There it is, the most misunderstood term of our time: artificial intelligence aka AI. It’s the single most hyped technology today that means everything and nothing.
Four companies in ten get described as AI company, without actually being one (reasons include confusing future plans with present, seeming more advanced, attracting VC money etc.).
My company Silo.AI isn’t one of those. It is full of hard-core ML, NLP and Computer Vision (CV) researchers, most with PhDs. Not everyone is a data scientist, but I’m sure everyone would benefit from understanding more about AI. Here’s what I’ve learnt during my 6 months with these amazing people at Silo.AI.
1. It’s OK for AI to be boring (for some)
When we build AI solutions, we are building them as “part of our clients’ existing workflows”. What does that mean? Well, the technical side focuses on the machine learning model and getting a sufficient amount of detailed enough data. First, we build a model, train it with training data, and then let the model interact with the actual data and wait for the outcome. This very simply put technical part is a big part, but it’s only one part of an AI solution.
Businesswise, “being part of a workflow” means that we are not disrupting everything the client has (they would never buy it), but instead, trying to build an AI solution as part of their already existing processes. We need to go business problem first.
In one case we had, our machine learning tool to predict flight delays became just another data input to the airline’s optimization system. Doesn’t sound super exciting first (many months’ project turns into a small button in the huge dashboard), but when you think about it, this tool can lead to a big impact, as better prepared delays = cost savings and happier passengers.
Equally boring may seem one of our computer vision quality control systems: the solution goes through at TONS of sewage pipe video material and flags anomalies in the waste water. What, is AI taking our jobs to stare at the surveillance tape for crap flow? Wouldn’t want any human to waste time in that.
2. Any business function can have an AI solution
As your grandma used to say: “Data is oil for artificial intelligence.”
Some business functions are inherently data driven. Any number heavy area where calculations and estimates are part of the day-to-day work, is a match made for AI. But what I’ve realized through my job is that any business function from HR, supply chain to legal can and should use AI.
Even HR and People operations?, you say. Oh, yes.
When you ask people to describe their jobs, they give you a few sentences about the tasks they enjoy the most. They probably won’t list the endless updating of reports and drafting contracts, scheduling meetings, and scrolling through email to remember what tomorrow’s workshop is about. These repetitive small tasks take a lot of time in any department, and they could all be given to AI, so that we people would have more time for the interesting and challenging human-suited work.
3. Finding the right problem is difficult
Although using AI in all the departments for the smallest of impact would be beneficial, clients won’t just go for any AI solution that they could benefit from. Because of time/budget/reality constraints, clients need to pick the one area where the solution will have most impact, a budget can be found and there is an urgent fire to put off.
Finding this type of problem takes time. My company has found it necessary to create Prestudy, Design sprint and PoC-like frameworks in order to discover these opportunities faster and more efficiently. It is hard to know what is possible and where to start from. Designing business driven AI solutions is (at least in my eyes) as hard as getting the model right.
4. Artificial intelligence can save us
With something as powerful as machine learning, you can potentially solve very, very complex problems. Such as the climate change.
This power is one of the reasons why I joined an AI company, and why so many people are so excited about AI.
I believe that technology can help us become better. It doesn’t mean that we can drop everything else we’re doing both as consumer (reducing flight time, recycling) and as businesses in our work, but as DeepMind has showed, making things more efficiently can save astonishing amounts of energy.
Because of this immense power, it is crucial to make sure AI efforts stand on a good ethical ground. It is scary how real-looking fake videos you can create to support your propaganda and to brainwash even the smartest of people. However, AI can also be used to fight this scam and to bring in metadata about the photos and videos online that otherwise would be near impossible to interpret as untrue.
5. You don’t need to code or build models, but understand what they can do
As I’m sure you’re aware, constant learning is the key in this fast-paced world. This has definitely become clear in the AI industry. Forget the endless categorizing into technical and non-technical people. AI is not too difficult to understand, once you get someone who speaks your language to explain. Also accepting you will always only grasp a limited amount at once will help you (the basics of any learning process).
First, start by taking the Elements of AI course that has enlightened more than 150 000 people. Continue by watching videos such as CGP Grey’s How machines learn or read Tim Urban’s Wait but Why posts on AI (part 1, part 2). If you want to go far on the technical side, take the renown Andrew Ng ML course. Google and learn. That’s what machine learning experts/software developers/most adults do all the time. Last, talk about this stuff with your friends, colleagues and family.
Understand and discover together. ❤️
