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.

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.

Moving on to another topic, let's explore the story of Goodreads. Goodreads was founded in December 2006 and launched in January 2007 by Otis Chandler and Elizabeth Khuri Chandler. In December 2007, the site already had 650,000 members and 10,000,000 books had been added. This growth was remarkable, considering the short time span. By July 2012, the site reported 10 million members, 20 million monthly visits, and thirty employees. Fast forward to July 2019, the site had a staggering 90 million members. Goodreads addressed what publishers call the "discoverability problem" by guiding consumers in the digital age to find books they might want to read. The theory behind Goodreads is that people will put more faith in book recommendations from a social network they build themselves.

During its first year of business, the company was run without any formal funding. However, in December 2007, the site received funding estimated at $750,000 from angel investors. This funding lasted Goodreads until 2009 when they received two million dollars from True Ventures. The success of Goodreads can be attributed to its unique rating system. After a user has rated 20 books on its five-star scale, the site begins making recommendations. Otis Chandler believed this rating system would be superior to Amazon's as it includes books a user has actually engaged with, rather than just browsing or purchasing them as gifts. This personalized approach to recommendations has proven to be effective, as users trust the recommendations made by a social network they have built themselves.

In addition to its recommendation system, Goodreads also facilitates reader interactions with authors through interviews, giveaways, authors' blogs, and profile information. The platform even has a special section dedicated to authors, providing them with suggestions for promoting their works on Goodreads.com. This feature is aimed at helping authors reach their target audience effectively. By 2011, "seventeen thousand authors, including James Patterson and Margaret Atwood" used Goodreads to advertise their books.

As we look at the history of artificial intelligence and the success story of Goodreads, we can identify some common points. Both AI systems and Goodreads rely on data and algorithms to provide value to their users. In the case of AI systems, the training computation, algorithms, and input data contribute to their capabilities. Similarly, Goodreads leverages data from its users' ratings and engagement with books to offer personalized recommendations. Both AI systems and Goodreads understand the importance of solid data in providing accurate and valuable results.

Furthermore, the exponential growth of AI capabilities, as seen in the doubling of training computation every 6 months, mirrors the growth of Goodreads' user base. From 650,000 members in its first year to a staggering 90 million members in just 12.5 years, Goodreads has witnessed remarkable growth. This growth can be attributed to its unique approach to book recommendations and its ability to address the discoverability problem faced by publishers.

Looking ahead, it is interesting to consider what might be next for both artificial intelligence and platforms like Goodreads. With the potential development of human-level artificial intelligence within the next few decades, we may see even more advancements in AI systems that can outperform humans in various domains. As for Goodreads, it will continue to evolve and adapt to the changing needs of readers and authors alike. There may be innovations in the recommendation system, further enhancing the personalized experience for users. Additionally, Goodreads could potentially expand its offerings to incorporate other forms of media, such as movies or music, further enhancing the discoverability aspect.

In conclusion, the brief history of artificial intelligence and the success story of Goodreads provide valuable insights into the power of data, algorithms, and user engagement. Both AI systems and Goodreads have leveraged these factors to deliver meaningful and personalized results to their users. As we move forward, it is essential to continue exploring the potential of AI and platforms like Goodreads to enhance our daily lives. By understanding the common points between these two domains, we can identify actionable advice:

• 1. Embrace the power of data: Whether you're working with AI systems or running a platform like Goodreads, solid data is crucial. Invest in data collection and analysis to gain meaningful insights and provide accurate results.

• 2. Prioritize user engagement: Both AI systems and Goodreads thrive on user engagement. Encourage active participation from your users and provide personalized experiences to keep them coming back for more.

• 3. Continuously innovate: The world is evolving rapidly, and it's crucial to stay ahead of the curve. Embrace new technologies, explore novel approaches, and adapt to the changing needs of your users to remain relevant and successful.

By following these actionable advice, you can unlock the potential of AI and user-driven platforms, leading to exciting developments and advancements in various fields.