The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it’s distorting our understanding of the field’s progress.
Read MoreError-riddled data sets are warping our sense of how good AI really is
Tag: Neural Networks
Investors Bet Flex Logic embedded FPGAs will catch Edge AI wave with $55M new funding
Geoff Tate, CEO and cofounder of Flex Logix Technologies, told us several years back that edge AI inferencing would be the embedded FPGA vendor’s biggest market through about 2023. Among the reasons was the crying need for an affordable inference engine that could also handle a range of applications as data processing and storage shifts from cloud data centers to edge devices.
Read MoreModel Scaling that’s both accurate and fast: Facebook AI…
The past several years have seen the rapid development of new hardware for training and running convolutional neural networks. Highly-parallel hardware accelerators such as GPUs and TPUs have enabled machine learning researchers to design and train more complex and accurate neural networks that can be employed in more complex real-life applications.
Read MoreTrending toward concept Building – A review of Model Interpretability for Deep Neural Networks
Explaining how deep neural networks work is hard to do. It is an active area of research in academia and industry. Data scientists need to stay current in order to create models that are safe and usable. Leaders need to know how to avoid the risk of unethical, biased, or misunderstood models. In this post, I breakdown trends in network interpretability applied to image data. Some of the approaches covered apply to non-image-based networks as well.
Read MoreArtificial Intelligence (AI) and Prosthetics: The new frontier
If you’ve found your way to this little part of the internet — then you obviously either love robotics, have played way too much Deus Ex: Human Revolution, or are just an avid technophile like myself who is fascinated by science, computers and how they are forging new paths toward a bionic future.
Read MoreCase Study: Industry use-cases of Neural Networks
Neural Network is a part of Artificial Intelligence, which uses a similar way for training the machine and predict some output as our mind learns new things and does prediction. In this article, we are going to discuss what is neural networks and their industry use cases.
Read MoreFederated Learning: A decentralized form of Machine Learning
Most major consumer tech companies that are focused on AI and machine learning now use federated learning – a form of machine learning that trains algorithms on devices distributed across a network, without the need for data to leave each device. Given the increasing awareness of privacy issues, federated learning could become the preferred method of machine learning for use cases that use sensitive data (such as location, financial, or health data).
Read MoreTesla Working on Full Self-Driving Mode, Extending AI Lead
Tesla’s goal to release its level 5 Full Self Driving (FSD) mode autopilot capability in 2021 was deemed unrealistic by the CEO of competitor Waymo in a recent interview. Tesla is the only autonomous vehicle manufacturer using real-time cameras, rather than pre-mapped Lidar (Light Detection and Ranging) to guide vehicle movement. Tesla also…
Read MoreBackpropagation in Neural Networks: How it helps?
Backpropagation in neural Network is vital for applications like image recognition, language processing and more. Neural networks have shown significant advancements in recent years. From facial recognition tools in smartphone Face ID, to self driving cars, the applications of neural networks have influenced every industry.
Read MoreWhy Is Object Detection So Messy?
Those working with Neural Networks know how complicated Object Detection techniques can be. It is no wonder there is no straight forward resource for training them. You are always required to convert your data to a COCO-like JSON or some other unwanted format. It is never a plug and play experience. Moreover, no diagram thoroughly explains Faster R-CNN or YOLO as there is for U-Net or ResNet. There are just too many details.
Read MoreNeural Networks In a Nutshell
I will try my level best to present a transparent view of how neural network works such that by the end of this article you might gain a solid knowledge of this topic. Since machine learning algorithms are now recursively used to predict various cases like cancer , stocks etc. the number of neural networks projects are growing at an exponential rate. We can also say that neural networks sits at the core of revolutionary machine learning projects.
Read MoreWhat is Artificial Intelligence? How does AI work, types and the future of it?
The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life.
Read MoreRetraining Machine Learning Model approaches
Generally machine learning models will be trained by some learning between set of input features and dependent feature or target variable. The aim of the model is to minimize the prediction error by applying or optimizing cost functions, and when we found some optimized models, we will deploy into the production and the aim is that model will generate accurate predictions on future unseen data as well so the goal is that model will predict the future unseen data as accurately as data used during the training period.
Read More2011: DanNet triggers deep CNN revolution
In 2021, we are celebrating the 10-year anniversary of DanNet, which, in 2011, was the first pure deep convolutional neural network (CNN) to win computer vision contests. Read about its history here.
Read MoreIBM AI helps to break down massive code to ease cloud migration
Cloud technology has certainly brought along more convenience, enabling anyone to access their photos, email and other applications anywhere and at anytime. But not all companies have moved everything to the cloud just yet. Those looking to migrate typically have to break down their apps into smaller chunks in a process dubbed refactoring — or restructuring their computer code without changing its intended functions.
Read MoreInventorBot: Using AI to generate new ideas in any Field
How a neural network trained on the US Patent Database can produce new and possibly useful inventions.
Read MoreWorking with the Lambda Layer in Keras
Working With The Lambda Layer in Keras. In this tutorial we’ll cover how to use the Lambda layer in Keras to build, save, and load models which perform custom operations on your data.
Read MoreDeep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI
Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI.
Read MoreHow to train a neural network from scratch
In this article, I will continue our discussion on artificial neural networks, and give an example of a very simple neural network written in python. The purpose of this series of articles I am writing is to give a full explanation of ANN’s from the ground up, with no hiding behind special libraries. Tensorflow is great for prototyping and production, but when it comes to education the only way to learn is to get a pencil and paper and get dirty in math.
Read MorePeeking into AI’s ‘black box’ brain — with physics
Cats aren’t dogs. Even modern AI knows that. But how exactly AI distinguishes cat images from those of dogs is not clear. Standard neural networks are akin to a black box, as even the people who program them often have little to no idea how they make decisions. It’s not as critical when it’s just a picture of a cute puppy or a kitten. But it becomes important when an AI tries to interpret, say, a sequence of weather images that show…
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