www.softwaredefinedtalk.com/slack) - new #upvoteplease channel for shameless (self) promotion. Subscribe to Software Defined Interviews Podcast (http://www.softwaredefinedinterviews.com/) - Cote on Tech Evangelism (http://www.softwaredefinedinterviews.com/75) CashedOut.coffee podcast (http://www.cashedout.coffee/). Send your postal address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) and we will send you a sticker. Brandon built the Quick Concall iPhone App (https://itunes.apple.com/us/app/quick-concall/id1399948033?mt=8) and he wants you to buy it for $0.99. Recommendations Brandon: Dr. Foster (https://www.netflix.com/title/80097034) on Neflix (https://www.netflix.com/title/80097034) Matt: Slint documentary Breadcrumb Trail (https://www.youtube.com/watch?v=GsRpS6XGiOs&t=). Coté: micro.blog (https://micro.blog/), where Coté now has cote.coffee (http://cote.coffee/) hooked up with some Instagram and Pinboard IFTTT wingdings. Drafts 5 seems fine. Coté needs help figuring out WTF “culture” is from a practical angle (https://twitter.com/cote/status/1050246624881061889).

In this episode of our Strata Data conference series, we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs. Fast Forward Labs was an Applied AI research firm and consultancy founded by Hilary Mason, who’s TWiML Talk episode remains an all-time fan favorite. My chat with Justin took place on the 1 year anniversary of Fast Forward Labs’ acquisition by Cloudera, so we start with an update on the company before diving into a look at some of recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge. To learn more about Cloudera and CFFL, visit Cloudera's Machine Learning resource center at cloudera.com/ml. For the complete show notes, visit https://twimlai.com/talk/185.  

Probabilistic Programming has been discussed as a programming paradigm that uses statistical approaches to dealing with uncertainty in data as a first class construct. On today’s podcast, Wes talks with Mike Lee Williams of Cloudera’s Fast Forward Labs about Probabilistic Programming. The two discusses how Bayesian Inference works, how it’s used in Probabilistic Programming, production-level languages in the space, and some of the implementations/libraries that we’re seeing. Key Takeaways * Federated machine learning is an approach of developing models at an edge device and returning just the model to a centralized location. By taking the averages of the edge models, you can protect privacy and distribute processing of building models. *Probabilistic Programming is a family of programming languages that make statistical problems easier to describe and solve. *It is heavily influenced by Bayesian Inference or an approach to experimentation that turns what you know before the experiment and the results of the experiment into concrete answers on what you should do next. * The Bayesian approach to unsupervised learning comes with the ability to measure uncertainty (or the ability to quantify risk). * Most of the tooling used for Probabilistic Programming today is highly declarative. “You simply describe the world and press go.” * If you have a practical, real-world problem today for Probabilistic Programming, Stan and PyMC3 are two languages to consider. Both are relatively mature languages with great documentation. * Prophet, a time-series forecasting library built at Facebook as a wrapper around Stan, is a particularly approachable place to use Bayesian Inference for forecasting use cases general purpose.

In this episode of the ARCHITECHT Show, Hilary Mason -- now GM of machine learning at Cloudera, and formerly founder of Fast Forward Labs, chief scientist at Bitly and more -- discusses a breadth of topics related to artificial intelligence, including what's exciting today in enterprise AI and machine learning, and how to discern the wheat from the chaff in AI research. Mason also goes into depth on the topic of data ethics, explaining why we're at a day of reckoning and how companies and data scientists can go about getting their ethics in order.

Today’s topic is machine learning and I’m talking to one of the brightest minds in the field, Hilary Mason. She’s the founder of Fast Forward Labs, a machine intelligence research company. She also advises startups through Accel, a prominent venture capital firm. If you’re interested in artificial intelligence and machine learning, I’m pretty sure you’ll love this episode.

In this episode of the ARCHITECHT Show, data scientist extraordinaire Hilary Mason covers a wide range of topics, including her path from Bitly to Cloudera—where she's now VP of research after the company acquired her applied research firm, Fast Forward Labs. Among other topics, Mason also discusses the state of AI readiness and adoption within large enterprises; the importance of getting "big data" pieces in place before jumping into AI; and who will actually do AI inside the companies that adopt it.

