The Rise of Artificial Intelligence through Deep Learning Yoshua Bengio 17 mins. TEDxMontreal 20170517
The Rise of Artificial Intelligence through Deep Learning | Yoshua Bengio | TEDxMontreal 173,625 views 2.4K 92 SHARE SAVE TEDx Talks Published on May 17, 2017 SUBSCRIBE 15M A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead? Yoshua Bengio believes that understanding the basics of AI is within every citizen’s reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all. ___________________________ Yoshua Bengio is one of the pioneers of Deep Learning. He is the head of the Montreal Institute for Learning Algorithms (MILA), Professor at the Université de Montréal, member of the NIPS board and co-founder of Element AI. With a PhD from McGill University (1991, Computer Science) and postdocs at MIT and AT&T Bell Labs, he holds the Canada Research Chair in Statistical Learning Algorithms, is a Senior Fellow of the Canadian Institute for Advanced Research and co-directs its program focused on deep learning. He is best known for his contributions to deep learning, recurrent nets, neural language models, neural machine translation and biologically inspired machine learning. https://mila.umontreal.ca/en/ https://www.elementai.com/
#ai  #explainer  #video  @YoshuaBengio 
14 minutes ago
Where AI is today and where it's going Richard Socher TedX 15 mins. 20171107
Where AI is today and where it's going. | Richard Socher | TEDxSanFrancisco 178,269 views 1.6K 135 SHARE SAVE TEDx Talks Published on Nov 7, 2017 SUBSCRIBE 15M Richard Socher is an adjunct professor at the Stanford Computer Science Department where he obtained his PhD working on deep learning with Chris Manning and Andrew Ng. He won the best Stanford CS PhD thesis award. He is now Chief Scientist at Salesforce where he leads the company’s research efforts in artificial intelligence. He previously founded MetaMind, a deep learning AI platform that analyzes, labels and makes predictions on image and text data. Richard Socher is Chief Scientist at Salesforce and an adjunct professor at the Stanford Computer Science Department. At Salesforce he leads the company’s research efforts and brings state of the art artificial intelligence solutions to Salesforce. Prior to Salesforce, Richard was the CEO and founder of MetaMind, a startup acquired by Salesforce in April 2016. Richard obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and won the best Stanford CS PhD thesis award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
#ai  #explainer  #video 
18 minutes ago
Machine Learning & Artificial Intelligence Crash Course Computer Science #34 20171101
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34 345,529 views 8.2K 129 SHARE SAVE CrashCourse Published on Nov 1, 2017 SUBSCRIBE 8.3M So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list... Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids SHOW MORE
#ai  #ml  #explainer  #video 
23 minutes ago
What is Artificial Intelligence (or Machine Learning)? Hubspot YouTube 20170130
What is Artificial Intelligence (or Machine Learning)? 382,888 views 3.2K 126 SHARE SAVE HubSpot Published on Jan 30, 2017 SUBSCRIBE 62K What is AI? What is machine learning and how does it work? You’ve probably heard the buzz. The age of artificial intelligence has arrived. But that doesn’t mean it's easy to wrap your mind around. For the full story on the rise of artificial intelligence, check out The Robot Revolution: http://hubs.ly/H0630650 Let’s break down the basics of artificial intelligence, bots, and machine learning. Besides, there's nothing that will impact marketing more in the next five to ten years than artificial intelligence. Learn what the coming revolution means for your day-to-day work, your business, and ultimately, your customers. Every day, a large portion of the population is at the mercy of a rising technology, yet few actually understand what it is. Artificial intelligence. You know, HAL 9000 and Marvin the Paranoid Android?
#ai  #definition  #explainer  #A+  @Hubspot  #video 
27 minutes ago
How A.I. Is Different in China Video McKinsey 20180128
How A.I. Is Different in China. Chris Thomas, McKinsey & Co Beijing. 3,179 views LIKE DISLIKE SHARE SAVE Jeffrey Towson Published on Jan 28, 2018 SUBSCRIBE Chris Thomas (McKinsey & Co - Head of Semiconductors for Asia and Global Head of Digital Strategy) talks with Jeffrey Towson about what is happening in artificial intelligence in China - and what everyone is getting wrong. Apologies for the quality of this vid - but the thinking is really great. By Professor Jeffrey Towson at Peking University Guanghua School of Management www.jefftowson.com
#ai  #china  #video  #A+ 
58 minutes ago
The State of Artifical Intelligence in China Kai-Fu Lee 20171103
The State of Artifical Intelligence in China - Kai-Fu Lee 13,597 views 227 5 SHARE SAVE The Artificial Intelligence Channel Published on Nov 3, 2017 SUBSCRIBED 75K Kai-Fu Lee is a vc, technology executive, writer, and computer scientist. Lee developed the world's first speaker-independent, continuous speech recognition system as his Ph.D. thesis at Carnegie Mellon. He later worked as an executive, first at Apple, then SGI, Microsoft, and then Google. Recorded November 2nd, 2017
#ai  #China  #status  #innovation  #advances  #applications  #A+ 
1 hour ago
Applied Artificial Intelligence: A Handbook For Business Leaders eBook: Mariya Yao, Adelyn Zhou, Marlene Jia: Kindle Store
Applied Artificial Intelligence: A Handbook For Business Leaders Kindle Edition by Mariya Yao (Author), Adelyn Zhou (Author), Marlene Jia (Author)
#ai  #book  #A+ 
1 hour ago
The Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence: Carlos E Perez: 9781978487529: Amazon.com: Books
The Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence Paperback – October 19, 2017 by Carlos E Perez (Author)
Deep Learning Artificial Intelligence involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. In addition to that, it is technology that can be extremely disruptive. The ramifications to society and even our own humanity will be profound. There are few subjects that are as captivating and as consequential as this. Surprisingly, there is very little that is written about this new technology in a more comprehensive and cohesive way. This book is an opinionated take on the developments of Deep Learning AI. One question many have will be "how to apply Deep Learning AI in a business context?" Technology that is disruptive does not automatically imply that its application to valuable use cases will be apparent. For years, many people could not figure out how to monetize the World Wide Web. We are in a similar situation with Deep Learning AI. The developments may be mind-boggling but its monetization is far from being obvious. This book presents a framework to address this shortcoming.
