As another example of future FL trends – enabling parallel training of deep learning models on distributed data sets while preserving data privacy is complex and challenging. One group of researchers has developed a federated learning framework FEDF for privacy-preservation coupled with parallel training. The framework allows a model to be learned on multiple geographically-distributed training data sets (which may belong to different owners) while not revealing any information of each data set as well as the intermediate results.

Nvidia and the University of Florida (UF) today announced plans to build the fastest AI supercomputer in academia. By enhancing the capabilities of UF's existing HiPerGator supercomputer with the DGX SuperPod architecture, Nvidia claims the system -- which it expects will be up and running by early 2021 -- will deliver 700 petaflops (one quadrillion floating point operations per second) of performance. Some researchers within the AI community believe that capable computers, in conjunction with reinforcement learning and other techniques, can achieve paradigm-shifting AI advances. A paper recently published by researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia found that deep learning improvements have been "strongly reliant" on increases in compute. And in 2018, OpenAI researchers released an analysis showing that from 2012 to 2018, the amount of compute used in the largest AI training runs grew more than 300,000 times with a 3.5-month doubling time, far exceeding the pace of Moore's law.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to …

The early muted gains were accelerated as the markets surged higher today. Investors will be keenly observing the developments on the new stimulus package in the US, with the old one expiring at the end of the month. Markets are hopeful after the European Union signed off on a 750-billion-euro package last night that drove European markets higher. Corporate earnings were also strong overall this morning, with majors such as Coca-Cola and IBM reporting stronger than expected quarterly reports. Our deep learning algorithms have gone through the data and have used Artificial Intelligence ("AI") technology to help you spot the Top Buys for today.

I started doing some home baking recently. It started, like with a lot of other people, during the pandemic lockdown period when I got tired of buying the same bread from the supermarket every day. In all honesty, my bakes are passable, not very pretty but they please the family, which is good enough for me. Yesterday I stumbled on a YouTube video on how a factory makes bread in synchronised perfection and it broke a bit of my heart. All the hard work kneading dough amounts to nothing compared to spinning motors tumbling through a mechanised giant bucket. As I watch rows and rows of dough rising in unison spirals up the proofing carousel then slowly rolling into a constantly humming monstrous oven to become marching loaves of bread, something died in me. When the loaves zipped themselves into sealed bags and dumped themselves into packing boxes, I tell myself that they don't have the same craftsmanship (in my mind) as someone who is making bread with love, for his family. But deep inside me, I understand that if bread depended on human bakers only, it would be a whole lot more expensive, a lot more people would go hungry.

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. After filtering out applications companies (e.g., companies that use ML to provide business analytics), tools that aren't being actively developed, and tools that nobody uses, I got 202 tools. Please let me know if there are tools you think I should include but aren't on the list yet! I categorize the tools based on which step of the workflow it supports. I don't include Project setup since it requires project management tools, not ML tools.

This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012. The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters -- 10 times more than previous competitors -- is surprisingly powerful.

This week, the internet was abuzz with news of Tumblr's declaration that it would ban adult content on its platform starting December 17. But aside from the legal, social and ethical aspects of the debate, what's interesting is how the microblogging platform plans to implement the decision. According to a post by Tumblr support, NSFW content will be flagged using a "mix of machine-learning classification and human moderation." Which is logical because by some estimates, Tumblr hosts hundreds of thousands of blogs that post adult content and there are millions of individual posts that contain what is deemed adult content. The enormity of the task is simply beyond human labor, especially fora platform that has historically struggled to become profitable.

In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food. In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food.

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