In a previous article, I discussed my long-term plan to learn more about machine learning, starting with the Elements of AI courses. While I'm only at the beginning of this journey, what I've learned so far has been very enlightening. It's tempting to see systems like Tesla's Full Self Driving (FSD) beta as a child that is learning by doing while we supervise and keep things safe. Eventually, we think, the "child" will grow up and be like us, and then maybe even be better at human driving than humans. After studying the basics more, it's clear that this isn't what machine learning does.

If you are over 30 years of age, you have witnessed a technology revolution that has grossly affected how we live: computers have come from being an oddity to an everyday feature in our households and places of work; the cellphone is ubiquitous; hardcopy letters by mail are rare as we can communicate instantaneously through email. And yet, what we have witnessed is only the beginning. Drs Hughes and Agrawal give us a glimpse of what is still to come, and what will be featured at the IFOS World Congress in Vancouver. Over the last five years there have been significant advances in high performance computing that have led to enormous scientific breakthroughs in the field of machine learning (a form of artificial intelligence), especially with regard to image processing and data analysis. These breakthroughs now affect multiple aspects of our lives, from the way our phone sorts and recognises photographs, to automated translation and transcription services, and have the potential to revolutionise the practice of medicine.

OCCRP – As new technologies offer a world of opportunities and benefits in many sectors, so too do they offer new avenues and for organized crime. It was true at the advent of the internet, and it's true for the growing field of artificial intelligence and machine learning, according to a new joint report by Europol and the United Nations Interregional Crime and Justice Research Center. At its simplest, artificial intelligences are human designed systems that, within a defined set of rules can absorb data, recognize patterns, and duplicate or alter them. In effect they are "learning" so that they can automate more and more complex tasks which in the past required human input. However, "the promise of more efficient automation and autonomy is inseparable from the different schemes that malicious actors are capable of," the document warned.

Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.

Having risen to fame on its superhuman performance at playing games, the artificial intelligence group DeepMind has cracked a serious scientific problem that has stumped researchers for half a century. With its latest AI program, AlphaFold, the company and research laboratory showed it can predict how proteins fold into 3D shapes, a fiendishly complex process that is fundamental to understanding the biological machinery of life. Independent scientists said the breakthrough would help researchers tease apart the mechanisms that drive some diseases and pave the way for designer medicines, more nutritious crops and "green enzymes" that can break down plastic pollution. DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease. "It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive.

Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed. AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat detection, online signals to system assurance, PNNL is advancing the frontiers of scientific research and national security by applying AI to scientific problems. For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. PNNL's AI research has been applied across a variety of domain areas from national security, to the electric grid and Earth systems.

DeepMind, the AI technology company that's part of Google parent Alphabet, has achieved a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold system has officially solved a protein folding grand challenge that has flummoxed the scientific community for 50 years. The advance inn DeepMind's AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development. The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days – a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste. You may have heard of'Folding@Home,' the program that allows people to contribute their own home computing (and formerly, game console) processing power to protein folding experiments. That massive global crowdsourcing effort was necessary because using traditional methods, portion folding prediction takes years and is extremely expensive in terms of straight cost, and computing resources.

The novel coronavirus 2019 (COVID-2019) first appeared in Wuhan city of China in December 2019. It spread rapidly all around the globe to be finally declared a global pandemic by the World Health organization on the 11th of March 2020. Covid-19 has resulted in countless numbers of lives lost, ruining multiple businesses all over the world and disrupting the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this pandemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available.

Following the revolutions in military affairs brought about by gunpowder and nuclear weapons, we find ourselves once again at the dawn of a new era of warfare: The Age of Autonomous Systems. Using cutting-edge technologies for military purposes, especially from the field of Artificial Intelligence, will radically transform how wars will be fought in the near future. LAWS (Lethal Autonomous Weapon Systems) is a critical acronym to understand warfare in the 21st century. LAWS encompass any weapon system with autonomy in its critical functions, namely one which can select (i.e., search for or detect, identify, track, and select) and attack (i.e., use force against, neutralise, damage or destroy) targets without human intervention[1]. While technically accurate, 'LAWS' is admittedly a less emphatic term than that used by a global coalition of Human Rights Watch-coordinated non-governmental organisations formed in October 2012 who are working to fully ban LAWS -- or as they call them, 'Killer Robots'.

DeepMind, the AI technology company that's part of Google parent Alphabet, has achieved a significant breakthrough in AI-based protein structure prediction. The company announced today that its AlphaFold system has officially solved a protein folding grand challenge that has flummoxed the scientific community for 50 years. The advance inn DeepMind's AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development. The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days – a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste. You may have heard of'Folding@Home,' the program that allows people to contribute their own home computing (and formerly, game console) processing power to protein folding experiments. That massive global crowdsourcing effort was necessary because using traditional methods, portion folding prediction takes years and is extremely expensive in terms of straight cost, and computing resources.