In a 1987 article in the Times Book Review, Robert Solow, a Nobel-winning economist at M.I.T., commented, "You can see the computer age everywhere but in the productivity statistics." Despite massive increases in computing power and the rising popularity of personal computers, government figures showed that over-all output per worker, a key determinant of wages and living standards, had stagnated for more than a decade. The "productivity paradox," as it came to be known, persisted into the nineteen-nineties and beyond, generating a huge and inconclusive body of literature. Some economists blamed mismanagement of the new technology; others argued that computers paled in economic importance compared to older inventions such as the steam engine and electricity; still others blamed measurement errors in the data and argued that once these were corrected the paradox disappeared. Nearly forty years after Solow's article, and almost three years since OpenAI released its ChatGPT chatbot, we may be facing a new economic paradox, this one involving generative artificial intelligence.

Artificial Intelligence (AI) is reframed as a cognitive engine driving a novel productivity revolution distinct from the Industrial Revolution's physical thrust. This paper develops a theoretical framing of AI as a cognitive revolution akin to written language - a transformative augmentation of human intellect rather than another mechanized tool. We compare AI's emergence to historical leaps in information technology to show how it amplifies knowledge work. Examples from various domains demonstrate AI's impact as a driver of productivity in cognitive tasks. We adopt a multidisciplinary perspective combining computer science advances with economic insights and sociological perspectives on how AI reshapes work and society. Through conceptual frameworks, we visualize the shift from manual to cognitive productivity. Our central argument is that AI functions as an engine of cognition - comparable to how human language revolutionized knowledge - heralding a new productivity paradigm. We discuss how this revolution demands rethinking of skills, organizations, and policies. This paper, balancing academic rigor with clarity, concludes that AI's promise lies in complementing human cognitive abilities, marking a new chapter in productivity evolution.

In the early hours of April 12, 1812, a crowd of men approached Rawfolds Mill, a four-story stone building on the banks of the River Spen, in West Yorkshire. This was Brontë country--a landscape of bleak moors, steep valleys, and small towns nestled in the hollows. The men, who'd assembled on the moors hours earlier, were armed with muskets, sticks, hatchets, and heavy blacksmith's hammers. When they reached the mill, those at the front broke windows to gain entry, and some fired shots into the darkened factory. But the mill's owner, William Cartwright, had been preparing for trouble.

Humanity has entered the Fifth Industrial Revolution (IR 5.0): a new and deeper collaboration between humans and artificial intelligence across the economy. While Industry 5.0 is believed to have started in 2020, the rise of AI in recent years has pushed it into overdrive - leading experts to say it is just now'coming.' Researchers predict this new revolution will be a'sensory leap' from today's AI -- which mostly interacts with human beings via text commands -- to so-called'multimodal interaction,' which will be much more human. And some are calling the shift the'Cognitive Age.' Imagine AI-powered robots that see, hear, touch and more, pooling fresh data from across those suites of sensors to synthesize that data with the vast arrays of digital data stored elsewhere online. Brain-computer interfaces, like Elon Musk's Neuralink, will also play a role in IR 5.0.

We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment.

I took six weeks off to raise a baby and everyone decided it was the time to declare the AI revolution imminent. It's hard not to take it personally. The tick-tock of new developments, each more impressive than the last – and each arriving on the scene faster than the last – hit its apogee last week with the near-simultaneous announcement of Google's Bard and Microsoft's Bing Chat. Since then, there's been possible permutation of the discourse, from millenarian claims of an imminent AI eschaton to rejection of the entire field as glorified autocomplete. I'm not here to settle that debate.

Did you miss a session at the Data Summit? If data is the new oil of the digital economy, artificial intelligence (AI) is the steam engine. Companies that take advantage of the power of data and AI hold the key to innovation -- just as oil and steam engines fueled transportation and, ultimately, the Industrial Revolution. In 2022, data and AI have set the stage for the next chapter of the digital revolution, increasingly powering companies across the globe. How can companies ensure that responsibility and ethics are at the core of these revolutionary technologies?

The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.

Hanson: Terry, thanks so much for joining this videocast or podvideo, I don't really know what to call it. When I started trying to conceptualize what I was getting at, I wanted to talk to people who had a clear and obvious perspective on what they thought AI is. And you're particularly unique, and special in this context, because you have been consistent since… Well, there's a great book that you have a chapter in and I think Jim Anderson edited in 1981, called "Parallel Models of Associative Memory". Sejnowski: It's interesting you brought that up because I met Geoff Hinton in San Diego in 1979 at a workshop he and Jim organized that resulted in that book. It was my first neural network workshop. We were all interested the same things. There was no neural network organization or community at that time – We were a bunch of isolated researchers working on our own. Hanson: And probably not well appreciated, by talking about neural networks, or neural modelling. Sejnowski: We were the outliers. But we had a great time talking with each other. Hanson: Going back to the book, you had a chapter called skeleton filters in the brain. I think that was the name of it. Perhaps not the best title in the world, but still… "Skeleton filters" is a little scary, I gotta say. But, it was a really incredibly easy read – I just read it the other day again. And, in it, you're really going in a subtle way from biophysics, modelling a neuron and referencing everybody, you know Cowen, and everybody who'd developed a differential equation, or anything up to semantics and cognition. But biophysical modeling, this kind of category you might associate with biophysics of neural modelling, in that neurons and circuits matter and that's what we're modelling, for that purpose – that's the purpose of it. For example, I think you mentioned Hartline and Ratliff, and Limulus crab retina. And this provided an enormous amount of data well into the 60s where people were actually modelling and there were predictions and it was very tightly tied to the crab. Sejnowski: By the way, although it's called a Horseshoe Crab, and looks like one, Limulus has eight legs, so it's an arachnid.

In this special issue we are reprinting our top stories of the past year. This article first appeared online in our "Hidden Truths" issue in June, 2021. If you could soar high in the sky, as red kites often do in search of prey, and look down at the domain of all things known and yet to be known, you would see something very curious: a vast class of things that science has so far almost entirely neglected. These things are central to our understanding of physical reality, both at the everyday level and at the level of the most fundamental phenomena in physics--yet they have traditionally been regarded as impossible to incorporate into fundamental scientific explanations. They are facts not about what is--"the actual"--but about what could or could not be. In order to distinguish them from the actual, they are called counterfactuals. Suppose that some future space mission visited a remote planet in another solar system, and that they left a stainless-steel box there, containing among other things the critical edition of, say, William Blake's poems.