Artificial intelligence is proving to be one of the pillars on which the metaverse will be built. Starting with the processing of user-generated data, continuing with generative AI models that create photorealistic virtual environments and avatars that resemble users, as well as the ability to recognise body movements and thus make the metaverse experience more natural. But AI will also spark new life into the digital characters that populate virtual worlds, such as non-human characters and personal assistants, and enable everyone to understand each other in their own language by translating speech simultaneously. Artificial intelligence could help create increasingly engaging and user-friendly experiences to maximise activity and engagement time as is the case in social networks today, and could act as a watchdog, stopping harassment before it even happens, as long as we don't have a problem with an intrusive AI listening to all our conversations and judging our every move. One of the difficulties we have today is to define with sufficient accuracy what the metaverse is, a term that has come to the fore more for marketing reasons than anything else.

Earlier this month a report by the UN's Intergovernmental Panel on Climate Change (IPCC) scientist believe that 1.5C will be reached by 2040, 10 years earlier than previously predicted, and it may even happen earlier if emissions aren't cut down in the coming years. What does a temperature rise of 1.5C mean? These changes are inevitable according to the report after accessing all scenarios. On a less "Humanity is doomed" note, artificial intelligence has been helping the fight to mitigate these effects. An international team of researchers led by the British Antarctic Survey (BAS) and The Alan Turing Institute have created a new artificial intelligence tool called IceNet to allow for a more accurate forecast into the Arctic seas ice condition aim to mitigate the impact on arctic wildlife and coastal communities affected by sea ice loss.

Monitoring field conditions and be notifying farmers, tracking livestock, piloting agricultural drones, and directing self-driving tractors in near real-time are some of the uses of 5G in the agricultural business. Improved crop yields, better quality produce, and higher profits are some of the outcomes of 5G in the agricultural industry. For manufacturing 5G allows for advanced predictive maintenance due to the ability to connect many more devices and capture more data with the avoidance of data loss this surplus in data allows for the accurate prediction of machine failure and similarly to agriculture, 5G allows for precision monitoring and control, creating an efficient and stable industry. However, the biggest downside of 5G is the fact that its waves are intercepted very easily due to its millimeter waves as the wavelength is shorter it can carry more data but goes a shorter range meaning for reliable signals there are going to have to be 5G cells everywhere. Now that everyone knows what 5G is and its capability I think we will be able to appreciate what the use of artificial intelligence with 5G means.

GPT-3 also known as Generative Pre-Trained Transformer 3 is an advanced autoregressive language model that uses deep learning to produce human-like text. A recent experience by the medical Paris-based firm, Nabla, specialising in healthcare technology used a cloud-hosted instance of GPT-3 to analyse queries by humans and produce a suitable answer to them. The bot was designed to ease the daily workload of doctors however the unpredictable nature of the software's responses made it inappropriate for interacting with patients in the real world; this was concluded after running a series of tests.

However, some people believe that there are some negative aspects that come along with the adoption of AI in specific work environments. They think that the problems that arise are more severe than the improvements that will be made to the current'working-life': Since the data that is being handled is overly sensitive, the case of errors, faults, and failures will inevitably end up costing the bank a lot of money and time. Although AI might increase the productivity of the specific job that is being replaced, this may lead to lower morale and effort from the other employees, as they will also begin to feel'replaceable'. In turn, this will cause a depreciation in their efficiency (resulting in firing/termination) or voluntary resignation. This will place an overall burden on places such as banks, as AI can replace the tellers, CSEs, and various officers.

On September 18th The Lancet Digital Health released an article called " Artificial intelligence in COVID-19 drug repurposing" (written by Yadi Zhou, PhD, Prof Fei Wang, PhD, Prof Jian Tang, PhD, Prof Ruth Nussinov, PhD, Prof Feixiong Cheng, PhD) in the article it explained how artificial intelligence is being used for drug reproposing,which is where an already existing is drug used to fight novel diseases such as COVID-19. Artificial intelligence is being used to speed up this process, with the exponential growth in computing power, memory storage and a plethora of data its only right for the medical sector to use this to speed up a process that help fight against the worlds latest threat that is COVID-19. So how is artificial intelligence being used to speed up this process, well the article explains how artificial intelligence used for extracting hidden patterns and evidence from biomedical data, which otherwise would have been done manually saving a considerable amount of time. In connection to the medical sector, artificial intelligence in medicine may be racially biased. Just like explained above artificial intelligence has transformed the healthcare and has really cut the time on many aspects of medicine such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged in a matter of second which is just incredible, but like all good things it comes with its flaws.

Artificial intelligence over recent years has been able to revolutionise our technological capabilities, particularly when executing mindless tasks of the likes of surveillance and data collection. Many of the roles currently undertaken by AI suit the computer's linear thought process. However, since recent breakthroughs in machine learning, AI may be developing the skill that previously was considered its downfall: creativity. But can we really call AI-generated images art? In 2018, Christie's became the first auction house to sell a piece of'art' created by AI.

New and more advanced technologies such as artificial intelligence (AI) are being incorporated to improve clinical diagnostics, giving scientists and doctors an opportunity to evolve healthcare. Recently, researchers have developed an AI algorithm in order to reduce the chances of a false diagnosis, and to help identify cancer with more ease. As like how doctors and nurses must be educated in order to do their jobs, AI algorithms must be competent enough in order to mundane tasks such as image analysis and decision making. In order to do so, we must instruct the AI explicitly what they should identify in an image given to the algorithm. In order to produce a successful AI algorithm, the computer system must be inputted with data that has a label or annotation and data points that the algorithm can recognize.

The use of artificial intelligence for behavioural analysis could provide doctors with valuable insights. Studies have shown that machine-learning algorithms, trained to judge the choice of words, are better able to distinguish between real and fake suicide notes, meaning their order and order in which they pick up signs of distress. Using such a system to monitor a patient's letter could provide a way to assess their risk of self-harm. Nasrallah says many people already use wearable devices to track their sleep patterns, which are closely linked to mental well-being. Both Nasrallah and Foltz stress that AI is not meant to replace human psychiatrists or to completely reinvent the wheel.

The Princeton Plasma Physics Lab (PPPL) has also used AI in their Fusion Recurrent Neural Network which aims to predict plasma disruptions ("a fast and anomalous loss of stability that can cause severe damage to plasma facing components"). The prediction system is led by William Tang, a professor in the Department of Astrophysical Sciences at Princeton and a principal research physicist at PPPL) and is considered to be "the first machine learning disruption predictor capable of consistently outperforming, on all metrics that matter, a simple "locked-mode" based predictor." In Eurofusion's paper on their advanced disruption predictor, they explain what the locked-mode is and how it is used in prediction: "When macroscopic instabilities start locking to the wall, the amplitude of the signal used to detect them (called locked mode) grows during the slowing down of their rotation. Therefore, the locked mode amplitude is routinely used as precursor of disruptions caused by this locking of instabilities to the wall.". One company working towards making nuclear fusion commercial is TAE Technologies (formerly known as Tri-Alpha Energy) and its CEO Michl Binderbauer claims that "commercialisation is coming in the next five years".