Hilary Mason is a huge name in the data science space, and she has an extensive understanding of what's happening in this space. Today, she answers these questions for us: What are the backgrounds of your typical data scientists? What are key differences between software engineering and data science that most companies get wrong? How should you measure the effectiveness of your work or your team's work as a data scientist for the best results? What is a good approach for creating a successful data product? How can we peak behind the curtain of black-box deep learning algorithms? Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link, or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast. Curtis: Today we hear from one of the biggest thinkers in the data science space, someone who DJ Patil endorses on LinkedIn for data science skills. She worked at bit.ly, the url shortener, and is a data scientist in residence at venture capital firm Accel Partners, a firm that helped fund some companies you may know, like Facebook, Slack, Etsy, Venmo, Vox Media, Lynda.com, Cloudera, Trifacta—and you get the picture. Ginette: The partner of this VC firm said that Accel wouldn’t have brought on just any data scientist. This position was specifically created because this particular data scientist might be able to join their team. Curtis: But beyond her position as data in residence with Accel, she founded a company that’s doing very interesting research, and today, she shares with us some of her experiences and perspective on where AI is headed. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how data and prediction shape our world. Ginette: A Vault Analytics production. Hilary: I'm Hilary Mason, and I'm the founder and CEO of Fast Forward Labs (Please note that Hilary is now the VP of Research at Cloudera). In addition to that, I'm a data science in residence for Accel Partners. And I've been working in what we now call data science, or even now call AI, for about twenty years at this point. Started my career in academic machine learning and decided startups were more fun and have been doing that for about 10,   12 years depending on how you count now, and it's a lot of fun! Ginette: Something I’d like to note here is there’s been a very recent change: Hilary’s company, Fast Forward Labs, and Cloudera recently joined forces, and Hilary’s new position is Vice President of Research at Cloudera. Now, one thing that Hilary talks to is where the data scientists she works with come from, which is a great example of the different paths people take to get into this field. Hilary I am a computer scientist, and I have studied computer science. It's funny because now at Fast Forward, our team only has only two computer scientists on it, and one of them is our general counsel, and one is me, and I'm running the business, so most of the people doing data science here come from very different backgrounds. We have a bunch of physicists, mathematicians, a   neuroscientist, a person who does brilliant machine learning design who was an English major, and so data science is one of those fields where one of the things I really love about it is that people come to it from so many different backgrounds, but mine happens to be computer science. The people on our team at Fast Forward   typically have a PhD in a quantitative field, such as physics, neuroscience, electrical engineering, and then have, through that, learned sufficient programming skill. One of the jokes I make about my team is that we're essentially a halfway house for wayward academics in the sense that we can absorb people and teach them to be good software engineers, help them understand the difference between theoretical machine learning an...

In this episode of the ARCHITECHT Show, Packet co-founder and CEO Zachary Smith explains his company's attempt to carve out its own cloud computing niche by offering "unopinionated" access to bare metal resources. Among other things, Smith discusses Packet's audience of tinkerers and DIYers; the unique benefits of custom hardware as a service; the challenge of trying to bootstrap a business in world of cloud giants; Packet's plan to power the advent of edge computing; and Smith's own journey from Juilliard to cloud hosting. In the news segment, co-hosts Derrick Harris and Barb Darrow (Fortune) discuss Rackspace buying Datapipe, Cloudera buying Fast Forward Labs, and Amazon's search for a new HQ.

This week's guest is Kathryn Hume. Kathryn is the President of Fast Forward Labs, which is an independent machine intelligence research company that helps organizations accelerate their data science and machine intelligence capabilities. If Fast Forward Labs sounds familiar, that's because we had their founder, Hilary Mason on a few months ago. We’ll link to that in the show notes. My discussion with Kathryn focused on AI adoption within the enterprise. She shared several really interesting examples of the kinds of things she’s seeing enterprises do with machine learning and AI, and we discussed a few of the various challenges enterprises face and some of the lessons her company has learned in helping them. I really enjoyed our conversation and I know you will too! You can find the notes for todays show here: https://twimlai.com/talk/20

My guest this time is Hilary Mason. Hilary was one of the first “famous” data scientists. I remember hearing her speak back in 2011 at the Strange Loop conference in St. Louis. At the time she was Chief Scientist for bit.ly. Nowadays she’s running Fast Forward Labs, which helps organizations accelerate their data science and machine intelligence capabilities through a variety of research and consulting offerings. Hilary presented at the O'Reilly AI conference on “practical AI product development” and she shares a lot of wisdom on that topic in our discussion. The show notes can be found at twimlai.com/talk/11.

