It's been more than 50 years since all four members of The Beatles released music, with their emotional classic'The End'. But despite just two members remaining - Sir Paul McCartney and Ringo Starr - the band is preparing for a comeback. McCartney has revealed that he is using artificial intelligence (AI) to'extricate' John Lennon's voice from an old demo to create the'final Beatles record.' While he's yet to name the song, it's likely to be a 1978 Lennon composition called Now And Then, according to the BBC. Speaking to Radio 4's Today programme, McCartney explained: 'We just finished it up and it'll be released this year.'

Artificial intelligence (AI) has proved a controversial matter in the music industry – resulting in legal tussles, job losses and a decline in musical quality. However, British pop icons the Pet Shop Boys argue that the technology can be used in a positive way in the creative process. The group's singer, Neil Tennant, said AI could'fill in the blanks' if a song has been left unfinished, such as when the composer is suffering from writer's block. Tennant and his bandmate Chris Lowe said they are looking at new technology as they prepare their'Dreamworld' greatest hits tour in Europe this summer. 'There's a song that we wrote a chorus for in 2003 and we never finished because I couldn't think of anything for the verses,' Tennant told the Radio Times.

At a Labour party conference-adjacent event in September, The World Transformed, Jeremy Corbyn was pictured waving an arm in front of an arcade cabinet bearing the words Thatcher's Techbase. The game – a modified version of 1994's famous infernal shooter, Doom II – sees players hunting down a resurrected, cyborg version of the former prime minister in a labyrinthine fortress. The images kicked off a minor media storm. "Pictured: Jeremy Corbyn plays video game that lets players kill Margaret Thatcher," said The Telegraph; the photos were featured in the Daily Mail, the Express and the Times. They even appeared on Have I Got News for You. Jim Purvis, the game's creator – who took and later tweeted the photos – was somewhat surprised.

The thrust of nuclear physics is studying the universe down to its smallest subatomic parts. Now, two physicists at the Department of Energy's Thomas Jefferson National Accelerator Facility have secured more than $2 million in federal funding dedicated to research projects that harness the power of data analytics to make that work faster and more efficient. David Lawrence and Chris Tennant are among 14 scientists at seven DOE national laboratories whose proposals were awarded a total of $37 million to be allocated over three years. "Artificial Intelligence and machine learning have the potential to transform a host of scientific disciplines and to revolutionize experimentation and operations at user facilities in the coming years," Chris Fall, director of DOE's Office of Science, said in announcing the funding. "These awards will help ensure America remains on the cutting edge of these critical technologies for science."

Automated theorem proving has long been a key task of artificial intelligence. Proofs form the bedrock of rigorous scientific inquiry. Many tools for both partially and fully automating their derivations have been developed over the last half a century. Some examples of state-of-the-art provers are E (Schulz, 2013), VAMPIRE (Kov\'acs & Voronkov, 2013), and Prover9 (McCune, 2005-2010). Newer theorem provers, such as E, use superposition calculus in place of more traditional resolution and tableau based methods. There have also been a number of past attempts to apply machine learning methods to guiding proof search. Suttner & Ertel proposed a multilayer-perceptron based method using hand-engineered features as far back as 1990; Urban et al (2011) apply machine learning to tableau calculus; and Loos et al (2017) recently proposed a method for guiding the E theorem prover using deep nerual networks. All of this prior work, however, has one common limitation: they all rely on the axioms of classical first-order logic. Very little attention has been paid to automated theorem proving for non-classical logics. One of the only recent examples is McLaughlin & Pfenning (2008) who applied the polarized inverse method to intuitionistic propositional logic. The literature is otherwise mostly silent. This is truly unfortunate, as there are many reasons to desire non-classical proofs over classical. Constructive/intuitionistic proofs should be of particular interest to computer scientists thanks to the well-known Curry-Howard correspondence (Howard, 1980) which tells us that all terminating programs correspond to a proof in intuitionistic logic and vice versa. This work explores using Q-learning (Watkins, 1989) to inform proof search for a specific system called non-classical logic called Core Logic (Tennant, 2017).