"It's more exhausting," says Afer, a deputy commander of the "Da Vinci Wolves", describing how one of the best-known battalions in Ukraine has to defend against constant Russian attacks. Where once the invaders might have tried small group assaults with armoured vehicles, now the tactic is to try and sneak through on foot one by one, evading frontline Ukrainian drones, and find somewhere to hide. Under what little cover remains, survivors then try to gather a group of 10 or so and attack Ukrainian positions. It is costly – "in the last 24 hours we killed 11," Afer says – but the assaults that previously might have happened once or twice a day are now relentless. To the Da Vinci commander it seems that the Russians are terrified of their own officers, which is why they follow near suicidal orders.

Rubik, a Ukrainian military drone pilot, had tracked the every move of one particular Russian soldier for weeks, with the promise of earning at least six points for killing him. In a war increasingly dominated by drones, Ukraine's military has launched a new score-based purchasing system for units to replenish their stocks, and Rubik -- his military nickname -- was looking to help his brigade cash in. On the "Brave1" platform launched by Ukraine's digital transformation ministry, new drones for the troops fighting Russia's invasion go for between two and a few dozen points.

For a while now, Tesla CEO Elon Musk has seemed awfully distracted. His past few years in non-Tesla activities include: buying and renaming Twitter; going all in on President Donald Trump's election campaign and then an obscure Wisconsin Supreme Court race; a lot of babymaking, plus attendant drama; and months spent standing up the so-called Department of Government Efficiency. Meanwhile, Tesla sales have slid as the electric-car maker faces fierce competition from Chinese manufacturers and rejection from buyers turned off by his politics. Now a new and unprecedentedly gigantic 1 trillion pay package proposal from the Tesla board will attempt to recenter Musk's focus on the automaker. Maybe "automaker" is the wrong term: For years now, Musk has argued that Tesla should be valued as an autonomous vehicle and robotics firm.

The governing board for the electric carmaker Tesla has put forward a pay package for CEO Elon Musk that could make him the world's first trillionaire -- but only if he meets a series of high-performance standards over the next 10 years. The proposal became public on Friday, as part of the company's regulatory filings. Musk is already considered one of the world's wealthiest businessmen, and one of his eye-popping pay packages from 2018 continues to be the subject of a legal battle. But if approved, the latest proposal would likely be the largest corporate pay package in United States history. Tesla shareholders will vote on the compensation scheme on November 6.

The camera zooms too close to the president's face; the table at which the tech executives are seated seems far too long. Mark Zuckerberg is there, and Bill Gates and Tim Cook and Satya Nadella and Sam Altman and on and on, a baker's dozen or so of Silicon Valley's most powerful people--cutthroat competitors all--united here to pledge allegiance to Donald Trump. The introduction from Trump is characteristically both overgilded and confusing: "It's an honor to be here with this group of people. And then, about 90 seconds in, the pandering begins. This was Donald Trump's dinner with tech leaders at the State Dining Room in the White House on Thursday evening, broadcast in part for all to see on C-SPAN.

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research team designed more than 36 million possible compounds and computationally screened them for antimicrobial properties. The top candidates they discovered are structurally distinct from any existing antibiotics, and they appear to work by novel mechanisms that disrupt bacterial cell membranes. This approach allowed the researchers to generate and evaluate theoretical compounds that have never been seen before -- a strategy that they now hope to apply to identify and design compounds with activity against other species of bacteria. "We're excited about the new possibilities that this project opens up for antibiotics development. Our work shows the power of AI from a drug design standpoint, and enables us to exploit much larger chemical spaces that were previously inaccessible," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and a member of the Broad Institute.

"These days at 70 years old you are still a child," China's Xi Jinping, 72, was translated as saying. "With the developments of biotechnology, human organs can be continuously transplanted, and people can live younger and younger, and even achieve immortality," Russia's Vladimir Putin, also 72, is reported to have replied. In reality, rounds of organ transplantation surgery aren't likely to help anyone radically extend their lifespan anytime soon. And it's a simplistic way to think about aging--a process so complicated that researchers can't agree on what causes it, why it occurs, or even how to define it, let alone "treat" it. This article first appeared in The Checkup, MIT Technology Review's weekly biotech newsletter.

The giant, remote-controlled vehicle -- somewhere between a tractor trailer, a tank and a Zamboni in appearance -- slowly rolled across the dry, brittle grass growing between the tangle of freeways making up the 101 and 23 interchange in Thousand Oaks. And as it rolled over the land, that fire incinerated any brush it encountered, leaving only a thin smoke cloud billowing from the top of the machine, some flashes of orange and red from behind its metal skirt and, in its wake, a desolate, smoldering black line. BurnBot isn't the fastest way to rid a landscape of dangerously flammable vegetation (it tops out at around 0.5 mph) but it can do something that traditional vegetation management techniques cannot: with almost surgical precision, it can kill the flammable brush sitting within feet of homes and highways on even the hottest and driest days and with virtually no safety risks or disruptions to daily life. On a recent summer afternoon, as wildland firefighters maneuvered the machine and mopped up the charred earth on a stretch of highway about 30 miles west of Los Angeles on the 101, a who's who of SoCal's wildfire leadership looked on -- from the California Department of Forestry and Fire Protection, local fire departments, Caltrans, the U.S. forest and park services, Southern California Edison and state Legislature. The sweet smoky smell of wildland fire permeated the hot midday air.

Differential privacy (DP) is the standard for privacy-preserving analysis, and introduces a fundamental trade-off between privacy guarantees and model performance. Selecting the optimal balance is a critical challenge that can be framed as a multi-objective optimization (MOO) problem where one first discovers the set of optimal trade-offs (the Pareto front) and then learns a decision-maker's preference over them. While a rich body of work on interactive MOO exists, the standard approach -- modeling the objective functions with generic surrogates and learning preferences from simple pairwise feedback -- is inefficient for DP because it fails to leverage the problem's unique structure: a point on the Pareto front can be generated directly by maximizing accuracy for a fixed privacy level. Motivated by this property, we first derive the shape of the trade-off theoretically, which allows us to model the Pareto front directly and efficiently. To address inefficiency in preference learning, we replace pairwise comparisons with a more informative interaction. In particular, we present the user with hypothetical trade-off curves and ask them to pick their preferred trade-off. Our experiments on differentially private logistic regression and deep transfer learning across six real-world datasets show that our method converges to the optimal privacy-accuracy trade-off with significantly less computational cost and user interaction than baselines.

We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich-Rubinstein distance, between general intensity measures based on observations from spatio-temporal point processes. Specifically, we model the observations by two weakly time-stationary point processes with spatial intensity measures $μ$ and $ν$ over the expanding window $(0,t]$ as $t$ increases to infinity, and establish sharp convergence rates of the empirical UOT in terms of the intrinsic dimensions of the measures. We assume a sub-quadratic temporal growth condition of the variance of the process, which allows for a wide range of temporal dependencies. As the growth approaches quadratic, the convergence rate becomes slower. This variance assumption is related to the time-reduced factorial covariance measure, and we exemplify its validity for various point processes, including the Poisson cluster, Hawkes, Neyman-Scott, and log-Gaussian Cox processes. Complementary to our upper bounds, we also derive matching lower bounds for various spatio-temporal point processes of interest and establish near minimax rate optimality of the empirical Kantorovich-Rubinstein distance.