By Lawrence Wang
Given the current economic climate, businesses are keenly interested in reducing costs, increasing efficiency and exploring new growth opportunities. Technological innovation to optimize existing processes can dramatically increase a company’s bottom line. The launch of his OpenAI’s ChatGPT in November 2022 marked a tipping point in how we use generative AI in the workplace, sparking a wave of change in how we work.
Generative AI utilizes models (such as algorithms and architectures) trained on large amounts of data collected through the public domain. Using this data, these generative AI models will be able to learn patterns, structures, and relationships, and generate new content and ideas based on the acquired knowledge. The underlying infrastructure behind generative AI models has been around for years, but through an easy-to-use user interface, OpenAI’s ChatGPT has made this technology accessible to more knowledge workers. rice field. Inside the large language model that powers ChatGPT, Transformers use self-attention to predict the next word in a sentence. This allows the model to come up with consistent answers for the average person.
Generative AI utilizes models (such as algorithms and architectures) trained on large amounts of data collected through the public domain. Using this data, these generative AI models will be able to learn patterns, structures, and relationships, and generate new content and ideas based on the acquired knowledge.
Given how easy it is to use generative AI models, and the fact that computing costs have dropped significantly over the past few months, there are startups leveraging them to create generative AI applications for office workers. increasing. From video call summaries to automated marketing copy creation, here are his five companies that can improve your daily workflow.
Guru is unique on this list in that it uses state-of-the-art vision models instead of large language models to enable developers to rapidly create interactive video AI products. Guru comes with out-of-the-box models that make it easy to analyze user movements in videos. Not only can we recognize specific movements of people and objects, but we can also understand how they behave. For example, swinging a golf club or climbing stairs. It also provides developers with the ability to perform time-series analysis, such as counting the number of times an action is performed or cropping a video to identify specific segments of behavior. With Guru, experts can not only explain how to perform a movement, but also display specific visual markers on the video so that the recipient can truly understand and imitate the movement.
So how can businesses leverage Guru? Physiotherapists can create apps that virtually perform custom physical therapy assessments and guide patients on how best to perform specific exercises. increase. Basketball teams can use this to compare and contrast how different players are shooting his 3-pointer, and see what players are doing leading up to each specific move. can be accurately identified.
So how can businesses leverage Guru? Physiotherapists can create apps that virtually perform custom physical therapy assessments and guide patients on how best to perform specific exercises. increase. Basketball teams can use this to compare and contrast how different players are shooting his 3-pointer, and see what players are doing leading up to each specific move. can be accurately identified.
Loopin records and summarizes video calls so your team can focus on what matters instead of taking notes during meetings. At the end of the meeting, a meeting summary of what was discussed is automatically sent to each participant, with action items laid out as next steps. What makes Loopin different from other similar tools is its ability to connect the dots between meetings. This allows your team to review relevant meeting notes without going through other applications.
If you are a software engineer, reflect will definitely speed up your software development cycle. It not only suggests the code you are writing, but also identifies code that can be optimized, making it easier to understand. Refact also has the ability to analyze your code and provide documentation on what your lines of code should do. There is also an AI chatbot that can have a conversation about the code in question. This allows anyone to better understand the syntax of your code, based on their specific context. This is especially useful for junior engineers and students just learning how to code.
Alicent is a Chrome extension that can play multiple roles as a powerful AI assistant. You can generate highly specific text for a variety of use cases, such as posting on social media or creating copy for landing pages. Alicent stands out because it can scan any content and provide a customized overview of that page with the voice of its AI assistant. Retail brand marketers can simply scan their product pages to instantly generate email copies and come up with tailored descriptions for each product in their inventory.
Retail brand marketers can simply scan their product pages to instantly generate email copies and come up with tailored descriptions for each product in their inventory.
Fabric is a unified collaborative workspace and command center for all your files, resolving digital chaos and chaos. Use semantic search to go through all your files and find what you need even if you can’t remember exactly where the files are. You can also instantly save website content, make notes and share with your colleagues.
So what’s next?
The five startups above are just a few of the many that are embracing generative AI to improve employee productivity. We are automating tasks that previously took far more time and resources to accomplish. As technology matures, I envision a future where each of us will have a multitude of AI helpers in different aspects of our professional lives. This reduces the amount of time we spend on the mundane and routine tasks that we have to do, while allowing us to do work that we really enjoy and truly add value to.
Lawrence Wang ’19
Lawrence Wang ’19
Lawrence Wang (’19) is a graduate of Columbia Business School. He is the founder of Butter Data, his B2B Saas product that automates data science for non-data teams, with a mission to democratize data insights for everyone. Prior to founding the company, he led data science for commerce communities at Instagram and Facebook Shopping. Lawrence founded Butter Data with the belief that every employee should have instant access to data-driven insights to make better decisions. He believes AI will transform the way we work, enhancing and optimizing existing workflows.
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