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Cupcakes and cocktails.

This is a summary of #AIChat (a 30 minute Twitter chat on artificial intelligence). Where my guest(s) cut through the artificial intelligence noise.

On February 12, 2019, my guests? Kerry Harrison (@copywriterkerry) and Richard Norton (@ericbratislava) from @wearetinygiant (https://www.tinygiant.io/) The topic? #AI #cupcakes #alcohol #augmentedcreativity

*** Mistakes are mine. ***

Here’s Q1 for @ericbratislava and @copywriterkerry

I love your #AI and #cupcakes idea (https://t.co/fZ2ZfBZd4e) . Q1) How does this demonstrate the potential of #AI and #humans working together? #AIChat pic.twitter.com/qDpI9wLOa8

— Nick Tang (@nickhtang) February 12, 2019

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The neural network came up with recipes we'd never have considered before. The human brought all her experience in cake making to dial up and down the ingredients to make them taste amazing.

— Kerryharrison (@copywriterkerry) February 12, 2019

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A1: Our AI cupcake project https://t.co/SY1OwYHgjl was all about human-AI collaboration. The neural network generated 80 recipes, which were weird and wonderful. We then collaborated with a cake expert to make them a reality.

— Kerryharrison (@copywriterkerry) February 12, 2019

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A1 Interesting as we are now seeing the surge of #AI vending machines although most are #ML not AI but everything from custom cupcakes to dentistry is being vending machine tested globally🌐🤔 #AIChat

— BrainBlender🤔🌐 (@BrainBlenderTec) February 12, 2019

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What a sweet fusion! 🧁 Thanks for introducing us to this. Looking forward to hearing more about how you augmented traditional recipe development with AI. #AIChat

— Adlib Software (@adlibsoftware) February 12, 2019

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As you continue to discuss Q1, we’re going to switch to #AI and #cocktails! Based on this article: https://t.co/HA6WaL53Hi #AIChat

— Nick Tang (@nickhtang) February 12, 2019

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Here’s Q2 for Eric (@ericbratislava) and Kerry (@copywriterkerry) from @wearetinygiant (https://t.co/n5q3Y7uz4J) Q2) How did the bartender feel when he was following the #AI recipe? #AIChat

— Nick Tang (@nickhtang) February 12, 2019

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#AIChat A2: TBH he was a little nervous. He was concerned the AI would replace him and take his job. But after a while playing with the AI-generated recipes, he realised that he could bring his experience and creativity to the mix. And he could create something very different.

— Kerryharrison (@copywriterkerry) February 12, 2019

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#AIChat For example, one of the AI-generated recipes had ice cream in it. Normally ice cream is put on the top of drinks (Coke float) but the mixologist decided to blend it in with the other ingredients. And it tasted delicious!

— Kerryharrison (@copywriterkerry) February 12, 2019

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A2 Therein is the issue as now you can have a bot bartender that makes the recipe identical every time but using #AI can vary to personal preference 🌐🤔 #AIChat

— BrainBlender🤔🌐 (@BrainBlenderTec) February 12, 2019

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What’s a Digital Twin? (#AIChat – Jan 10/19)

My guest? @RobTiffany. The topic? The value of digital twins to model machines, processes and businesses. Mistakes are mine. Not Rob’s. P.S. The goal of #AIChat? To help you cut through the artificial intelligence noise.

** 10 min until #AIChat. Join me from 2PM ET to 2:30PM ET My guest? @RobTiffany. The topic? The value of digital twins to model machines, processes and #business P.S. This is a 30 minute #AIChat. P.P.S. A S/O to @vfiorese_ for suggesting @RobTiffany as an #AIChat guest. ** pic.twitter.com/TtsZHNs5RJ

— Nick Tang (@nickhtang) January 10, 2019

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Twitter Universe.

Welcome @RobTiffany!

It’s a 30 minute #AIChat.

Let’s hit the ground running.

