Tagged in AmalgamAmalgam offers complete and optimized solutions for all your Artificial Intelligence challenges, from hardware to storage, to data center, to modeling, and beyond.
Chinese Lab Build the World Largest Model and Challenges Google… and the western as a whole.08 June, 2021
The Beijing Academy of Artificial Intelligence (BAAI), AKA OpenAI of China, recently launched the latest version of Wudao, a pre-trained deep learning model that is called “the world largest model ever”. The model has an incredible 1.75, wait for it…. TRILLION parameters. But how big is it really? Well, the model is 10 times bigger than the OpenAI GPT-3 model which, until now, has the title of the best model for language generation tasks. Another feature of Wudao is that the model was built and
João Paulo Martins
VGGNets are back03 June, 2021
If you have studied the deep learning timeline history, you may know that the big moment was in 2012 when Alex Krizhevsky presented a deep convolutional architecture able to improve the state-of-the-art error on ImageNet dataset by 11%. After that, it was proved that deep learning era had come. Two years later a better architecture was proposed by Karen Simonyan and Andrew Zisserman from the University of Oxford. In this model, the main difference was the number of layers, where they have s
Igor Muniz
Consumer vs Server-Grade Hardware and why you can't do Business-Grade work with the former02 June, 2021
In any market or industry, quality work at scale requires purpose-built high-performance tools. In a previous article (The Eight Challenges You'll Face With On-Premise Artificial Intelligence [https://amalgam.ai/posts/the-eight-challenges-youll-face-with-on-premise-artificial-intelligence/] ), we promised to cover this topic more in-depth in a separate post. Well, we like to keep our promises, so here we are. It turns out that 3 of the Challenges we listed (Servers, Networking, and Storage) ar
Adriano Marques
Calculating Material Stress Using Deep Learning27 April, 2021
If you are an engineer or get some classes of its subjects in college, you know resistances of material can give you a huge headache! Determining the stress a material is under, for example, requires complex equations created by mathematicians, engineers, and physicists. Such difficult that most part of the time we need computers to solve it. However, it seems that MIT researchers discovered a new method to simplify our lives, and guess what, using AI of course! By applying some computer visio
Igor Muniz
Nvidia GTC 202112 April, 2021
Nvidia's annual GTC starts today and you don't want to lose this opportunity. Every year Nvidia comes to show their exciting advancements in GPU technology and as artificial intelligence is directly linked to GPUs, you should expect a lot of AI content there, becoming a paradise for data scientist, machine learning engineers, and AI enthusiasts. You can also watch content about automation, health care, and quantum computing, all for free. But if you have extra money, I will recommend doing a wor
Igor Muniz
Potatoes Cluster: Training a Deep Learning Model Using a 100 Potatoes01 April, 2021
Have you ever heard about battery potatoes? Did you know that it is also possible to process information using potatoes? In this post we are going to show you how it’s possible to create a cluster of potatoes, that not only serves as a power supply but also processes data in a very cheap way. With that, we could achieve the performance of a Nvidia GTX 1060 using 100 potatoes connected with nails and clips. Building the Cluster In order to build our cluster, we are going to need some other item
Igor Muniz
Creating a Kubernetes environment on AWS with kOPS05 March, 2021
Have you ever wondered about deploying a cluster controlled by Kubernetes on AWS (or any other Cloud Provider) without suffering? Well, this post is definitely to you. I won't compare tools or methods like Terraform [https://www.terraform.io/], CloudFormation [https://aws.amazon.com/pt/cloudformation/] and many others. This is just a simple post about deploying a cluster, managed by Kubernetes, using the most basic commands and enabling Rolling Updates, with kOPS. In order to accomplish this
Rhuan Silva
The Art of Hiring Data Scientists09 February, 2021
The amount of companies hiring out Data Scientists thinking that they're going to come in, wave a wand and solve all problems with AI is appalling. This is not good for the industry, the companies, or the Data Scientists. Disrupting an industry takes a lot more than hiring a couple of Data Scientists and handing them a wishlist. Solving real problems with Artificial Intelligence requires experience across several technical domains that start with the hardware and ends with the production model,
Adriano Marques
Adopting Quantum Computing in 202109 February, 2021
Interesting take on adopting Quantum Computing in 2021, but I still think that there is a lot to overcome before most companies can actually use Quantum Computing for problems that really matter. Most people don't know, but Quantum Computers are a thing already, the problem is that they're not as powerful right now as we need it to be, and it is very hard to program them and get good results out of it. More on this topic here: https://www.analyticsinsight.net/if-not-then-organizations-should
Adriano Marques
Quantum Brain06 February, 2021
Just read a very interesting article on an intelligent material that re-arranges itself in response to a learning stimulus. This is very exciting, and I think, the way to go when it comes to embedding Artificial Intelligence to all things around us. It also shows how the concept of Artificial Intelligence is a lot more encompassing than the usual definitions can accommodate. Here is a cool article explaining how it works: https://scitechdaily.com/the-first-steps-toward-a-quantum-brain-an-inte
Adriano Marques
Distributed learning in an on-premise cluster08 January, 2021
Have you tried any distributed learning algorithms? If you are just starting out in this area, I have my doubts, but if you have been on this path for a few years, you might have faced one of those models. The incredible development of the machine learning area in the last decade has not only brought a new state of the art to several problems but has also taken processing optimization and parallelization to another level. With increasingly larger models, any common machine or even a single sup
Igor Muniz
Machine Learning Reproducibility: A Kaggle Competition Use-Case16 December, 2020
Even though Reproducibility in Machine Learning is a theme that people hear about now and then, we still see that people are practicing it only to a certain degree. Even between Kaggle [https://www.kaggle.com/] competition winners, we still see a lot of hard-to-reproduce code in Notebooks. Our goal here is to outline some reproducibility elements and how we tackled them in a recent competition. First, what reproducibility stands for in Machine Learning? During a Machine Learning project, we hav
Fernando Camargo
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