Google Bard Presents Timelines for Computer and AI Development

This set of questions and answers presents a historical perspective on when things happened.

My idea here is to use these data points and events in the development of a science fiction tale. But the info presented is useful for general reference.

Give me a timeline of advances in computer power from 1955 to present.

Sure, here is a timeline of advances in computer power from 1955 to present:

1955: The UNIVAC I is released. It is the first commercial computer to use vacuum tubes, and it has a speed of 1,900 operations per second.
1965: The IBM 360 is released. It is the first computer to use integrated circuits, and it has a speed of 17,000 operations per second.
1971: The Intel 4004 is released. It is the first microprocessor, and it has a speed of 740,000 operations per second.
1975: The Apple II is released. It is the first personal computer to be mass-produced, and it has a speed of 1.2 MHz.
1981: The IBM PC is released. It is the first IBM-compatible computer, and it has a speed of 4.77 MHz.
1985: The Macintosh is released. It is the first personal computer to use a graphical user interface, and it has a speed of 8 MHz.
1993: The Pentium processor is released. It is the first 32-bit processor, and it has a speed of 60 MHz.
2000: The Pentium 4 processor is released. It is the first 64-bit processor, and it has a speed of 1.4 GHz.
2006: The Intel Core 2 Duo processor is released. It is the first dual-core processor, and it has a speed of 2.13 GHz.
2011: The Intel Core i7 processor is released. It is the first quad-core processor, and it has a speed of 3.4 GHz.
2017: The Apple A11 Bionic processor is released. It is the first 7-nanometer processor, and it has a speed of 2.39 GHz.
2022: The Apple M2 processor is released. It is the first 5-nanometer processor, and it has a speed of up to 3.5 GHz.

As you can see, the pace of advancement in computer power has been exponential. In just over 60 years, we have gone from computers that could only perform a few thousand operations per second to computers that can perform billions of operations per second. And this trend is only going to continue. As we continue to develop new technologies, such as quantum computing, we can expect to see even more dramatic advances in computer power in the years to come.

Give me a similar timeline for big iron computers, mainframes please.

Sure, here is a timeline of advances in big iron computers, mainframes, from 1955 to present:

1955: The UNIVAC I is released. It is the first commercial mainframe computer, and it has a speed of 1,900 operations per second. 1964: The IBM System/360 is released. It is the first mainframe computer to use integrated circuits, and it has a speed of 17,000 operations per second. 1971: The IBM System/370 is released. It is the first mainframe computer to use multiple processors, and it has a speed of 100,000 operations per second. 1983: The IBM 3090 is released. It is the first mainframe computer to use 32-bit processors, and it has a speed of 1 million operations per second. 1990: The IBM ES/9000 is released. It is the first mainframe computer to use 64-bit processors, and it has a speed of 10 million operations per second.
2000: The IBM zSeries is released. It is the first mainframe computer to use multicore processors, and it has a speed of 100 million operations per second. 2010: The IBM zEnterprise is released. It is the first mainframe computer to use 128-bit processors, and it has a speed of 1 billion operations per second.
2020: The IBM z15 is released. It is the first mainframe computer to use 256-bit processors, and it has a speed of 10 billion operations per second.

As you can see, the pace of advancement in mainframe computer power has been just as impressive as the pace of advancement in personal computer power. In just over 60 years, we have gone from mainframe computers that could only perform a few thousand operations per second to mainframe computers that can perform billions of operations per second. And this trend is only going to continue. As we continue to develop new technologies, such as quantum computing, we can expect to see even more dramatic advances in mainframe computer power in the years to come.

It is important to note that mainframe computers are not just faster than personal computers. They are also more reliable, secure, and scalable. This is why mainframe computers are still used by many large organizations for critical applications, such as banking, insurance, and telecommunications.

Give me a timeline of ML and AI and LLM development from 1950 to present.

Sure, here is a timeline of ML and AI and LLM development from 1950 to present:

1950: Alan Turing publishes "Computing Machinery and Intelligence" which proposes a test of machine intelligence called The Imitation Game.
1952: Arthur Samuel develops a program to play checkers, which is the first to ever learn the game independently.
1957: Frank Rosenblatt introduces the perceptron, a simple neural network that can be trained to recognize patterns.
1965: Marvin Minsky and Seymour Papert publish "Perceptrons", which shows that perceptrons are limited in their ability to learn complex patterns.
1974: Geoffrey Hinton, David Rumelhart, and Ronald Williams introduce backpropagation, a method for training neural networks that is much more effective than previous methods.
1980: Tomaso Poggio and Christopher Koch propose the computational theory of visual cortex, which provides a theoretical framework for understanding how the brain processes visual information.
1986: Yann LeCun trains a convolutional neural network to recognize handwritten digits, which is a major breakthrough in the field of computer vision.
1997: IBM's Deep Blue defeats Garry Kasparov, the world chess champion, in a chess match. This is a major milestone in the field of artificial intelligence, as it shows that a computer can now beat a human at one of the most complex games in the world.
2006: Geoffrey Hinton, Ruslan Salakhutdinov, and Ilya Sutskever introduce the word2vec algorithm, which is a powerful method for representing words as vectors.
2012: Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton introduce the AlexNet neural network, which achieves state-of-the-art results on the ImageNet image classification challenge.
2017: OpenAI's GPT-2 language model is released, which is able to generate text that is indistinguishable from human-written text.
2022: Google's PaLM language model is released, which is able to perform a wide range of tasks, including answering questions, generating text, and translating languages.

As you can see, the development of ML, AI, and LLMs has been rapid in recent years. This is due to a number of factors, including the availability of large datasets, the development of new algorithms, and the increasing power of computers. It is likely that this trend will continue in the years to come, and we can expect to see even more impressive advances in ML, AI, and LLMs in the future.

Conclusion

That is the end of our conversation and its transcript for now. In this case Bard’s replies are more or less the result you’d expect from a polished search. Worthwhile tabulation I think. Hope you enjoyed it. As always Comments Criticisms and Suggestions are welcome! Thanks for reading.