#ai  #book  #deeplearning  @CarlosPerez  #tl  #SME 
1 hour ago
AI Knowledge Map: how to classify AI technologies Francesco Corea 20108029
AI Knowledge Map: how to classify AI technologies A sketch of a new AI technology landscape A shorter version of this article appeared first on Forbes The article has also been awarded with the Silver badge by KDnuggets as one of the most read and shared in August 2018. I. Introductory thoughts I have been in the space of artificial intelligence for a while, and I am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fix boxes is often not worth the benefits of having such a “clear” framework (it is a generalization of course, cause sometimes they are extremely useful). When it comes specifically to artificial intelligence, I do also think that many of the categorizations out there are either incomplete or unable to capture strong fundamental links and aspects of this new AI wave. So let me first tell you the rationale for this post. Working with strategic innovation agency Chôra, we wanted to create a visual tool for people to grasp at a glance the complexity and depth of this toolbox, as well as laying down a map that could help people orientating in the AI jungle. You should look at the following graph as a way to organize unstructured knowledge into a sort of ontology with the final aim not to accurately represent all the existing information on AI but rather to have a tool to describe and access part of that information set. What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway on pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. The utility of the final work should therefore help you achieve three things: making sense of what is going on and have a map to follow the path; understanding where machine intelligence is used today (with respect to where was not used for in the past); understanding what and how many problems are reframed to make possible for AI to tackle them (if you are familiar with the work of Agrawal et al., 2018 those are direct consequences of the drop in cost of prediction technologies
#ai  #landscape  #framework  #industry  #applications  #A+  @FrancescoCorea 
2 hours ago
These are the 8 major forces shaping the future of the global economy World Economic Forum 201810
These are the 8 major forces shaping the future of the global economy From atoms to bytes. Image: REUTERS/Desmond Boylan (CUBA - Tags: SOCIETY BUSINESS AGRICULTURE) - GM1E9AN0M3801 This article is written in collaboration with Visual Capitalist 11 Oct 2018 Jeff Desjardins Founder and editor of Visual Capitalist Latest Articles How to protect the 2020 Olympics from cyberattackers RAND Corporation 19 Oct 2018 Africa is helping the drone industry get off the ground. Here’s how Seth Berkley 19 Oct 2018 China has made a shocking food production discovery – electro culture Sean Fleming 19 Oct 2018 More on the agenda Explore context China Explore the latest strategic trends, research and analysis The world is changing faster than ever before. With billions of people hyper-connected to each other in an unprecedented global network, it allows for an almost instantaneous and frictionless spread of new ideas and innovations. Combine this connectedness with rapidly changing demographics, shifting values and attitudes, growing political uncertainty, and exponential advances in technology, and it’s clear the next decade is setting up to be one of historic transformation. But where do all of these big picture trends intersect, and how can we make sense of a world engulfed in complexity and nuance? Furthermore, how do we set our sails to take advantage of the opportunities presented by this sea of change?
#strategy  #drivers  #innovation 
16 hours ago
Billion Dollars Or Bust: A Scorecard Of Whether 75 Promising Startups Became Unicorns
Billion Dollars Or Bust: A Scorecard Of Whether 75 Promising Startups Became Unicorns Biz Carson Forbes Staff A sk DoorDash CEO and cofounder Tony Xu how his restaurant delivery startup was able to raise nearly $1 billion at over a $4 billion valuation, just three years after he could only attract $60 million in funding (at a $600 million valuation), and he just laughs. “The best way to fundraise is to build a good business because everything else is out of your control,” says Xu, 34. “I wish I had a magic wand that says ‘Investors, do this!’ and then they do it, but that’s not how it works.” Xu and his DoorDash are graduates of Forbes' Class of 2015 Next Billion Dollar Startups, our annual list of 25 private companies we think are on their way to achieving unicorn status. In DoorDash’s case, the firm had to overcome waning investor interest in the crowded food-delivery space and expand beyond restaurants, like its deal with Walmart to deliver groceries. Three years later, DoorDash’s total funding now stands at $978 million, of which $785 million was locked in this year. “Capital is accruing to the winners,” says a triumphant Xu. San Francisco’s DoorDash is one of the biggest success stories among the Next Billion-Dollar Startups, we have selected over the last four years with the help of Chapel Hill, North Carolina venture firm TrueBridge, which evaluates hundreds of candidates based on revenues, operating strategies and competitive challenges.  Considering that the vast majority of venture-backed firms fail, a full one-third of the startups on our list have already gone on to reach or surpass the $1 billion valuation mark either via new funding rounds, like DoorDash, acquisitions or initial public offerings. From the initial 25 companies on our 2015 list, nine are now unicorns and most of those companies passed the billion-dollar mark in 2017, a heady year for the stock market and for tech valuations. According to Pitchbook data, 34 US-based companies reached unicorn valuations in 2017. So far this year some 27 startups became worth $1 billion as of August 1. “I think what you’re seeing is a reflection of how much potential there still is and how many new incredible ideas there are,” says Thumbtack CEO Marco Zappacosta, 33, whose services marketplace company debuted on the 2015 list and grew into an unicorn later that year. In addition to Thumbtack, other Next Billion-Dollar Startups that have become unicorns include Opendoor, a new online real estate marketplace, and Procore, a maker of software for the construction industry.
#startups  #list 
18 hours ago
The silent shapers of healthcare services McKinsey 201810
The silent shapers of healthcare services By Neha Patel; Lisa Foo; and Saum Sutaria, MD Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article The US healthcare services industry is at a tipping point, but who—or what—is driving the undercurrents of change? Over the past five years, institutional investors have been quietly shaping parts of the healthcare industry. Private equity (PE) investors, for example, have begun to consolidate several markets, including ambulatory surgery, hospitalist staffing, and home health, undertaking more than $50 billion in total transactions. Institutional investors’ focus on healthcare services—healthcare delivery and its enablers—is likely to continue, given industry trends. Ongoing growth in health expenditures, the degree of medical waste, and industry fragmentation signal high upside potential. Furthermore, the impact on the industry could be even greater in coming years. Institutional investors have been learning from their experience and will likely be using those lessons as they inject hundreds of billions in capital into healthcare in the next five years. These new investments have the potential to drive structural shifts in ways that are more direct and proactive than have been used before. Health systems must decide how they want to respond—inaction is no longer an option. As they consider their responses, the systems need to answer two questions: Do they want to shape the industry on their own or alongside the institutional investors? And, how can they transform their business models to be sustainable as the industry evolves?
