Most people find it a pain to receive parcels in wide time slots like, say, between 8 a.m. and 5 p.m., so when delivery startup Paack offered a service in which everyone could narrow that window down to one hour, with no extra charge, it had a challenge on its hands. The startup's routing engine worked but needed to be more efficient. Enter Prowler.io, a Cambridge, U.K.-based machine-learning startup that bills itself as a decision-making platform for any company with complex problems to solve. Paack's contact at venture firm Balderton in London introduced it to Prowler in February 2018, and within months its delivery vans and trucks were being coordinated by an intelligent, digital simulation. With the beta test over, Paack's CEO, Fernando Benito, sees a potential benefit to his bottom line. Some of his startup's deliveries are now 15% more efficient, he tells Forbes.

TORONTO, ONTARIO – According to a new research report by the market research and strategy consulting firm, Global Market Insights, Inc, penetration of AI technologies in the retail market will exceed 8 billion USD by 2024. The AI in retail market is driven by increasing investments across the globe. This growing interest can be attributed to the wide applications of machine learning, predictive analytics, and deep learning. Furthermore, AI is set to unleash the next phase of the digital disruption in retail – and the major players in the sector are ramping up their digitalization efforts as a result. With a storied history in the AI and data analytics space, the founder and CEO of Daisy Intelligence, Gary Saarenvirta, is today working at the forefront of the disruption taking place globally in retail.

Researchers from the University of Southern California, Pinscreen, and Microsoft developed a deep learning-based method that can generate full 3D hair geometry from single-view images in real time. This is the first deep learning project that can render hair in real time, the team said. "Realistic hair modeling is one of the most difficult tasks when digitizing virtual humans," the researchers said. "In contrast to objects that are easily parameterizable, like the human face, hair spans a wide range of shape variations and can be highly complex due to its volumetric structure and level of deformability in each strand." Using NVIDIA TITAN Xp GPUs with the cuDNN-accelerated PyTorch deep learning framework, the researchers trained their convolutional neural network on a dataset comprised of over 40,000 different hairstyles and 160,000 corresponding 2D orientation images rendered from random views.

Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning–based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. By exploiting the scalability of ExPecto, we characterized the regulatory mutation space for human RNA polymerase II–transcribed genes by in silico saturation mutagenesis and profiled 140 million promoter-proximal mutations. This enables probing of evolutionary constraints on gene expression and ab initio prediction of mutation disease effects, making ExPecto an end-to-end computational framework for the in silico prediction of expression and disease risk.

Kairos snaps up EmotionReader, which can scan faces in a crowd and tell how audiences are reacting. EmotionReader, an Enterprise Ireland-backed facial recognition start-up, has been acquired by Miami-based Kairos in an undisclosed "multimillion-dollar" deal. Artificial intelligence (AI) then analyses viewer attention and emotional response, enabling media and brand owners to collect actionable insights and analytics for video. 'In our mission to fix biases in today's face recognition algorithms, we're thrilled to welcome to Kairos some of the best deep-learning talent in the world' – BRIAN BRACKEEN The company is the brainchild of Dr Padraig O'Leary and Dr Stephen Moore, and it was founded only last year. Moore, working from his Singapore base, is understood to have built an impressive R&D team in the south-east Asian country.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology – or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as'looting other software.' And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

Researchers from the Universitat Autonoma de Barcelona, Carnegie Mellon University and International Institute of Information Technology, Hyderabad, India, have developed a technique that could allow deep learning algorithms to learn the visual features of images in a self-supervised fashion, without the need for annotations by human researchers. To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive information about every image. However, collecting and manually annotating these images requires huge amounts of time, resources, and human effort. "We aim to give computers the capability to read and understand textual information in any type of image in the real-world," says Dimosthenis Karatzas, one of the researchers who carried out the study, in an interview with Tech Xplore. Humans use textual information to interpret all situations presented to them, as well as to describe what is happening around them or in a particular image.

Researchers from the Universitat Autonoma de Barcelona, Carnegie Mellon University and International Institute of Information Technology, Hyderabad, India, have developed a technique that could allow deep learning algorithms to learn the visual features of images in a self-supervised fashion, without the need for annotations by human researchers. To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive information about every image. However, collecting and manually annotating these images requires huge amounts of time, resources, and human effort. "We aim to give computers the capability to read and understand textual information in any type of image in the real-world," says Dimosthenis Karatzas, one of the researchers who carried out the study, in an interview with Tech Xplore. Humans use textual information to interpret all situations presented to them, as well as to describe what is happening around them or in a particular image.

While AI can lift competition and productivity, it also can act as a great leveler, putting smaller players on the same footing as goliaths. Take pharmaceutical research, for example. Large companies have the budget and resources to physically test millions of drug candidates, giving them an advantage over startups and researchers. But smaller labs can achieve similar results by harnessing neural networks that simulate how a potential drug molecule will bind with a target protein. Deep learning can help smaller companies and other researchers discover promising drug treatments by improving the speed and accuracy of molecular docking, the process of computationally predicting how and how well a molecule binds with a protein.

Recently, there has been a huge rise in the implementation of artificial intelligence solutions, with new deep learning architectures being built and deployed across various industries. Deep learning works primarily because of the vast amount of input data on which the deep neural net is trained. Hence, having a good labeled training dataset marks the first step in developing a highly accurate AI solution. Preparing labeled training datasets for Computer Vision problems is a painstaking task that involves image processing, manipulation, and finally image labeling. Thus, while dealing with hundreds and thousands of images, programmatic image manipulation and processing stands out as an efficient option for AI dataset creators. So getting familiar with image processing libraries is a convenient first step in creating a custom AI Solution.