Can you recall a time when ChatGPT did not dominate practically every discourse concerning the future of work?
Despite its youth—OpenAI introduced the chatbot in November 2022—the generative AI tool has made a transformative impact on practically every industry, not just technology. It has a significant influence on marketing and sales, product development, and service operations.
Roughly two-thirds of workers (72%) use generative AI daily to complete tasks related to their jobs, according to a recent McKinsey study.
Around 40% of organizations employ generative AI to speed up procedures and make staff more efficient.
Potential in the Future
It’s reasonable to expect some casualties from AI’s impact—the World Economic Forum estimates that AI would result in 83 million worldwide job losses by 2027.
Accenture predicts that generative AI tools could impact 40% of working hours and automate up to 30% of employment by the mid-2030s.
While the majority of the respondents polled by McKinsey acknowledge which generative AI will cause a crucial or major impact, especially within information-based work like banking and pharmaceutical products, this impact and acceptance of new methods of working will also add 9% to global revenue.
Manufacturing-based sectors like aerospace, automotive, and advanced electronics will see less disruption.
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Make artificial intelligence work for you
The McKinsey research also emphasizes how we’re adopting generative AI to speed up monotonous processes, allowing people to focus on more creative endeavours, which is its core benefit.
The most common use is creating initial drafts of text documents (9%), followed by personalized marketing (8%), summarizing text documents (8%), and spotting patterns in client demands (7%). ChatGPT is also used by 5% of employees to draft technical documentation and anticipate trends or anomalies, and 4% to build new product designs.
AI is transforming the IT business, particularly for software developers. Tools like CoPilot, Tabnine, and Amazon CodeWhisperer aid in the speedier and more precise creation of code. To fix bugs and prevent data breaches, CoPilot use the OpenAI Codex language model, Tabnine employs open-source code, and Amazon CodeWhisperer employs a big language model. The VentureBeat Job Board has hundreds of openings at firms that are actively hiring and deploying generative AI.
Zoom, San Jose, Machine Learning Engineer
As a Machine Learning Engineer at Zoom, you will tackle cutting-edge AI challenges and deploy models that consistently expand Zoom’s service in areas like as summarization, topic classification, language modelling, and coreference resolution. As a result, you will be required to do autonomous research with little supervision, interact with other researchers on larger-scale projects, and lead junior engineers on their research/engineering assignments.
STELLANTIS, Auburn Hills, Prompt Engineer/Generative AI Engineer
The purpose of your job as a Prompt Engineer/Generative AI Engineer is to design, create, revise, and optimize AI-generated text prompts to make sure they are correct, interesting, and relevant for a variety of applications. It contains NLP models and cues that drive language model and conversational AI system performance and effectiveness. You will use generative models and prompt engineering to develop new and creative AI products.
Apple, Cupertino, Machine Learning Engineer
Apple’s Video Computer Vision organization is developing new technologies for future Apple products, with an emphasis on ML-based solutions for real-time image and video processing. As a Machine Learning Engineer, you will help push the boundaries of 3D computer vision technologies by working with real-world data and a highly complex machine learning system to deliver computer vision neural networks and algorithms, involving training, evaluation, and failure analysis, as well as an end-to-end pipeline and metrics for fast model evaluation and repetitions.