#hc  #innovation  #startups  #VCs  #investment  @McKinsey  VCs  healthcare  Innovative 
18 hours ago
The Next Billion-Dollar Startups Forbes 20181015
Where are the next unicorns? They’re coming from a wide range of industries—from esports and online education, to trucking and 3-D printing, to luggage and shoes.
To compile our annual list of the Next Billion-Dollar Startups, we teamed up with TrueBridge Capital Partners to ask nearly 200 venture capital firms to nominate the companies they thought were most likely to become unicorns. We narrowed down the field from over 100 businesses to the 25 below by looking at revenue, funding and their most recent valuation. They’re presented in alphabetical order.
#startups  #analytics  #ML  #list  #hc  #others  #2018 
19 hours ago
The Big Leap Toward AI at Scale BCG 20180613
The Big Leap Toward AI at Scale JUNE 13, 2018 Philipp Gerbert , Sukand Ramachandran , Jan-Hinnerk Mohr , and Michael Spira After decades of false starts and unfulfilled expectations, artificial intelligence (AI) has now gone mainstream. Companies are successfully applying AI to a wide variety of current and novel processes, products, and services, and this success comes none too soon; AI is essential for business to address the ballooning complexity brought on by digitization and big data.  Visionary executives have started to imagine the potential for implementing AI throughout their companies. For example, BCG estimates that AI could help the top ten banks generate an additional $150 billion to $220 billion in annual operating earnings. As pioneers across industries strive to reap these rewards by scaling up AI, however, they are stumbling against what we call the “AI paradox”: it is deceptively easy to launch AI pilots and achieve powerful results. But it is fiendishly hard to move toward “AI@scale.” All sorts of problems arise, threatening to undercut the AI revolution at its inception.
#ai  #bcg  #A+ 
2 days ago
davidstaas (@davidstaas) | Twitter
davidstaas @davidstaas Follows you President, NinthDecimal | Bridging the physical and digital worlds through mobile. Product & marketing guy, runner, foodie, dad, and Wahoo on the west coast. San Francisco ninthdecimal.com Joined December 2008
#lbs  #vendor  @DavidStaas 
2 days ago
Hemingway Editor
Hemingway App makes your writing bold and clear. The app highlights lengthy, complex sentences and common errors; if you see a yellow sentence, shorten or split it. If you see a red highlight, your sentence is so dense and complicated that your readers will get lost trying to follow its meandering, splitting logic — try editing this sentence to remove the red. You can utilize a shorter word in place of a purple one. Mouse over them for hints.
#research  #tools  #writing  #editing 
2 days ago
Vala Afshar (@ValaAfshar) | Twitter
Vala AfsharVerified account @ValaAfshar Chief Digital Evangelist @Salesforce | Blog: @HuffPost @ZDNet | Show: @DisrupTVShow | Book: ªªhttp://bit.ly/tposbe ºº Boston huffpost.com/vala-afshar/ Joined March 2011
#marketing  #digital  #tl  #rr 
2 days ago
Alignable (@Alignable) | Twitter
Alignable @Alignable The #smallbusiness network Boston, MA alignable.com Joined March 2012
About
With millions of connections across more than 20,000 local communities, Alignable is the free network where small business owners build trusted relationships and generate referrals.

Members use Alignable to get the industry answers they need, connect within their local business community, and increase word-of-mouth for their business.

Headquartered in Boston, Alignable was made public in 2014 and is venture-backed by Mayfield Fund, Recruit Strategic Partners, Saturn Partners, NextView Ventures and Lead Edge Capital.
#smb  #social  #network  +DanGilmartin 
2 days ago
How Amazon Rebuilt Itself Around Artificial Intelligence WIRED 20180201
INSIDE AMAZON'S ARTIFICIAL INTELLIGENCE FLYWHEEL How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company. AUTHOR: STEVEN LEVYBY STEVEN LEVY IN EARLY 2014, Srikanth Thirumalai met with Amazon CEO Jeff Bezos. Thirumalai, a computer scientist who’d left IBM in 2005 to head Amazon’s recommendations team, had come to propose a sweeping new plan for incorporating the latest advances in artificial intelligence into his division. He arrived armed with a “six-pager.” Bezos had long ago decreed that products and services proposed to him must be limited to that length, and include a speculative press release describing the finished product, service, or initiative. Now Bezos was leaning on his deputies to transform the company into an AI powerhouse. Amazon’s product recommendations had been infused with AI since the company’s very early days, as had areas as disparate as its shipping schedules and the robots zipping around its warehouses. But in recent years, there has been a revolution in the field; machine learning has become much more effective, especially in a supercharged form known as deep learning. It has led to dramatic gains in computer vision, speech, and natural language processing.
#ai  #casestudy  +Amazon  #A+ 
4 days ago
Collaborative Intelligence - Humans and AI Are Joining Forces HBR 201807
Collaborative Intelligence: Humans and AI Are Joining Forces H. James WilsonPaul R. Daugherty FROM THE JULY–AUGUST 2018 ISSUE
#ai  #augmented  #HBR 
4 days ago
Twitter
closes $15M funding round” helps 800k+ online retailers optimize -commerce by analyzing…
e-commerce  startup  ATL  from twitter
5 days ago
KelaHealth
Surgery should be informed by data. Lots of it.
KelaHealth is a software platform that combines predictive algorithms with high-impact interventions to reduce surgical complications. We use millions of data points to inform each patient's comprehensive risk profile, then recommend the best interventions to mitigate those risks.
#hc  #startup  #ml  #complications  #surgery 
5 days ago
kēlaHealth (@kelahealth) | Twitter
kēlaHealth @kelahealth Bringing insights from millions of patients to every patient. San Francisco, CA kelahealth.com Joined July 2016
#hc  #startup  #ml  #complications 
5 days ago
Why We Started kēlaHealth and How It Teaches Us to Dare Greatly
Why We Started kēlaHealth and How It Teaches Us to Dare Greatly In 1910, President Theodore Roosevelt gave a speech that described “the man in the arena.” This passage emphasizes the sacrifice required to do something hard, and the worthiness of trying and daring despite the challenges and inevitable failures. For many of us in the startup world, this passage resonates deeply. In the face of the endless hurdles to overcome, coupled with the staccato string of mistakes to resolve, we desperately glean solace in knowing that at least good ol’ Teddy Roosevelt won’t label us as those ‘cold and timid souls who neither know victory nor defeat’. Harsh, Teddy.
