Launch festival April 2017

Hi ya,

A few months ago I got a founder ticket to the Launch Festival in San Francisco for April 7th and 8th. After three whirlwind days in the Bay Area here are the things that stuck out to me. Personally for me / Move Correctly my best meeting was actually with Greg, the CEO of Fit3d – since he provided so much actionable, real advice. It’s just fantastic to exchange ideas with some-one in the same industry. Now here are my notes about Launch:

 

Machine Learning

1. The flywheel for Machine Learning – was articulated by Rob May, the CEO of Talla, is the way ML becomes better the more data there is, makes better algorithms, makes a better product, drives more use and more data … So it’s really about who can build the best/ quickest flywheel for your use case / industry, and in that sense (at least the ML companies want to give that impression) this is the time to make major investments in this area, as the first (successful) movers will have a big advantage.

2. Zorroa – are doing really impressive visual recognition from videos – where they can search inside videos / images just as if you were googling documents. So eg locating any scenes where ‘the Rock’ appears, then narrowing it down if it happens in a bank, and further where there is a Lambo in the scene.. Currently you need to plug into their REST API,  but they mentioned a SAAS app in a few months…

3. Corto – their demo of a chatbot analytics interface to a pharmaceutical genomics data was impressive, with hypergraphs / nodes flowing and a lot of complex words in the presentation, so I have no doubt the tech is solid. Their team is chock-full of smart guys, with eg one of the leading AGI guys – Ben Goertzel as Chief Scientist. What wasn’t clear for me is who will sell their product and what is their value prop?

4. The PAC framework by Rob May again, which essentially states that any company should evaluate how they want to use Machine Learning in these categories:

A) P for Predict, eg. in recruitment which candidates will perform best, in sales which products etc.

B) A for Automate, could be easing workflow, say for example NLP (natural language processing) transcribing recruitment interview notes.

C) C for Classify – say classifying best resumes into different buckets quickly

Now apply these questions across your customers, across Product, across Operations, and you should start to identify good opportunities where to apply ML.

5. Talla is a customer service bot either for IT or HR, that’s been trained to answer IT / HR questions, with a UI either in Slack or Microsoft teams. Their target market is in mid-size companies.

6. Kylie.ai –  created a customer service bot, integrated with eg ticketing systems like Zendesk. They’d ‘clone’ employee personalities and create a response integrated into existing UI’s eg on Zendesk, Salesforce, SAP etc, which the human customer service agent can review, modify, approve / send.

Cannabis

The Cannabis market is yuuge apparently as it warranted it’s own vertical, next to healthcare, drones, ML etc. Interesting companies included:

  1. Leaf – built a small growing unit looking like fridge, which automates home growing. Sold about 1M of them in advance and are taking orders for 2018..
  2. Alula Hydro, who have created a hydroponic, nutrient delivery system for industrial growers. The 20K industrial growing management system apparently can raise yields from a crappy 1K per pound to 5K-6K per pound.
  3. Baker, who are making a CRM / loyalty / online store SAAS for dispensaries.

 

Miscellaneous notes

  1.  How to get 1000 applicants for a job ad – by Tucker Max from Book in a box
    • Start with the hook – explain the why / the mission of the company, and if some-one doesn’t believe in that clearly they’re not a fit.
    • Sell the role – Talk like you would talk to a friend about the job. Ditch all the standard corporate lingo about ‘mission critical, pro-active go-getter with a nose for global synergies’. 
    • Pleasure and Pain – explain why the company / role is awesome, but clearly also the downsides of the job, no point in trying to sugar-coat stuff.
    • Testimonials / social proof – we use these in any ads (for soda, cars, books etc), why not for job ads?
    • Finally – the actual ‘boring’ details
  2. VR / AR – was actually a bit underwhelming – nothing really that stood out. Yes, it’s cool to get a new, different type of camera or get analytics off VR, but…eh.
  3. In hardware – Megabots was really cool, just in terms of lighting up any 6-year old inside all of us – Robots with fun big weapons (WEABOONS!) and picking up cars 😉

 

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