A pair of studies analyzing the effects of AI on our planet have been released and the news is fairly grim. Greenpeace studied the emissions generated from the production of the semiconductors used in AI chips and found that there was a fourfold increase in 2024. This analysis was completed using publicly available data. Many of the big chipmakers like NVIDIA rely on companies like Taiwan Semiconductor Manufacturing Co and SK Hynix Inc. for the components of GPUs and memory units. Most of this manufacturing happens in Taiwan, South Korea and Japan, where power grids are primarily reliant on fossil fuels.

We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses to determine which charity to expose them to in a donation solicitation. The design balances two competing objectives: optimizing the outcomes for the subjects in the experiment (``cumulative regret minimization'') and gathering data that will be most useful for policy learning, that is, for learning an assignment rule that will maximize welfare if used after the experiment (``simple regret minimization''). We evaluate alternative experimental designs by collecting pilot data and then conducting a simulation study. Next, we implement our selected algorithm. Finally, we perform a second simulation study anchored to the collected data that evaluates the benefits of the algorithm we chose. Our first result is that the value of a learned policy in this setting is higher when data is collected via a uniform randomization rather than collected adaptively using standard cumulative regret minimization or policy learning algorithms. We propose a simple heuristic for adaptive experimentation that improves upon uniform randomization from the perspective of policy learning at the expense of increasing cumulative regret relative to alternative bandit algorithms. The heuristic modifies an existing contextual bandit algorithm by (i) imposing a lower bound on assignment probabilities that decay slowly so that no arm is discarded too quickly, and (ii) after adaptively collecting data, restricting policy learning to select from arms where sufficient data has been gathered.

Google has pledged to no longer build AIs for the fossil fuel industry as it further distances itself from controversial developments. A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent. In an interview with CUBE's John Furrier, the leader of Google's CTO office, Will Grannis, said that Google will "no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations." The pledge from Google Cloud is welcome, but it must be taken in a wider context.

Google has announced it will no longer build custom AI or machine learning tools for oil and gas companies. The move comes in response to a report from Greenpeace called'Oil in the Cloud,' which identified the tech giant as one of three main tech companies helping fossil fuel companies expand their extraction projects. While Google still has a number of current contracts it says it will honor, a company spokesperson said that moving forward it will no longer build custom AI or machine learning algorithms'to facilitate upstream extraction in the oil and gas industry.' The spokesperson pointed out that the company receives just $65million in annual revenue through Google Cloud from oil and gas companies, less than one percent of the total revenue from cloud services. Greenpeace was encouraged by Google's response and called on other major tech companies to follow suit, saying it has already had'productive conversations' with many.

The tech industry faces criticism for the significant energy used to power its computing infrastructure. In response, the major tech companies have made data centers more efficient, and worked to ensure they're powered at least in part by renewable energy. These changes are a step in the right direction, but don't come close to tackling the problem. Most large tech companies continue to rely heavily on fossil fuels, and when they do commit to efficiency goals, these are not open to public scrutiny and validation. Researchers Lotfi Belkhir and Ahmed Elmeligi estimate that the tech sector will contribute 3.0–3.6% of global greenhouse emissions by 2020, more than double what the sector produced in 2007 (Belkhir and Elmeligi, 2018).

Bee brains have evolved to be so energy efficient that they may be able to count using only four nerve cells, scientists have found. Simulations with a brain model used just four nerve cells and found this simplistic organ would be able to count up to, and beyond, five. The small number of nerve cells needed to count indicates that brain size is not as important as brain organisation, scientists claim. Bee brains have evolved to be so energy efficient that they can count using only four brain cells, scientists have found. Simulations showed the simple brain was capable of counting small quantities by closely studying one item at a time.

Apple's got a new robot. You can't buy this'bot, though -- it's only for Apple's use. The robot, named Daisy, takes apart old iPhones so that the valuable materials in the devices, like gold, can be extracted. It's an improved version of "Liam," the recycling robot that Apple revealed in 2016 to take apart iPhone 6 phones. Daisy can disassemble 200 iPhones an hour, Apple said in a press release on Thursday extolling the virtues of its latest droid. And Daisy can take apart nine different versions of the iPhone, a step up from Liam's limited capabilities that only allowed it to dismantle the iPhone 6.

Apple harvested almost 40 million worth of gold from recycled gadgets last year, and is now deploying robots to take iPhones apart in a major environmental push. In its latest annual environmental responsibility report, which was published last week, Apple explained that it gathered 2,204 pounds of recycled gold during its fiscal year 2015. The gold, which weighs more than a ton, is worth 39.6 million. Apple recovered more than 63 million pounds of various materials via its "take-back" recycling initiatives in 2015, according to the company's environmental report. The tech giant gathered over 23 million pounds of steel, making it the most recycled material, and more than 13 million pounds of plastics.

Apple has revealed a 29 armed robot that can rip apart an iPhone in 11 seconds for recycling. It is hoped the machine will help recycle silver, tungsten and other metals from the handsets. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. It has already been installed near Apple's HQ in Cupertino, and it plans to build a second in Europe.