Can you remember a time before ChatGPT dominated almost every conversation about the future of work?
Despite its relative infancy—OpenAI launched the chatbot in November 2022—the generative AI tool has had a groundbreaking effect on almost every industry, and not just tech. It’s particularly making an impact on marketing and sales, product development and service operations.
In fact, according to a recent McKinsey report, generative AI is now being used by 79% of workers and 22% use it regularly to complete their day-to-day work tasks.
Generative AI is also being used by around 40% of organizations to speed up processes and make workers more efficient.
It’s fair to assume that AI’s impact will include some casualties along the way—the World Economic Forum has estimated that AI will result in 83 million global job losses by 2027.
Separately, Accenture has estimated that 40% of all working hours could be affected by generative AI tools, and by the mid-2030s, up to 30% of jobs could be automated.
And while the majority of those surveyed by McKinsey admit that generative AI will cause significant or disruptive change, particularly in knowledge-based work such as banking and pharmaceuticals, this disruption and adoption of a new way of working will also add to revenue to the tune of 9% globally.
Conversely, manufacturing-based businesses including aerospace, automotive and advanced electronics will experience less disruptive effects.
The McKinsey report also highlights how we’re using generative AI to speed up repetitive tasks so that workers can focus on more creative endeavors, which is its primary advantage.
Crafting first drafts of text documents is its most popular use at 9%, followed by personalized marketing (8%), summarizing text documents (8%) and identifying trends in customer needs (7%). ChatGPT is also being used by 5% of workers to draft technical documents and forecast trends or anomalies, while 4% are using the tool to create new product designs.
For tech workers, software engineers in particular, AI is also proving to be invaluable when it comes to creating code faster and more accurately. These include CoPilot, which is driven by the OpenAI Codex language model, and can assist in debugging and preventing data breaches while also writing entire lines of code. Similarly, Tabnine, which is compatible with Python, Rust, Go, Java and Javascript, uses open-source code to offer complete suggestions.
And then there’s Amazon CodeWhisperer. It uses a large language model (LLM) trained on AWS services, and suggests and monitors code as you write it, based on the text prompts initially provided by users.
As the adage goes, if you can’t beat them, join them—and the VentureBeat Job Board is the perfect place to start your search. It features thousands of jobs at companies that are harnessing generative AI and actively hiring, such as the three below.
As a Machine Learning Engineer at Zoom you will be working across various natural language processing (NLP) areas like summarization, topic segmentation, language modeling and coreference resolution to solve cutting edge AI problems and deploy models that constantly advance Zoom’s offering. As such, you will be expected to carry out independent research without much supervision, collaborate with other researchers on larger scale projects, and provide directions to junior engineers on their research/engineering tasks. Get more information here.
As a Prompt Engineer/Generative AI Engineer, your role is to design, develop, refine and optimize AI-generated text prompts to ensure they are accurate, engaging and relevant for various applications. It includes NLP models and prompts that drive the performance and effectiveness of language models and conversational AI systems. You will work with generative models and implement prompt engineering to create new and innovative AI products. View additional details here.
The Video Computer Vision organization at Apple is working on exciting technologies for future Apple products, and is focusing on ML-based solutions around real-time image and video. As a Machine Learning Engineer you will help push the boundaries of 3D computer vision technologies and join a team of highly accomplished and deeply technical engineers and researchers to work with real-world data and a highly complex machine learning system in order to deliver computer vision neural networks and algorithms, including training, evaluation and failure analysis, along with end-to-end pipeline and metrics for fast model evaluation and iterations. See the full job description here.
Futureproof your career today via the VentureBeat Job Board.
Head over to our on-demand library to view sessions from VB Transform 2023. Register Here
Can you remember a time before ChatGPT dominated almost every conversation about the future of work?
Despite its relative infancy—OpenAI launched the chatbot in November 2022—the generative AI tool has had a groundbreaking effect on almost every industry, and not just tech. It’s particularly making an impact on marketing and sales, product development and service operations.