#hc  #startup  #ml 
5 days ago
ICMCS'18 - Keynote talk of Prof. Rachid Benlamri - YouTube
CMCS'18 - Keynote talk of Prof. Rachid Benlamri
#hc 
5 days ago
Q3 2018: An entrepreneurs’ market leads to digital health’s biggest quarter yet | Rock Health | We're powering the future of healthcare. Rock Health is a seed and early-stage venture fund that supports startups building the next generation of technolo
Q3 2018: An entrepreneurs’ market leads to digital health’s biggest quarter yet At the close of its third quarter, 2018 is already the most-funded year ever for digital health startups. There has never been a better time to raise money, with founders securing larger and more frequent rounds. Yet numbers alone don’t tell the full story of the progress made in digital health thus far in 2018.
5 days ago
Human-Level Intelligence or Animal-Like Abilities Adnan Darwiche ACM 201810
Human-Level Intelligence or Animal-Like Abilities? By Adnan Darwiche Communications of the ACM, October 2018, Vol. 61 No. 10, Pages 56-67 10.1145/3271625
#ai  #status  #approaches  #critique  #academic  #A+  #video 
5 days ago
Learning Topic Models – Provably and Efficiently | April 2018 | Communications of the ACM
Learning Topic Models – Provably and Efficiently By Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu Communications of the ACM, April 2018, Vol. 61 No. 4, Pages 85-93 10.1145/3186262 Comments
5 days ago
From Biological Systems to Machines, Learning is the key Siemens
As sensors proliferate in power and sheer numbers, vast opportunities are emerging in the field of machine learning, making ever more data available locally and through information networks.
#ml 
5 days ago
Piazza • Ask. Answer. Explore. Whenever.
The incredibly easy, completely free Q&A platform Save time and help students learn using the power of community Wiki style format enables collaboration in a single space Features LaTeX editor, highlighted syntax and code blocking Questions and posts needing immediate action are highlighted Instructors endorse answers to keep the class on track Anonymous posting encourages every student to participate Highly customizable online polls Integrates with every major LMS
#software  #tools  #faqs  #faqfind 
5 days ago
Ahrefs Academy: Learn how to apply Ahrefs' tools & data in your Digital Marketing
Ahrefs Academy The best tools and data are worth nothing if you don’t know how to use them. Browse our video tutorials to learn more about Ahrefs and get better at digital marketing. Marketing with Ahrefs Ahrefs might be a bit overwhelming to a newcomer. Take this short course to learn about our major features and how to apply them in your marketing.
#marketing  #digital  #search  #tutorial 
5 days ago
Google Search Operators: The Complete List (42 Advanced Operators)
Google Search Operators: The Complete List (42 Advanced Operators) Joshua Hardwick May 22, 2018 850 shares 29 Comments
#google  #search  #operators 
5 days ago
Applied Artificial Intelligence: A Handbook For Business Leaders eBook Mariya Yao, Adelyn Zhou, Marlene Jia 2018
Applied Artificial Intelligence: A Handbook For Business Leaders Kindle Edition by Mariya Yao (Author), Adelyn Zhou (Author), Marlene Jia (Author)
#ai  #ebook  #A+  @MariayYao 
5 days ago
Why Applied Machine Learning Is Hard Jason Brownlee 20171222
Why Applied Machine Learning Is Hard by Jason Brownlee on December 22, 2017 in Machine Learning Process How to Handle the Intractability of Applied Machine Learning. Applied machine learning is challenging. You must make many decisions where there is no known “right answer” for your specific problem, such as: What framing of the problem to use? What input and output data to use? What learning algorithm to use? What algorithm configuration to use? This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm. In this post, you will discover the intractable nature of designing learning systems and how to deal with it. After reading this post, you will know: How to develop a clear definition of your learning problem for yourself and others. The 4 decision points you must consider when designing a learning system for your problem. The 3 strategies that you can use to specifically address the intractable problem of designing learning systems in practice. Let’s get started.
#ml  #tutorial  #advice  #bestpractices  #explainer  @JasonBrownlee  #A+ 
5 days ago
Model-Based Machine Learning (Early Access): Table of Contents
Table of Contents How can machine learning solve my problem? Introduction to the book, who it is for and how to read it.
#ml  #book  #explainer  #courses  #tutorial  #concepts  #glossary 
5 days ago
Machine Learning Flashcards
Machine Learning Flashcards 300 digital flashcards in DRM-less print-quality png, web-quality png, PDF, Anki, and SVG for $12 Buy Now - Get Access Forever Memorize Key Concepts Machine learning is a broad field, encompassing parts of computer science, statistics, scientific computing, and mathematics. There are hundreds of concepts to learn. These flashcards are designed to help you memorize key concepts in machine learning rapidly and enjoyably.
#ml  #glossary  #explainers  #flashcards  #A+ 
5 days ago
Devoted Health (@DevotedHealth) | Twitter
Devoted Health @DevotedHealth We’re on a mission to make healthcare the way it should be: caring, affordable, always there when you need it. Waltham, MA devoted.com Joined September 2017
#hc  #payer  #innovation  #disruptive  @DevotedHealth 
5 days ago
Chris Albon (@chrisalbon) | Twitter
Chris AlbonVerified account @chrisalbon Using data and AI to fight for something that matters. Data Scientist @DevotedHealth. Wrote Machine Learning w/ Python Cookbook. Cofounded @NewKnowledgeAI. PhD.
#ai  #ml  #SME  #hc  @DevotedHealth 
5 days ago
Jason Brownlee (@TeachTheMachine) | Twitter
Jason Brownlee @TeachTheMachine Making Developers Awesome At Machine Learning Melbourne, Australia MachineLearningMastery.com Joined November 2013
#ml  #nlp  #ai  #dl  #tl  #SME  #explainers  #tutorials  #courses  #A+ 
5 days ago
(Holger Mueller (@holgermu) | Twitter
Holger Mueller @holgermu VP & Principal Analyst, Constellation Research - 28 year Enterprise Software Veteran San Diego enswmu.blogspot.com Joined December 2010
#technology  #analyst  @Constellation  @RayWang 
5 days ago
Natural Language… by George-Bogdan Ivanov [PDF/iPad/Kindle]
Natural Language Processing For Hackers Learn to build awesome apps that can understand people Bogdan Ivanov Understand the whole process of what is Natural Language Processing, not just bits and pieces. Build practical application, with real-world data. Crawl, c
#nlp  #ebook  #explainer 
6 days ago
Linguistic Knowledge in Natural Language Processing 20180804
Linguistic Knowledge in Natural Language Processing Ever since diving into Natural Language Processing (NLP), I’ve always wanted to write something rather introductory about it at a high level, to provide some structure in my understanding, and to give another perspective of the area — in contrast to the popularity of doing NLP using Deep Learning.