The O'Reilly Radar Podcast: Thinking critically about AI, modeling language, and overcoming hurdles.This week, I sit down with Hilary Mason, who is a data scientist in residence at Accel Partners and founder and CEO of Fast Forward Labs. We chat about current research projects at Fast Forward Labs, adoption hurdles companies face with emerging technologies, and the AI technology ecosystem—what's most intriguing for the short term and what will have the biggest long-term impact.Here are some highlights: Missing wisdom There are a few things missing [from the AI conversation]. I think we tend to focus on the hype and eventual potential without thinking critically about how we get there and what can go wrong along the way. We have a very optimistic conversation, which is something I appreciate. I'm an optimist, and I'm very excited about all of this stuff, but we don't really have a lot of critical work being done in things like how do we debug these systems, what are the consequences when they go wrong, how do we maintain them over time, and operationalize and monitor their quality and success, and what do we do when these systems infiltrate pieces of our lives where automation may have highly negative consequences. By that, I mean things like medicine or criminal justice. I think there's a big conversation that is happening, but the wisdom still is missing. We haven't gotten there yet. Making the impossible possible I'm particularly intrigued at the moment by being able to model language. That's something where I think we can't yet imagine the ultimate applications of these things, but it starts to make things that previously would have seemed impossible possible, things like automated novel writing, poetry, things that we would like to argue are purely human creative enterprises. It starts to make them seem like something we may one day be able to automate, which I'm personally very excited about. The impact question is a really good one, and I think it is not one technology that will have that impact. It's the same reason we're starting to see all these different AI products pop up. It's the ensemble of all of the techniques that are falling under this umbrella together that is going to have that kind of impact and enable applications like the Google Photos app, which is my favorite AI product, or self-driving cars or things like Amazon's Alexa, but actually smarter. That's a collection of different techniques. Making sentences and languages computable We've done a project in automated summarization that I'm very excited about—that is applying neural networks to text, where you can put in a single article and it will extract; this is extractive summarization. It extracts sentences from that article that, combined together, contain the same information in the article as a whole. We also have another formulation of the problem, which is multi-document summarization, where we apply this to Amazon product reviews. You can put in 5,000 reviews, and it will tell you these reviews tend to cluster in these 10 ways, and for each cluster, here's the summary of that cluster review. It gives you the capability to read or understand thousands of documents very quickly. ... I think we're going to see a ton of really interesting things built on the techniques that underlie that. It's not just summarization, but it's making sentences and languages computable. Adoption hurdles I think the biggest adoption hurdle [for emerging technologies]—there are two that I'll say. The one is that sometimes these technologies get used because they're cool, not because they're useful. If you build something that's not useful, people don't want to use it. That can be a struggle. The second thing is that people are generally resistant to change. When you're in an organization and you're trying to advocate for the use of a new technology to make the organization more efficient, you will likely run into friction. In those situations, it's a matter of time and making the people who are most resistant look good.

The O'Reilly Radar Podcast: Thinking critically about AI, modeling language, and overcoming hurdles.This week, I sit down with Hilary Mason, who is a data scientist in residence at Accel Partners and founder and CEO of Fast Forward Labs. We chat about current research projects at Fast Forward Labs, adoption hurdles companies face with emerging technologies, and the AI technology ecosystem—what's most intriguing for the short term and what will have the biggest long-term impact.Here are some highlights: Missing wisdom There are a few things missing [from the AI conversation]. I think we tend to focus on the hype and eventual potential without thinking critically about how we get there and what can go wrong along the way. We have a very optimistic conversation, which is something I appreciate. I'm an optimist, and I'm very excited about all of this stuff, but we don't really have a lot of critical work being done in things like how do we debug these systems, what are the consequences when they go wrong, how do we maintain them over time, and operationalize and monitor their quality and success, and what do we do when these systems infiltrate pieces of our lives where automation may have highly negative consequences. By that, I mean things like medicine or criminal justice. I think there's a big conversation that is happening, but the wisdom still is missing. We haven't gotten there yet. Making the impossible possible I'm particularly intrigued at the moment by being able to model language. That's something where I think we can't yet imagine the ultimate applications of these things, but it starts to make things that previously would have seemed impossible possible, things like automated novel writing, poetry, things that we would like to argue are purely human creative enterprises. It starts to make them seem like something we may one day be able to automate, which I'm personally very excited about. The impact question is a really good one, and I think it is not one technology that will have that impact. It's the same reason we're starting to see all these different AI products pop up. It's the ensemble of all of the techniques that are falling under this umbrella together that is going to have that kind of impact and enable applications like the Google Photos app, which is my favorite AI product, or self-driving cars or things like Amazon's Alexa, but actually smarter. That's a collection of different techniques. Making sentences and languages computable We've done a project in automated summarization that I'm very excited about—that is applying neural networks to text, where you can put in a single article and it will extract; this is extractive summarization. It extracts sentences from that article that, combined together, contain the same information in the article as a whole. We also have another formulation of the problem, which is multi-document summarization, where we apply this to Amazon product reviews. You can put in 5,000 reviews, and it will tell you these reviews tend to cluster in these 10 ways, and for each cluster, here's the summary of that cluster review. It gives you the capability to read or understand thousands of documents very quickly. ... I think we're going to see a ton of really interesting things built on the techniques that underlie that. It's not just summarization, but it's making sentences and languages computable. Adoption hurdles I think the biggest adoption hurdle [for emerging technologies]—there are two that I'll say. The one is that sometimes these technologies get used because they're cool, not because they're useful. If you build something that's not useful, people don't want to use it. That can be a struggle. The second thing is that people are generally resistant to change. When you're in an organization and you're trying to advocate for the use of a new technology to make the organization more efficient, you will likely run into friction. In those situations, it's a matter of time and making the people who are most resistant look good.

