AI is used to compose music, suggests recipes and make investment decisions, but a company has designed a system that can edit human genes. California-based Profluent Bio developed a system capable of creating a range of bespoke cures for disease by developing molecules that have never existed in nature. The AI was trained on a database of 5.1 million CRISPR-associated (Cas) proteins, allowing it to create potential molecules that could be used in gene editing. The system then narrowed down the results to four million sequences, allowing it to identify the gene editor the team named OpenCRISPR-1. Experiments showed OpenCRISPR-1 performed as well as Cas proteins, but it also reduced the impact on off-target sites by 95 percent.

An AI has designed anti-microbial proteins that were then tested in real life and shown to work. The same approach could eventually be used to make new medicines. Proteins are made of chains of amino acids. The sequence of those acids determine the protein's shape and function. Ali Madani at Salesforce Research in California and his colleagues used an AI to design millions of new proteins, then created a small sample of those to test whether they worked.

In a study published in Science today, Berger and her colleagues pull several of these strands together and use NLP to predict mutations that allow viruses to avoid being detected by antibodies in the human immune system, a process known as viral immune escape. The basic idea is that the interpretation of a virus by an immune system is analogous to the interpretation of a sentence by a human. "It's a neat paper, building off the momentum of previous work," says Ali Madani, a scientist at Salesforce, who is using NLP to predict protein sequences. Berger's team uses two different linguistic concepts: grammar and semantics (or meaning). The genetic or evolutionary fitness of a virus--characteristics such as how good it is at infecting a host--can be interpreted in terms of grammatical correctness.

For a long time, being out at sea meant being out of sight and out of reach. And all kinds of shenanigans went on as a result - countries secretly selling oil and other goods to countries they're not supposed to under international sanctions rules, for example, not to mention piracy and kidnapping. The problem is that captains can easily switch off the current way of tracking ships, called the Automatic Identification System (AIS), turning their vessels into "ghost ships". But now thousands of surveillance satellites have been launched into space, and artificial intelligence (AI) is being applied to the images they take. There's no longer anywhere to hide - even for ghost ships.

There was no one home when Avisheh Madani arrived to tour a San Francisco rental property. No one human, that is. Madani, 35, used a code from an app to unlock the door and was greeted immediately by a robot. "It was definitely weird," she said. The robot, really a moveable video monitor, is the brainchild of Zenplace, a rental management company based in San Francisco and expanding quickly across the nation.

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f ( x ) given a budget. In our setting, it is not practical to request samples of  f ( x ) at precise input values due to the formidable cost of precise experimental setup. Rather, we may request a constrained experiment, which is a subset r of the input space for which the experimenter returns  x  in r and  f ( x ). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location  x  of the next observation increases. Our goal is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We introduce cost-sensitive policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic functions and functions derived from real-world experimental data indicate that our policies outperform random selection, that the model-based policies are superior to model-free ones, and give insights into which policies are preferable overall.