Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.

These and many other insights are from Capgemini's Reinventing Cybersecurity with Artificial Intelligence Report published this week. Capgemini Research Institute surveyed 850 senior executives from seven industries, including consumer products, retail, banking, insurance, automotive, utilities, and telecom. Enterprises headquartered in France, Germany, the UK, the US, Australia, the Netherlands, India, Italy, Spain, and Sweden are included in the report. Please see page 21 of the report for a description of the methodology. Capgemini found that as digital businesses grow, their risk of cyberattacks exponentially increases.

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.

The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correctly predicting all 4 types and $0.86$ for correctly predicting at least 2 types. Furthermore, we investigate the possible uses of a fine-tuned BERT model for personality-specific language generation. This is a task essential for both modern psychology and for intelligent empathetic systems.

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.

Dozens of databases of people's faces are being compiled without their knowledge by companies and researchers, with many of the images then being shared around the world, in what has become a vast ecosystem fueling the spread of facial recognition technology. The databases are pulled together with images from social networks, photo websites, dating services like OkCupid and cameras placed in restaurants and on college quads. While there is no precise count of the data sets, privacy activists have pinpointed repositories that were built by Microsoft, Stanford University and others, with one holding over 10 million images while another had more than two million. The face compilations are being driven by the race to create leading-edge facial recognition systems. This technology learns how to identify people by analyzing as many digital pictures as possible using "neural networks," which are complex mathematical systems that require vast amounts of data to build pattern recognition.

The current crop also includes applications of on-demand manufacturing, augmented reality, music-assisted learning, interactive video, and online music creation. The Aussie team got the nod for their knack at using AI to separate, classify and up-res audio "for the purpose of audio search, discovery, recommendation, personalization, and quality enhancement," which works with studio, UGC audio and, maybe, live mobile communication, a statement from Techstars Music reads. Software engineer and chief of Mawson and Popgun Stephen Phillips paid tribute to his fellow Brisbanites with a tweet. Two more AI startups from Mawson are ready for take off. Both Replica and SuperRes have joined the Techstars Music 2019 program in LA. White says she's "excited and super grateful" to be a member of Techstars' 2019 class of music-based startups.

LG Electronics has tapped Dr. Darin Graham as the head of the LG Artificial Intelligence (AI) Lab in Toronto. Graham is an expert on AI networks and will be responsible for strengthening LG's North American AI capacity through strategic partnerships with industry and academia in Canada. Previously, Graham helped establish founding operations of the Vector Institute, a world-renowned artificial intelligence research organization. Further, he brings over 20 years of experience in leading innovative projects that bring creative ideas to the market. Graham has led several international research and development programs and was pivotal in building Canada's AI ecosystem.

The promise of autonomous cars has no doubt been an exciting and intoxicating one for technophiles everywhere, not to mention those who see driving as a chore that must be endured, rather than enjoyed. There's no shortage of autonomy evangelists - Tesla CEO Elon Musk is a famous example, Ford reckons it will have its first truly self-driving car ready by 2021 and BMW and Mercedes have similar timelines - while ridesharing companies and Silicon Valley tech giants like Uber and Google are also bullish on fully-autonomous vehicles. And why wouldn't they be? Without a driver taking a cut of fares, profit margins for those businesses would explode, while also giving the public cheaper rides and potentially freeing them from the financial burden of owning a car. But according to one of the world's biggest manufacturers of the advanced sensors that make autonomous cars possible, the utopian vision of completely hands-off, point-to-point driving is highly unlikely for the foreseeable.

Businesses are increasing the pace of investment in AI systems to defend against the next generation of cyberattacks, a new study from the Capgemini Research Institute has found. Two thirds (69%) of organizations acknowledge that they will not be able to respond to critical threats without AI. With the number of end-user devices, networks, and user interfaces growing as a result of advances in the cloud, IoT, 5G and conversational interface technologies, organizations face an urgent need to continually ramp up and improve their cybersecurity. AI-enabled cybersecurity is now an imperative: Over half (56%) of executives say their cybersecurity analysts are overwhelmed by the vast array of data points they need to monitor to detect and prevent intrusion. Accordingly, almost half (48%) said that budgets for AI in cybersecurity will increase in FY2020 by nearly a third (29%).