Let me add a disclaimer. This is simply a point of view and can’t possibly adequately cover both topics in the detail they deserve.
First, observations on the learning AI/ML:
1. Stop trying to rationalize AI , it is so misunderstood by so many, from investors to product people. Let’s just simplify this: AI is just another innovation stemming from the need to do things more efficiently and faster, in this case, it can stem from marketing (wanting to know the customer and make shopping & buying more convenient) to medical where its more research-oriented designed to dramatically speed up research, data analysis, and scenario design, to manufacturing that is about finding data-driven ways to optimize processes and product innovations. The barrier to entry in AI/ML is simply lower than its ever been. AI innovation is NOT new; the innovation is in the speed, lower costs, processing, and accessibility of open source to proliferate tools that can be used in business innovation. Focus on what you can access and how that applies to your business vs big topics of “creepy factor” and other generalized sound bites.
2. The type of data you have will dictate what form of AI/Machine learning you want to apply and the approach. Start with the data and that will narrow your learning curve. This one speaker tried to talk broadly about AI across several industries and various types of AI/ML, it’s just too broad a topic to start with a theory on such legacy analytics and modeling processes. That will make you a great conversationalist but won’t help you understand how it applies to your present or future business. Start with the data and the answers you want to derive… don’t get hung up on the semantics of deep learning, neural networks, recommenders etc… focus on the business problem and the approach to math approach will follow. If you don’t have data, don’t let that stop you, there are many outlets to buy Trained Datasets in specific categories that will get the ball started. do some research,, wish more meetups talked about the process and workarounds vs. the act itself.
3. We need better teachers and speakers. Seems we either get a quant person that over complicates the topic and gets in the weeds or we have deal makers spouting generalizations to inspire investments? We need something in the middle, Hoping Brand marketers and more product management minds can begin to step up and help connect the two and we begin to see more collaboration between the business people the product people, the marketing people and the engineering/and data science communities. Point in case, you have AI people talking about data, but don’t really know the Data industry, they just assume all will benchmark off the big players? data is available for a cost from many sources, and we need mixed points of view that help navigate this, not just talk algorithms.
4. So many people are asking the wrong questions? What is AI? How is it used? Is it potentially creepy? I was shocked at the similarities. etc… This is not how to start. refer to #2 and when you go to events have a set of key things you need answered for your business and narrow that. Generalizations are better served on the side. As an organizer of the event, try to understand these through your interactions at the event. Your networking charter may not match the bulk of what the attendees are looking for?
How Meetups Win or Fail: I love the proliferation of networking groups and meetups, yet so few sustain momentum. Not a shocker given how often people change jobs, and support meetups episodically. There are a few ground rules if you have any hopes of succeeding? I’m sure anyone who has set up a Meetup has watched their videos on how to make it successful, I’d encourage everyone to do that again. so a few foundational rules I think all should follow to create connections and recurring collaborators:
1. Don’t let one speaker dominate? therefore so many events have switched to fast-paced speaking sessions. Unless you have an Iconic speaker, very few should speak more than 15 minutes in a meet up. This also includes that annoying person that keeps asking questions and those that want to be the smartest person in the room.. Moderators, STEP UP and own this.
2. Don’t shortcut speaker preparedness! Nothing worse than walking in and hearing, “the speaker felt he/she didn’t need slides”, I personally, almost walk out at that point. If you are attending and there is not a speaker outline of the session that the speaker prepared, I’d sit next to the door so you can escape without distraction. If you speak, give it the respect and don’t wing it. If you are the organizer, don’t accept it and be willing to cut them out if they don’t prepare. Always be prepared with a backup, even if a back means, you cut them short and resort to open discussion or group breakout discussions. Unless you get the CEO of Google, Amazon, Apple, Microsoft, require speakers to prepare and submit slides before the event.
3. The venue matters so does the food. One person leaving an event, said: “there are so many meetups in the Bay Area you could literally eat out for a year without paying“. That is how many think in the Bay Area, when giving up their evening to an event like this. Make sure the venue is accommodating. Nothing more depressing than walking into a cold team room with folding chairs and white walls. How does that inspire collaboration or creative thinking after a long day at work? Ask for support and don’t just do meets ups believing that the networking alone will make up for a poor venue, in a bad place with airplane bags of peanuts. Take the extra time to make it worth coming back to. Great minds in a room aren’t enough today with all the competing networking events. Before you begin this, find sponsors that will support you and make that a mission.
4. Know your audience. This should be the first one, but I’m so amazed that very few networking groups poll the audience or really invest in learning the audience at the events. Many rely on the mixer part, but group audience analysis is important to the sustainability of this and how to get future speakers on topics that matter to the return collaborators. This doesn’t mean go around the room and have 20-100 people share their background, but you can interactively get a feel for what their real interests are and their potential roles in making this a true network collaboration vs. just listening to people present through continuous polling and documenting the results. NOTE: its what’s called, “doing a warm-up”… invest in it and you’ll see amazing results from what you find out.