#nlp  #glossary  #explainer 
6 days ago
CS 124 From Languages to Information Stanford 2018
CS 124/LINGUIST 180 From Languages to Information
#nlp  #course  @Stanford  #2018 
6 days ago
Know thy customer: Salesforce and Marriott envision a smart, voice-connected future SiliconANGLE 20180925
Know thy customer: Salesforce and Marriott envision a smart, voice-connected future BY MARK ALBERTSON UPDATED 23:50 EST . 25 SEPTEMBER 2018 One company is the world’s largest hospitality conglomerate, opening a new hotel every 14 hours. The other enterprise is the fastest-growing of the largest U.S. software companies. Together, the two firms want to connect with customers in new ways using technology from a diverse ecosystem that includes Apple Inc. and Amazon.com Inc. Marriott International Inc. and Salesforce Inc. shared the keynote stage at the latter’s Dreamforce conference in San Francisco on Tuesday, and comments from the leaders of both organizations highlighted how artificial intelligence and digital assistant technology are making their way into societal mainstream. What’s powering Salesforce and major customers such as Marriott is connectivity, technology that is wrapping mobile usage with central enterprise platforms to deliver the richest possible customer experience. “We are all deeply connected,” said Salesforce Chairman and co-Chief Executive Marc Benioff at his keynote address on Tuesday afternoon. “In the fourth industrial revolution, everything is connected and behind all of this is the customer.”
#digitalassistant  #nlp  #application  +Salesforce  +Marriott  #hospitality  voice-connected 
6 days ago
Natural Language Processing in the kitchen LA Times 201312
Natural Language Processing in the kitchen Los Angeles Times DATABASE: The Times California Cookbook website. By Anthony Pesce Nat­ur­al Lan­guage Pro­cessing is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage, and it’s ripe with pos­sib­il­it­ies for news­gath­er­ing. You usu­ally hear about it in the con­text of ana­lyz­ing large pools of le­gis­la­tion or oth­er doc­u­ment sets, at­tempt­ing to dis­cov­er pat­terns or root out cor­rup­tion. I de­cided to take it in­to the kit­chen for my latest pro­ject: The Times Cali­for­nia Cook­book re­cipe data­base. In this post Where to start? Enter NLTK Deep­er ana­lys­is Wrap­ping it up The first phase of the pro­ject, the hol­i­day edi­tion, launched with more than 600 hol­i­day-themed re­cipes from The Times Test Kit­chen. It’s a large num­ber, but there’s much more to come next year – we have close to an­oth­er 5,000 re­cipes staged and nearly ready to go. With only four months between the concept stage of the site and launch, the Data Desk had a tight time frame and lim­ited re­sources to com­plete two par­al­lel tasks: build the web­site and pre­pare the re­cipes for pub­lic­a­tion. The biggest chal­lenge was pre­par­ing the re­cipes, which were stored in The Times lib­rary archive as, es­sen­tially, un­struc­tured plain text. Pars­ing thou­sands of re­cords by hand was un­man­age­able, so we needed a pro­gram­mat­ic solu­tion to get us most of the way there. We had a pile of a couple thou­sand re­cords – news stor­ies, columns and more – and each re­cord con­tained one or more re­cipes. We needed to do the fol­low­ing: Sep­ar­ate the re­cipes from the rest of the story, while keep­ing the story in­tact for dis­play along­side the re­cipe later. De­term­ine how many re­cipes there were – more than one in many cases, and counts up to a dozen wer­en’t par­tic­u­larly un­usu­al. For each re­cipe, find the name, in­gredi­ents, steps, prep time, servings, nu­tri­tion and more. Load these in­to a data­base, pre­serving the re­la­tion­ships between the re­cipes that ran to­geth­er in the news­pa­per.
#nlp  #casestudy 
7 days ago
Blog - Salesforce Research: How to talk to your database 20170829
How to talk to your database By Victor Zhong A vast amount of today’s information is stored in relational databases. These databases provide the foundation of systems such as medical records, financial markets, and electronic commerce. One of the key draws of relational databases is that an user can use a declarative language to describe the intended query instead of writing a collection of function calls. The details of how the query is executed is abstracted away in the implementation details of the database management system, avoiding the need to modify application code due to changes in database organization. Despite the meteoric rise in the popularity of relational databases, the ability to retrieve information from these databases is limited. This is due, in part, to the need for users to understand powerful but complex structured query languages. In this work, we provide a natural language interface to relational databases. Through this interface, users communicate directly with databases using natural language as opposed to through structured query languages such as SQL 1. Our work is two-fold: first, we introduce Seq2SQL, a deep neural network for translating natural language questions over a given table schema to corresponding SQL queries. Next, we release WikiSQL, a corpus of 87,000 hand-annotated instances of natural language questions, SQL queries, and SQL tables extracted from HTML tables from the English Wikipedia. WikiSQL is orders of magnitude larger than comparable datasets 2.
#speech  #recognition  #application  #interface  #SQL  #database  @Salesforce 
7 days ago
Improving end-to-end speech recognition models Salesforce Research 20171214
Improving end-to-end speech recognition models By Yingbo Zhou Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model. Since the models are all trained separately with different training objectives, improving one of the components does not necessarily lead to performance improvement of the whole system. This makes improving of the system performance difficult. End-to-End models address the aforementioned problem by jointly train all components together with a single objective, and thus simplifies the training process significantly. However, learning end-to-end models are also challenging, since The number of parameters is commonly in the order of millions, which makes it very easy to overfit. The training objective and testing metrics are commonly different due to optimization limitations, which may lead to inferior models. We tackle these two challenges by 1) improving the regularization of the model during training, and 2) using policy learning to optimize directly on the performance metric. Both approaches are highly effective and improve the performance of the end-to-end speech model significantly.
