LoRa (Long Range) radio uses little power and can communicate at up to three miles in urban areas and five miles or more in the open. Many drone operators now use a repeater, carried on another drone, to extend the reach
First-person view (FPV) drones are quickly becoming a key weapon in the Ukraine conflict. There is rapidly developing drone warfare involving thousands of drones every month. Both Russia and Ukraine have fielded jammers and drone guns firing radio waves to knock out drone communications.
Most recently a Russian group claims to have developed a ‘magic radio’ for FPVs which is highly resistant to jamming. A physicist with the handle DanielR evaluated the device minutely in a detailed Twitter thread. While the technology is not astounding, what is interesting is that the device uses cheap, off-the-shelf components.
After a first round in which the judge refused a few arguments, things have gotten tightened up a bit.
New artists – from photographers and game artists – have joined the lawsuit
New arguments have been added:
In an effort to expand what is copyrighted by artists, the complaint makes the claim that even non-copyrighted works may be automatically eligible for copyright protections if they include the artists’ “distinctive mark,” such as their signature, which many do contain.
AI companies that relied upon the widely-used LAION-400M and LAION-5B datasets — which do contain copyrighted works but only links to them and other metadata about them, and were made available for research purposes — would have had to download the actual images to train their models, thus made “unauthorized copies.” to train their models.
The suit claims that the very architecture of diffusion models themselves — in which an AI adds visual “noise” or additional pixels to an image in multiple steps, then tries to reverse the process to get close to the resulting initial image — is itself designed to come as close to possible to replicating the initial training material. The lawsuit cites several papers about diffusion models and claim are simply ‘reconstructing the (possibly copyrighted) training set’.
This third point is likely the actual meat of the suit; but they haven’t spelled it out quite as sufficiently as I think they should have. To me, the questions that are really the crux of the question are:
Do large-scale models work by generating novel output, or do they just copy and interpolate between individual training examples?
Whether training (using copyrighted art) is covered by fair use or qualifies as a copyright violation.
Determining what autonomous driving algorithms do in difficult life-and-death situations is a real problem. Until now, many have likened it to the famous ‘trolley problem‘.
There is a runaway trolley barreling down its tracks. Ahead, on the tracks, there are five people tied up and unable to move. The trolley is headed straight for them but you are standing in the train yard next to a lever. If you pull this lever, the trolley will switch to a different set of tracks. However, you notice that there is one person on the side track. You have two (and only two) options:
Do nothing, in which case the trolley will kill the five people on the main track.
Pull the lever, diverting the trolley onto the side track where it will kill one person.
The problem asks which is the more ethical option? Or, more simply: What is the right thing to do?
Analysts have noted that the variations of these “Trolley problems” largely just highlight the difference between deontological and consequentialist ethical systems. Researchers, however, are finding that distinction isn’t actually that useful for determining what autonomous driving algorithms should do.
Instead, they note that drivers have to make many more realistic moral decisions every day. Should I drive over the speed limit? Should I run a red light? Should I pull over for an ambulance?
For example, if someone is driving 20 miles over the speed limit and runs a red light, then they may find themselves in a situation where they have to either swerve into traffic or get into a collision. There’s currently very little data in the literature on how we make moral judgments about the decisions drivers make in everyday situations.
Researchers developed a series of experiments designed to collect data on how humans make moral judgments about decisions that people make in low-stakes traffic situations, and from that developed the Agent Deed Consequence (ADC) model.
The approach is highly utilitarian. It side-steps complex ethical problems by simply collecting data on what average people would consider ethical or not. The early research for ADC claims the judgements of the average people and ethics experts very often match; even if they were not trained in ethics. This more utilitarian approach may be sufficient for some tasks, but inherently is at risk from larger issues ‘If everyone jumped off a bridge, would you?” It’s often referred to as the Bandwagon Fallacy. Decisions made by the masses is something even Socrates argued against in The Republic.
Energy Vault (NYSE: NRGV) has licensed six additional EVx gravity energy storage systems in China after starting construction of the world’s first facility near Shanghai.
After trying, then giving up on, battery technology (which is not exactly eco friendly), Energy Vault is designing and building facilities that essentially recreate the physics of the most popular form of energy storage – pumped hydro – but with giant movable blocks.
The design goes like this: Energy Vault installations use excess renewable energy at low times to lift massive composite blocks. Then, when the energy is once again needed on the grid, the blocks are lowered and the potential energy is turned to kinetic energy from the dropping blocks. That lowering then spins generators that supply electricity to the grid. The company believes it will be able to achieve a respectable round-trip efficiency (RTE) of over 80% with its current design.
Like many other projects, the devil is in the details – and there is growing skepticism. There is almost no solid technical details being released by the company. Many have questioned how the designs will achieve the astounding 80% efficiency when the gold standard of pumped hydro can’t usually achieve that rate. There’s no information on how the motors work or just about all the details that would be required to figure out if the claims are even remotely feasible.
The Northwest Passage is a sea lane between the Atlantic and Pacific oceans through the Arctic Ocean, along the northern coast of North America. For centuries, such a trade route to Asia was sought.
