Technology
Google appeals judge’s decision forcing app store competition on Android
Today, Google filed its official notice of appeal against the district court ruling and jury verdict in Epic v. Google. Judge James Donato’s ruling earlier this week would force the company to distribute third-party app stores on Google Play and drop requirements that Google Play apps use its billing system, among other competition-friendly changes.
Google had said it would be appealing the verdict. “As we have already stated, these changes would put consumers’ privacy and security at risk, make it harder for developers to promote their apps, and reduce competition on devices,” Google VP of regulatory affairs Lee-Anne Mulholland said in a blog post on Monday. “Ultimately, while these changes presumably satisfy Epic, they will cause a range of unintended consequences that will harm American consumers, developers and device makers.”
Donato’s ruling this week said that Google’s changes must go into effect starting November 1st, 2024, and they would stay in effect until November 1st, 2027.
Servers computers
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Technology
All Pixel Watches finally get “Full Battery Notification” feature
Google is rolling out a much-requested Quality of Life (QoL) feature for Pixel Watch devices. You may have been longing for a method to determine your smartwatch’s full charge without the need to physically check it. Google has finally addressed the issue with the “Full Battery Notification” feature for all Pixel Watch 3 series and older models.
In a blog post, Google has officially confirmed the rollout of “Full Battery Notification.” The way the feature works is pretty simple but neat. Basically, it sends your smartphone linked to the Pixel Watch a notification letting you know that your wearable has reached 100% charge. So, you’ll know immediately when your smartwatch is ready for you to put it back on. This is especially useful when you’re away from the watch’s charging dock.
“Full Battery Notification” wide rollout for Pixel Watch devices has started
Thanks to the new feature, those moments of constantly checking your wearable while charging are over. While it’s a pretty simple feature on paper, it contributes to a better overall user experience for Pixel Watch devices. The “Full Battery Notification” is available on all Pixel Watch models and will be enabled automatically. This means you won’t need to do anything to receive alerts.
Google began testing this in 2023
It’s noteworthy that Google first tested the feature in 2023. The company sent it to some units but never carried out a mass rollout for everyone. Additionally, the feature was quickly lost by those who had received it at the time. Since then, a large number of Pixel Watch users have been demanding the return of the feature for everyone. Months later, the users’ request is becoming a reality.
We don’t know why the Mountain View giant took so long to deliver a seemingly simple feature on paper. Anyway, the wait is finally over. It would also be wonderful if the feature could work bidirectionally in the future. That is, when your smartphone reaches 100% charge, your smartwatch will receive a notification.
Technology
Tesla unveils its ‘Cybercab’ robotaxi
Tesla has introduced a robotaxi called Cybercab during its “We, Robot” event at Warner Bros. Discovery’s studio in California, six months after Elon Musk revealed that the company was going to launch one. Musk made his way to the stage on a Cybercab, which has no steering wheels or pedals, announcing that “there’s 20 more” where it came from. He talked about how our current modes of transportation “suck” and how how cars are on standby all the time. A car that’s autonomous could be used more, he said. “With autonomy, you get your time back… Autonomous cars are going to become 10 times safer.”
He said the costs of autonomous transport will be so low that they will be comparable to mass transit. In time, he said the operating cost of the robotaxi to be 20 cents a mile, 30 to 40 cents with taxes. He confirmed to the audience that people will be able to buy one and that Tesla expects to sell the Cybercab for below $30,000. He even envisions a future wherein people own several, managing a fleet like “shepherd,” that can earn them money through a ridesharing network. When asked when the model will be available, he replied that Tesla will start by making fully autonomous unsupervised Full Sell Driving available on the Model 3 and Model Y in Texas and California. Musk said that the Cybercab is expected to go into production before 2027, but he himself admitted that he tends to be “highly optimistic with timeframes.” And he does — he said way back in 2019 that Tesla will “have over a million robotaxis on the road” within a year.
Talking about the Cybercab’s technology, he said that it uses AI and vision. Tesla has long dropped radars and sensors that other robotaxis like Waymo’s use extensively. Because of that, he said that it doesn’t need expensive equipment, and Tesla can keep manufacturing costs low. Notably, the Cybercab doesn’t come with a charging port and uses inductive charging instead.
Reuters reported back in April that Musk ordered the company to “go all in” on robotaxis built on its small-vehicle platform. Musk previously said that the model was going to be unveiled on August 8, but he later announced that the company’s robotaxi event will be pushed back to October after he requested “an important design change to the front.” The delay would also give the company extra time to “show off a few other things,” he explained. The Cybercab that Tesla presented to the audience today is all silver and seems to have taken design cues from the Cybertruck. It doesn’t have a back windshield and has doors that open upwards.
In addition to reporting the robotaxi’s existence, Reuters revealed in April that Tesla scrapped its plans for an affordable, $25,000 electric vehicle. While Musk called it a lie, another report by Electrek backed Reuters‘ story and cited “sources familiar with the matter” who reportedly told the publication that the low-cost EV’s development has been postponed.
After talking about the Cybercab, Musk briefly introduced the Robovan — an autonomous van that can carry up to 20 people and transport goods. It’ll get the costs of travel down even further, he said, since it could transport big groups like sports teams. Finally, Musk brought out a parade of Tesla’s humanoid Optimus robots. Musk said Tesla has made dramatic progress on its development over the past year and that in the future, it can teach your kids, mow your lawn and even be your friend. He believes Tesla could sell its Optimus robots for between $20,000 to $30,000.
