South African-based Artificial Intelligence (AI) software & solutions veteran and founder of Cortex Logic – Dr. Jacques Ludik – has been awarded a premium accolade at Africa's Tech Week event, underlying a life dedicated to AI and Data Science Innovation. Ludik, an Africa-based smart technology entrepreneur and AI investor/ AI ecosystem builder, holds a PhD in Computer Science and has amassed 25 years' experience in the study and exploitation of AI & Data Science in real world applications. Ludik was formally a founder of Bennit AI, Mosaic, SynerG and CSense Systems, the latter being Africa's first AI company sold to General Electric in 2011. Over the course of his career Ludik has published a wide range of papers on AI, Advanced Analytics, Machine Learning and Data Science and is a big supporter of AI for social good. He is currently founder & CEO of Cortex Logic as well as founder & president of the Machine Intelligence Institute of Africa (MIIA).

For generations healthcare has been episodic – someone gets sick or breaks a bone, they see a doctor, and then they might not see another one until the next time they get sick or injured. Now, as emerging technologies such as artificial intelligence open up new possibilities for the healthcare industry in the Fourth Industrial Revolution, policymakers and practitioners are developing new ways to deliver continuous healthcare for better outcomes. Consumers already expect access to healthcare providers to be as smart and easy as online banking, retrieving boarding passes and making restaurant reservations, according to Kaiser Permanente CEO Bernard J Tyson. Nearly three-quarters of Americans with health insurance (72%), for example, say it's important that their health insurance provider uses modern communication tools, such as instant message and two-way video. Innovative healthcare organizations such as Kaiser Permanente are listening.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

He has demanded "goddamned steam" to power the Navy's aircraft carriers and prefers a wall to drones and other technology to secure the country's southern border. He has rejected the scientific consensus on climate change and repeatedly, wrongly, pointed to occasional wintry weather as proof that he's right. And this week, amid a safety scare involving Boeing's 737 MAX 8 and MAX 9 airplanes, President Trump complained that modern jets are "too complex to fly." He added: "I see it all the time in many products. Always seeking to go one unnecessary step further, when often old and simpler is far better."

A drone has been likened to a grasshopper because of its unique ability to leap into the air using its specially designed legs. The cargo-carrying automated vehicle is equipped with legs that let it jump 6.5 feet (two metres) into the air, taking off almost vertically. The craft, dubbed the Sparrow, costs £30,000 ($40,000) and can fly up to 62 miles (100km) at a speed of 112 mph (180kph). Delivery firms are pioneering a host of new technologies to tackle the last mile of deliveries. It is hoped the vehicles can cut the inefficiencies, and hence costs, of the final stage of delivery, in which packages are taken from a central hub to your door.

HEJERE, ETHIOPIA - Canada joined much of the world in barring the Boeing 737 Max 8 jet from its airspace on Wednesday, saying satellite tracking data show possible but unproven similarities between the Ethiopian Airliner crash that killed 157 people and a previous crash involving the model five months ago. The decision left the U.S. as one of the few remaining countries to allow the planes to keep flying. Canadian Transport Minister Marc Garneau said a comparison of vertical fluctuations found a "similar profile" to the Lion Air crash that killed 187 people in October. Garneau emphasized that the data are not conclusive but crossed a threshold that prompted Canada to bar the Max 8. He said the new information indicated the Ethiopian Airliner jet's automatic system kicked in to force the nose of the aircraft down after computer software determined it was too high.

An extended 5km (3.1 miles) no-fly zone for drones has come into force around airports in the UK after reported sightings at Gatwick, Heathrow and Dublin airports in recent months grounded hundreds of flights and left thousands stranded. Previously, only a 1km (0.6 mile) exclusion zone was in place. But despite the negative reputation they have received, the use of drones isn't all bad. From finding missing people to delivering takeaways, here are some of the ways the unmanned aircraft can be beneficial. A Norfolk man who went missing in June last year was only found when a police drone spotted him stuck on a marsh.

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.

Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely match the samples' true malicious/benign (1/0) target labels. However, there are often a number of other sources of contextual metadata for each malware sample, beyond an aggregate malicious/benign label, including multiple labeling sources and malware type information (e.g., ransomware, trojan, etc.), which we can feed to the classifier as auxiliary prediction targets. In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss. We find that incorporating multiple auxiliary loss terms yields a marked improvement in performance on the main detection task. We also demonstrate that these gains likely stem from a more informed neural network representation and are not due to a regularization artifact of multi-target learning. Our auxiliary loss architecture yields a significant reduction in detection error rate (false negatives) of 42.6% at a false positive rate (FPR) of $10^{-3}$ when compared to a similar model with only one target, and a decrease of 53.8% at $10^{-5}$ FPR.

MIT has responded to Donald Trump's bizarre suggestion that its scientists are better at flying planes than trained pilots. In a strange tweet posted on Tuesday morning, the president had suggested that computer scientists from the Massachusetts Institute of Technology were required to to fly new planes, because they are so "complicated". "Pilots are no longer needed," he wrote. The post appeared to be a response to the Ethiopian Airlines disaster, in which 157 people were killed in a crash that had strange parallels to another accident involving the same model of plane just months before. Authorities across Europe have temporarily grounded all of the same planes in an attempt to stop further disasters.