How this Houston innovator is making AI accessible, personal, and safe

HOUSTON INNOVATORS PODCAST EPISODE 198

Anshumali Shrivastava joins the Houston Innovators Podcast to share the revolutionary work ThirdAI is doing for artificial intelligence. Photo via rice.edu

Anshumali Shrivastava's career has evolved alongside the rise of artificial intelligence. Now, he believes his company represents the future of the industry's widespread implementation.

Shrivastava, who's also a professor at Rice University, founded ThirdAI, pronounced "third eye," in 2021 to democratize artificial intelligence through software innovations. As Shrivastava explains on the Houston Innovators Podcast, AI processes have historically been run on larger, less accessible computing hardware. ThirdAI's tools are able to run on a regular central processing unit, or CPU, rather than the more powerful graphics processing unit, or GPU.

"We focus on the problems that people are facing in the current AI ecosystem," Shrivastava says on the podcast. "Right now, if you are to build some of the large-language models and (linear programming) models, you need a lot of computing power, dedicated engineers to move it, and, even if you are using fully managed services, it's costly and there are a lot of privacy implications because you have to move data around."



These are some of the challenges for AI development, and, as Shrivastava points out, this process isn't even accessible for 90 percent of the world that lacks the infrastructure to do it. And, even for companies that can afford to invest in the dedicated GPU hardware, there's a global chip shortage.

ThirdAI's solution? Enable AI processes on the hardware that is accessible — CPUs.

"That is what our product offerings are — the AI ecosystem on commodity infrastructure," he says, explaining that ThirdAI's goal is also to improve existing AI applications.

One specific AI application that ThirdAI is making more effective for its customers is search tools in ecommerce. The need to make online shopping searches as quick and as accurate as possible directly affects the company's ability to complete the transaction. Wayfair tapped into ThirdAI's tech to address its latency in its on-site searching.

"Their problem was in the domain of making language models and search engines, which are AI based, very efficient," Shrivastava says. "We were able to bring down (latency) significantly with our technology."

One AI application that's taken off over the past few years is the chat-based model — led by OpenAI's ChatGPT. As exciting as the prospect of navigating information via chatbot is, many companies, understandably, have privacy concerns.

ThirdAI created PocketLLM to address this concern. The platform, which ThirdAI offers for free, operates completely on the harddrive of the owner of the data, meaning your data stays with you.

"ChatGPT has shown the world what is possible," Shrivastava says, explaining that 80 or 90 percent of use cases are people or companies wanting to take their knowledge and data and turn it into an AI chat tool. "What people want is a ChatGPT-kind of agent on their data, but they don't want their proprietary data to be leaked to the outer world."

"Because I can build AI where your data is, your data never have to leave your ecosystem. PocketLLM is a demonstration of that capability," Shrivastava continues. "It's a tool that uses our software stack and essentially looks at your data and builds this chat agent and offers you an air-gapped privacy."

Shrivastava explains that the tool can be used for enterprises or even personal applications — like navigating past conversations on email, seeing as no email provider seems to have an optimized search option.

PocketLLM is just one thing ThirdAI is working on. Shrivastava explains that the company is developing an entire ecosystem of tools that can be used on CPUs.

"If AI is going to be an agent, it better be personalized," he says, explaining that no one AI will have the right answer for everyone.

Shrivastava shares more about the future of both AI and ThirdAI, which is growing to keep up with demand. Listen to the interview here — or wherever you stream your podcasts — and subscribe for weekly episodes.

ThirdAI's new PocketLLM app is free to use and completely secure. Photo via Getty Images

Houston startup launches innovative chat tool on its mission to democratize AI

smart tech

Artificial intelligence has a big potential to disrupt the technology industry, and one Houston company that was founded by a computer science professor at Rice University, is fast on its way to help lead that future now in a convenient and affordable way.

Founded by Anshumali Shrivastava and Tharun Medini, a recent Ph.D. who graduated under Shrivastava from Rice's Department of Electrical and Computer Engineering, ThirdAI is building AI deep learning tools that aim to be sustainable and scalable to fit the changing needs of the industry. The company is on a mission to democratize AI, Shrivastava tells InnovationMap.

Shrivastava likes to use the word efficiently when describing what makes ThirdAI different, and how its programs can teach AI via multiple avenues to be what he refers to as “1,000 times more efficient.”

“The carbon footprint of these models are off the charts, and so expensive,” Shrivastava. “We believe this could be made efficient. … We use the same ideas that were developed, but we do it on a massive scale.”

ThirdAI's latest tool is a multilingual ChatGPT-like AI training tool PocketLLM app. Announced earlier this month, the tool is free. According to the company, users have access to a personalized chatbot that understands what the user is searching within documents, and can be fine-tuned to help elaborate your thoughts through a neural search.

ThirdAI's PocketLLM app is free to use. Image courtesy of ThirdAI

The app is private and secure and runs on deep-learning algorithms according to Vinod Iyengar, head of product at ThirdAI, and no one — not even ThirdAI — has access to the documents except the user.

“Tools exist to help people search text files, but that requires sharing your data with third parties,” says Iyengar in a news release. “Our solution is private and secure, powered by deep learning algorithms. And it returns results lightning fast.”

The process includes the user installing the app, uploading any text document files, and clicking "train." Minutes later, you have an AI tool that can process the information in those documents.

“The neural search encourages you to elaborate on your thoughts with details in the discover window and see the difference in results,” says Shrivastava in the release. “It can also be fine-tuned to your tastes by selecting the relevant option and hitting the update button to re-train."

In September of 2021, ThirdAI — pronounced "third eye" — raised $6 million in seed funding. The round was invested in by three California-based VCs — Neotribe Ventures and Cervin Ventures, which co-led the round with support from Firebolt Ventures. The technology ThirdAI is working with comes from 10 years of deep learning research and innovation. The company's technology has the potential to make computing 15-times faster, the company reports.

