Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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AI has made it much cheaper to build new software; you can iterate much faster to test improvements for your users. However, as your product matures, you can expect it will be harder to see changes that drive a significant lift to your north-star metrics.
The cost of measuring your success with AI based on metrics like the number of lines of code generated comes with a greater risk of false positives in your institutional knowledge about what matters for users of your AI app.
As AI evolves out of the labs and into production applications, evaluations have matured beyond the academic benchmarks to optimize models for user experience more directly. Engineers must consider how their maintenance efforts will scale to keep up the exponential increase in candidate features they're exploring as they efficiently filter out those that cannot be associated with a significant improvement for users and businesses.
Drawing the wrong conclusions about what is key to your AI app can hurt the evolution of your product for many experiment sprints to come. Protecting your knowledge discovery is the most valuable thing you can do with your AI initiative.
AI flexibly integrates the most relevant context to model high-quality reasoning traces. By understanding how updates in your AI app affect your business KPIs and learning from successes and failures, AI can assist in designing and implementing future experiments.
In the earliest stages of a company's maturity, the most valuable interactions with AI might occur in an IDE. However, even as pipelines stabilize and services scale, AI will be helping teams collaborate to form a consensus around the next great idea.
The benefits of fostering a culture of experimentation are compounding for early adopters as it becomes cheaper to iterate. Big tech is already flying in this regard, running 200K experiments each year!
LLMs with tools and agents have been great for quick one-off tasks. But like that online GIF converter you visit to update your slides, there's no reason to return tomorrow. When was the last time you went back through your chatGPT history?
Experimentation helps you overcome the illusion that data and model artifacts offer a moat. Models come and go, so if you're trying to build a defensible AI product, invest in ways to develop knowledge that transcends the details of the currently en-vogue weights. Invest in a culture of experimentation.
Agile AI engineering with an integrated development and experiment platform.
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