The O’Reilly Bots Podcast: Hilary Mason, Jimi Smoot, and Roger Chen on what AI means now.Something remarkable is happening in the world of artificial intelligence. At the O’Reilly AI Conference in New York, people weren’t just talking about AI as a far-off dream; they were talking about AI as something that exists in real products today. In this episode of the O’Reilly Bots podcast, I talk with three artificial-intelligence practitioners about the real practice of AI: Hilary Mason, Jimi Smoot, and Roger Chen. Hilary Mason, founder and CEO of Fast Forward Labs, a startup that conducts research on machine intelligence, says that today’s AI “gives us a capability that would have seemed like magic even five years ago, and yet that capability is not nearly as interesting as the fact that the app is actually useful.” We also talk about the potential of AI-generated content, and some products that provide a glimpse of what a new AI-written world might look like. My second conversation is with Jimi Smoot, founder and CEO of Vesper, a hybrid AI and human assistant that helps executives with tasks like scheduling and travel arrangements. AI that augments human functions is likely to be a facet of the next economy. Smoot says that “early in the process with a new user, having a human touch is critical to developing trust.” Finally, Roger Chen, co-chair of the O’Reilly AI Conference, talks about what the term AI really means, the origins of the AI Conference, and how companies can implement AI now.  “I think a lot of these interfaces that we call AI and bots are just going to be known as seamless, great experiences and interfaces,” he says. O’Reilly’s upcoming Bot Day on October 19, 2016, in San Francisco, will provide more insight on AI for bots. Other links: Google’s "Deep Dream" paper, illustrating how a neural network can be used to turn an ordinary photograph into a dream-like composite Composing classical music using neural networks Video highlights from the O’Reilly AI Conference Brief, from Fast Forward Labs, a summarization engine that uses AI to extract the most interesting sentences from long passages of text

The O’Reilly Bots Podcast: Hilary Mason, Jimi Smoot, and Roger Chen on what AI means now.Something remarkable is happening in the world of artificial intelligence. At the O’Reilly AI Conference in New York, people weren’t just talking about AI as a far-off dream; they were talking about AI as something that exists in real products today. In this episode of the O’Reilly Bots podcast, I talk with three artificial-intelligence practitioners about the real practice of AI: Hilary Mason, Jimi Smoot, and Roger Chen. Hilary Mason, founder and CEO of Fast Forward Labs, a startup that conducts research on machine intelligence, says that today’s AI “gives us a capability that would have seemed like magic even five years ago, and yet that capability is not nearly as interesting as the fact that the app is actually useful.” We also talk about the potential of AI-generated content, and some products that provide a glimpse of what a new AI-written world might look like. My second conversation is with Jimi Smoot, founder and CEO of Vesper, a hybrid AI and human assistant that helps executives with tasks like scheduling and travel arrangements. AI that augments human functions is likely to be a facet of the next economy. Smoot says that “early in the process with a new user, having a human touch is critical to developing trust.” Finally, Roger Chen, co-chair of the O’Reilly AI Conference, talks about what the term AI really means, the origins of the AI Conference, and how companies can implement AI now.  “I think a lot of these interfaces that we call AI and bots are just going to be known as seamless, great experiences and interfaces,” he says. O’Reilly’s upcoming Bot Day on October 19, 2016, in San Francisco, will provide more insight on AI for bots. Other links: Google’s "Deep Dream" paper, illustrating how a neural network can be used to turn an ordinary photograph into a dream-like composite Composing classical music using neural networks Video highlights from the O’Reilly AI Conference Brief, from Fast Forward Labs, a summarization engine that uses AI to extract the most interesting sentences from long passages of text

Hilary Mason, founder at Fast Forward Labs and Data Scientist in Residence at Accel Partners, debunks some of the myths around startups being "data-driven." In addition, she tackles some complex but critical topics and translates them for the rest of us. This episodes includes... 1) A clear definition of what data science actually is (and should be) 2) Hard truth about how much a startup should actually value its data 3) The evolution of the field of data science, who should use it, and where it's going and why Follow Hilary @hmason and visit fastforwardlabs.com to learn more. And let me know what you think of the show -- tweet me (Jay Acunzo) @jayacunzo. You can also subscribe to receive every episode plus weekly insights and resources about gaining startup traction: goo.gl/4eP9Ch