Below is an example of an article, written under the heading "Final article." The content under the headings "Title of article", "Supporting information", "Content to mention" and "Sentences to include" is the information from which the article was based. The end of the example is marked with ###.
Format the content in the following sequence: 1.Attention - one to two sentences to grab the reader's attention with compelling content to spark curiosity and engagement. 2.Content - describe the subject of the article according to instructions above and based off of the content below. 3.Action - 100 words to inspire readers to take action. Include a compelling call-to-action on how they can participate in the AI trend.
Title of article:
Generative AI is making AI deployment easier - an exponential feedback loop
Scale AI - an AI service which builds, compares, and deploys large language model apps. Scale AI is dedicated to building the next-generation supply chain and boosting industry performance by leveraging AI technologies. Scale AI creates productivity gains across industries thanks to AI-powered supply chain optimization. Scale AI generates AI-powered supply chain intellectual property (IP) and new business opportunities. It supports a wide range of AI use cases, including autonomous vehicles, robotics, e-commerce, mapping, and natural language processing. At the core of Scale AI’s platform is a network of human annotators who work in collaboration with machine learning algorithms. They are a leading figures in automating data-related processes to improve efficiency and provide high-quality data for AI algorithms.
Content to mention:
- 'Self-replicating machines' concept applied to AI: "A self-replicating machine is a type of autonomous robot that is capable of reproducing itself autonomously using raw materials found in the environment, thus exhibiting self-replication in a way analogous to that found in nature."
- AI is providing development in 'Back-end software development' and 'Front-end software development' - there are stages of deploying an application which involve the back-end - creating the actual code, and front-end - creating a user experience for interacting with the software. AI is proving to be a helpful assistant in both spheres. In the back-end, assistants like GitHub copilot are reportedly making programming 10% to 25% faster. On the front-end, AI web developer tools are making creating user-friendly, intuitive applications of AI software more accessible to non-specialists.
Sentences to include:
- Generative AI is becoming increasingly involved in automating stages of deploying AI services, tracing a path that spectacularly resembles the Sci-Fi concept of 'self-replicating machines'. -Self-replicating machines are (by-and-large) fictional devices which can create copies of themselves by utilizing the raw resources around them. Generative AI is inching closer to embodying this make-believe.
- The implications of this parallel have started influencing the AI deployment process, rendering approaches more efficient, incisive, and downright easier because generative AI is both the creator and the implementer of AI models. -Take Scale AI, a Canadian company which boosts industry performance with AI technologies from machine vision to language processing. On one hand, they are focused on creating AI applications for you. But they also dabble in what they peddle. Their data services employ both human annotators and machine learning algorithms, a widespread practice now in the AI training industry. -Even if you're a small-scale developer working with ready-made data, generative AI is playing an increasingly important role in your business. On the back-end, AI assistants like GitHub copilot are reportedly improving coding tasks by up to 25%. Simultaneously, genAI is making front-end development of user experiences easier by the day, allowing even non-specialists to create user-friendly, intuitive applications, vastly widening the user base and application spectrum of AI -By automating steps of its own creation, generative AI is in yet another way an unprecedented phenomnen in technological evolution. It is starting to generate itself. If we learned anything from the history of life, it is that self-referential dynamics are unpredictable and richer than anything comprehensible to human imagination. As a consumer or as an AI creator, we all have a role in this exponential feedback loop. We have the power to shape it, refine it, and help it grow.
Turning to generative AI for written content is not always optimal. Always perform this cost benefits analysis:
I could have written this post with a language model. But I didn't. You're looking at that antiquated humanoid writing.
Why didn't I? The reason is that it takes time to generate text. And there are times when you lose more time relying on AI than what you stand to gain.
Consider these costs you must invest when generating written content with genAI:
- Explicitly write out the context, audience, their interests.
- Set a framework for the content, such as whether you want a catchy hook, or a call to action, or want to instill fear or hope.
- Describe the goal for the content and what you want to deliver (People often this that this is the only thing a prompt consists of. It isn't.)
- Check if the generated content is appropriate and factually correct.
When you're editing a whole book, or writing many posts to the same audience, with the same format with only slight variation in the subject matter, it sure as hell is worth to invest in a developing a stable prompt and letting an LLM take on most of the workload.
I'm not about to do you like that. I want my content to be original, factual and diverse. So I'm committed to writing it organically, a la carte, intentionally evading cookie cutter outputs.
It's important to perform this cost-benefits analysis for your written content too. How important is your personal tone for you? How much content of similar subject and tone are you writing at a time? Ultimately, will it take more time to instruct a model or write the content yourself?
Please write a LinkedIn article about [one sentence summary of future article here]. Use the example above for formatting, but do not include any information from the example above. Use the example above only as a stylistic guide and a sample for organizing the content. Below is the title of the article, marked under the heading "Title of article". Below is information that I would like to embed in the article, under the heading "Supporting information". Not all the information under the heading "Supporting information" must be integrated in the article. Only use information under the heading "Supporting information" which is directly related to the subject of the article. Embed sentences located under the heading "Sentences to include" verbatim into the LinkedIn post. Only utilize the most relevant information to the subject matter and information most easily accessible to a beginner reader who does not specialize in machine learning or AI. Be sure to explain the content for an adult beginner learner in AI. Make the content enticing and relevant for entrepreneurs, business leaders and creatives. Keep the article to a length of 250 to 500 words.