Servers computers
Dell PowerEdge FC630 Blade Server Review & Overview | Memory Install Tips | How to Configure DDR4
The Dell PowerEdge FC630 is an enterprise level blade server that goes into the FX2 or FX2S enclosure. In our video, we will provide an overview specifically focused on memory and CPUs. We will review how to install your RAM upgrades as well as how to properly configure memory inside your FC630 server. We will provide a few general tips and tricks to make your installation process as smooth as possible. Sit back, relax and enjoy the Dell FC630 memory tutorial video.
The Dell FC630 blade server is part of Dell’s 13th Generation. They operate with dual Intel Xeon E5-2600 V3 or V4 series processors, which is an LGA2011-3 CPU socket. We recommend the E5-2620v3 for lower end applications, E5-2660v3 or E5-2670v3 as value CPUs and E5-2690v4 or E5-2695v4 or E5-2697v4 or E5-2699v4 for higher end applications. There are 24 DIMM slots on the motherboard and runs on DDR4 memory. The 13th Gen is the first generation of DDR4 based blade servers from Dell. With the FC630, you can utilize 2133 MT/s, 24000 MT/s or 2666MT/s. Do note, 2666 will clock down to 2400MT/s, which is technically the fastest speed. Size options include 4GB, 8GB, 16GB, 32GB or 64GB. There are two types of memory you can utilize with your Dell PowerEdge FC630 blade server: ECC Registered (RDIMM) or Load Reduced (LRDIMM). We recommend RDIMMs for lower sizes such as 4GB 8GB or 16GB. However we recommend LRDIMMs for higher DIMM sizes such as 32GB or 64GB. With LRDIMMs you can reach double the overall memory capacity compared to RDIMMs. RDIMMs – The max memory configuration is 768GB via (24 x 32GB) DDR4 PC4-19200T-R 2400MHz ECC Registered Server Memory. LRDIMMs – The max configuration is 1.5TB via (24 x 64GB) DDR4 PC4-19200T-L 2400MHz Load Reduced Server Memory.
There are two Intel processors. Each CPU runs 12 memory slots. There are 4 Memory Channels per CPU. There are 3 DIMM slots per Memory Channel. The memory channels are extremely important. If you are not loading 24 DIMMs, then you want to make sure you split your DIMMs evenly across the various memory channels. This is very easy to identify. Dell has labeled and color coded to help you easily load your memory upgrades. You always want to start your install at the beginning of the memory channel. White is the first DIMM slot in each memory channel followed by black second and then green third. The proper memory configuration would be in sets of 4 such as 4, 8, 16 or 24 DIMMs to maintain an even load balance/distribution across the FC630 memory channels. In our video, we will discuss the channels in more depth and we will actually load and upgrade our FC630 server.
The highly versatile Dell PowerEdge FC630 and will work for a wide variety of applications such as hosting and virtualization. Do you want to buy a refurbished Dell PowerEdge FC630? You can custom configure your own FC630 blade server on our website or contact sales@cloudninjas.com for quotes.
Interested in buying a refurbished Dell PowerEdge FC630 Blade Server ? Please visit: https://cloudninjas.com/
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Technology
Can AI really compete with human data scientists? OpenAI’s new benchmark puts it to the test
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More
OpenAI has introduced a new tool to measure artificial intelligence capabilities in machine learning engineering. The benchmark, called MLE-bench, challenges AI systems with 75 real-world data science competitions from Kaggle, a popular platform for machine learning contests.
This benchmark emerges as tech companies intensify efforts to develop more capable AI systems. MLE-bench goes beyond testing an AI’s computational or pattern recognition abilities; it assesses whether AI can plan, troubleshoot, and innovate in the complex field of machine learning engineering.
AI takes on Kaggle: Impressive wins and surprising setbacks
The results reveal both the progress and limitations of current AI technology. OpenAI’s most advanced model, o1-preview, when paired with specialized scaffolding called AIDE, achieved medal-worthy performance in 16.9% of the competitions. This performance is notable, suggesting that in some cases, the AI system could compete at a level comparable to skilled human data scientists.
However, the study also highlights significant gaps between AI and human expertise. The AI models often succeeded in applying standard techniques but struggled with tasks requiring adaptability or creative problem-solving. This limitation underscores the continued importance of human insight in the field of data science.
Machine learning engineering involves designing and optimizing the systems that enable AI to learn from data. MLE-bench evaluates AI agents on various aspects of this process, including data preparation, model selection, and performance tuning.
From lab to industry: The far-reaching impact of AI in data science
The implications of this research extend beyond academic interest. The development of AI systems capable of handling complex machine learning tasks independently could accelerate scientific research and product development across various industries. However, it also raises questions about the evolving role of human data scientists and the potential for rapid advancements in AI capabilities.
OpenAI’s decision to make MLE-benc open-source allows for broader examination and use of the benchmark. This move may help establish common standards for evaluating AI progress in machine learning engineering, potentially shaping future development and safety considerations in the field.
As AI systems approach human-level performance in specialized areas, benchmarks like MLE-bench provide crucial metrics for tracking progress. They offer a reality check against inflated claims of AI capabilities, providing clear, quantifiable measures of current AI strengths and weaknesses.
The future of AI and human collaboration in machine learning
The ongoing efforts to enhance AI capabilities are gaining momentum. MLE-bench offers a new perspective on this progress, particularly in the realm of data science and machine learning. As these AI systems improve, they may soon work in tandem with human experts, potentially expanding the horizons of machine learning applications.
However, it’s important to note that while the benchmark shows promising results, it also reveals that AI still has a long way to go before it can fully replicate the nuanced decision-making and creativity of experienced data scientists. The challenge now lies in bridging this gap and determining how best to integrate AI capabilities with human expertise in the field of machine learning engineering.
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