Larry Ellison’s Ambitions Beyond Having Fun and Surrounding Himself with Beautiful Things

For decades, Larry Ellison reveled in being the Silicon Valley executive who really knew how to have a good time. He spent as much as $200 million building a Japanese-inspired imperial villa near Palo Alto, Calif., bought the sixth-largest Hawaiian island and dated and married and divorced with never-ending zeal. Few paid much attention to exactly what his database company, Oracle, did. Sometimes, neither did Mr. Ellison. He did not show up for his keynote talk at Oracle’s annual convention in San Francisco in 2013 because he was on his yacht trying to win the America’s Cup, which he did. A biography about him was titled, "The Difference Between God and Larry Ellison: God Doesn’t Think He’s Larry Ellison."

A New Path Laid Down by His Friend Elon Musk

Now 80 years old and married for the fifth or possibly the sixth time, Mr. Ellison is expanding his ambitions beyond having fun and surrounding himself with beautiful things. Following a path laid down by his friend Mr. Musk, who has at least six companies that feed off one another, Mr. Ellison also appears to be planning to grow his corporate empire. Oracle keeps emerging as a possible bidder for TikTok, the wildly popular video app that Congress has decreed needs to divest itself of its ownership by the Chinese internet company ByteDance or be banned in the United States.

The Ellison Family’s Ambitions

The tech moguls have been unleashed. Mr. Musk, backed by President Trump, has obliterated the lines between public and private. He is blowing up government agencies and using his vast wealth to try to sway elections. Mr. Ellison, who may be closer to Mr. Trump than any mogul this side of Mr. Musk, appears to want nothing less than to bring the country under the benevolent sway of artificial intelligence, which he has said will bring about an era of bounty and harmony. One company, even if it is as successful as Oracle, might not help him get close to this goal. Several, though, might.

The Quest for Data

Mr. Ellison’s quest for data has hit setbacks. In November, a federal court in California gave final approval to a settlement over a class-action suit that accused Oracle of improperly capturing and selling individuals’ online and offline data without their permission. Oracle agreed to pay $115 million without admitting wrongdoing. During Mr. Ellison’s flamboyant heyday in the 1990s, he provided a striking contrast to what was then a relatively sober Silicon Valley. He described his office style as "management by ridicule." After Oracle had a self-inflicted near-death experience, he explained, "Oracle is run by adolescents. And that includes me."

Conclusion

The Ellison family’s ambitions are shifting from having fun and surrounding themselves with beautiful things to growing their corporate empire. With a fortune of $175 billion, there is not much left for Mr. Ellison to buy that would seriously dent his wallet. He is planning to combine thousands of databases into one enormous electronic repository, which can be mined by A.I. That will cure diseases and fix everything else, he told Tony Blair, the former British prime minister, at a symposium on reinventing government held in Dubai in February.

Frequently Asked Questions

Q: What are Larry Ellison’s ambitions?
A: Larry Ellison’s ambitions are expanding beyond having fun and surrounding himself with beautiful things to growing his corporate empire.

Q: What is Oracle’s role in TikTok?
A: Oracle is a possible bidder for TikTok, a wildly popular video app that Congress has decreed needs to divest itself of its ownership by the Chinese internet company ByteDance or be banned in the United States.

Q: What is the significance of the algorithm in the context of TikTok?
A: The algorithm is crucial to TikTok’s success, and it seems unlikely that any new owner would be able to replicate its magic.

Q: How does Elon Musk fit into this picture?
A: Elon Musk, a friend of Larry Ellison’s, has laid down a path for Mr. Ellison to follow, and Mr. Musk’s ownership of Twitter (now called X) has likely influenced Mr. Ellison’s interest in TikTok.

When major disasters hit and structures collapse, people can become trapped under rubble. Extricating victims from these hazardous environments can be dangerous and physically exhausting. To help rescue teams navigate these structures, MIT Lincoln Laboratory, in collaboration with researchers at the University of Notre Dame, developed the Soft Pathfinding Robotic Observation Unit (SPROUT). SPROUT is a vine robot — a soft robot that can grow and maneuver around obstacles and through small spaces. First responders can deploy SPROUT under collapsed structures to explore, map, and find optimum ingress routes through debris. 

“The urban search-and-rescue environment can be brutal and unforgiving, where even the most hardened technology struggles to operate. The fundamental way a vine robot works mitigates a lot of the challenges that other platforms face,” says Chad Council, a member of the SPROUT team, which is led by Nathaniel Hanson. The program is conducted out of the laboratory’s Human Resilience Technology Group. 

First responders regularly integrate technology, such as cameras and sensors, into their workflows to understand complex operating environments. However, many of these technologies have limitations. For example, cameras specially built for search-and-rescue operations can only probe on a straight path inside of a collapsed structure. If a team wants to search further into a pile, they need to cut an access hole to get to the next area of the space. Robots are good for exploring on top of rubble piles, but are ill-suited for searching in tight, unstable structures and costly to repair if damaged. The challenge that SPROUT addresses is how to get under collapsed structures using a low-cost, easy-to-operate robot that can carry cameras and sensors and traverse winding paths. 

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How a Flexible Robot Helps Find Survivors Inside Collapsed Buildings
Video: MIT Lincoln Laboratory

SPROUT is composed of an inflatable tube made of airtight fabric that unfurls from a fixed base. The tube inflates with air, and a motor controls its deployment. As the tube extends into rubble, it can flex around corners and squeeze through narrow passages. A camera and other sensors mounted to the tip of the tube image and map the environment the robot is navigating. An operator steers SPROUT with joysticks, watching a screen that displays the robot’s camera feed. Currently, SPROUT can deploy up to 10 feet, and the team is working on expanding it to 25 feet.

