Data, analytics and AI in 2018: Some hopes and pointers


When pondering what to write about looking forward to 2018 I had a shortlist of three hot topics:

  1. AR
  2. Blockchain
  3. Artificial Intelligence (AI)
I didn't choose AR as I think it will remain a specialist tool, although cool apps like Star Chart that my family love exist and Pokemon Go showed how addictive usage in games can be, it's still early days for tool kits like ARKit to make a break through app.

Blockchain is still probably at least a year off. Given the co-ordination needed in business process innovation it takes a bit longer to get into the mainstream. It appears that the processing speed is also a bit of a impediment at the moment. I am watching this field with interest though as it has potential to change the way companies process transactions. (Edit: although this is now the subject of a 15below tech take)

Which leaves AI. I chose this not just because it's been my key interest my whole adult life, but also because it is making another big step into the mainstream. Most people will have interacted with AI already for years in its application in detecting credit fraud, computer game opponents, and recommendation services. More recently with voice assistants such as Siri or Alexa adding language processing. This year self-driving cars are going to push computer vision and real time decision making.

I would expand this topic to include data science and analytics. AI relies on data, so good data hygiene and processing is vital. Also doing something interesting with that data often includes analysing it to show the information within. I suspect that in 2018 another nudge for investment this year is going to be GDPR, as marketers need to provide more value in exchange for permission to use data and send communications.


So, reading about what might be coming up in 2018 I would like to share three interesting posts from consultancies about AI and data analytics in 2018. The first is Top 10 AI technology trends for 2018 from pwc - An exciting year and prospects to look forward to! To me Explainable AI is the next goal to provide a competitive advantage. Using AI to predict outcomes is great, but if the model can't explain why it has made a prediction it is difficult to know how to improve it, or the limits of how you can trust it. This is a pretty big difference to how humans learning and provide feedback.

 The next article was a great overview of current landscape for analytics, AI, and automation from McKinsey, data analysis skills and under-utilised assets are definitely an area of weakness that 2018 could start improving. With all the potential that could be unlocked, it seems a no-brainer.

 It’s difficult to sum up Machine learning evolution in a simple infographic, but think pwc did a good job - just bear in mind reality is a lot more nuanced. Also AI predictions, on technology used, more then 5 years out are usually wrong! It's hard to argue with the general trend of combining techniques. This is something that I have observed since I started studying AI. Where neural nets were abandoned for a decade in favour of other techniques. Now with much larger corporate focus with giants like Apple, Google, and Facebook all having substantial investment and looking for a competitive edge, I'd hope that this wouldn't be the case.

 But what about the data scientist building a lot of this infrastructure? One key concern I have picked out from looking around on twitter to gauge opinion, is below

worth bearing in mind if you are introducing predictive analytics. Your model reflects the data that you had at the time and choices made during the training process. How are you going to check the performance and effectiveness going forward? Especially if you don't have an Explainable AI.

 The second is how important the foundation is, Sean Taylor highlights that a lot of the value of data scientists work is how it is used downstream

So think about the benefits that a good data analytic culture can bring. Not all of the value will come from where this work is done. If it's done right then the learning will be occurring in other roles.

 In the past year I have been looking at some of the business aspects of AI. So this year my resolution has been to brush up on the more technical aspects. Expect to see my write about the DataCamp Data Science courses and others that I take in 2018.

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