#speech  #recognition  #research  #A+  @Salesforce  #nlp 
7 days ago
The Natural Language Decathlon Salesforce Research 20180620
The Natural Language Decathlon By Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher Introduction Deep learning has significantly improved state-of-the-art performance for natural language processing tasks like machine translation, summarization, question answering, and text classification. Each of these tasks is typically studied with a specific metric, and performance is often measured on a set of standard benchmark datasets. This has led to the development of architectures designed specifically for those tasks and metrics, but it does not necessarily promote the emergence of general NLP models, those which can perform well across a wide variety of NLP tasks. In order to explore the possibility of such models as well as the tradeoffs that arise in optimizing for them, we introduce the Natural Language Decathlon (decaNLP). This challenge spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, database query generation, and pronoun resolution. The goal of the Decathlon is to explore models that generalize to all ten tasks and investigate how such models differ from those trained for single tasks. For this reason, performance on the Decathlon is measured by an aggregate decaScore, which combines the standard metrics for each of the ten tasks.
#nlp  #tasks  #research  #status  #applications  #A+  @Salesforce  @RichardSocher 
7 days ago
Mercedes unveils independent voice assistant MBUX for latest fleet | Mobile Marketer 20180111
Mercedes unveils independent voice assistant MBUX for latest fleet AUTHOR Robert Williams PUBLISHED Jan. 11, 2018 SHARE IT POST SHARE TWEET Brief: Mercedes-Benz introduced a digital assistant that bypasses the need for voice-enabled systems from tech giants Amazon and Google, according to The Verge. The Mercedes-Benz User Experience (MBUX) will be added as standard equipment in its next generation of compact cars as early as this year, the company announced at the Consumer Electronics Show in Las Vegas this week. MBUX has natural-language processing (NLP) capabilities that can respond to slang or indirect requests. For example, saying "Hey, Mercedes, I'm too cold" will prompt the system to automatically raise the inside temperature a few degrees instead of requiring a driver to request the exact temperature. Asking questions like "Do I need sunglasses tomorrow in Miami?" are answered with weather forecasts for the region.
#chatbot  #digitalassistant  #vehicles  #casestudy  #AI  #nlp  +Mercedes 
7 days ago
Dish Network offers live customer service with Apple Business Chat | Mobile Marketer 20180723
Dish Network offers live customer service with Apple Business Chat AUTHOR Robert Williams PUBLISHED July 23, 2018 SHARE IT POST SHARE TWEET Brief: Dish Network announced that it is the first TV provider to provide real-time customer support on Apple's Business Chat, which lets companies have private conversations and make sales on Apple's Messages app. Dish said its satellite TV subscribers can use Business Chat to reach a live customer service representative and make purchases of pay-per-movie movies or live sporting events with Apple Pay, according to a press release. Discover, Four Seasons, Harry & David, Hilton, The Home Depot, Lowe’s, Marriott, NewEgg, T-Mobile, TD Ameritrade, Wells Fargo, 1-800-Flowers and Apple are among the companies that use Business Chat, per TechCrunch. Apple Business Chat doesn't show customer contact information to an agent, giving people complete control of sharing their contact and payment information. Customers can leave a conversation with Dish at any time and pick it up later, even on other Apple devices linked to the user's Apple ID. The conversation never times out and ends when a customer deletes the message thread in the Messages app.
#chat  #casestudy  #brands  #customersupport  #nlp  +Apple  +DishNetwork 
7 days ago
Twitter
“Know thy customer: and envision a smart, -connected future” …
voice-connected  from twitter
7 days ago
How Salesforce aims to get an edge in the artificial intelligence race SiliconANGLE 20180927
How Salesforce aims to get an edge in the artificial intelligence race BY MARK ALBERTSON UPDATED 17:26 EST . 27 SEPTEMBER 2018 The driver in a car accident takes a picture of the damaged vehicle and sends it to an insurer for a coverage quote on the spot. A hat retailer uses data analytics to tweak its marketing formula and more than 60 percent of recipients suddenly open their messages in an email campaign. A hotel guest checks in and issues voice commands to an in-room personal assistant, ordering a rental car from the guest’s preferred company that shows up outside the lobby a half-hour later. Is this the future of artificial intelligence, or is it a mad vision of computers run amok? In fact, these are all actual use cases presented during Dreamforce 2018 in San Francisco this week (pictured), and they underscore a theme that occupied much of the conversation among 170,000 attendees. Conference organizer Salesforce has been working hard on artificial intelligence since it rolled out Einstein two years ago and it may be making significant progress in a challenging and often overhyped field. The stakes for Salesforce and every other company seeking to build a viable business in the AI space are high. It takes a significant capital investment to do it right, yet customers demand it. Doubling down on Einstein “What’s coming next is AI,” Ulrich Spiesshofer, president and chief executive of global industrial solutions giant ABB Ltd., said during a conversation on Wednesday with Salesforce co-CEO Marc Benioff on a Dreamforce stage. “We need to be leading in AI use as an industry.” ABB, which announced a significant in-house expansion of Salesforce’s Einstein AI technology this week, has built its business largely on the capabilities of intelligent industrial robots. The company created its own viral marketing stir last year when it had one of its robots conduct an orchestra in Pisa, Italy, while accompanied by the famed tenor Andrea Bocelli. “We’re using AI combined with unique hardware to create a completely unique market,” Spiesshofer told Benioff.
#ai  #nlp  #competition  #platforms  +Salesforce  #Einstein 
7 days ago
Forrester Wave - Machine Learning Data Catalogs Alation 201805
n our 29-criteria evaluation of machine learning data catalogs (MLDCs) providers, we identified the 12 most significant ones — Alation, Cambridge Semantics, Cloudera, Collibra, Hortonworks, IBM, Infogix, Informatica, Oracle, Reltio, Unifi Software, and Waterline Data — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise architecture (EA) professionals make the right choice.
#data  #catalogs  #ML  #vendors  #analystreport  @Forrester  +  Alation 
7 days ago
Alation - Enterprise Collaborative Data Platform
Alation Alation’s enterprise collaborative data platform empowers employees inside of data-driven enterprises to find, understand, and use the right data for better, faster business decisions. Alation combines the power of machine learning with human insight to automatically capture information about what the data describes, where the data comes from, who’s using it and how it’s used. Alation is based in sunny Redwood City and funded by Andreessen Horowitz, Bloomberg Beta, Costanoa Ventures, Data Collective, General Catalyst Partners, Harmony Partners, Icon Ventures, and Stanford StartX. Customers include Albertsons, eBay, Pfizer, Square, and some of the world’s largest finance firms.
