Since its rise to stardom, AI has had an unquestionably positive effect on our lives. Think of Siri, your friendly personal assistant embedded in your iPhone. What about Netflix that uses AI algorithms to recommend our next movie? But let’s sit down and ponder upon the darker side of artificial intelligence: Its potential to replace all our jobs.
Instead of worrying about AI taking over our current jobs and rendering us all out of work, we should be thinking about what AI and machines still cannot achieve... for we will need their help to turn our far-reaching dreams into reality.
Watch out. If your job is highly-predictable, you can easily be replaced by machines! Technologies such as artificial intelligence will automate 50% of the current activities that our workers carry out today in industries like accommodation and food services, manufacturing, transportation and warehousing, agriculture, retail and mining.
In this latest briefing note - WHAT’S NOW AND NEXT IN ANALYTICS, AI, AND AUTOMATION - by McKinsey Global Institute, personalized advertising is one of the best use cases for AI and machine learning.
Find out how big data and CRM can provide your customers with superior personalized service through dynamic pricing, better customer responsiveness, improved decision making, and enhanced management of leads and clients.
We managed to interview a couple of Wrangle Malaysia attendees - from a variety of ICT and non-ICT backgrounds - on what their thoughts were on a plethora of topics such as how big data and data science applied to their organizations and individuals, the types of skill sets required to be a data scientists and how attending Wrangle Malaysia would help them think about their big data strategies and projects.
Will these 2017 big data, AI and IoT predictions ring true? Among them: AI will be hot and will be used in conversations between buyers and suppliers in the form of chatbots and digital assistants; deep learning will finally become a reality; and, early adopters of machine learning will have a huge advantage because the system would have begun learning about the business sooner.
Analytics involving 3,000 couples – married or otherwise – presented a correlation between the price of an engagement ring and the length of marriage. If you are spending a large part of your salary on your other half’s diamond ring, be prepared to split up. But then again, in data science, correlation is not always causation.
Last month a company sold 100 units of a product without having conducted any prior marketing activity. Then it ran a direct mail campaign at the start of this month. By the end of this month, it had sold an additional 112 units. Did the campaign work? The fact that there was a campaign and an increase in sales (a correlation) doesn’t necessarily mean that the campaign was responsible for the increase (causation).
This is how we explain Big Data to our neighbors. The Big Data Big Insights infographic is one of many MDEC’s efforts in explaining the concept of big data and analytics to the layman.