Impactful storytelling
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Make clear end of your journey for others
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People do not remember facts, they remember stories
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Make user THE hero, the center of story
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Make your vision a story
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If you use influencers invite them to debugging and tweaking
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Venture capitalists want transformation, not disruption or revolution
- How is my product going to change their life?
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If highly technical, make parallels to mundane events
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In B2B
- Find out what does fuel the buyer ‒ they should see a change
Adventures in building a hardware startup
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Make something people want
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Hire people better than you
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There is very little common between prototype and mass production
- Known known is good
- Known unknown ok
- Unknown unknown will kill your startup
- Known unknown ok
- Noone can deliver what we promise
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How to avoid delusion of Kickstarter?
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Ship quickly, get it to the people in a small amount
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“Selling” prototypes
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Barely works
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Offer a few on Kickstarter (100-1000 pieces)
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Prototype should be big and ugly
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Get an Advisory board
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Offer 1 %
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They have the knowledge you don’t have
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Mass production
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Hardware club ‒ help for startups
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Dragon innovations
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Start designing with the factory
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It takes approx 1 year to make the HW
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Creative problem solving using server less and ML
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Microsoft has cool Azure automated IMG classificators for non-ML specialists
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Basically a 1 hour Microsoft Azure promotion with some of the most basic NN classification information
Interview with Jakub Nesetril, VP Product Development at Oracle
- Do not outsource, but use services ‒ they save time&energy and tent to be way deeper than your implementation
CONFIG-LESS. SERVER-LESS. EFFORT-LESS.
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Do less ‒ type less, think less, do more ‒ whole presentation
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Setting up a server is hard: slicing, load balancing, latency…
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Solution? Use Zeit ‒ server pushing in one command
- Builds from package.json (Node.js) or Dockerfile
- Free minimalistic version
- After :now: command you get a new link
- Once the build is completed, you can directly access the build via given link
- Then it can be pushed as main version
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Look more into Next.js
- Embrace the filesystem
- Embrace conventions
- Make it lightweight and flexible, obsessively
- One command to develop: next
- One command to build: next build
- One command to export statically: next export
- One command to server-render: next start
People & algorithms: Build AI-driven features that don’t cause harm
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Many problems
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ML models get things wrong and we can’t always explain the results
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Underlying bias problem in the data
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Data is not inherently objective or fair
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Removing bias requires human intervention
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Examples
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Accuracy at face recognition white × black faces
Google Photos, y'all fucked up. My friend's not a gorilla. pic.twitter.com/SMkMCsNVX4
— Jacky lives on @jalcine@playvicious.social now. (@jackyalcine) June 29, 2015 -
FB showing year review of death in family with balloons around it
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Design can make intelligent features seem less scary and more worthy of trust
- Google search ‒ tries to look like “machine, human untouched” feed of truth
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Ask the (uncomfortable) questions
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Ask questions about data sources
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Design methods for feedback
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More at AI for Good
Power of programming
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The best way of helping others, the world is not by working for nonprofit, not giving money, but
- Becoming a software developer!
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Developers break the rule of supply and demand
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Our work multiplies, it costs the same to make code for 5 or 5 milion People
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WhatsApp was initially created by only 40 people yet it improved live to milions
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Bots on the net: The good, the bad, and the future
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Bots are going to be a multibilion industry
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Bussines see savings × customers do not trust them = gap
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What if people couldn’t distinguish between human and bot = Turing test
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1st to pass the test for some people in their time (1966)
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Specialized AI or Microworlds
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Help you buy a shirt, fix a computer or get weather information
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Work well in their space, but can’t expand
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Don’t need to learn
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State-of-the-art for most problems
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Encoding behavior exposes bias
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Latent bias ‒ build-in
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Selection bias ‒ see People & algorithms: Build AI-driven features that don’t cause harm
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Interaction Bias ‒ acquired when the bot communicate with us and reflect it back
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Loss aversion ‒ people need to gain 150 $ after lost 100 $ to feel even
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Statistical learning ‒ there is roof to how much data increases the accuracy
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Algorithms drive ML chatbots; Big Data is the fuel
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What to do?
- Self identifying rules: “I am a bot.”
- Licencing: A licence to operate a certain bot
The age of Assistance
- The web changed the world 20 years ago
- Mobile-first changed the world in less than 10 years ago
- Getting internet to even more people ‒ 5 bilion by now
- AI-first!
- Assistants are really good at accessing directly really deep points in other apps
- Specific place in Street view; translating sentences; setting an alarm;
- Personalization
- Remembering context, favorites, work, home; asking for personal info gathered from emails; finding pictures
- 3rd party integrations ‒ for near future
- Going to be a revolution, because AI saves time for users
- Assistants are really good at accessing directly really deep points in other apps