AI used to develop vaccine » Gadget Flow


The first drug developed by artificial intelligence is a vaccine for the flu. Smart Algorithms for Medical Discovery (SAM) was given information about compounds that help the immune system along with those that don't. From this input, SAM created a flu vaccine that has so far proven effective in animals.

Researchers release the first vaccine fully developed by AI program


A team of researchers at Flinders University in South Australia has created a vaccine that is considered to be the first human drug to be fully designed by artificial intelligence. Drugs have been previously designed with the help of computers. However, this vaccine was independently designed by an AI software known as SAM or Search Algorithm for Ligands. Nikolai Petrovsky, professor at Flinders University who also led the development said that its name has been derived from the task it was assigned to perform which was searching the universe for all possible compounds for a good human drug also known as a ligand. Petrovsky, also a Research Director for an Australian company, Vaxine added that the AI software was first taught about the set of compounds which activate the immune system in human beings and a set of compounds which do not.

Australian Researchers Have Just Released The World's First AI-Developed Vaccine


A team at Flinders University in South Australia has developed a new vaccine believed to be the first human drug in the world to be completely designed by artificial intelligence (AI). While drugs have been designed using computers before, this vaccine went one step further being independently created by an AI program called SAM (Search Algorithm for Ligands). Flinders University Professor Nikolai Petrovsky who led the development told Business Insider Australia its name is derived from what it was tasked to do: search the universe for all conceivable compounds to find a good human drug (also called a ligand). "We had to teach the AI program on a set of compounds that are known to activate the human immune system, and a set of compounds that don't work. The job of the AI was then to work out for itself what distinguished a drug that worked from one that doesn't," Petrovsky said, who is also the Research Director of Australian biotechnology company Vaxine.

AI-created flu vaccine starts testing in US


The flu vaccine is getting a boost from AI. The flu vaccine isn't perfect, but Australian scientists are trying to make it work better. Researchers at Flinders University in South Australia have developed a way to use artificial intelligence to create a "turbocharged" flu vaccine. A vaccine created with the computer program -- Smart Algorithms for Medical Discovery, or Sam for short -- started clinical trials in the US about a week ago, Flinders University Professor Nikolai Petrovsky said in an email to CNET. Petrovsky told the Australian Broadcasting Corporation that Sam can be trained and can then learn to create new drugs.

Full text of the G20 Osaka leaders' declaration

The Japan Times

We will work together to foster global economic growth, while harnessing the power of technological innovation, in particular digitalization, and its application for the benefit of all. We are resolved to build a society capable of seizing opportunities, and tackling economic, social and environmental challenges, presented today and in the future, including those of demographic change. This recovery is supported by the continuation of accommodative financial conditions and stimulus measures taking effect in some countries. However, growth remains low and risks remain tilted to the downside. Most importantly, trade and geopolitical tensions have intensified. We will continue to address these risks, and stand ready to take further action. Fiscal policy should be flexible and growth-friendly while rebuilding buffers where needed and ensuring debt as a share of GDP is on a sustainable path. Monetary policy will continue to support economic activity and ensure price stability, consistent with central banks' mandates. Central bank decisions need to remain well communicated.

Neural network vaccinations protect against hacking


A programming technique that works on the same principle as disease-preventing vaccinations could safeguard machine learning systems from malicious cyber-attacks. The technique was developed by the digital specialist arm of Australia's national science agency, the CSIRO, and presented recently at an international conference on machine learning, held in Long Beach, California, US. Machine learning systems, or neural networks, are becoming increasingly prevalent in modern society, where they are pressed into service across a wide range of areas, including traffic management, medical diagnosis, and agriculture. They are also critical components in autonomous vehicles. They operate from an initial training phase, in which they are fed tens of thousands of possible iterations of a given task.

Researchers develop 'vaccine' against attacks on machine learning


Algorithms'learn' from the data they are trained on to create a machine learning model that can perform a given task effectively without needing specific instructions, such as making predictions or accurately classifying images and emails. These techniques are already used widely, for example to identify spam emails, diagnose diseases from X-rays, predict crop yields and will soon drive our cars. While the technology holds enormous potential to positively transform our world, artificial intelligence and machine learning are vulnerable to adversarial attacks, a technique employed to fool machine learning models through the input of malicious data causing them to malfunction. Dr Richard Nock, machine learning group leader at CSIRO's Data61 said that by adding a layer of noise (i.e. an adversary) over an image, attackers can deceive machine learning models into misclassifying the image. "Adversarial attacks have proven capable of tricking a machine learning model into incorrectly labelling a traffic stop sign as speed sign, which could have disastrous effects in the real world. "Our new techniques prevent adversarial attacks using a process similar to vaccination," Dr Nock said. "We implement a weak version of an adversary, such as small modifications or distortion to a collection of images, to create a more'difficult' training data set.

Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data Machine Learning

Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of cases and of the mortality occurs in sub-Saharan Africa, mostly among children. Although malaria prediction systems are central to the 2016-2030 malaria Global Technical Strategy, currently these are inadequate at capturing and estimating the burden of disease in highly endemic countries. We developed and validated a computational system that exploits the predictive power of current Machine Learning approaches on 22-years of prospective data from the high-transmission holoendemic malaria urban-densely-populated sub-Saharan West-Africa metropolis of Ibadan. Our dataset of >9x104 screened study participants attending our clinical and community services from 1996 to 2017 contains monthly prevalence, temporal, environmental and host features. Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0.1 to -0.05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting. LEMPS is well-suited for malaria prediction, where there are multiple features which are correlated with one another, and trading-off between regularization-strength L1-norm and L2-norm allows the system to retain stability. Data-driven systems are critical for regionally-adaptable surveillance, management of control strategies and resource allocation across stretched healthcare systems.

Learning When-to-Treat Policies Machine Learning

Any solution to the "policy learning" problem needs to deal with numerous difficulties, including how to incorporate robustness to potential selection bias as well as fairness constraints articulated by stakeholders, and there have been several notable advances that address these difficulties over the past few years. One limitation of this line of work, however, is that the results cited above all focus on a static setting where a decision-maker only sees each subject once and immediately decides how to treat the subject. In contrast, many problems of applied interest involve a dynamic component whereby the decision-maker makes a series of decisions based on time-varying covariates. In medicine, if a patient has a disease for which all known cures are invasive and have serious side effects, their doctor may choose to monitor disease progression for some time before prescribing one of these invasive treatments. Meanwhile, a health inspector needs to not only choose which restaurants to inspect, but also when to carry out these inspections.

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis


The Global Technical Strategy for Malaria Elimination 2016–2030 [1] recommends that countries should integrate effective surveillance as a core intervention in their malaria policies. As such, the World Health Organization (WHO) recently provided guidelines to support measurements of the most important parasitological and entomological indicators [2]. Effective entomological surveillance requires detailed quantitative understanding of key biological attributes which influence overall potential of vector populations to transmit Plasmodium to humans [3]. Such attributes may include the likelihood with which specific Anopheles populations bite humans as opposed to the other available vertebrate hosts, i.e. the human blood indices (HBI), defined as proportion of all mosquito blood meals obtained from humans [4, 5]. Other attributes include parasite infection rates, i.e. the proportion of females infected with Plasmodium [6], survivorship, i.e. whether the mosquitoes can live long enough to allow complete sporogonic development of Plasmodium inside them [7], mosquito susceptibility to insecticides commonly used to control them [8], and the location of mosquito biting, i.e. indoors or outdoors, and how it overlaps in space and time with humans [9–12].