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.

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 that are 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.

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. Many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

Collaborative filtering, as defined by Wikipedia, is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. These predictions are specific to the user but use information gleaned from many users.

This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes. Collaborative filtering algorithms often require (1) users' active participation, (2) an easy way to represent users' interests, and (3) algorithms that are able to match people with similar interests.

A key problem of collaborative filtering is how to combine and weight the preferences of user neighbors. Sometimes, users can immediately rate the recommended items. As a result, the system gains an increasingly accurate representation of user preferences over time. In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about challenges in the performance of the recommendation.

One typical problem caused by the data sparsity is the cold start problem. As collaborative filtering methods recommend items based on users' past preferences, new users will need to rate a sufficient number of items to enable the system to capture their preferences accurately and thus provide reliable recommendations.

Looking at the brief history of artificial intelligence and the concept of collaborative filtering, it is evident that AI systems have come a long way in a relatively short period. The advancements in training computation, algorithms, and input data have propelled AI to surpass human capabilities in various domains. As we move forward, the development of transformative AI and human-level artificial intelligence seems promising.

To leverage the power of collaborative filtering, here are three actionable pieces of advice:

1. Encourage active user participation: To enhance the accuracy of collaborative filtering recommendations, it is crucial to engage users actively. Encourage them to provide their preferences and opinions to create a rich dataset for the system to learn from.

2. Develop efficient algorithms: As the user-item matrix used for collaborative filtering can be large and sparse, it is essential to develop algorithms that can handle such data sparsity. Invest in research and development to optimize recommendation systems' performance and overcome challenges posed by large datasets.

3. Address the cold start problem: New users often face difficulties in receiving accurate recommendations due to the lack of past preferences. By implementing strategies to prompt new users to rate a sufficient number of items, the system can quickly capture their preferences and provide reliable recommendations.

In conclusion, artificial intelligence has evolved rapidly, showcasing its potential to outperform humans in various tasks. The combination of advancements in training computation, algorithms, and input data has paved the way for transformative AI in the near future. Collaborative filtering offers a powerful approach to personalized recommendations, but it requires active user participation, efficient algorithms, and solutions to the cold start problem. By addressing these considerations, we can further enhance the capabilities and benefits of AI systems.