The brief history of artificial intelligence: The world has changed fast – what might be next? Just 10 years ago, no machine could reliably provide language or image recognition at a human level. But, as the chart shows, AI systems have become steadily more capable and are now beating humans in tests in all these domains.

The brief history of artificial intelligence: The world has changed fast – what might be next? Just 10 years ago, no machine could reliably provide language or image recognition at a human level. But, as the chart shows, AI systems have become steadily more capable and are now beating humans in tests in all these domains.

Training computation is measured in floating point operations, or FLOP for short. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers. All AI systems that rely on machine learning need to be trained, and in these systems training computation is one of the three fundamental factors driving the capabilities of the system. The other two factors are the algorithms and the input data used for the training.

For the first six decades, training computation increased in line with Moore’s Law, doubling roughly every 20 months. Since about 2010, this exponential growth has sped up further, to a doubling time of just about 6 months. This rapid growth in training computation has played a significant role in the advancements of AI systems.

In her latest update, Cotra estimated a 50% probability that such “transformative AI” will be developed by the year 2040, less than two decades from now. This prediction aligns with the beliefs of many AI experts who also believe that there is a real chance that human-level artificial intelligence will be developed within the next few decades. Some even speculate that it will exist much sooner.

Moving on to another topic, let's discuss "Daily Active Users (DAU) vs. Monthly Active Users (MAU) in SaaS." DAU refers to the total number of unique users on a given day, considering both new users and existing users who have logged in or taken an action within the app. On the other hand, "monthly active users" represent the number of people who have opened and engaged with the app in the past month.

The DAU/MAU ratio is essentially a measure of "stickiness" or frequent engagement with the product by returning users. It indicates how often users return to engage with the app within a given time frame. For example, if the DAU/MAU ratio is 10%, it means that customers return 3 out of every 30 days to engage with the app.

The average ratio for SaaS companies typically falls between 10-20%, indicating a moderate level of stickiness. However, what is considered "good" in terms of the percentage depends on various factors such as the app medium and whether the app is free or paid.

Now that we have discussed both artificial intelligence and SaaS metrics, we can draw some common points between the two. Both fields have experienced significant advancements in recent years. AI systems have become increasingly capable, surpassing human performance in various domains. Similarly, SaaS companies strive to improve user engagement and stickiness by analyzing metrics such as DAU and MAU.

Furthermore, there is a parallel between the exponential growth in training computation for AI systems and the need for SaaS companies to track and improve their DAU/MAU ratio. Both factors play a crucial role in driving the capabilities and success of their respective domains.

In conclusion, the world of artificial intelligence has witnessed remarkable progress, with AI systems surpassing human performance in numerous domains. The future holds the possibility of transformative AI and human-level artificial intelligence within the next few decades. Additionally, SaaS companies focus on metrics such as DAU and MAU to measure user engagement and improve stickiness.

To capitalize on these advancements and metrics, here are three actionable pieces of advice:

1. Embrace AI: Stay updated with the latest developments in artificial intelligence and explore how it can benefit your business. Consider incorporating AI-driven solutions to enhance your products or services.

2. Optimize User Engagement: Analyze your DAU/MAU ratio and identify areas for improvement. Implement strategies to increase user engagement, such as personalized experiences, regular updates, and valuable content.

3. Foster Innovation: Encourage a culture of innovation within your organization. Stay open to new ideas and technologies that can drive growth and improve your product offerings. Foster collaboration between different teams to leverage their expertise and create groundbreaking solutions.

By staying informed, focusing on user engagement, and fostering innovation, businesses can navigate the evolving landscape of AI and SaaS to thrive in the future.