This article was originally published on AssemblyAI and re-published to TOPBOTS with permission from the author. Diffusion Models are generative models which have been gaining significant popularity in the past several years, and for good reason. A handful of seminal papers released in the 2020s alone have shown the world what Diffusion models are capable of, such as beating … [Read more...] about Introduction to Diffusion Models for Machine Learning
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DALL·E 2, Explained: The Promise And Limitations Of A Revolutionary AI
DALL·E 2 is the newest AI model by OpenAI. If you’ve seen some of its creations and think they’re amazing, keep reading to understand why you’re totally right — but also wrong. OpenAI published a blog post and a paper entitled “Hierarchical Text-Conditional Image Generation with CLIP Latents” on DALL·E 2. The post is fine if you want to get a glimpse at the results … [Read more...] about DALL·E 2, Explained: The Promise And Limitations Of A Revolutionary AI
Transformers And Multimodal: The Same Key For All Data Types
The world of Machine Learning is undoubtedly fascinating, constantly growing, and capable of touching the most diverse sectors, from medicine to space racing, from catering to big manufacturing. There are countless fields of application for this technology and just as many techniques that have been developed over the decades, but they all have one thing in common: … [Read more...] about Transformers And Multimodal: The Same Key For All Data Types
Data Capitalism: Innovation, Extraction, Social Conscience
How to advance social capital in a world increasingly governed by surveillance capitalism? About a year ago, I read Atlas of AI by Kate Crawford, a brilliant analysis of the extractive processes that govern the field of machine learning, from environmental resource allocation to the harvesting of our political fabric to data privacy infringements. This article … [Read more...] about Data Capitalism: Innovation, Extraction, Social Conscience
Similarity-Based Image Search for Visual Art
Similarity-based image search, also known as content-based image retrieval, has historically been a challenging computer vision task. This problem is especially difficult for visual art, because it is less obvious as to what a metric of “similarity” should be defined as and who should set that standard for art. For example, when I upload a photo of a wall mural … [Read more...] about Similarity-Based Image Search for Visual Art
Unconstrained Chatbots Condone Self-Harm
WARNING. This post contains references to self-harm and suicide. It includes conversations between a human and DialoGPT, with the sole purpose of surfacing the danger of uncontrolled AI. If you or a loved one are dealing or have dealt with suicidal thoughts, I suggest skipping this article. In the context of an accelerating mental health crisis, Natural Language … [Read more...] about Unconstrained Chatbots Condone Self-Harm
Amazon Releases 51-Language Dataset for Language Understanding
Imagine that all people around the world could use voice AI systems such as Alexa in their native tongues. One promising approach to realizing this vision is massively multilingual natural-language understanding (MMNLU), a paradigm in which a single machine learning model can parse and understand inputs from many typologically diverse languages. By learning a shared data … [Read more...] about Amazon Releases 51-Language Dataset for Language Understanding
How To Structure Intent In Chatbots And Gather Useful Feedback
I recently collaborated on several projects involving chatbots and had the opportunity to discuss with industry experts about the main difficulties that are often encountered in this type of project. While it is becoming easier and easier to build conversational assistants, it looks like there are some problems that emerge systematically as the chatbot grows, as a consequence … [Read more...] about How To Structure Intent In Chatbots And Gather Useful Feedback
NeurIPS 2021 – 10 Papers You Shouldn’t Miss
The 2021 edition of the most beloved conference in Artificial Intelligence is here to end the year with a ‘grand finale’. The growth of the conference hasn’t ceased: last year’s conference — which we also reviewed — had 1899 main-track papers accepted, compared to this year’s 2334. Some of the published papers have been on arxiv.org for some time now and have … [Read more...] about NeurIPS 2021 – 10 Papers You Shouldn’t Miss
Eliminating AI Bias
Evidence of AI Bias The primary purpose of Artificial Intelligence (AI) is to reduce manual labour by using a machine’s ability to scan large amounts of data to detect underlying patterns and anomalies in order to save time and raise efficiency. However, AI algorithms are not immune to bias. AI Bias has presented itself in several forms, with some examples highlighted … [Read more...] about Eliminating AI Bias