The usual way of evaluating prediction output is with the accuracy metric, where we indicate a match (1) or a no match (0). However, this does not provide enough granularity to effectively assess OCR performance. We should instead use error rates to determine the extent to which the OCR transcribed text and ground truth text (i.e. A common intuition is to see how many characters were misspelled. While this is correct, the actual error rate calculation is more complex than that.

New AI software developed by researchers at Flinders University shows promise for enabling timely support ahead of relapse in patients with severe mental illness. The AI2 (Actionable Intime Insights) software, developed by a team of digital health researchers at Flinders University, has undergone an eight-month trial with psychiatric patients from the Inner North Community Health Service, located in Gawler, South Australia. The digital tool is tipped to revolutionise consumer-centric timely mental health treatment provision outside hospital, with researchers labelling it as readily available and scalable. In the trial of 304 patients, the AI2 software found that 10% of them were at increased risk of not adhering to treatment plans by failing to take medication or disengaging with health services. This led to interventions which clinicians believe could have prevented the patient from relapsing and experiencing a deterioration of their mental health.

Mammograms have decreased since the pandemic, due to COVID restrictions, which means fewer people had the chance for early detection. Thankfully, artificial intelligence is helping, not just with early detection but better accuracy to reduce false positives.

Artificial intelligence will likely reshape the world in the coming decades. Autonomous machines will be able to hear, see, learn, think, and make decisions. This will drive productivity and innovation across virtually every industry, from retail and robotics to marketing and mobility. From an investor's perspective, these technologies will also create incredible wealth. In fact, research from Ark Invest indicates that AI will add $30 trillion to global equity by 2037.

Newly appointed Minister for Industry, Science and Technology, Christian Porter. The federal government has unveiled its first action plan dedicated to establishing Australia as a global leader in developing and adopting responsible artificial intelligence (AI). Industry, Science and Technology Minister Christian Porter said the benefits of AI include protecting the environment, improving health outcomes, promoting smart cities, and boosting the economy. "AI could contribute more than $20 trillion to the global economy by 2030, and the AI Action Plan will help us leverage opportunities for AI to further strengthen the economy and improve the quality of life of all Australians, while ensuring that the development and adoption of AI is guided by appropriate safeguards, privacy and ethical considerations," he said. The government allocated $124.1 million in funding through the May budget to deliver some of the plan's key measures.

GANs (Generative Adversarial Networks) are a subset of unsupervised learning models that utilize two networks along with adversarial training to output "novel" data which resembles the input data. More specifically, GANs typically involve "a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G [1]." Conditional GANs are a modification of the original GAN model, later proposed by Mehdi Mirza and Simon Osindero in the paper, "Conditional Generative Adversarial Nets" (2014). In a cGAN (conditional GAN), the discriminator is given data/label pairs instead of just data, and the generator is given a label in addition to the noise vector, indicating which class the image should belong to. The addition of labels forces the generator to learn multiple representations of different training data classes, allowing for the ability to explicitly control the output of the generator. When training the model, the label is usually combined with the data sample for both the generator and discriminator.

Cloud-based platform Dosis uses AI to help patients and clinicians tailor their medication plans. Shivrat Chhabra, CEO and co-founder, tells us how it works. When and why was Dosis founded? Divya, my co-founder and I founded Dosis in 2017 with the purpose of creating a personalised dosing platform. We see personalisation in so many aspects of our lives, but not in the amount of medication we receive.

In a world of deep fakes, facial recognition and machine learning, an ethical framework to guide the development and rollout of AI is becoming …

The second commitment, dubbed "AI use Ontarians can trust," says that risk-based rules will be implemented to ensure safe, equitable and secure use of …

Scientists from Oxford University have used a machine learning algorithm to discover every lion has its own identifiable and trackable roar. Previous research has shown that lions roar to communicate with other members of their pride and scare off foes. But we still know little about how they recognize which animal has made the call. The Oxford scientists tried to find out by designing a device called a "biologger" that's attached to a lion's existing GPS collar to record audio and movement data. They then associated each call with a lion by cross-referencing the data through their recordings of roars.