Google says it’s rolling out a new feature called ‘AI Inbox,’ which summarizes all your emails, but the company promises it won’t train its models on your emails.
On Thursday, Google announced a new era of Gmail where Gemini will be taking over your default inbox screen.
Google argues that email has changed since 2004, as users are now bombarded with hundreds of emails every week, and volume keeps rising.
Stephen Wolfram, a physicist, computer scientist and founder of Wolfram Research, has been hunting for a theory of everything since his first days as a particle physicist at Caltech. Wolfram put that mission to the side to focus on his business, but the success of artificial intelligence and computational science has encouraged Wolfram to pick up the quest to understand the universe once again, with renewed vigour.
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This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about AI in 2023 and Things we learned about LLMs in 2024.
It’s been a year filled with a lot of different trends.
2026 (Nature Neuroscience)
• AstroREG, a resource of enhancer–gene interactions in human primary astrocytes, generated by combining CRISPR inhibition (CRISPRi), single-cell RNA-seq and machine learning.
This study reveals how distal DNA ‘switches’ control gene activity in human astrocytes. Using CRISPRi screens and single-cell RNA-seq, we map enhancer–gene links, highlight Alzheimer’s disease-related targets and introduce a model that predicts additional regulatory interactions.
Thanks to their work from the 1980s and onward, John Hopfield and Geoffrey Hinton have helped lay the foundation for the machine learning revolution that started around 2010.
The development we are now witnessing has been made possible through access to the vast amounts of data that can be used to train networks, and through the enormous increase in computing power. Today’s artificial neural networks are often enormous and constructed from many layers. These are called deep neural networks and the way they are trained is called deep learning.
A quick glance at Hopfield’s article on associative memory, from 1982, provides some perspective on this development. In it, he used a network with 30 nodes. If all the nodes are connected to each other, there are 435 connections. The nodes have their values, the connections have different strengths and, in total, there are fewer than 500 parameters to keep track of. He also tried a network with 100 nodes, but this was too complicated, given the computer he was using at the time. We can compare this to the large language models of today, which are built as networks that can contain more than one trillion parameters (one million millions).
Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development of computational models inspired by the brain’s layered organization, also known as deep neural networks (DNNs), have recently opened new exciting possibilities for research in this area.
By comparing how DNNs and the human brain process information, researchers at Peking University, Beijing Normal University and other institutes in China have shed new light on the underpinnings of visual processing. Their paper, published in Nature Human Behavior, suggests that language actively shapes how both the brain and multi-modal DNNs process visual information.
A new wave of GoBruteforcer botnet malware attacks is targeting databases of cryptocurrency and blockchain projects on exposed servers believed to be configured using AI-generated examples.
GoBrutforcer is also known as GoBrut. It is a Golang-based botnet that typically targets exposed FTP, MySQL, PostgreSQL, and phpMyAdmin services.
The malware often relies on compromised Linux servers to scan random public IPs and carry out brute-force login attacks.
Detecting cancer in the earliest stages could dramatically reduce cancer deaths because cancers are usually easier to treat when caught early. To help achieve that goal, MIT and Microsoft researchers are using artificial intelligence to design molecular sensors for early detection.
The researchers developed an AI model to design peptides (short proteins) that are targeted by enzymes called proteases, which are overactive in cancer cells. Nanoparticles coated with these peptides can act as sensors that give off a signal if cancer-linked proteases are present anywhere in the body.
Depending on which proteases are detected, doctors would be able to diagnose the particular type of cancer that is present. These signals could be detected using a simple urine test that could even be done at home.