When building SPROUT, the team overcame a number of challenges related to the robot’s flexibility. Because the robot is made of a deformable material that bends at many points, determining and controlling the robot’s shape as it unfurls through the environment is difficult — think of trying to control an expanding wiggly sprinkler toy. Pinpointing how to apply air pressure within the robot so that steering is as simple as pointing the joystick forward to make the robot move forward was essential for system adoption by emergency responders. In addition, the team had to design the tube to minimize friction while the robot grows and engineer the controls for steering.

While a teleoperated system is a good starting point for assessing the hazards of void spaces, the team is also finding new ways to apply robot technologies to the domain, such as using data captured by the robot to build maps of the subsurface voids. “Collapse events are rare but devastating events. In robotics, we would typically want ground truth measurements to validate our approaches, but those simply don’t exist for collapsed structures,” Hanson says. To solve this problem, Hanson and his team made a simulator that allows them to create realistic depictions of collapsed structures and develop algorithms that map void spaces.

SPROUT was developed in collaboration with Margaret Coad, a professor at the University of Notre Dame and an MIT graduate. When looking for collaborators, Hanson — a graduate of Notre Dame — was already aware of Coad’s work on vine robots for industrial inspection. Coad’s expertise, together with the laboratory’s experience in engineering, strong partnership with urban search-and-rescue teams, and ability to develop fundamental technologies and prepare them for  transition to industry, “made this a really natural pairing to join forces and work on research for a traditionally underserved community,” Hanson says. “As one of the primary inventors of vine robots, Professor Coad brings invaluable expertise on the fabrication and modeling of these robots.”

Lincoln Laboratory tested SPROUT with first responders at the  Massachusetts Task Force 1  training site in Beverly, Massachusetts. The tests allowed the researchers to improve the durability and portability of the robot and learn how to grow and steer the robot more efficiently. The team is planning a larger field study this spring.

“Urban search-and-rescue teams and first responders serve critical roles in their communities but typically have little-to-no research and development budgets,” Hanson says. “This program has enabled us to push the technology readiness level of vine robots to a point where responders can engage with a hands-on demonstration of the system.”

Sensing in constrained spaces is not a problem unique to disaster response communities, Hanson adds. The team envisions the technology being used in the maintenance of military systems or critical infrastructure with difficult-to-access locations.

The initial program focused on mapping void spaces, but future work aims to localize hazards and assess the viability and safety of operations through rubble. “The mechanical performance of the robots has an immediate effect, but the real goal is to rethink the way sensors are used to enhance situational awareness for rescue teams,” says Hanson. “Ultimately, we want SPROUT to provide a complete operating picture to teams before anyone enters a rubble pile.” 

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Executive Summary

Artificial intelligence (AI) companies often claim that Artificial General Intelligence (AGI) is just around the corner. However, the latest models still require additional training to reach their full potential. Scale AI, a company that has played a key role in building advanced AI models, has developed a platform that can automatically test a model across thousands of benchmarks and tasks, pinpoint weaknesses, and flag additional training data to enhance their skills.

The Challenges of Training AI Models

Training AI models requires a significant amount of data and human labor. Large language models (LLMs) are trained on vast amounts of text scraped from books, the web, and other sources. However, turning these models into helpful, coherent, and well-mannered chatbots requires additional "post-training" in the form of humans who provide feedback on a model’s output.

Introducing Scale Evaluation

Scale AI has developed a new tool called Scale Evaluation, which automates some of this work using Scale’s own machine learning algorithms. The tool allows model makers to go through results and slice and dice them to understand where a model is not performing well, then use that to target the data campaigns for improvement.

Case Study: Reasoning Capabilities

In one instance, Scale Evaluation revealed that a model’s reasoning skills fell off when it was fed non-English prompts. The tool highlighted the issue and allowed the company to gather additional training data to address it.

Industry Reaction

Jonathan Frankle, chief AI scientist at Databricks, a company that builds large AI models, says that being able to test one foundation model against another sounds useful in principle. "Anyone who moves the ball forward on evaluation is helping us to build better AI," Frankle says.

The Future of AI Testing

Scale’s new tool offers a more comprehensive picture by combining many different benchmarks and can be used to devise custom tests of a model’s abilities, like probing its reasoning in different languages. The company’s AI can take a given problem and generate more examples, allowing for a more comprehensive test of a model’s skills.

Conclusion

The development of Scale Evaluation highlights the need for more comprehensive testing of AI models. As AI continues to advance, it is crucial that we have tools in place to ensure that these models are safe, trustworthy, and effective.

Frequently Asked Questions

Q: What is Scale Evaluation?
A: Scale Evaluation is a new tool developed by Scale AI that automates some of the work involved in testing and evaluating AI models.

Q: How does Scale Evaluation work?
A: The tool uses Scale’s own machine learning algorithms to test a model across thousands of benchmarks and tasks, pinpoint weaknesses, and flag additional training data to enhance their skills.

Q: What are the benefits of Scale Evaluation?
A: The tool allows model makers to go through results and slice and dice them to understand where a model is not performing well, then use that to target the data campaigns for improvement.

Q: How will Scale Evaluation impact the development of AI?
A: Scale’s new tool offers a more comprehensive picture by combining many different benchmarks and can be used to devise custom tests of a model’s abilities, like probing its reasoning in different languages. The company’s AI can take a given problem and generate more examples, allowing for a more comprehensive test of a model’s skills.