Anshumali Shrivastava is an associate professor of computer science at Rice University. Photo via rice.edu

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How Houston innovators played a role in the historic Artemis II splashdown

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Research from Rice University played a critical role in the safe return of U.S. astronauts aboard NASA’s Artemis II mission this month.

Rice mechanical engineer Tayfun E. Tezduyar and longtime collaborator Kenji Takizawa developed a key computational parachute fluid-structure interaction (FSI) analysis system that proved vital in NASA’s Orion capsule’s descent into the Pacific Ocean. The FSI system, originally developed in 2013 alongside NASA Johnson Space Center, was critical in Orion’s three-parachute design, which slowed the capsule as it returned to Earth, according to Rice.

The model helped ensure that the parachute design was large enough to slow the capsule for a safe landing while also being stable enough to prevent the capsule from oscillating as it descended.

“You cannot separate the aerodynamics from the structural dynamics,” Tezduyar said in a news release. “They influence each other continuously and even more so for large spacecraft parachutes, so the analysis must capture that interaction in a robustly coupled way.”

The end result was a final parachute system, refined through NASA drop tests and Rice’s computational FSI analysis, that eliminated fluctuations and produced a stable descent profile.

Apart from the dynamic challenges in design, modeling Orion’s parachutes also required solving complex equations that considered airflow and fabric deformation and accounted for features like ringsail canopy construction and aerodynamic interactions among multiple parachutes in a cluster.

“Essentially, my entire group was dedicated to that work, because I considered it a national priority,” Tezduyar added in the release. “Kenji and I were personally involved in every computer simulation. Some of the best graduate students and research associates I met in my career worked on the project, creating unique, first-of-its-kind parachute computer simulations, one after the other.”

Current Intuitive Machines engineer Mario Romero also worked on Orion during his time at NASA. From 2018 to 2021, Romero was a member of the Orion Crew Capsule Recovery Team, which focused on creating likely scenarios that crewmembers could encounter in Orion.

The team trained in NASA’s 6.2-million-gallon pool, using wave machines to replicate a range of sea conditions. They also simulated worst-case scenarios by cutting the lights, blasting high-powered fans and tipping a mock capsule to mimic distress situations. In some drills, mock crew members were treated as “injured,” requiring the team to practice safe, controlled egress procedures.

“It’s hard to find the appropriate descriptors that can fully encapsulate the feeling of getting to witness all the work we, and everyone else, did being put into action,” Romero tells InnovationMap. “I loved seeing the reactions of everyone, but especially of the Houston communities—that brought me a real sense of gratitude and joy.”

Intuitive Machines was also selected to support the Artemis II mission using its Space Data Network and ground station infrastructure. The company monitored radio signals sent from the Orion spacecraft and used Doppler measurements to help determine the spacecraft's precise position and speed.

Tim Crain, Chief Technology Officer at Intuitive Machines, wrote about the experience last week.

"I specialized in orbital mechanics and deep space navigation in graduate school,” Crain shared. “But seeing the theory behind tracking spacecraft come to life as they thread through planetary gravity fields on ultra-precise trajectories still seems like magic."

UH breakthrough moves superconductivity closer to real-world use

Energy Breakthrough

University of Houston researchers have set a new benchmark in the field of superconductivity.

Researchers from the UH physics department and the Texas Center for Superconductivity (TcSUH) have broken the transition temperature record for superconductivity at ambient pressure. The accomplishment could lead to more efficient ways to generate, transmit and store energy, which researchers believe could improve power grids, medical technologies and energy systems by enabling electricity to flow without resistance, according to a release from UH.

To break the record, UH researchers achieved a transition temperature 151 Kelvin, which is the highest ever recorded at ambient pressure since the discovery of superconductivity in 1911.

The transition temperature represents the point just before a material becomes superconducting, where electricity can flow through it without resistance. Scientists have been working for decades to push transition temperature closer to room temperature, which would make superconducting technologies more practical and affordable.

Currently, most superconductors must be cooled to extremely low temperatures, making them more expensive and difficult to operate.

UH physicists Ching-Wu Chu and Liangzi Deng published the research in the Proceedings of the National Academy of Sciences earlier this month. It was funded by Intellectual Ventures and the state of Texas via TcSUH and other foundations. Chu, founding director and chief scientist at TcSUH, previously made the breakthrough discovery that the material YBCO reaches superconductivity at minus 93 K in 1987. This helped begin a global competition to develop high-temperature superconductors.

“Transmitting electricity in the grid loses about 8% of the electricity,” Chu, who’s also a professor of physics at UH and the paper’s senior author, said in a news release. “If we conserve that energy, that’s billions of dollars of savings and it also saves us lots of effort and reduces environmental impacts.”

Chu and his team used a technique known as pressure quenching, which has been adapted from techniques used to create diamonds. With pressure quenching, researchers first apply intense pressure to the material to enhance its superconducting properties and raise its transition temperature.

Next, researchers are targeting ambient-pressure, room-temperature superconductivity of around 300 K. In a companion PNAS paper, Chu and Deng point to pressure quenching as a promising approach to help bridge the gap between current results and that goal.

“Room-temperature superconductivity has been seen as a ‘holy grail’ by scientists for over a century,” Rohit Prasankumar, director of superconductivity research at Intellectual Ventures, said in the release. “The UH team’s result shows that this goal is closer than ever before. However, the distance between the new record set in this study and room temperature is still about 140 C. Closing this gap will require concerted, intentional efforts by the broader scientific community, including materials scientists, chemists, and engineers, as well as physicists.”

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This article originally appeared on EnergyCapitalHTX.com.