ChatGPT is a version of GPT-3, a large language model also developed by OpenAI. Language models are a type of neural network that has been trained on lots and lots of text. Because text is made up of sequences of letters and words of varying lengths, language models require a type of neural network that can make sense of that kind of data. Recurrent neural networks, invented in the 1980s, can handle sequences of words, but they are slow to train and can forget previous words in a sequence. In 1997, computer scientists Sepp Hochreiter and Jürgen Schmidhuber fixed this by inventing LTSM (Long Short-Term Memory) networks, recurrent neural networks with special components that allowed past data in an input sequence to be retained for longer. LTSMs could handle strings of text several hundred words long, but their language skills were limited.

Microsoft just showed how artificial intelligence could find its way into many software applications--by writing code on the fly. At the Microsoft Build developer conference today, the company's chief technology officer, Kevin Scott, demonstrated an AI helper for the game Minecraft. The non-player character within the game is powered by the same machine learning technology Microsoft has been testing for auto-generating software code. The feat hints at how recent advances in AI could change personal computing in years to come by replacing interfaces that you tap, type, and click to navigate into interfaces that you simply have a conversation with. The Minecraft agent responds appropriately to typed commands by converting them into working code behind the scenes using the software API for the game.

Microsoft just showed how artificial intelligence could find its way into many software applications--by writing code on the fly. At the Microsoft Build developer conference today, the company's chief technology officer, Kevin Scott, demonstrated an AI helper for the game Minecraft. The non-player character within the game is powered by the same machine learning technology Microsoft has been testing for auto-generating software code. The feat hints at how recent advances in AI could change personal computing in years to come by replacing interfaces that you tap, type, and click to navigate into interfaces that you simply have a conversation with. The Minecraft agent responds appropriately to typed commands by converting them into working code behind the scenes using the software API for the game.

Patrick Henry Winston was, by all standards, a rock star in the field of Artificial Intelligence. In 1970, Patrick wrote his Ph.D. thesis, in which he explored -- under the improvisational supervision of his advisor, Marvin Minsky -- the theoretical difficulties of learning, and wrote in Lisp a blocks-world program that could perceive blocks and block-enabled architectures (e.g. That computer program was able to learn to generalize its existing knowledge when comparing a baseline example architecture with a new example, and specialize its existing knowledge when comparing a baseline example with a near miss. That was the first effort ever in making machines learn things in ways that resemble how humans learn things. Some say that was "real" Machine Learning, much unlike statistical Machine Learning and neural-net Machine Learning, whereby programmers would program their computers to slavishly crunch through hundreds of billions of data points, which is nothing like how people learn new things, but has become popular because the theory behind them are much more understood and much easier to implement, and because this kind of big-data crunching is practically allowed for due to the tremendous computing power that we have today.

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Last fall, Google unveiled a breakthrough artificial intelligence technology called BERT that changed the way scientists build systems that learn how people write and talk.

Artificial intelligence is a concept that seems tailor-made for the intelligence community. The ability to sort through massive amounts of data, seeking out patterns large and small, anomalies that warrant further investigation, that's what intelligence analysts do already. Imagine what they could achieve when augmented by AI? Dean Souleles, chief technology advisor for the Office of the Director of National Intelligence, said on Agency in Focus – Intelligence Community that the IC is working now to lay the foundation for adopting AI. "You cannot build a house without a solid foundation. The foundation of AI is data and computational technology," Souleles said. "The intelligence community has spent much of the last decade on a program we call ICITE, the information technology enterprise of the IC. And that's been about modernizing the technology infrastructure. And that is about getting cloud technology throughout the community, making basic computational capability available to our technologists just as it is in the private sector. But that's not good enough, because the new era of computation requires sophisticated kinds of computing. We talk about GPUs, graphical processing units, or tensor processing units (TPUs), or neuromorphic chips or field programmable gate arrays, or any of the wide variety of things that are the specialized computation that enable AI computation. And we need to make the investments in those things."

Artificial intelligence is a concept that seems tailor-made for the intelligence community. The ability to sort through massive amounts of data, seeking out patterns large and small, anomalies that warrant further investigation, that's what intelligence analysts do already. Imagine what they could achieve when augmented by AI? Dean Souleles, chief technology advisor for the Office of the Director of National Intelligence, said on Agency in Focus – Intelligence Community that the IC is working now to lay the foundation for adopting AI. "You cannot build a house without a solid foundation. The foundation of AI is data and computational technology," Souleles said. "The intelligence community has spent much of the last decade on a program we call ICITE, the information technology enterprise of the IC. And that's been about modernizing the technology infrastructure. And that is about getting cloud technology throughout the community, making basic computational capability available to our technologists just as it is in the private sector. But that's not good enough, because the new era of computation requires sophisticated kinds of computing. We talk about GPUs, graphical processing units, or tensor processing units (TPUs), or neuromorphic chips or field programmable gate arrays, or any of the wide variety of things that are the specialized computation that enable AI computation. And we need to make the investments in those things."

I always felt that our modern society was becoming more and more lackadaisical with our privacy and data. No-one really seemed to care about privacy or how their data was being used. Then came along the EU's GDPR regulation and to some degree things changed. I think the thing that changed was that we began to realise there was some value in our data, that that value could be greater to others than we thought, and actually, protecting that data wasn't the primary concern of the people we were sharing it with. But did it change our behaviour?