#data  #enterprise  #curation  #catalog  #findability  #search  #collaboration  #vendor 
7 days ago
How to solve 90% of NLP problems: a step-by-step guide 20180124
How to solve 90% of NLP problems: a step-by-step guide Using Machine Learning to understand and leverage text. How you can apply the 5 W’s and H to Text Data! For more content like this, follow Insight and Emmanuel on Twitter. Text data is everywhere Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (NLP). NLP produces new and exciting results on a daily basis, and is a very large field. However, having worked with hundreds of companies, the Insight team has seen a few key practical applications come up much more frequently than any other: Identifying different cohorts of users/customers (e.g. predicting churn, lifetime value, product preferences) Accurately detecting and extracting different categories of feedback (positive and negative reviews/opinions, mentions of particular attributes such as clothing size/fit…) Classifying text according to intent (e.g. request for basic help, urgent problem)
#nlp  #explainer  #tutorial 
7 days ago
Implementing Enterprise AI – Online workshop Ajit Jaokar 201807
Implementing Enterprise AI – Online workshop July 26, 2018 By ajit Leave a Comment Implementing Enterprise AI – Online workshop By Ajit Jaokar and Cheuk Ting Ho   Early bird discounted rate $599 USD     Introduction Launched for the first time, jointly delivered by Ajit Jaokar and Cheuk Ting Ho – Implementing Enterprise AI workshop is an online workshop targeting developers and strategists.  The workshop enables you to develop a personal strategic case study for implementing Enterprise AI. Some knowledge of Python is good, but it is not mandatory. The workshop focuses on the professional deployment of AI in the Enterprise along with the underlying business case The professional deployment of AI in Enterprises differs from the content in a typical training course. In larger organisations, the Data Science function typically spans three distinct roles: The Data Engineer, the Data Scientist and the DevOps Engineer. The Data Scientist is primarily responsible for developing the Machine Learning and Deep Learning algorithms. The Data Engineer and The DevOps Engineer roles work in conjunction with the Data Scientist to manage the product/service lifecycle. The workshop is based on the following considerations a)      Emphasis on the full AI pipeline b)      Understanding the business case for Enterprise AI c)      Understanding the practical implementation considerations for Enterprise AI d)      Adopting a pragmatic approach to balance against the media hype e)      Developing a case study as part of
#AI  #workshop  #course  #tutorial  #explainer  @AjitJaokar 
7 days ago
How to Model Human Activity From Smartphone Data JasonBrownlee 20180917
How to Model Human Activity From Smartphone Data by Jason Brownlee on September 17, 2018 in Deep Learning for Time Series Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field. Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks, or CNNs, have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering. In this tutorial, you will discover the ‘Activity Recognition Using Smartphones‘ dataset for time series classification and how to load and explore the dataset in order to make it ready for predictive modeling. After completing this tutorial, you will know: How to download and load the dataset into memory. How to use line plots, histograms, and boxplots to better understand the structure of the motion data. How to model the problem, including framing, data preparation, modeling, and evaluation. Let’s get started.
#analytics  #ML  #sensors  #mobile  #data  #HAR  @JasonBrownlee  #A+ 
7 days ago
Deep Learning For Natural Language Processing Jason Brownlee 2018
Deep Learning for Natural Language Processing Develop Deep Learning Models for your Natural Language Problems   $37 USD Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects. About the Ebook:
#NLP  #tutorial  #explainer  #ebook  #A+  #tl  @JasonBrownlee 
7 days ago
Links to articles on NLP
Compiled 20181014
Top Articles and Videos about Nlp on Pocket
Top Articles and Videos about Natural Language on Pocket
5 Free Resources for Getting Started with Deep Learning for Natural Language Processing
7 Applications of Deep Learning for Natural Language Processing
5 Fantastic Practical Natural Language Processing Resources
Deep Learning Research Review Week 3: Natural Language Processing – Adit Deshpande – CS Undergrad at UCLA ('19)
Natural Language Processing in the kitchen - Data Desk - Los Angeles Times
Top Books on Natural Language Processing
Natural Language Processing for Beginners: Using TextBlob
How Consumer-Focused AI Startups Are Breaking Down Language | TechCrunch
Top Articles and Videos about Nlp on Pocket
Natural Language Processing Key Terms, Explained
NLP News - NLP for beginners, dialogue & sentence representations | Revue
How to solve 90% of NLP problems: a step-by-step guide
GitHub - oxford-cs-deepnlp-2017/lectures: Oxford Deep NLP 2017 course
10 Common NLP Terms Explained for the Text Analysis Novice - Data Science Central
Analyze and Understand Text: Guide to Natural Language Processing - Federico Tomassetti - Software Architect
Introduction to Natural Language Processing (NLP) - Algorithmia Blog
How Natural Language Processing Is Used - DZone Big Data
NLP vs CI Who is The King of Chatbot? – UX Planet
How to get started in NLP – Towards Data Science
Chat Bots — Designing Intents and Entities for your NLP Models
NLP - Neuro-Linguistic Programming - Life Coach Directory
Natural Language Processing companies & examples | Apiumhub
The Essential NLP Guide for data scientists (codes for top 10 NLP tasks)
Natural Language Processing and Machine Learning – Chatbots Magazine
Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found
Deep Learning Research Review: Natural Language Processing
Ask HN: Needs advice on learning NLP | Hacker News
NLP Highlights | Free Listening on SoundCloud
Amazon Comprehend - Natural Language Processing (NLP) and Machine Learning (ML)
A Word is Worth a Thousand Vectors | Stitch Fix Technology – Multithreaded
Get started with NLP (Part II): overview of an NLP workflow
How To Get Into Natural Language Processing
#NLP  #keysources  #links  #news  #explainers  #A+  #2018  #PEH 
7 days ago
What is Practical AI? Reflektion 20171108
What is Practical AI? BY: CAMERON CONAWAY • NOVEMBER 8, 2017 Practical AI (Artificial Intelligence) is the valuable application of intelligence rendered by machines. Practiced AI is rooted in present-day use cases, and is separate from the futuristic promises and predictions of what artificial intelligence may be able to accomplish. The term artificial intelligence, coined in 1955 by Dartmouth math professor John McCarthy, has undergone changes in its scope since its initial use. As the field of artificial intelligence has expanded, two phenomena have played particularly important roles in our collective understanding of what it means:
#ai 
7 days ago
Identity Resolution & People-Based Marketing | Acxiom
Acxiom Provides the Data and Technology Foundation for the World's Best Marketers
Acxiom provides the data foundation for the world’s best marketers. We enable people-based marketing everywhere through a simple, open approach to connecting systems and data that drives seamless customer experiences and higher ROI. A leader in identity and ethical data use for nearly 50 years, Acxiom helps thousands of clients and partners around the globe work together to create a world where all marketing is relevant. Acxiom is a registered trademark of Acxiom LLC. For more information, visit Acxiom.com.
