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.

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.

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.