For the first time in history, AI is available for all. To benefit from ChatGPT and other ground-breaking tools, it's good to get hands-on and play. There is a new skill to learn - Prompt Engineering.
AI (Artificial Intelligence) as a term was coined already in the 1950s and started to gain new interest during the 1980s and 90s with neural networks and computer vision. Natural Language Processing (NLP) took focus in 2000s with models that were able to understand human language. In recent years, Large Language Models (LLMs) – transformers – have revolutionized the game by being able to consume large amounts of data without supervised learning.
OpenAI’s GPT (Generative Pre-trained Transformer) model gained significant attention as it was able to perform various natural language processing tasks, such as text generation, summarization, and question-answering. ChatGPT, using GPT model as its foundation, made the ground-breaking technology available to everyone, attracting users in record time.
“Generative AI was a user interface problem that was solved. ChatGPT gave a good user interface to an already existing service. The advantages of these tools are accessibility and ease-of-use” says Lukas Lundin, Data and AI Go-to-market Manager at Microsoft, who has a front-row seat to all the latest developments.
Listen to Data Insiders podcast episode with Lukas Lundin on Spotify.
Other recent tools from OpenAI are Codex, which can convert human language into code and transform one piece of code into another programming language and DALL-E that generates images from human language.
Lukas Lundin advises that organizations should encourage everyone to experiment with these technologies and practice prompt engineering, which is becoming an essential skill to every information worker.
You don't need to be a data scientist or know anything about machine learning. What you need to know about is prompt engineering, which is basically a fancy term for asking good questions.
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Lundin understands that organizations have concerns about AI, especially regarding security and privacy. Microsoft has recently brought GPT models to everyday productivity tools such as Word, Outlook and PowerPoint and provides access to OpenAI’s services in Azure environment. This enables companies to stay in control of their data. One solution to safeguard that the models are not abused, to generate malicious code or spam for example, is Azure Open AI Service’s content moderation filtering that runs both the input prompt (questions) and generated content through an ensemble of classification models that detect the misuse.
The explosion of pre-trained models has made companies to experiment and find ways to incorporate these technologies into their tasks and operations. Lukas Lundin sees four categories of use cases getting most traction in an enterprise space:
New use cases are emerging all the time as organizations and individuals are exploring the possibilities of AI.
This is the first time in history we can offload intelligent tasks to a computer. We had the industrial revolution where machines became autonomous and replaced a lot of manual labour. Now we will have the same happening with information work.
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Lukas Lundin is an AI enthusiast both at work and outside the office. He is using AI tools to create marketing content, write summaries and emails, but also in his free time to find new recipes and interesting sights to visit. “On a vacation in Tokyo, I visited different districts and Bing Chat worked as a travel guide during the trip,” he explains.
Looking ahead, Lundin expects that transformer models will work as gateways to different AI services.
“I think, in the future, we will have a GPT model that takes in questions and accesses various other models and resources to complete the task. It will be able to orchestrate whole pipelines,” Lundin predicts.
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