The AI Revolution - Part 3

In Part 1, have discussed the overall market potential of AI for the next 30 years, Part 2 touched on the various industries potentially can benefit from AI. In part 3, will give a high overview the role of data in Artificial Intelligence.

Defining the Role of Data

There is nothing new about data. Every interesting application ever written for a computer has data associated with it. Data comes in many forms – some organised, some not. What has changed is the amount of data. Some people find it almost terrifying that we now have access to so much data that details nearly every aspect of most people’s lives, sometimes to a level that even the person doesn’t realize. In addition, the use of advanced hardware and improvements in algorithms make data the universal resource for AI today.

Today, applications collect data manually, as done in the past, and also automatically, using new methods. However, it’s not a matter of just one to two data collection techniques; collection methods take place on a continuum from fully manual to fully automatic.

Raw data doesn’t usually work well for analysis purpose. We will discover the need to define the truth value of the data to ensure the analysis outcomes match the goals set for applications in the first place.

First Step: To Collect Data

Second Step: To Analyse Data

Third Step: To Suggest Hypotheses or Actions/ Reanalyse New Data Periodically

First Step – To Collect Data

One way to get a dataset is from observing user behaviours or other types of behaviours. For example, let’s say you run a website that sells things online. So, an e-commerce or an electronic commerce website where you offer things to users at different prices, and you can just observe if they buy your product or not, the number of viewers in every product page, the favorite product by viewers.

A very common way of acquiring data is to download it from a website or to get it from a website or to get it from a partner. Thanks to the open internet, you can download for free, keep in mind licensing and copyright. This could be a great way to get started on the application.

Second Step – To Analyse Data

The second step is to then analyse the data. Your data science team have a lot of ideas about what is affecting the performance of your sales funnel. For example, the overseas customers are scared off by the international shipping costs, which is why a lot people go to the Purchasing Page but don’t actually Purchase. They may suggest that you should spend fewer advertising price during certain period because few people will go online to buy at that time.

So, a good data science team may have many ideas and so they try many ideas to get good insights.

Third Step – To Suggest Hypothesis

The data science team will distil these insights down to a smaller number of hypothesis, about ideas of what could be going well and what could be going poorly as well. Such as incorporating shipping costs rather than having it as a separate line item.

When you take some of these suggested actions & deploy these changes to your website, you then start to get new data back as users behave differently now that you advertise differently at the time of siesta or have a different Purchasing policy.

Then your data science team can continue to collect data & analyse the new data periodically to see if they can come up with even better hypothesis or even better actions over time. The above process applies similarly to various industries & processes, particularly those activities that involve in repetitive environment.

When you combine poorly collected, ill-formed data with algorithms that don’t actually answer your questions, you get output that may actually lead your business in the wrong direction, which is why AI is often blamed for inconsistent or unreliable results. Asking the right question, obtaining the correct data, performing the right processing, and then correctly analysing the data are all required to make data acquisition the kind of tool you can rely on.

Data do takes us to a new height. For example, let’s say you run a website that sells things online, ultimately priorities and goals to focus on are Cost Reduction and Increase Operating Efficiency. In addition, ultimately to increase Revenue Gain by providing Competitive Pricing i.e Price Bundling and attractive Promotions, Rewards & Personalisation.

In healthcare, AI can provide tremendous help in analyzing complex medical data such as X-rays, CT scans, and different screenings and tests. Using the patient’s data and external knowledge sources such as clinical research, medical professionals can build a personalized treatment path for everyone.

Besides on-site clinical decision support, AI can also be used to provide real-time medical advice to patients. The AI doctor app uses speech recognition to consult with patients, checks their symptoms against a database, and offers them adequate treatments.

In Malaysia, genome.my, a web services provides software in healthcare such as image classification, object detection, text classification & sentiment analysis using natural language processing. In addition, Twistcode Technologies uses artificial intelligence in healthcare to improve patients’ lives and create more efficient, sustainable and accessible healthcare systems. Twistcode, a local entrepreneur, twist algorithms, applications, repetitive process, codes serial to parallel, from CPU to GPGPU, specialised in supercomputing and has its own self-assembled GPU based very high-end servers to facilitate compute processing acceleration and artificial intelligence in healthcare.

In conclusion, AI doesn’t work without data. It consumes data in order to learn. Big data refers to the massive sets of data that are now available for this purpose. There is simply an enormous amount of data available of all types. This includes images, audio, and text data.