2018 has been where AI and analytics surprised everyone. A large amount of time spent on research and experimentation has given us a clearer picture of what AI can do. But in 2019, organizations and professionals are going to invest in how these innovations can be used for real-life value-creating applications. In this article, we discuss AI and Analytics trends in three areas
- Data Safety
- Explanable AI
- Industry applications
It is clear that companies are realizing how valuable data can be and how it can drastically change the decision-making process, for better. This led companies to collect more and more data about consumers. Fun fact – Facebook even keeps track of where your mouse pointer moves on the screen. Fine movements of ours are being recorded and processed to make our experience better. But it’s not so easy for these companies.
Consider the Equifax data breach that happened not too long back. For those of you who do not know, Equifax is a credit reporting company. In our case, this means that it holds very sensitive information about citizens. More than 800 million of them. In the data breach, over personal data of 143 million Americans were compromised. Some of the information over which hackers were able to gain access to included name, address, and even social security number. The company’s security department said it “was aware of this vulnerability at that time, and took efforts to identify and to patch any vulnerable systems.” Equifax admitted it was aware of the security flaw a full two months before the company says hackers first gained access to its data.
There are many such examples of data breaches. Consequently, people are becoming more and more aware of their digital footprints. Government and lawmakers are realizing that data is no more just bunch of files on a computer. It has the potential to do good and to do the worst. This leads to the question – How safe are we digitally? What measures are being taken by the companies to safeguard our privacy?
Analytics is seen as a potential way to tackle this problem. Tools like user analytics try to understand steps a hacker takes when he crawls through the security walls of any company. It tries to understand if any user activity is different from the usual. For example a sudden spike in money withdrawal from a particular bank account. Deep Learning, arguably the most advanced form of analytics available to use is used on similar lines. Like user analytics, it focuses on anomalous behavior. The advantage is that it does it at a very micro level and is faster at detecting any such hazard, which is very important to take preventive measures.
Today we hear about deep learning as if it is Harry Potter’s, Elder Wand. Deep Learning has given some surprising results. Consider the following painting created by AI, which got sold for $432k at an auction.
In case you are wondering what kind of signature is at the bottom right, it is the objective function of the GAN (Generative Adversarial Network) that produced this art. Now we know why Math is often called ‘Art’.
But as awesome it may seem, deep learning is still considered as a black box method. Meaning, we are not perfectly clear on what’s going under the hood. Consider the use of Deep Learning in Cancer Detection. The algorithm, out of 100 cancer positives, might detect 90 of them. But wrongly predicting even 10% is fatal. Moreover, we don’t know why did the algorithm make those 10 errors. This puts a limitation on deep learning. In addition, company executives cannot trust major decisions of a company with some algorithm that might be accurate, but can’t explain why. This is where explanable AI comes into the picture. So that we can go from ‘may be’, ‘should be’ and ‘can be’ to ‘is’ or ‘isn’t’. Explanable AI is an active research area.
It’s a cliche to say AI will transform every possible industry on the earth. So let’s skip to the part where we specifically talk about a few selected industries and the ways.
In this nation of 1.25 B, where people are increasingly going online to shop, logistic is something every e-commerce focuses on the most. By forecasting demand and analyzing shopping patterns, companies can optimize their logistics. Thanks to real-time GPS data, weather data, road maintenance data and fleet and personnel schedules integrated into a system looking at historical trends, the most optimised routes and time are selected for delivery. Warehouse automation is something that has reduced the mundane repetitive work and has made processes faster and seamless.
Personalization or the ability of the algorithms to go to the micro level details and offer the best recommendation could be very well used in education. When you and I studied, we probably sat in a classroom where we had 50 classmates who. Now some of us understood what was being taught and some of us didn’t. It was impossible to teach each one of us in a way we could understand. Because there wasn’t any unique way. AI can open doors to this kind of personalization and enhance our learning experience with multiple folds.
We all know how dreadful disasters are. Life and property get destroyed in seconds when n earthquake or Tsunami strikes. In other cases like famine, a farmer’s livelihood gets disturbed. What if analytics can be used to predict the next earthquake or predict what crop would get the best yield? Startups like Cropin technologies provide smart solutions to farmers.
In 2015, researchers at the Vrije University Institute for Environmental Studies in Amsterdam published a research paper exploring how analyzing Twitter activity can help to detect flood events. The researchers found that by combining data from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity, disaster relief organizations can “gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods.”
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