A northern route was discovered in 1850 by the Irish explorer Robert McClure. Scotsman John Rae explored a more southerly area in 1854 through which Norwegian Roald Amundsen made the first complete passage in 1903–1906 (yes, it took 3 years since they would be frozen in the ice all winter). The passage was known for its many disastrous expeditions. Despite the incredible feat, nearly year-round ice pack made this traversal impractical.
In recent years, however, artic sea ice has been receding. In 2013, a Chinese shipping line successful made passage by the 73,500 ton Nordic Orion. The company expressed interest in continuing the route more frequently as winter sea ice recedes.
But it’s not just big companies, you can now take one of these trips yourself – if you have the money. In just the last few years, tour companies are starting to make regular trips through the Northwest passage. Costs range from around $10,000 – $50,000.
Another World Adventures / Classic-Sailing – traverse the passage on the 124′ long Tecla – an actual wooden sailing ship. It must be popular, the 26 night 2023-2024 winter trip is sold out.
Adventure Canada offers partial tours through the Northwest Passage
Dutch company Holoconnects showed off its Holobox at CES. The selling point is it can bring realistic holograms into conference venues, hotels and more. The Holobox has an 86-inch, transparent 4K LCD screen for a life-size and realistic-looking holographic projection. Because of the lighting behind the screen, it creates the illusion of a 3D projection or hologram.
This works a bit better for multiple viewers compared to head tracking techniques that only work for one viewer.
Takashi Yoshinaga created a version that uses HoloLens2 and allows you to pull the person out of the holobox.
Want to know how policy is generated and how governments evaluate challenges and future direction? Companies like Moss Adams present interesting research they do.
In this interesting discussion, Richard Florida (author of Rise of the Creative Class and award winning commentary on socio-economic urban studies) points out the misconceptions and changes facing cities in 2023 and beyond. He gives a really interesting summary of how things are (which is very different than what the media tells us), and will likely change, since Covid.
It’s an interesting take on how cities are changing and likely futures.
This seems like quite a shock for a big company that is clearly dominating the marketplace – but the reason is likely strategic. Unity has been on a tear acquiring lots of tool and support companies in the last few years to make Unity the engine that has everything you need – from rendering, to marketing, to authoring, to storefronts.
Still, as the economic reigns tighten, this likely can’t continue. There was already a disastrous attempt to start charging fees for this once free engine, as well as announcing 3% layoffs on some of it’s more speculative VFX division.
Rowhammer is a DRAM memory security vulnerability discovered in June 2014 (paper here). It demonstrates a security problem in which programs can modify memory they should not have access too. In the paper, they note how DRAM memory cells interact electrically between themselves by leaking their charges, possibly changing the contents of nearby memory rows that were not addressed in the original memory access. This circumvention of the isolation between DRAM memory cells results from the high cell density in modern DRAM, and can be triggered by specially crafted memory access patterns that rapidly activate the same memory rows numerous times.
The row hammer effect has been used in some privilege escalation computer security exploits (Paper here). Google’s Project Zero demonstrated two working privilege escalation exploits based on the row hammer effect in 2015. Since then, there has been a back and forth war of fixes and new exploits – some even involving ways to circumvent ECC (error-correcting) DRAM.
Now we fast forward to today, and there is another way to manipulate bits – RowPress (Paper here). Instead of ‘hammering’ neighbor rows with certain write patterns, this method involves manipulating the length of time the aggressor row is left open when reading it. When a computer accesses a chunk of memory, it opens the rows to the cells storing the desired data and transfers it to the CPU. The researchers show you can use clever methods to manipulate how long that row is left open. When an attacker row is left open the optimal amount, you can affect nearby victim rows:
We show that keeping a DRAM row (i.e., aggressor row) open for a long period of time (i.e., a large aggressor row on time, tAggON) disturbs physically nearby DRAM rows. Doing so induces bitflips in the victim row without requiring (tens of) thousands of activations to the aggressor row. We characterize RowPress in 164 off-the-shelf DDR4 DRAM chips from all three major manufacturers and find that RowPress significantly amplifies DRAM’s vulnerability to read-disturb attacks (i.e., greatly reduces the minimum number of total aggressor row activations to cause at least one bitflip, ACmin.
The methods they use are VERY clever. They started on a FPGA-based test beds to test the idea, then moved to PC’s. This required a deep knowledge of memory hardware and involves clever manipulation of the memory controller and cache systems (section 6.2 of the paper). The summary in the comments was great:
With respect to knowing how physical memory maps to their process memory, they allocated a 1GB hugepage and use a technique called DRAMA to determine the row-column mapping.
To keep their target row open, they take advantage of the fact (new to me) that multiple cache blocks will live on the same physical row, which means that repeated accesses to those blocks can influence the memory controller to keep that row open. They also empty the processor cache between each iteration so that they can be sure that they will hit the actual RAM. To bypass the target row refresh (TRR) mechanisms that have been implemented to counter traditional RowHammer attacks, they also toggle a large number of dummy rows so that the TRR will pick up on those rather than the actual aggressor rows, since TRR implementations apparently have a small number of candidate aggressor rows.
