Koneoppimisen soveltamisesta journalismipalvelussa 14.11.2017 Vesa Lindqvist
Let’s start with a quick ”Turing test” NO. 1: • Kilimanjaro is a mountain of 19,710 feet covered with snow and is said to be the highest mountain in Africa. The summit of the west is called “Ngaje Ngai” in Masai, the house of God. Near the top of the west there is a dry and frozen dead body of leopard. No one has ever explained what leopard wanted at that altitude. NO. 2: • Kilimanjaro is a snow-covered mountain 19,710 feet high, and is said to be the highest mountain in Africa. Its western summit is called the Masai “Ngaje Ngai,” the House of God. Close to the western summit there is the dried and frozen carcass of a leopard. No one has explained what the leopard was seeking at that altitude.
Which one is translated from Japanese to English by Google Translate? (The other one is original text) Source: The Great A.I. Awakening, NYT
Lightning fast development
• Only 24 hours earlier, Google would have translated the same Japanese passage as follows: •
Kilimanjaro is 19,710 feet of the mountain covered with snow, and it is said that the highest mountain in Africa. Top of the west, “Ngaje Ngai” in the Maasai language, has been referred to as the house of God. The top close to the west, there is a dry, frozen carcass of a leopard. Whether the leopard had what the demand at that altitude, there is no that nobody explained.
Source: The Great A.I. Awakening, NYT
But there’s still time for us humans to do translation work…
• “Machine translation is by no means solved. Google Neural Machine Translator can still make significant errors that a human translator would never make, like dropping words and mistranslating proper names or rare terms, and translating sentences in isolation rather than considering the context of the paragraph or page. There is still a lot of work we can do to serve our users better.”, Google Research, 9/2016
But for how many months?
Or perhaps weeks?
What should a media company do right now?
• HS has explored the use of artificial intelligence and machine learning in the media business. Funding from Tekes. • The outcome of the survey resulted in the following conclusions: 1. Making use of artificial intelligence and machine learning is vital to the success of a media company in the near future 2. In the media sector artificial intelligence is not yet widely utilized 3. Raising knowledge and skills is important 4. The best way to explore the topic is to make bold experiments in co-operation with expert organizations
AI in Media Create Artificial Intelligence (AI) solutions for digital journalism service Ultrahack Sprint II 17.-19.11.2017
What is Ultrahack?
• Ultrahack combines hackathons, an innovation platform and accelerators in a unique manner. Ultrahack builds a community where the best talent and leading industry partners could meet and collaborate towards future innovations. Ultrahack is an ultra long development runway with leading industry players, novel challenges and never-before-published API's and tools. • AI IN MEDIA - Challenge is to create Artificial Intelligence (AI) solutions for digital journalism service • Prize for the winner is 10 000€
Use cases for AI in a digital journalism service
For users: personal content delivery (e.g. quality content recommendations without creating filter bubbles)
Try HS.fi/sinulle
Use cases for AI in a digital journalism service
For users/business: content classification (e.g. automated keyword/concept extraction from text, pictures and video) - Talking points this week - Automatic comment moderation - Ad targeting - Search - Keywords/tags
Use cases for AI in a digital journalism service
For users: text summarization (e.g. trustworthy method for shortening text) - Headlines - Summaries - Text collections
But how to make the summarization trustworthy?
8/2016, Google Research
Use cases for AI in a digital journalism service
For business: content demand prediction (e.g. method to predict demand of an article before it is published) For business and journalists: How to create structured data from unstructured data sources for content creation (e.g. event data)
For business: Create methods for understanding users better For whole media industry: super AI solutions
Practicalities and expected outcomes
•
Available APIs for chosen developers (under strict contract) – HELSINGIN SANOMAT CONTENT API – GOOGLE ANALYTICS API
•
HS mentor and organizer for the teams – Vesa Lindqvist
•
Judges – Vesa Lindqvist/HS – Esa Mäkinen/HS – Lari Tuominen/Qvik – Roman Yangarber/Helsingin Yliopisto
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Expected outcomes: HS expects to find between 1-5 new artificial intelligence solutions and good teams to carry out AI-pilots during Q4/16Q1/17
Two ways to approach machine learning technology aqquisitions
• Buy technology
• Develop technology
• Pros:
• Pros:
– Fast time-to-market – Cheaper
• Cons: – Limited understanding – ”black box” effect
– Better understanding – Enhanced trust
• Cons: – Slower time-to-market – Can be expensive
Third option: best of both worlds – to do co-operation with early phase start-ups
HS: AI prototypes with Finnish start-ups
Machine learning research co-operation(s) - iMEDL project •
Media organizations have an urgent and growing need to organize and classify massive streams of big-data content according to complex classification schemes and categories. The need exists on two sides: internal and external. ``Internal'' means to help journalists / content producers to create better content, and help them situate it intelligently and appropriately within the context of the media landscape. ``External'' means to help content find the most appropriate targets among the user audience, where the content is most relevant / most needed. Currently many media organizations must resort to manual processes for tagging of content, which has clearly proven to be extremely problematic in terms of quality, uniformity, descriptiveness, speed and overall effectiveness. Roman Yangarber
This project will develop state-of-the-art analytics methodologies to resolve the problem for both internal users and external users, according to complex and interacting preferences. Moreover, in day-to-day production deployment is crucial that these preferences must not need to be elaborated explicitly, but must be learned implicitly via automatic understanding the deep semantics of the content. This understanding is achieved via neural networks and deep learning.
Machine learning research co-operation(s) – Immersive Automation
Early 2017 robot reporting
Late 2017 robot productivity
The need to act now has been widely identified - good
We need learn how to apply AI/ML technologies
• Business people need a better understanding about ML technologies and we need to learn to speak proper ”data science” • An example of a helpful way to think about machine learning application(s)
Koneen tavoite
Mitä ennustetaan
Pelisäännöt
Opetusaineisto
Raportointi ja jatkokoulutus
Summary
• Machine learning technologies develop with amazing speed • Making use of artificial intelligence and machine learning is vital to the success of a media company in the near future • In the media sector artificial intelligence is not yet widely utilized • Raising knowledge and skills is important • The best way to explore the topic is to make bold experiments in cooperation with expert organizations