Here’s Q1) Can you define what’s a digital twin? #AIChat

— Nick Tang (@nickhtang) January 10, 2019

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A1: In it’s simplest form, a digital twin is a digital representation of a physical object. This could be a machine, a person, or even a process. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A1.1: For some history, digital twins were originally created by NASA to have high-fidelity models of spacecraft for design and troubleshooting. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A1 follow-up. Is there such a thing as a wrong #digital #twin #AIChat?

— Nick Tang (@nickhtang) January 10, 2019

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A1.2 A digital twin can be wrong and therefore deliver bad results if it’s properties or attributes don’t match the physical object precisely. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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Q 1.3 Can a digital twin be more observable than the object it represents and does that make it less than a twin? #aichat

— spara (@spara) January 10, 2019

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A1.3: Digital twins tell the story of the machine #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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Still in use. DART https://t.co/n6IdVbRUg7 #aichat

— spara (@spara) January 10, 2019

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A1.4: Since a #DigitalTwin can carry more measured and calculated data than the physical object, I think it makes it a super twin. In a well-functioning system, the twins will drive all your operations. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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It does, but it makes it seem that the digital twin is the system controller. #aichat

— spara (@spara) January 10, 2019

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Here’s Q2 for @RobTiffany (it’ll help you with his definition of #digital twin in Q1)

How does a digital twin link to model machines, processes and business? #AIChat

— Nick Tang (@nickhtang) January 10, 2019

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A2: For starters, I believe that #digitaltwins should be at the heart of any #IoT system since you can’t understand the telemetry #data being collected without modeling the schema or shape of the object producing the data to provide context. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A2.1: When physical machines and people are connected to an #IoT system via a #digital thread of near real-time data, the digital twin is brought to life. You can see the latest state of the physical object as well as its history by viewing the digital twins. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A2.2: Beyond being a mirror of physical objects, digital twins can also carry rules, KPIs and #Analytics with them that are triggered when new #data arrives. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A2.3: The #DigitalTwin can map measure values to sensors from a physical object but it can also contain virtual or calculated values that are derived dynamically. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A digital twin does a great job of properly organizing the data relevant to a physical object which makes it easier for advanced analytics, #MachineLearning, and #DeepLearning to do their thing. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A2.4: The more #data your #DigitalTwins accumulate, the better the results will be from #AI. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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A2.5: Classes of machines that are the same as each other can be improved when the #digitaltwin of one machine solves a problem that can be shared across a fleet. #aichat

— Rob Tiffany 🇺🇸 (@RobTiffany) January 10, 2019

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No. #AI won’t take over a journalist’s job: #AIChat summary: Jan 11/19

#AIChat is a Twitter Chat where my guest(s) cut through the artificial intelligence.

#AIChat session with @the_claus and Professor @NeilMaiden

Talking about @inject_en

Mistakes are mine.

Q1) How does @inject_eu work? #AIChat

— Nick Tang (@nickhtang) January 11, 2019

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A1 So, INJECT is a new digital tool to support journalists to write more original journalism, more quickly than now. It combines NLP, information retrieval, entity extraction and recommender tech to present journalists with content to inspire new angles on stories #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1 followup: Does someone sort through the information INJECT gathers to make sure that there’s no bias in the article? #AIChat

— Nick Tang (@nickhtang) January 11, 2019

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A1. The team maintains an editorial board to manipulate news creatively from different better researched sources #AIChat. But creative ideas can come from all sources, so more sources are better…

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1 followup: It is hard to find journalistic content without ANY bias. Since we are all (still) humans, everything we write is based on our upbringing etc

— Claus Hesseling (@the_claus) January 11, 2019

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INJECT could help journalists to overcome their own biases – in helping the to see "opposing" things #AIchat