#data  #vendor  #source  #tl  #identifyresolution  @Acxiom  #A+ 
7 days ago
Innovative Uses of Third Party Data Acxiom 20181007
Innovative Uses of Third-Party Data Subscribe to our Weekly Blog Summary Email Address Sign Me Up Follow Doug Hurst and Derek Bowman August 07, 2018 Audience Data, third party data Last year, companies spent over $10 billion on third-party audience data for advertising and marketing purposes. It’s clear that third-party data remains a vital tool in marketers’ arsenals to help them better understand and target their best customers and find more like them. As consumers continue to generate unprecedented amounts of data, leveraging third-party data is becoming even more valuable, expanding the realm of possibilities with audience segmentation, modeling and personalization. Use cases for audience data continue to expand, providing fertile ground for marketers to supplement their rapidly growing first-party data sets to guide marketing strategy. Let’s examine three use cases in particular that represent innovative ways to maximize value from third-party data.
#data  #thirdparty  #uses  #marketing  #ecommerce  @Axciom  #A+ 
7 days ago
Investing in AI: When natural language processing pays off VentureBeat 20180929
Investing in AI: When natural language processing pays off ARTHUR COLEMAN, ACXIOM RESEARCH SEPTEMBER 29, 2018 12:10 PM Image Credit: raindrop74 / Shutterstock MOST READ What I learned by bringing down LinkedIn.com Chattanooga celebrates its growing tech scene while acknowledging struggles with inclusivity Survey: AR/VR startups are leaving international money on the table Trump administration defends FCC’s repeal of net neutrality rules Ninja will play Fortnite with a woman if you pay him or if you’re Ellen UPCOMING EVENTS VB Summit 2018: The best in AI. An invite-only executive event. Oct. 22 - 23 BLUEPRINT: Mar. 26 - 28 Don’t miss the AI event of the year for the C-suite: VB Summit, October 22 & 23 in Mill Valley, CA. Join execs VP+ to dive deep into how AI is transforming every aspect of business. Request your invite today. Reserve now! For the past 18 months, my teams at Acxiom Research have worked extensively with a specific form of artificial intelligence called natural language processing (NLP). Our most exciting NLP development is called ABBY — our first artificially intelligent employee. But I’m not just here to talk about ABBY. I’m here to talk about the potential of NLP and how to decide if it’s a technology your own company should be exploring. I want to leave you with two thoughts about NLP: Recommended videosPowered by AnyClip Google Drops Out Of Contention For A $10 Billion Defense Contract   NOW PLAYINGGoogle Drops Out Of Contention For A $10 Billion Defense Contract U.S. Senator Seeks An Official Investigation Of Google Google+ Is Dead Alphabet Shuts Google+ Social Site After User Data Exposed TV Execs Say Big Companies Have To Work Together Google Stocks Drop After Security Glitch Goes Public Microsoft's $7.5 Billion GitHub Deal Set For EU Approval Has Google Abandoned Its Vow Not To 'Be Evil'? U.S., European Regulators Investigating Google Glitch The Windows 10 Cumulative Update Causes Problems For HP Computers First, the open source technology around NLP is so robust you can easily build “on the shoulders of giants” and create amazingly effective NLP applications right now using just a small, highly-focused team and a platform approach.
#nlp  #AI  #design  #advice  #guidelines  #critique  #A+ 
7 days ago
Enterprise AI - An Applications Perspective Data Science Central Ajit Jaokar
Enterprise AI - An Applications Perspective Posted by Vincent Granville on October 10, 2018 at 6:00amView Blog By Ajit Jaokar and Cheuk Ting Ho. Version One: Release date: Oct 24. Exclusively on Data Science Central with free access. Introduction Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a simple and practical roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk's new course on Implementing Enterprise AI.  After reading this book, the reader should be able to Understand Enterprise AI use cases De-mystify Enterprise AI Understand what problems Enterprise AI solves (and does not solve) The term ‘Enterprise’ can be understood in terms of Enterprise workflows. These are already familiar through deployments of ERP systems like SAP or Oracle Financials. We consider both core Enterprise and the Wider enterprise workflows (including supply chain). The book is thus concerned with understanding the touchpoints which depict how Enterprise workflows could evolve when AI is deployed in the Enterprise.
#ai  #enterprise  #applications  #book  +AjitJaokar  #A+ 
7 days ago
Otter Voice Notes - Otter.ai
Remember, search, and share your voice conversations Otter is the smart note-taking and collaboration app that business people, students, and journalists use to get more value from meetings, calls, video conferences, interviews, lectures, and wherever important conversations happen.
#nlp  #speech  #startup  #meeting  #notes  #podcasts  @SamLiang 
8 days ago
Natural Language Processing is Fun! Adam Geitgey Medium 201818
Natural Language Processing is Fun! How computers understand Human Language This article is part of an on-going series on NLP: Part 1, Part 2. You can also read a reader-translated version of this article in 普通话. Computers are great at working with structured data like spreadsheets and database tables. But us humans usually communicate in words, not in tables. That’s unfortunate for computers.
#nlp 
8 days ago
Machine Learning Vs. Artificial Intelligence: How Are They Different? 20180711
Machine Learning Vs. Artificial Intelligence: How Are They Different? Terence Mills CommunityVoice Forbes Technology Council CommunityVoice i POST WRITTEN BY Terence Mills Terence Mills, CEO of AI.io and Moonshot is an AI pioneer and digital technology specialist. Connect with him about AI or mobile on LinkedIn
#ml  #ai  #comparison  #definition 
8 days ago
Twitter
"Price of Obamacare Plans Takes Surprise Drop" Premium cost within ‘silver’ tier of health coverage down…
Insurance  from twitter
8 days ago
Twitter
RT : You can stay updated on with local news stations in the path of the storm with .

Ad…
HurricaneMichael  from twitter
9 days ago
CIO upfront: The making of a digital human ‘cardiac coach’ CIO 20180905
CIO upfront: The making of a digital human ‘cardiac coach’ Intelligent digital humans are filling the gaps in traditional ways to support cardiac patients. Marie Johnson of the Centre for Digital Business and FaceMe CEO Danny Tomsett explain how.
#hc  #digital  #speech  #applications  #AI  #nlp 
9 days ago
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