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Build and deploy software collaboratively with the power of AI without spending a second on setup.

Industry is already using data to remake culture. To reverse the tide—to make culture more equitable—we need to decode that data for ourselves.

Post45 data collective The Post45 Data Collective peer reviews and houses literary and cultural data from 1945 to the present on an open-access website designed, hosted, and maintained by Emory University’s Center for Digital Scholarship. VIEW OUR DATA HOW TO SUBMIT Terms of use These terms have been derived from Dataverse Project's recommendations for best

Learn the art of image prompting with the help of Google AI.

Variation kicks in when you look at the later years, consider multiple marriages, divorce, separation, and opposite-sex versus same-sex relationships. This chart breaks it all down.

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

Since launching Spotify’s brand-new AI DJ in beta a few weeks back, Premium listeners across the U.S. and Canada have had the chance to experience our personalization capabilities in a whole new way. We’ve already seen so much love for DJ—both on-platform and across social media—and we’re not even out to 100% of users yet.... Read more »

A fable

Chatbots, Alternatives to Chatbots, and Testing

Create stunning heatmaps, data visualizations and interactive maps with iipmaps.com - the powerful mapping platform for data storytelling and geographic insights.

Find and translate all newspapers in the world

Find out when it’s your time for the glasses and hearing aid. Time is undefeated.

“Two weeks ago today, a satellite whirled above Washington on its way around the earth and shot photographs from 400 miles up that could change the way...

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.

archived 18 Mar 2024 16:39:47 UTC

Discover the architecture of Time-LLM and apply it in a forecasting project with Python

Rest of World analyzed 3,000 AI images to see how image generators visualize different countries and cultures.

I was relaxing on a beach during my summer leave when I received a mail from a reader that asked me if it is technically possible to write a virus using Python. The short answer: YES. The longer answer: yes, BUT… Let’s start by saying that viruses are a little bit anachronistic in 2021… nowadays other kinds of malware (like worms for example) are far more common than viruses. Moreover, modern operative systems are more secure and less prone to be infected than MS-DOS or Windows 95 were (sorry Microsoft…) and people are more aware of the risk of malware...

If you’re a fan of neural networks, you’ve probably heard of the ELU, GELU and SiLU activation functions.  However these activation functions still are not so common, in this post we are going to know them a little more elu: The Exponential Linear Unit is a smooth approximation to the rectifier function. The main advantages is that generates negative outputs, which aid in guiding the network’s weights and biases in the desired directions, and the main cons is that increases the computation time. import numpy as np import seaborn as sns import matplotlib.pyplot as plt def elu(x,alpha): return [z if z 0 else alpha*(np.exp(z) -1) for z in x] x = np.random.randint(-10,10, 100) alpha = .5 y = elu(x, alpha) and plot to check graphically: plt.style.use('ggplot') g = sns.lineplot(x=x, y=y) g.axhline(0, ls='--', color='gray') g.axvline(0, ls='--', color='gray') g.set(ylim=(-1.5, 2)) plt.legend(labels=['elu']) plt.title('Exponential Linear Unit (ELU)') plt.xlabel('x') plt.ylabel('y') plt.show() The plot: /*! elementor - v3.7.4 - 31-08-2022 */ .elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$='.svg']{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block} gelu: Gaussian Error Linear Unit is used for LM and transformer models like GPT-2 and BERT, this functions prevents the problem of vanishing gradients also unlike ELU, it has a continuous derivative at 0, which can sometimes make training faster. import math def gelu(x): return [0.5 * z * (1 + math.tanh(math.sqrt(2 / np.pi) * (z + 0.044715 * math.pow(z, 3)))) for z in x] y = gelu(x) plt.style.use('ggplot') g = sns.lineplot(x=x, y=y) g.axhline(0, ls='--', color='gray') g.axvline(0, ls='--', color='gray') g.set(xlim=(-4, 4)) g.set(ylim=(-0.5, 2)) plt.xlabel('x') plt.ylabel('y') plt.legend(labels=['gelu']) plt.title('Gaussian Error Linear Unit (GELU)') plt.show() The output:   silu: Sigmoid Linear Units, it  serves as a smooth approximation to the ReLU  def sigmoid(x_elem): return 1/(1 + np.exp(-x_elem)) def silu(x, theda = 1.0): return [x_elem * sigmoid(theda *x_elem) for x_elem in x] y = silu(x) plt.style.use('ggplot') g = sns.lineplot(x=x, y=y) g.axhline(0, ls='--',... Continue Reading

Recruiters are eager to use generative AI, but a Bloomberg experiment found bias against job candidates based on their names alone

How common are wide age gaps between spouses? These are the age differences through the lens of the 2022 five-year American Community Survey.

Find out if you’re under cyber-attack here #CyberSecurityMap #CyberSecurity