‘The development of full artificial intelligence could spell the end of the human race’, Stephen Hawkins once famously and ominously declared. Hawkins thought artificial intelligence (AI) could outcompete humanity (think Terminator). Why? Because of AI’s amazing ability to rapidly and independently adapt and redesign. Thankfully, so far this doomsday prediction has proved to be very far from reality. While the future has not been written, AI is currently making positive and impactful contributions across multiple fields to aid humans in tasks that we simply could not achieve without intelligent machine help.
This week is a celebration of our symbiotic relationship with clever AI. Machine Learning week Europe is live in Berlin from the 5th to the 7th of October 2022, with five conferences and hundreds of attendees including AI visionaries. Read on to find out how machine learning is helping to find rare earth elements, learn about intelligent programmable particles, and discover how deep learning can be used for forest fire debris analysis. So, if AI ever says to you – ‘I’ll be back’ – don’t panic! It just means that once its finished working on the current task for you it will be back to help (probably).
Subjectivity and bias in existing Forensic Science methodologies can unfortunately distort the decision-making process in determining whether ignitable liquid residue is present or not in the debris from a fire. Investigating decision theory, statistical and machine learning (ML) methodologies in evaluating evidence from fire debris is the research focus of Professor Michael E Sigman and Mary R Williams, MS, University of Central Florida. Their results demonstrate how forensic analysts could rely more on computers and ML to transition from making categorical to more nuanced statements about the value of evidence, possibly reducing flawed testimony, based only on human interpretation.
Inorganic phosphors unlocked the potential of LEDs to produce white light, revolutionising commercial lighting. Despite an enormous number of known inorganic compounds, however, finding compounds which could host rare earth elements to become new inorganic phosphors is tedious and time-consuming. Could advances in machine learning guide better material discovery? Jakoah Brgoch at the University of Houston, Texas, USA, thinks so. He and his team created a series of machine learning algorithms to identify candidate materials from a database of over 100,000 compounds.
Programmable matter can change its physical properties according to user input or autonomous sensing. Researchers are using amoebots to understand programmable matter. Dr Giovanni Viglietta, Assistant Professor at the Japan Advanced Institute of Science and Technology, has developed a mathematical technique for the self-organisation of individual particles to achieve a complex task. He demonstrates that, when properly programmed, amoebots perform tasks such as solving the fundamental shape-formation problem. Dr Viglietta is also exploring innovative approaches based on machine learning and discusses his plans to apply reinforcement learning techniques to create a more efficient and reliable algorithm.