This article is about Artificial Intelligence
Top technologies to learn in Artificial Intelligence
By NIIT Editorial
Published on 17/12/2020
The COVID-19 pandemic might have brought the world to a standstill but also acted as an eye-opener for CEOs who’ve come to a multi-pronged realization. First, supply lines must be untangled to mitigate dependencies on a single source, and second, increase in-house automation for value chains to sustain with least human intermediation. No wonder then that Artificial Intelligence comes across as a lively prospect. Corroborating the same line of events, Gartner predicts the emergence of five new technology categories in their hype cycle namely Small Data, Generative AI, Composite AI, Responsible AI, and Things as Customers.
The interest in AI is compounded by job seekers who as per the job site, indeed, are increasingly vying for selected openings across the domain. From June 2019 to July 2020, AI-related job searched have withered a meteoric increase of 106% on the platform. That being the case, readers should be acquainted with topline AI skills and expertise, that will help them find a suitable job in the market.
Artificial Intelligence Technologies
Below are the most valued skills/technologies in the AI industry, that recruiters are most after:
Natural Language Generation (NLG)
It does exactly what it sounds like. This software process converts data from a tough-to-understand format into a consumable one for human consumption. A wide-scale application is being hailed in data analysis that involves crunching numbers and subsequent reporting. NLG can be customized to manufacture results in a data format of your choosing from bar graphs to pie charts and more. Job aspirants interested in AI technology must investigate NLG so they can off-load the mass of data to be processed onto their algorithms that are less susceptible to miscalculations.
AI speech recognition capabilities have been continually getting better with each upgrade in transcription software. For instance, Google’s AI-learning voice assistant Echo uses speech recognition techniques to understand human voice input, converts it into a machine-compatible format to be read by the code, and then back to a human-readable format to be interpreted by analysts. From in-car navigation systems to smart assistants and medical facilities, applications of speech recognition are omnipotent.
Virtual Agents is just another name for conversational AI. Techniques such as computer generation, animation, along AI are used to deploy human-sounding bots. Chatbots have become a default customer expectation and a de facto service experience at contact centers. Whereas the cost to businesses in employing customer service representatives is huge, the same (or even better) results can be accomplished by bots that would ensure a shorter margin of error.
The business value chain generates data from multiple sources. Data sourcing and sampling is a key part of decision management, a process that can be augmented with AI. Smart insights can help stakeholders analyse key trends with predictive analytics. AI’s intervention also helps mitigate unnecessary exposure to risk and optimises organizational resources. Decision-makers can unravel revenue-linked factors like which demographics is their key contributor and customer preferences so requisite steps can be effectuated. The three sectors that are presently experiencing the most commotion from AI experts towards decision management are fintech, insurance, and e-commerce.
Machine learning is arguably the most important capability of AI that lends it a self-learning, ever-improving attire. ML algorithms run on training-data, which enables it to learn through experiences without programmatically being asked to do so. Whether it is adaptive learning in artificial intelligence or reinforcement learning in artificial intelligence, they all fall under the umbrella of machine learning. With vivid applications across industries such as pattern detection, exploratory data analysis, and making predictions, AI experts with a specialty are widely sought after through the jobs market.
Learning in AI, or rather by it, is possible thanks to deep learning. The reason it is labeled deep is because the neural networks have multiple layers that run, well, deep and make learning through AI programming a reality. Since it is a subset of machine learning, it tweaks the programming model in each turn, leans from the new outcome, and continues to self-improve. Operating with massive sets of data it finds applications in facial recognition, image colorization, virtual assistants, and transcription software among others. Advancements in deep learning hold the key to the future of AI technology.
Robotic Process Automation
RPA refers to software that replicates human-responses in digital systems, only doing it with superior accuracy than their human counterparts. The response actions to be automated are repetitive and hitherto being initiated by the workforce. Robotic process automation is applied to tasks like accessing local applications, enabling API frameworks, data scraping, data extraction, and performing calculations. Experts working with Artificial Intelligence technology are expected to have operational knowledge of RPA enablement.
Machine intelligence can be augmented with the active participation of peer-to-peer networks. A P2P network stands for decentralization and transparency, factors due to which, they are heavily used in the financial sector vis-a-vis blockchain technology. Although their widespread use is yet to be experienced in relation to AI learning, due to debates around their actual impact, nevertheless artificial intelligence learnings include inter-disciplinary domains and P2P networks could one day be one amongst them.
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