Artificial Intelligence and Machine Learning are more and more popular in a startup world, first of all, because they can really help the business and secondly, make it look special and fresh. On the other hand, there is no need to use it in each startup. Before applying it to the business model, just take a look at the list of really, really must-known facts about Machine Learning.
Understand Machine Learning and AI technology
We totally get it, having “AI” in your startup is a great fundraising hook, but Machine learning was not invented to gather funds (mostly). It might be the ace up your’s sleeve, but it also can be just a buzzword or worse, badly invested money. Basically, if you understand how the principles work, you should be able to estimate if Machine Learning is something that your startup needs. Bear in mind those 10 points while making your decision:
1. Machine Learning (ML) doesn’t equal Artificial Intelligence (AI)
Let’s make clear that Machine Learning and Artificial Intelligence (AI) are not the same things. Yes, Machine learning is a part of AI, but those shouldn’t be taken as synonymous. Machine learning is based on the amount of available data and its analysis.
2. Quality and Quantity of data
Machine learning is essentially learning from data. Although a scenario where small human-like robots are sitting at school and noting down most important information from the class and playing games after coming back home - is appealing, let’s leave this image for Sci-Fi movies. Data here is the king and the queen. Without qualitative training data and right learning algorithms, you can’t do much in Machine Learning world.
Why the quality data matters? And also, why the more of it, the better? Well, Machine Learning algorithm discovers patterns which are already included in the training data. Most of the hard work is a data transformation. The time and effort put into selection and algorithm adjustments is nothing to compare with data cleansing and feature engineering. However, it is necessary if we want it to work.
3. Machine Learning is about data and algorithms but.. data always wins
No matter how good the algorithms are, no matter how advanced it seems, especially when deep learning comes in, the data is the secret ingredient that makes it all work. It is possible to have a very simple algorithm which works perfectly with good data but never the other way around. Even the most sophisticated algorithm can’t do much if the data is not good enough. On the other hand, remember, that feeding it with information isn’t enough - the context depends on you.
4. Simple models for less data
We have covered the topic of (quality) ‘more is more’ when it comes to Machine Learning and data, but if you have less data, it is also possible. Bear in mind that the models are trained from patterns in the data based on defined parameters. There is actually a lot of math coming into it but important is to keep the models as simple as possible.
Additionally, the models should be renewed regularly to get the most of it.
5. ML discovers only correlations not casual relationships and needs context
As we mentioned before, the context comes from the human factor. Machine Learning algorithm identify correlations, but they can’t understand the environment of the data which might be relevant or not. If you don’t take into account the facts surrounding the information, it will be most likely misleading. Let’s take as an example a very common use of ML: recommendations. Imagine, that while recommending related products the algorithm was not fed with the information that one specific product had extremely low price a year ago and in the historical data it shows that it was a great deal. Now, the product is over-recommended with its casual price and… it just won’t work. Do you get the point?
6. Machine Learning is all around us
No matter if you see it or not - machine learning is a tool used by many companies in different industries. It can be helpful when it comes to marketing and advertising (as recommendation engines in Netflix, Spotify, YouTube and so on), searchability in eCommerce, programmatic advertising, marketing forecasting, customer segmentation just to name a few. However, Machine Learning has a more significant impact till now in another field - full of data - healthcare. It also saves lives: Computer-aided detection (CADe) and diagnosis (CAD) do a lot of a good job especially in medical imaging, where learning from examples is essential. Another valuable use of Machine Learning can be found in finances and insurance. If you are searching for more everyday-life AI and ML examples, we have got you covered:
7. Human error can kill it
If the system fails, usually it is caused by the human and not by the algorithm. Systematic error, bias or wrong training data input - the machine did it? Nope. Though, it is okay to make mistakes and learn from them.
8. Not really going to destroy humanity
At least, not soon! Being proactive and considering regulations regarding AI and Machine Learning is a good thing but let’s not freak out by the AI machines uprise and escalation of a war against humanity. AI algorithms are an inevitable part of our everyday actions (at least if you are using Google, Siri, Netflix, Spotify, YouTube, Google Maps and so on, so yeah, pretty often, right?) and like every tool it can be used for a good or bad cause.
9. Not necessary in every startup
In a startup ecosystem, it seems quite appealing to use AI and Machine Learning while pitching investors. It also might give a huge advantage over competitors but… not in each business model Machine Learning is a good idea. It all depends on the industry, and there are some startups which are doing just great particularly in the SaaS field. However, regardless of AI and ML-based tools which can help in running the business and scaling up, if you don’t have enough quality data or can’t really adjust it later on, it won’t change much. Artificial Intelligence and Machine Learning are tools, and as with every other tool, if you don’t know how to use it, it can’t do the magic trick on its own. For startups, the problem is lack of people who can handle it, of course, next to the needed resources. This is why many startups outsource their Machine Learning work. It is necessary for startup founders to understand that some business models need AI from the beginning, others can apply it later to solve business problems because it doesn’t influence that much a product’s or service’s basic value proposition while some don’t need it at all as implementation in their core business strategy. If your startup is not “AI-first”, then it is better to focus on choosing the right tech stack and maybe add some AI or ML charm later on, when you have enough data and valuable insights to use it.
10. Self-fulfilling prophecy
This term comes from psychology but can also apply to Artificial Intelligence and especially Machine Learning. The decisions and the input which you give today will definitely affect the training data, and when the system includes biases into the model, it will generate new training data with those strengthen biases and so on… It might not seem that obvious but just think about an example, if the credit scoring algorithm has some biases, some people who should get it to finally buy a house or get a loan to study abroad and so on won’t qualify from the bank’s point of view, and it is just because the data scientist haven’t done the job right. Being responsible and not creating self-fulfilling prophecies is out of the question, or else it might actually harm others.
Is Machine Learning for a startup a good idea?
If you chose to implement AI technology and Machine Learning in your startup environment as a product’s or service’s core, there always should be a tech-savvy with you who understands the technical part itself, knows how it works and can explain insights like for example why Python is a good choice for Machine learning. On the other hand, it might be safer to outsource it but let’s keep in mind that implementing sophisticated technology like this requires a lot of gathered clean and labelled data, which is not always a case in a startup ecosystem.