In little more than 1,600 words voicing his scepticism over net zero policies, Tony Blair this week propelled himself and his increasingly powerful institute back into the national debate. In the past eight years, the former prime minister has built a global empire employing more than 900 people across more than 40 countries, providing policy advice to monarchs, presidents and prime ministers. But while Blair's thinktank has brought him influence in his post-Downing Street career, it has also renewed scrutiny on his political views and how they are shaped by his commercial relationships. The Labour MP James Frith said on Wednesday: "I give congratulations to the marketing department at the Tony Blair Institute (TBI), who have managed to time it brilliantly to get maximum coverage." Patrick Galey, the head of fossil fuel investigations at the nongovernmental organisation Global Witness, said: "Blair's well-documented links to petrostates and oil and gas companies ought to alone be enough to disqualify this man as an independent and reliable arbiter of what's possible or commonsense in the energy transition."

I'm a professional journalist with a deep scepticism of how emotionless AI could be used in the arts, but I decided to put my doubts to one side and see if the likes of ChatGPT and DALL-E could help me whip up some last-minute Valentine's Day inspiration. After all, isn't using robots to save our disorganised butts the real meaning of Valentine's Day? Of course it is, so I decided to see if I could get a romantic love poem and some cover art ready for a Valentine's Day card using artificial intelligence tools in the form of the ChatGPT chatbot, and the DALL-E AI art generator. And the results were... horrific. I have to start by reiterating that I have never used AI tools before today, so do bear with me if I sound a little noobish.

"I last sat down in 2005," Geoffrey Hinton often says, "and it was a mistake". Now 73, the British computer scientist has spent his entire career driving forward the field of artificial intelligence (AI), doing almost all of his work while standing due to back injuries beginning in his teens. By the time he reached his 50s, Mr Hinton's back problems were so severe that he decided to just stop sitting down. These days, when travelling by car he lies sprawled across the back seat - and he eats "like a monk at the altar" by kneeling on a foam cushion before the table. "If you let it completely control your life, it doesn't give you any problems," he told Cade Metz, a journalist for The New York Times, who has detailed his life in the book Genius Makers.

Thomas Jefferson, the American statesman and third US president, was many things (including, notoriously, a slave-owner). But whatever else he was (or wasn't), he was a firm believer in what he called the "suffrage of the people" -- what today we'd call democracy. The democracy he had in mind, of course, wasn't a truly "general suffrage" of all citizens: in its most ambitious form it enfranchised only male taxpayers and soldiers. It was also far removed from the classical ideal set by Ancient Athens, in which all eligible citizens gathered regularly to debate and settle policy. Still, even Jefferson's limited and strictly "representative" version of democracy required something vital if it was to function properly: not just an able and knowledgeable public service, but a well-informed voting public.

There has been a considerable amount of hype around Artificial Intelligence (AI) and Machine Learning (ML) technologies in the last five or so years. So much so that AI has become somewhat of a buzzword – full of ideas and promise, but something that is quite tricky to execute in practice. At present, this means that the challenge we run into with AI and ML is a healthy dose of scepticism. For example, we've seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then failing to deliver. In turn, the ongoing evolution and adoption of these technologies is consequently knocked back.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of artificial intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and causality of the models. Pearl also went on record to say that deep learning was stuck with curve fitting and called it "sacrilege". From the point of view of the mathematical hierarchy, Pearl said that no matter how well the data is manipulated, it's still a curve-fitting exercise. This a very controversial accusation coming from Pearl, who was awarded the ACM Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of artificial intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and causality of the models. I see dozens of "Data Science Institutes" erected across the country, I read their manifestos and I check their advisory boards. Causality does not seem to be on their agenda. Which makes one doubt whether the Ladder has been internalized and where this hype will end.

Artificial intelligence in cybersecurity has recently made several headlines: "The Future of Cybersecurity: Artificial Intelligence" (Cybersecurity.CIOReview), "How AI is the Future of Cybersecurity" (Infosecurity Magazine) and "AI in Cyber Security Market to Grow at an Exorbitant Pace" (P&S Market Research). These headlines make seasoned cybersecurity professionals wary. We've seen other emerging technologies receive similar attention, and we've seen many of them fail to live up to their expectations. In this article, we will build a real-world perspective on AI in cybersecurity. We will explore where scepticism regarding AI in cybersecurity is justified, how the technology can provide tangible value, and what to look for in an AI-driven cybersecurity provider.

The National Transportation Safety Board said in a preliminary report released on Thursday that the Uber SUV being tested in Tempe detected the woman and eventually concluding "an emergency braking manoeuvrer was needed to mitigate a collision". But the system couldn't activate the brakes, the NTSB said. It could be decades before fully autonomous vehicles are on the roads (cars where drivers never have to touch the wheel), but the intervening period - in which human driver intervention is required as a backup - could prove more dangerous. "We need a healthy level of scepticism for the ability of humans and machines to function together", said Mr Crist. Drivers are at risk of becoming complacent, and the few extra seconds it could take for a human to intervene and recover a situation could be deadly. The report also warns that connected autonomous vehicles - which use the internet to monitor and communicate with surrounding cars - are at risk of being hacked.

Tone of voice is key for a brand like Virgin Holidays, especially when it comes to grabbing the attention of email subscribers. Previously, the company has relied on copywriters to encapsulate its quirky and adventurous attitude. However, with email campaigns taking up extensive amounts of time and resources (with little pay-off) – the strategy was failing to work. I recently heard from Saul Lopez, Customer Lifecycle Lead at Virgin Holidays, about the brand's decision to bring AI into the mix, specifically to optimise subject lines using AI marketing technology from Phrasee. Here's more on the reasons why this approach has turned around the travel brand's email strategy, plus a few general benefits of using AI.