The last two years have been some of the most exciting and highly anticipated in Automatic Speech Recognition’s (ASR’s) long and rich history, as we saw multiple enterprise-level fully neural network-based ASR models go to market (e.g. Alexa, Rev, AssemblyAI, ASAPP, etc). The accelerated success of ASR| The Gradient
One of the goals of AI research is to teach machines how to do the same things people do, but better. In the early 2000s, this meant focusing on problems like flying helicopters [https://www.youtube.com/watch?v=M-QUkgk3HyE] and walking up flights of stairs [https://www.youtube.com/| The Gradient
What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-intensive and engineering-first efforts that scale to ever larger training sets| The Gradient
LLM-based chatbots’ capabilities have been advancing every month. These improvements are mostly measured by benchmarks like MMLU, HumanEval, and MATH (e.g. sonnet 3.5, gpt-4o). However, as these measures get more and more saturated, is user experience increasing in proportion to these scores? If we envision a future| The Gradient
Introduction Imagine yourself a decade ago, jumping directly into the present shock of conversing naturally with an encyclopedic AI that crafts images, writes code, and debates philosophy. Won’t this technology almost certainly transform society — and hasn’t AI’s impact on us so far been| The Gradient
The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structural| The Gradient
A brief overview and discussion on gender bias in AI| The Gradient
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.| The Gradient
Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?| The Gradient
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.| The Gradient
Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.| The Gradient
On the the pivotal role that Deep Learning has played as a key enabler for advancing single-cell sequencing technologies.| The Gradient
On fish counting – a complex sociotechnical problem in a field that is going through the process of digital transformation.| The Gradient
In this article, we will talk about classical computation: the kind of computation typically found in an undergraduate Computer Science course on Algorithms and Data Structures [1]. Think shortest path-finding, sorting, clever ways to break problems down into simpler problems, incredible ways to organise data for efficient retrieval and updates.| The Gradient
Once considered a forbidden topic in the AI community, discussions around the concept of AI consciousness are now taking center stage, marking a significant shift since the current AI resurgence began over a decade ago.| The Gradient
"In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of human intelligence, they defy| The Gradient
Anything that looks like genuine understanding is just an illusion.| The Gradient
This essay first appeared in Reboot. Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both evocative of and directly derived from children’s cartoons — might show up uncritically in the New Yorker? More than ever, the| The Gradient
A collection of the best technical, social, and economic arguments Humans have a good track record of innovation. The mechanization of agriculture, steam engines, electricity, modern medicine, computers, and the internet—these technologies radically changed the world. Still, the trend growth rate of GDP per capita in the world's frontier| The Gradient
The debate around artist compensation in AI art, and some possible solutions to the problem| The Gradient
A mystery Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive completions to user prompts (NYT coverage). They are a delicate combination of a radically simplistic algorithm with massive amounts of data and computing power. They are trained by playing a guess-the-next-word| The Gradient
