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Artificial Intelligence Advances Food Safety

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Landing AI is helping food producers overcome not only the limitations of their human workforce but of traditional machine vision as well, using machine …


Why do birds crash into solar panels?

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Billions of birds die annually from collisions with windows, communication towers, wind turbines, and other human-made objects. One reason is that birds see a reflection of the sky in the object and think they're flying into an unobstructed path. This is even a problem for solar panel facilities, which see up to 138,000 bird deaths per year in the US from collisions with equipment. Though damage to the solar panels is minimal, officials worry about the impact these structures have on local wildlife. To combat the problem, the Department of Energy (DOE) has awarded Argonne National Laboratory $1.3 million to develop a system that can automatically monitor bird activity.


Embedded computing development kit for artificial intelligence (AI)-based machine vision offered by AAEON

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TAIPEI, Taiwan – AAEON Technology in Taipei, Taiwan, and Aotu.ai in Santa Clara, Calif., are introducing the BrainFrame Edge AI Developers Kit (DevKit) for an Intel artificial intelligence (AI) computer to enable system integrators rapidly to create and deploy smart machine vision applications. The BrainFrame Edge AI DevKit helps create solutions such as machine vision-based access control, uniform compliance, manufacturing automation, and video analytics. BrainFrame scales and configures easily and enables a connected camera to become a continuously monitoring Smart Vision system. BrainFrame's automatic algorithm fusion and optimization engine has VisionCapsules, Aotu.ai's open source algorithm packaging format. These self-contained capsules have a negligible memory footprint and include all necessary code, files, and metadata to describe and implement a machine learning algorithm.


Exploring exploration: comparing children with RL agents in unified environments

AIHub

Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different. Traditionally, reinforcement learning (RL) research takes place in grid-world-like settings or other 2D games, whereas children act in the real world which is rich and 3-dimensional.


Training our humans on the wrong dataset

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I really don't want to say that I've figured out the majority of what's wrong with modern education and how to fix it, BUT When we train (fit) any given ML model for a specific problem, on which we have a training dataset, there are several ways we go about it, but all of them involve using that dataset. Say we're training a model that takes a 2d image of some glassware and turn it into a 3d rendering. We have images of 2000 glasses from different angles and in different lighting conditions and an associated 3d model. How do we go about training the model? Well, arguable, we could start small then feed the whole dataset, we could use different sizes for test/train/validation, we could use cv to determine the overall accuracy of our method or decide it would take to long... etc But I'm fairly sure that nobody will ever say: I know, let's take a dataset of 2d images of cars and their 3d rendering and train the model on that first.


Demystifying the Language of Text Processing in Organizations

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Text Processing can transform the unstructured data into insightful information with the help of machine learning models. Organizations are now clamouring data to improve their businesses. An increase in Demand for customer-services has prompted the organizations to generate and utilize data on an everyday basis. But the humongous amount of data retrieved by organizations is unstructured and unsegregated. This creates a significant challenge for organizations to get an insight into the functionality of their businesses.


Top 10 Digital Transformation Trends For 2021

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No one could have predicted where 2020 would take us: The last six months alone have produced more digital transformation than the last decade, with every transformation effort already underway finding itself accelerated, and at scale. While many of my digital transformation predictions from a year ago benefited from this shift, others were displaced by more urgent needs, like 24/7 secure and reliable connectivity. What does this mean for 2021? Will core technologies like AI and data analytics still dominate headlines, or will we see newer, previously emerging technologies take the lead? Only time will tell, but here are my top ten digital transformation predictions for 2021.


Recurrent Neural Network Maths

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So, there are tons of content on neural networks but, rarely are these focused on the maths. Even if there is such content, it's so much complex that we just leave it. And I believe, without that deeper understanding of how maths is actually working, it's really difficult to get the proper intuition and it will always be a magical blackbox to us. This is my third blog in the series. In case you haven't checked my previous articles I would highly recommend to do so, since I'll building concepts on top of those.