In fact, according to a recent McKinsey report, generative AI is now being used by 79% of workers and 22% use it regularly to complete their day-to-day work tasks.
Generative AI is also being used by around 40% of organizations to speed up processes and make workers more efficient.
Event
VB Transform 2023 On-Demand
Did you miss a session from VB Transform 2023? Register to access the on-demand library for all of our featured sessions.
Future outlook
It’s fair to assume that AI’s impact will include some casualties along the way—the World Economic Forum has estimated that AI will result in 83 million global job losses by 2027.
Separately, Accenture has estimated that 40% of all working hours could be affected by generative AI tools, and by the mid-2030s, up to 30% of jobs could be automated.
And while the majority of those surveyed by McKinsey admit that generative AI will cause significant or disruptive change, particularly in knowledge-based work such as banking and pharmaceuticals, this disruption and adoption of a new way of working will also add to revenue to the tune of 9% globally.
Conversely, manufacturing-based businesses including aerospace, automotive and advanced electronics will experience less disruptive effects.
Making AI work for you
The McKinsey report also highlights how we’re using generative AI to speed up repetitive tasks so that workers can focus on more creative endeavors, which is its primary advantage.
Crafting first drafts of text documents is its most popular use at 9%, followed by personalized marketing (8%), summarizing text documents (8%) and identifying trends in customer needs (7%). ChatGPT is also being used by 5% of workers to draft technical documents and forecast trends or anomalies, while 4% are using the tool to create new product designs.
For tech workers, software engineers in particular, AI is also proving to be invaluable when it comes to creating code faster and more accurately. These include CoPilot, which is driven by the OpenAI Codex language model, and can assist in debugging and preventing data breaches while also writing entire lines of code. Similarly, Tabnine, which is compatible with Python, Rust, Go, Java and Javascript, uses open-source code to offer complete suggestions.
And then there’s Amazon CodeWhisperer. It uses a large language model (LLM) trained on AWS services, and suggests and monitors code as you write it, based on the text prompts initially provided by users.
As the adage goes, if you can’t beat them, join them—and the VentureBeat Job Board is the perfect place to start your search. It features thousands of jobs at companies that are harnessing generative AI and actively hiring, such as the three below.
Machine Learning Engineer, Zoom, San Jose
As a Machine Learning Engineer at Zoom you will be working across various natural language processing (NLP) areas like summarization, topic segmentation, language modeling and coreference resolution to solve cutting edge AI problems and deploy models that constantly advance Zoom’s offering. As such, you will be expected to carry out independent research without much supervision, collaborate with other researchers on larger scale projects, and provide directions to junior engineers on their research/engineering tasks. Get more information here.
Prompt Engineer/Generative AI Engineer, STELLANTIS, Auburn Hills
As a Prompt Engineer/Generative AI Engineer, your role is to design, develop, refine and optimize AI-generated text prompts to ensure they are accurate, engaging and relevant for various applications. It includes NLP models and prompts that drive the performance and effectiveness of language models and conversational AI systems. You will work with generative models and implement prompt engineering to create new and innovative AI products. View additional details here.
Machine Learning Engineer, Apple, Cupertino
The Video Computer Vision organization at Apple is working on exciting technologies for future Apple products, and is focusing on ML-based solutions around real-time image and video. As a Machine Learning Engineer you will help push the boundaries of 3D computer vision technologies and join a team of highly accomplished and deeply technical engineers and researchers to work with real-world data and a highly complex machine learning system in order to deliver computer vision neural networks and algorithms, including training, evaluation and failure analysis, along with end-to-end pipeline and metrics for fast model evaluation and iterations. See the full job description here.
Futureproof your career today via the VentureBeat Job Board.
VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.
Author: Aoibhinn Mc Bride, Jobbio
Source: Venturebeat
Reviewed By: Editorial Team
