Collaborating Authors

REPSOL is a global multi-energy provider and digital transformation trailblazer


Reimagining business for the digital age is the number-one priority for many of today's top executives. We offer practical advice and examples of how to do it right. Repsol is a global multi-energy provider that strives to drive the evolution towards a low-emissions energy model. Repsol's objective is to be a net-zero emissions company by 2050. Repsol was the first energy company to set this ambitious objective in line with the Paris Agreement and the United Nations Sustainable Development Goals.

Lidar Crop Classification with Data Fusion and Machine Learning


Crop type maps are frequently generated using remotely sensed data acquired by sensors mounted on satellites, manned aircraft or unmanned aerial vehicles (UAVs or'drones'), the most popular being multispectral sensors mounted on satellites. Aerial multispectral sensors are more frequently employed where imagery with very high spatial resolution is required. However, the use of Lidar data for crop type mapping is still uncommon. This article outlines research done on creating crop type maps using Lidar, Sentinel-2 and aerial data along with several machine learning classification algorithms for differentiating four crop types in an intensively cultivated area. Lidar data is becoming ever-more widely available as more aerial surveys are conducted, UAV-Lidar sensors are becoming more prevalent and Earth observation satellites are being fitted with Lidar sensors.

Hydroponics Market to Grow Exponentially Stoked by Emergence of Advanced Artificial Intelligence-based Systems, reports Fortune Business Insights


Top companies covered are Heliospectra AB, Signify Holdings, Terra Tech Corp., Argus Control Systems, American Hydroponics, Scotts Miracle-Gro, LumiGrow, Village Farms, Green Sense Holdings, Urban Cultivator, other key market players profiled

Artificial Intelligence and IoT can improve travel experience


In interaction with Media, Aamir Junaid Ahmad, CEO - BusAndTicket, a technocrat himself, shared how the company is planning to use technology for improving bus ticket booking and travel experience for its customers. With the AI advancement in the world, we will soon implement new ways the technology can improve customer experience. AI and IoT together will give more personalized ticket booking experience and will help users find the best deals and recommendations to fulfill their travel plans with ease. The more they use the service, the more information will be available to further customize the search results. is coming up for the first time with the concept of dynamic pricing in bus ticket booking using the Analytical Benefits of AI.

Google's Model Search: An Open Source Platform for Finding Optimal ML Models


I hope this article gave you a quick overview of Google's Model search. If you are interested in finding more about its features, there are a bunch of stuff on their Github repository that I haven't covered such as: It will be interesting to see whether this framework is going to be actually used in challenges such as Kaggle competitions or whether if it's not going to be used by the community and it just goes to waste! (hopefully not). I think there have been a lot of papers about NAS, but most of them don't make it into frameworks.

KIPO Publishes Examination Guidelines on Artificial Intelligence


The Korean Intellectual Property Office (KIPO) announced Patent Examination Guidelines for key technology areas related to the Fourth Industrial Revolution, including machine learning based artificial intelligence ("AI"), on January 18, 2021. In the Examination Guidelines for AI, KIPO outlines specific guidelines on description and novelty/inventiveness requirements for different categories of AI inventions (e.g., AI model training invention and AI application invention, as depicted below), in addition to eligibility requirements which correspond to that of computer-related inventions. In particular, KIPO's Examination Guidelines provide examples of various AI inventions with practical drafting tips on enablement (Article 42(3)(i) of Patent Act) and inventiveness requirements (Article 29(2)). Under Article 42(3)(i), the description of an invention shall be written clearly and fully so that a person with ordinary skill in the art (POSITA) to which the invention pertains can easily practice the claimed invention. For an AI invention, KIPO suggests that the description of the technical problem, solution, and specific technical configuration (e.g., training data, data preprocessing, trained model, and loss function, etc.) be included to enable a POSITA to practice the claimed invention, unless the technical configuration is well known in the art.

The Power of Scale


On May 2020, OpenAI introduced GPT-3 which is the third iteration of GPT language generation model series. The model boasted of a capacity of 175 billion parameters, more than 10 times than any other language model created before it. And to say that the model was a significant improvement would be an understatement. It could write essays on any topic without any inconsistencies in the output text. When trained on code samples, it could generate small code snippets (which were actually functional!) by getting a description of the task in English by the user. A group of developers trained it to update financial statements on Microsoft Excel based on casual description of transactions.

Can We Be Friends with Robots?


To determine how close humans and robots can become, we need a clear understanding of what, exactly, friendship is, and defining friendship isn't easy. Our friendships are made, maintained, and repaired all the time. Hopefully, we all have friends and believe, deep in our hearts, that the one ship that won't sink is friendship. Although friendship plays such a profound role in our lives that research links it to both emotional and physical well-being, people disagree about what makes friendship special and how far the bonds can go. Can we be friends with people who do things we find unconscionable?

Algorithm helps artificial intelligence systems dodge "adversarial" inputs


In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action -- steer right, steer left, or continue straight -- to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels?