It’s almost impossible to start searching for new technologies, browsing through industry tech papers, tech mags etc. without hearing the two magic letters: AI. Over the past few years, interest in artificial intelligence has rocketed with no sign of abating. But in the stampede to build an AI career, students fall into the following four main traps.
Thinking That Artificial Intelligence Is a Singular Technology.
As study groups and tech magazines are flush with AI fever, they tend to speak of AI as one all-encompassing technology. Even the question, “What is our AI career-strategy?” presumes that AI is one silver bullet. Au contraire, the AI we see and know today is actually the result of multiple technologies (e.g. computer vision, natural language processing, generative adversarial networks, and more) layered together. There is no single AI technology as such – rather there is a diverse set of applications of AI techniques to train computers for solving problems so that the repetitive tasks are reduced. This can take various forms ranging from computers recognising distinctively between images and voice commands, to decision trees built to deliver the best out of many options for a layman customer, to driverless vehicles navigating the road etc.
As a career oriented individual endeavours to apply AI to its problem-solving skills, it’s important to understand that to “do AI” requires a more focused approach. The key is to understand the problem’s core complexities, assess the end-user and the gaps, as well as prioritise the processes that application of specific AI technologies can make even efficient.
There are multiple non-traditional application areas of AI today in which an aspiring student can strive to make a career in. An application area with a large loss component hence higher vulnerability is the Banking sector. Fraud detection using AI could be the investment yielding the highest return in the coming future. Few prominent successful examples would be Citibank’s partnership with Feedzai, Danske Bank’s has been helped by Teradata to modernize their fraud detection processes thereby reducing their purported 1,200 false positives per day.
If you are looking towards the service sector (be it in healthcare or hospitality), then customer service automation could be an area of priority. International airlines on an average receive thousands of customer queries a day– twice as many in times of weather disturbances.AIchatbots have helped companies in communicating efficiently with passengers and deliver phenomenal experience. According to a report by SITA, 68% of airlines and 42% of airports are using AI-driven chatbots. In fact, Artificial Intelligence is one of the top 5 emerging technologies for airline companies with 52% of them looking to invest in AI in the coming period.
Ignoring the Data link
AI is about finding patterns and making consequent useful predictions based on large structured/unstructured datasets (image, speech, text etc.). Artificial intelligence only functions with sufficient, high-quality data. The data however is useless by itself, instead what makes this data meaningful is its proper utilization. There are several questions that need to be addressed to self at this point. Do you understand the concepts of data repositories? Are you aware of the concepts of Database Management System, or how do they translate into meaningful data organisation at the repository? Are you good with data analysis? The saying “garbage in, garbage out” illustrates how important it is to get quality data that you can analyse; if not, your output would be questionable. Data cleansing can take a lot of time so ensure that your data input parameters are tight.
Neglecting to Build Sufficient Talent
For all the negativity AI attracts in the media related job cannibalisation, it actually presents a golden opportunity for the labour market. Today demand is great and talent is scarce. New career opportunities have and will continue to present themselves. As companies gear up to meet the age of data, non-digitally native companies have started to invest in data-savvy AI professionals – those that know how to leverage the benefits of data (data strategists), construct them (data engineers), manipulate them (data scientists) and optimise them (data visualisers and modellers). One approach to start is to create your own group of likeminded experts. These will be trained, speciality experts with knowledge in strategy, data engineering and data science etc. to start with. While on rotation in various functions, these experts would also train the in-house talent for the work to be done.
Ignoring the role of a ‘Policing Entity’
Last mistake happens while learning about industry level deployments of AI models. Industries have to be kept vigilant in sourcing, capturing and utilizing new data for the AI models. Data is an ever-evolving organism, it’s sources and contents change constantly. Each AI model that rolls out has to go through a rigorous model-validation process to ensure no biases have crept in and that variables have been used in the most appropriate manner. The models are also continuously evaluated to prevent adverse effects and ensure reliability and performance.
The potency of AI lies in its facility to take any perpetual task and make it more methodical, freeing us to deliver more creative output and better customer experiences. AI also has the ability to move businesses from insight to action and monetise data to improve ROI. For organisations that can harness the power of AI, the pay-off can be significant. Aspiring students are expected to look forward to these organisations for a bright future in AI.