"We lived and breathed data science," DataRobot CEO Jeremy Achin says of himself and his cofounder Tom de Godoy. "And we asked ourselves, 'How would we automate our jobs?'" DataRobot wants to make machine learning so simple that a business analyst with basic training can run predictive models without breaking a sweat. The Boston-based startup just raised a $206 million Series E funding round led by Sapphire Ventures to expand the business, which sells software that helps companies across industries develop and deploy in-house AI models. The billion-dollar valuation makes it the highest-ranking of the "picks-and-shovels" startups featured on Forbes' inaugural AI 50 list (meaning the companies that provide tools to help their customers develop their own AI).

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

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H3 Dynamics has partnered with Curitiba-based EPH Engineering in Brazil, a firm that specializes in hydropower design, dam inspections and safety plans, to launch a turnkey dam inspection solution that combines AI-enabled damage assessment and HYCOPTER fuel cell drones capable of flying 3.5 hours at a time. With over 5,000 dams submitted to the Brazilian Dam Safety Plan, and two recent collapse incidents causing more than 300 deaths and major environmental damage, Brazilian authorities have tightened inspection and upkeep requirements in the country. "Many accident reports show that problems were not detected by instrumentation but by visual observation. Drones can help, but due to the large dimensions of these structures we need much longer flight times." Some of the dams are so large that they would require months of battery-powered drone flights to fully scan their surfaces.

Drug repositioning is an attractive cost-efficient strategy for the development of treatments for human diseases. Here, we propose an interpretable model that learns disease self-representations for drug repositioning. Our self-representation model represents each disease as a linear combination of a few other diseases. We enforce the proximity between diseases to preserve the geometric structure of the human phenome network - a domain-specific knowledge that naturally adds relational inductive bias to the disease self-representations. We prove that our method is globally optimal and show results outperforming state-of-the-art drug repositioning approaches. We further show that the disease self-representations are biologically interpretable.

Oblivious to those depending on bananas for their favourite protein shake or breakfast fix, a deadly fungus has sneaked up on banana plantations in South America, threatening the fruit's future. An artificial intelligence-powered smartphone app developed for banana farmers can come in handy to stem further spread of the disease, its makers from Colombia, India and U.S., said. The artificial intelligence-powered tool built into the app called Tumaini – which means "hope" in Swahili- can detect pathogens at an early stage and help fast-track control and mitigation efforts, according to a statement by researchers who designed the application. The app has been developed by scientists from the Colombia-based International Center for Tropical Agriculture (CIAT), the Imayam Institute of Agriculture and Technology (IIAT), Tamil Nadu in India, and Texas A&M University, in the United States. The tool is being tested on three main banana-producing continents: Asia, Latin America and Africa. It has been tried in Colombia in South America; the Democratic Republic of Congo, Benin and Uganda in Africa; and India and China in Asia.

Complete report on Machine Learning in Finance market report spread across 100 pages, list of tables & figures, profiling 10 companies.

Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work focus on a new method for color texture analysis considering all color channels in a more intrinsic approach. Our proposal consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel edges that cover spatial patterns throughout individual image color channels, while between-channel edges tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through a concise topological characterization of the modeled network in a multiscale approach with radially symmetric neighborhoods. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks, and also has the most stable performance between datasets, achieving $98.5(\pm1.1)$ of average accuracy against $97.1(\pm1.3)$ of MCND and $96.8(\pm3.2)$ of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, where results show that SSN also has higher performance and robustness.

In 2018, Brazil had over 230 million cows chewing their cuds – about 22% of the 1.02 billion cows living on this planet – and consequently was the world's largest exporter of beef that year. With the country's revenues from agriculture reaching $84.6 billion, it makes sense that there would be some agriculture technology (agtech) startups cropping up in the Land of the Holy Cross. The Brazilian Institute of Geography and Statistics has noted the efforts this South American country has been making to advance the use of technology in farming. For example, tractor use in the country has grown by almost 50% in the past decade while crop irrigation use has increased by 52%. The agricultural sector is now working hand-in-hand with the tech world to capture big data and turn it into insights for "precision farming," something we talked about in our article on 6 IoT in Agriculture Solutions from AgTech Startups.

The Modi 2.0 is all set to launch a national Artificial Intelligence (AI) Programme soon, which will see the formation of a task force under Principal Scientific Advisor K Vijay Raghavan to identify projects and initiatives in which to implement the AI technology. The policy will also include a national artificial intelligence centre, which has been delayed because of a long-standing tiff between NITI Aayog and the Ministry of Electronics and Information Technology (MeitY) on which will be the department that will anchor the project. The proposed policy and the centre could finally see the light of day as the finance ministry has cleared the NITI Aayog's Rs 7,000-crore plan. "The expenditure finance committee has cleared the spending of Rs 7,000 crore till 2024-25. The NITI Aayog is likely to be the line ministry for this initiative. The Cabinet note is being circulated by NITI Aayog and is likely to be cleared soon," said a government official.