This blog is not about how Jira is too complex and over-engineered with features I don’t need.
Those complaints are well articulated by others.
… Over the years I have arrived at the conclusion that Jira contradicts my values as a member of a dev team. It represents a way of thinking and working that goes against my beliefs. Read More
Monthly Archives: August 2020
Simplifying GRUs, LSTM and RNNs in General
This article discusses how sequence models work and some of their application.
Sequence models are a special class of deep neural networks that have applications in machine translation, speech recognition, image captioning, music generation, etc. Sequence problems can be of varying types where the input X and output Y might both be sequences with either the same length or different lengths. It can also be that only one of X or Y is a sequence. Read More
GPT-3, explained: This new language AI is uncanny, funny — and a big deal
Last month, OpenAI, the Elon Musk-founded artificial intelligence research lab, announced the arrival of the newest version of an AI system it had been working on that can mimic human language, a model called GPT-3.
In the weeks that followed, people got the chance to play with the program. If you follow news about AI, you may have seen some headlines calling it a huge step forward, even a scary one.
I’ve now spent the past few days looking at GPT-3 in greater depth and playing around with it. I’m here to tell you: The hype is real. It has its shortcomings, but make no mistake:GPT-3 represents a tremendous leap for AI. Read More
Architectures Every Data Scientist And Big Data Engineer Should Know
Comprehensive and Comparative List of Feature Store Architectures for Data Scientists and Big Data Professionals.
Feature store has become an important unit of organizations developing predictive services across any industry domain.
… This blog post highlights the features supported by different Feature Store frameworks, that are primarily developed by different leading industry giants. Read More
Spamouflage Goes to America
Social media accounts from the pro-Chinese political spam network Spamouflage Dragon started posting English-language videos that attacked American policy and the administration of U.S. President Donald Trump in June, as the rhetorical confrontation between the United States and China escalated.
The videos were clumsily made, marked by language errors and awkward automated voice-overs. Some of the accounts on YouTube and Twitter used AI-generated profile pictures, a technique that appears to be increasingly common in disinformation campaigns. The network did not appear to receive any engagement from authentic users across social media platforms, nor did it appear to seriously attempt to conceal its Chinese origin as it pivoted toward messaging related to U.S. politics. Read More
Assessing Demographic Bias in Named Entity Recognition
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition(NER) systems for English across different demographic groups with synthetically generated corpora. Our analysis reveals that models perform better at identifying names from specific demo-graphic groups across two datasets. We also identify that debiased embeddings do not help in resolving this issue. Finally, we observe that character-based contextualized word representation models such as ELMo results in the least bias across demographics. Our work can shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics. Read More
Characterizing Implicit Bias in Terms of Optimization Geometry
We study the implicit bias of generic optimization methods, such as mirror descent, natural gradient descent,and steepest descent with respect to different potentials and norms, when optimizing under determined linear regression or separable linear classification problems. We explore the question of whether the specific global minimum (among the many possible global minima) reached by an algorithm can be characterized in terms of the potential or norm of the optimization geometry, and independently of hyperparameter choices such as step-size and momentum. Read More
AI Magic Makes Century-Old Films Look New
Denis Shiryaev uses algorithms to colorize and sharpen old movies, bumping them up to a smooth 60 frames per second. The result is a stunning glimpse at the past.
On April 14, 1906, the Miles brothers left their studio on San Francisco’s Market Street, boarded a cable car, and began filming what would become an iconic short movie. Called A Trip Down Market Street, it’s a fascinating documentation of life at the time.
… Well over a century later, an artificial intelligence geek named Denis Shiryaev has transformed A Trip Down Market Street into something even more magical. Read More
How China uses facial recognition to control human behavior
When facial recognition is everywhere, anything you do is fair game for public shaming and punishment.
Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks — to help deal with violent crimes, terrorist threats and human trafficking. And while it’s still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China.
China’s facial recognition system logs nearly every single citizen in the country, with a vast network of cameras across the country. Read More
FaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best,this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans.
To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on Deep-Fakes [1], Face2Face [59], FaceSwap [2] and NeuralTextures [57] as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available2and contains a hidden test set as well as a database of over1.8million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data,we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers. Read More
Rick's Cafe AI 