— Claus Hesseling (@the_claus) January 11, 2019

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A1 The heart of INJECT is a set of creative search algorithms that operationalize expertise of experienced journalists in strategies. User choices to explore, for example numbers and evidence, or human angles, or comedy, invoke different creative search algorithms #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1. Unlike robot-journalism and other #AI uses, INJECT does not seek to replace any key element of a journalist’s work. Instead, it uses different technologies to enhance/empower journalists operating in less and less time, and fewer resources. Journalists control the AI #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1 Our INJECT team are currently designing new algorithms to manipulate the creative news index (now over 10million articles) to discover new, evolving, breaking and under-reported angles on topics. These new features should be available by the summer #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1 In background, 24/7, INJECT generates creative indexes of news stories. Using RSS, it reads and makes sense of about 15000 stories published each day from 300+ titles in 6 languages, and builds a rich index from which to generate creative guidance for journalists #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A1 Furthermore, to support hyper-local and specialist journalism, INJECT’s algorithms can read and make sense of local news and other types of archive, so that INJECT’s creativity support can exploit these information assets #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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Many journalists are afraid of algorithms and #AI – but INJECT's mission is to create AI tools that support journalists #AIchat

— Claus Hesseling (@the_claus) January 11, 2019

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As we’re talking Q1, here’s Q2 for Professor @NeilMaiden and @the_claus How does @inject_eu help journalists and their newsrooms? #AIChat

— Nick Tang (@nickhtang) January 11, 2019

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A2: #AIChat. INJECT is different from other search engines. Engines like Google look to return the best-fit that you asked for. INJECT takes what you ask for, and gives you something a little different. Many people struggle with idea, at first. I think of it as ‘Googlisation’

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2 Journalists also use INJECT in unpredicted by creative ways. For example some freelancers use it to prepare for interviewing people, others to discover new stories and outlines to pitch to editors #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2) NYT or WaPo have great archives – and if you use it right it can be a goldmine. But smaller news outlets often don't have to resources to do that. INJECT could provide help here #AIchat

— Claus Hesseling (@the_claus) January 11, 2019

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A2 #AIChat By ‘smart’ archive, we mean that we attach machine-readable semantics to published articles. We can extract content, tag it with inferred meanings, and manipulate these INJECT inferences in the tool’s reasoning engines

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2 #AIChat. Yes, Claus raises a key issue. Many media organisations are seeking to make smarter use of their #news archives, and journalists also want to be inspired by what they themselves have written before

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2. Our studies with INJECT reveal that INJECT works better to support more original feature articles. So our recent focus has been to support news and media businesses focused on quality #journalism. #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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Follow up Q in response to A2: It's great that you are surfacing smaller publications!! Given the problems we've had with fake news lately, how do you ensure the news (source) is credible? #AIchat

— Mia Dand (@MiaD) January 11, 2019

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A2 #AIChat. Mia – we have chosen a set of over 300 publication titles in many countries across 3 languages, to reduce the risk of bias, echo chambers etc. But this approach is not foolproof. So INJECT seeks to empower journalists to avoid ill-researched sources

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2: Adding to that: Even big new organisations get things wrong. So far there is no AI to solve this #AIchat

— Claus Hesseling (@the_claus) January 11, 2019

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A2: Plus: With INJECT also smaller publications can make us of AI and machine learning. With our tool they can turn their "dumb" archive into "smart" archive #AIChat

— Claus Hesseling (@the_claus) January 11, 2019

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A2 Our studies of INJECT newsrooms have shown that its use improved the novelty of articles written by journalists over a period of months. Journalists generated not only new angles but also new content and new forms of content #AIChat

— Neil Maiden (@NeilMaiden) January 11, 2019

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A2) and an essential one to keep in mind. Even with the AI software, it’s still up to the reporter to make sense of it all. In order to create a well balanced article. #AIChat

— Nick Tang (@nickhtang) January 11, 2019

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Well-said @nickhtang I am a big fan of quality journalism and any technology that helps surface content from the smaller publications that get overshadowed by the big outlets…!!! #AIchat

— Mia Dand (@MiaD) January 11, 2019

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#AIChat One wider observation is that journalists seek more original news stories. This requires #creative thinking of different forms. In this sense INJECT is different for uses of machine learning tools in news

— Neil Maiden (@NeilMaiden) January 11, 2019

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#AIChat Thanks to Nick and Mia. You can find out more about our INJECT project and product at https://t.co/AmHC0aW6Zx

— Neil Maiden (@NeilMaiden) January 11, 2019

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