It’s almost impossible to start searching for new technologies or browse industry technical papers and technical journals without hearing the two magic letters of AI. Interest in artificial intelligence has skyrocketed over the past few years and shows no signs of slowing down. But in the rush to build an AI career, a student falls into her next four major traps.
We believe that artificial intelligence is a unique technology.
With so much AI enthusiasm in conferences and technical journals, they tend to talk about AI as one overarching technology. Even the question “What is our AI career strategy?” assumes that AI is one of the silver bullets. In contrast, the AI we see and know today is actually the result of layering multiple technologies (computer vision, natural language processing, generative adversarial networks, etc.). No such single AI technology exists. Rather, there are various applications of AI technology to train computers to solve problems and reduce repetitive tasks. This can range from computers that distinguish and recognize images and voice commands, to decision trees built to offer lay customers the best of many options, to self-driving cars navigating roads. can take shape.
As career-minded individuals seek to apply AI to their problem-solving skills, it is important to understand that “doing AI” requires a more focused approach. The key is understanding the core complexities of the problem, assessing end users and gaps, and prioritizing processes that can be made more efficient by applying specific her AI technologies.
Today, there are several non-traditional applications of AI in which aspiring students can strive to build their careers. An application area that is highly vulnerable due to the large loss component is the banking sector. Fraud detection using AI could be the best paying investment in the future. Some notable successes are our partnership with Citibank. feedzaiDanske Bank’s Teradata Reduce 1,200 false positives per day by modernizing the fraud detection process.
If you’re considering a service sector (such as healthcare or hospitality), customer service automation could be a priority area. An international airline, on average, he receives thousands of customer inquiries a day. This doubles in bad weather. AI chatbots are helping companies communicate effectively with their passengers and deliver an amazing experience. According to a SITA report, 68% of airlines and 42% of airports are using AI-powered chatbots. In fact, artificial intelligence is one of the top five emerging technologies for airlines, with 52% of them considering investing in AI in the future.
ignore data link
AI is about finding patterns and resulting useful predictions based on large structured and unstructured data sets (images, audio, text, etc.). Artificial intelligence cannot function without sufficient, high-quality data. However, data by itself is not useful. Instead, what makes this data meaningful is its proper use. At this point, I have a few questions that I need to address myself. Do you understand the concept of data repositories? Are you aware of the concepts of database management systems, or do you know how they translate into a meaningful organization of data in a repository? Data Analysis are you good at The saying “garbage in, garbage out” shows how important it is to have quality data that you can analyze. Otherwise the output will be questionable. Make sure your data input parameters are rigorous, as data cleansing can take a long time.
neglect to develop enough talent
Despite all the negativity AI attracts in its cannibalization of media-related jobs, it actually offers a great opportunity in the labor market. Today, demand is high and manpower is scarce. New career opportunities have existed and will continue to exist. As companies prepare for the data age, non-digital native companies are beginning to invest in data-savvy AI experts. These professionals know how to take advantage of data (data strategists), structure data (data engineers), and work with data. Them (data scientists) and optimization (data visualizers and modelers). One of his approaches to getting started is to create his own group of like-minded professionals. These are first trained by experts with knowledge in strategy, data engineering, data science, etc. These professionals rotate in various functions while training in-house personnel to do the work they do.
Ignore the role of “policing entity”
The final mistake comes while learning about industry-level deployment of AI models. The industry needs to pay attention to sourcing, collecting and using new data for AI models. Data is an evolving organism, its sources and content are constantly changing. Each AI model that you roll out must go through a rigorous model validation process to ensure no bias is introduced and variables are used in the most appropriate way. Models are continuously evaluated to prevent adverse impacts and to ensure reliability and performance.
AI’s potential lies in its ability to handle any persistent task and make it more orderly, allowing it to deliver more creative output and better customer experiences. AI also has the ability to move the business from insight to action and monetize data to improve his ROI. For those organizations that can harness the power of AI, the rewards can be great. Aspiring students are expected to look to these organizations for a bright future in AI.
References:
- www.emerj.com
- www.sita.aero
- www.thechatbot.net
