A lot of paper is published daily about Machine Learning while billions of Dollars is invested in AI and Data Science all over the world daily.
Eric Beinhocker said ‘there are physical technologies which evolve at the pace of science and social technologies which evolve at the pace at which humans can change- much slower.
Could this mean that data science, machine learning, data analytics might keep evolving at pace with science and technological advancements but the impact and benefits may evolve slower due to the slower adoption by man- slow to trust (see social technology being slow)
Here are some predictions about AI and Data Science (oh and AI) based on the above;
1. Data Science and AI roles will continue to favour Specialization
Newbies or budding data analysts and scientists may be aspiring to become Unicorns in data science, seasoned data scientists and analysts know that just because someone knows how to do something doesn’t mean they should do it. Although a multi-talented performer has value but that may not necessarily be a comparative advantage when building and scaling large data science teams.
2. Executive Understanding of Data Science and AI Will Become More Important
It may be ridiculous to realize that often times the major limiting factor to enjoying the best of data science or data analytics is the ability of the actual decision makers to consume and understand data. Organizing in house trainings that can help an organization’s personnel mature in this light ameliorate the situation.
3. Data Science and AI Ethics Will Continue to Gain Momentum
This is already morphing into a distinct discipline thanks to incidences like the Cambridge Analytica Scandal and Amazon scrapping its secret AI tool that showed a bias against women. Ensuring that data and all its application is used in an ethical manner is becoming an established discipline and can only continue to be so.
4. More Confusion When Tools Converge
Remember the saying that too many cooks spoil the broth? There are a lot of tools out there packed with or offering similar (if not same) functionalities. This is the best recipe for confusion because in reality, there are multiple tools available to do the same task and new entrants into the industry may not know why or which tool to pick per time. An organization with an appreciation for teams with various technical abilities and skills may be the ones that enjoy the best of the myriad of tools available.
5. Hype and Definitions in May Shift
The hype was mostly on ‘big data’ initially before moving on to ‘data science’ around 2015. It is possible that 2020 will be the year that ‘AI’ could overtake and seize domination of the conversation because it is not so much about big data as much as what you are able to do with it. AI helps us make more efficient use of big data.