I'm part-way through writing a book about the history of fake news, so I'm well aware that people making stuff up is not new. But what is new is the reach that troublemakers have, whether their actions are deliberate or accidental. Social media and the wider web changed the game for mischief-makers, and made it easier for the rest of us to be inadvertently hoodwinked online (see: the odd "Goodbye Meta AI" trend that I wrote about this week for the Guardian). The rise of generative AI since the release of ChatGPT in 2022 has also supercharged the risks. While early research suggests our biggest fears about the impact of AI-generated deepfakes on elections are unfounded, the overall information environment is a puzzling one.

We have made it – almost – through another year without being churned into paste by a super-intelligent AI, conscripted into a Martian work camp by an insane billionaire or forced offline by a Carrington event. Even in the absence of civilisation-altering events it's been a busy year. But the advantage of a slow week (I hope that isn't tempting fate) is that you can reflect on the past 12 months and realise that, sometimes, there's only a few stories that really matter. The Guardian has confirmed it was hit by a ransomware attack in December and that the personal data of UK staff members has been accessed in the incident. "We believe this was a criminal ransomware attack, and not the specific targeting of the Guardian as a media organisation," said Guardian Media Group's chief executive, Anna Bateson and the Guardian's editor-in-chief, Katharine Viner.

Hey guys! this is my first blog on the History of Deep Learning it is very interesting please read it. It all started in the 1960s after the world war countries are spending there more money on research and US was the main contender for that, at that time in the US there was lots of research happening in the field of Computer Science and Space. There was this guy called Frank Rosenblatt this guy conceptualize this thing called Perceptron. He said that I have invented something that can learn and it is very close to our actual human neuron and this was a big thing back then because media play a big role they promote perceptron on a large scale and this is the starting of A.I. At that time Frank Rosenblatt made a statement about perceptron that caused a heated controversy Rosenblatt's statement, The New York Time reported the perceptron to be " the embryo of an electronic computer that [the navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence" this was huge there was a lot of research and projects happening on perceptron.

One of the most controversial and widely discussed topics of all time… Artificial Intelligence. From film to books to blogs, the benefits and dangers of AI is debated amongst academics and common people alike. When we think of AI, we think of a robot with the ability to think, talk, and interact like a human, resulting in robots cognizant enough to take over earth and destroy the human race. Fictional AI systems like JARVIS from the Iron Man and Avengers movies and Agent Smith in the Matrix movies are what most think of when they are trying to narrow down an image of Artificial Intelligence, but AI does not always have to become human-like. In fact, current AI programs are not even developed enough to parallel the fictional JARVIS. Much of the development of AI systems are for cybersecurity purposes, detection of cyber threats and split second responses are the ultimate goal for cybersecurity AI but this amount of efficiency is quite distant.

Ten years ago, IBM made a gamble. Through a monumental advertising and PR campaign, it promised that its AI technology–Watson–would transform the health care industry as we know it. A decade and billions of dollars later, Watson Health is being sold for parts. What went wrong with IBM's "moonshot?" And what does Watson's failure tell us about the promise of AI for health care?

Tbtech looks at how technology has restructured the relationship between patients, healthcare providers, and the health system. Click here to listen to the podcast'The digital transformation of healthcare' While many industries worldwide have discussed digitization for many years, the Covid-19 pandemic forced the healthcare system to change rapidly. As mentioned by Satpal Biant, Head of Public Sector, UK & Ireland SAP, "the transformation that has taken place since the start of the pandemic has ingrained digitization into the fabric of our society for both business and pleasure." Within the UK, the NHS has changed quickly over the last few years by introducing various digital systems, which have provided patients with a modern experience and a centralized place where doctors can see all patient information. Telemedicine, artificial intelligence-enabled medical devices, and blockchain electronic health records are just a few concrete examples of digital transformation in healthcare.

Firstly: what is a sunspot? Sunspots are a temporary phenomena on the Sun's photosphere that appear darker than the surrounding areas. The reason why I have selected the sunspots dataset for time series analysis is sunspots appear on an 11-year solar cycle, meaning we should expect to see a seasonality component to the data. I will be modelling the seasonality trend using two different methods, the ARMA model and LSTM model. The data that will be used is from 1749 to 2013 and is the monthly average at each month.

Renew Democracy Initiative chairman, chess grandmaster and political activist Garry Kasparov discusses losing a chess match to a computer and the implications AI has for the future. The man widely considered to be one of the greatest chess players of all time said humans shouldn't fear artificial intelligence. Garry Kasparov told FOX Business' Gerry Baker on "WSJ at Large," those who are warning AI will replace us are just wrong. "I'm really concerned about the doomsayers, all these doomsayers that are trying to terrorize our minds," he said. "And maybe we should stop watching too many Hollywood movies because the future is for our making." "I'm arguing that we have to work with machines, and there's the endless opportunities that will actually bring more benefits than problems, as it's happened many times before."

It's tough to do much of anything involving technology these days without running into a virtual assistant. Pick up your Android phone or Chromebook, and there's Google Assistant waiting for a chat. Apple's got Siri, poor Samsung's got Bixby, and even random companies like Bank of America are getting in on the action with their own woefully unnecessary A.I. personalities (sorry, "Erica"). We've talked plenty about the reasons why everyone and their mother wants you to get friendly with their flavor of robot aid -- and why that, in turn, has led to what I call the post-OS era, in which a device's operating system is less important than the virtual assistant threaded throughout it. It's no coincidence that Google is slowly expanding Assistant into a platform of its own, and what we're seeing now is almost certainly just the tip of the iceberg.

In September 2017, IBM announced a ten-year partnership with MIT, based around a $10 million investment into artificial intelligence research. Scientists, professors, and students working in the field were encouraged to submit their ideas for new avenues of study. A.I. is rapidly becoming a major focus of modern technology, and its importance to our everyday lives will only increase over the next few years. IBM and MIT both want to be on the forefront of the latest developments – but this partnership goes far beyond the technology itself. The lab will consider the societal impact of A.I. alongside its efforts to push the technology forward.