Gear Computers 10 things to know about Apple's new M5 Pro and M5 Max MacBook Pros The latest versions of Apple's MacBook Pro laptops include M5 chips with revamped architecture to bring performance upgrades across the board. The new computers look similar, but the guts have gotten revamped. We may earn revenue from the products available on this page and participate in affiliate programs. Apple's latest MacBook Pro refresh landed today with two new processors, the M5 Pro and M5 Max, built on what the company calls its Fusion Architecture. We have already been using the vanilla M5 chip in the latest version of the Apple Vision Pro headset, but these new MBP models crank up the power level even more.

Why it's high time we stopped anthropomorphising ants We have long drawn parallels between ants and humans. Now we are comparing the insects to computers. Pollution is making many cities unlivable for their human inhabitants, but it is also tearing ant families and communities apart. Ants recognise each other by sniffing a thin layer of hydrocarbons on the outside of their exoskeletons; each colony has a specific "smell". But a new study reveals that ozone emissions can change the structure of these hydrocarbons.

The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.

Points were sampled uniformly in the bands denoted by dashed lines. We posit that these barriers are due, at least in part, to the sensitivity of decision trees to transformations of the input resulting from greedy construction and simple decision rules. Of these, key limitation is the latter; even if we replace greedy construction with a perfect tree learner, simple distributions can nonetheless require an arbitrarily large axis-aligned tree to fit.

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances

But, they are limited to unimodal, Gaussian approximations of state uncertainty.