According to Ion Stoica, co-founder of Databricks and Anyscale, and also a senior professor of computer science at Berkeley, 2023 will be the year of "distributed AI frameworks." Needless to say, he has already had a hand in creating such a tool, in the form of Anyscale's open source Ray platform. Among other uses, Ray helps power OpenAI's groundbreaking ChatGPT. I interviewed Stoica to find out what Ray does exactly and, more generally, what is needed to scale AI software in this new era of generative AI. We also discuss the latest in "sky computing," a term Stoica and his Berkeley team introduced in 2021, in a paper that proposed a new form of cloud computing based around interoperability and distributed computing.

The future of AI is distributed, said Ion Stoica, co-founder, executive chairman and president of Anyscale on the first day of VB Transform. And that's because model complexity shows no signs of slowing down. "For the past couple of years, the compute requirements to train a state-of-the-art model, depending on the data set, grow between 10 times and 35 times every 18 months," he said. Just five years ago the largest models were fitting on a single GPU; fast forward to today and just to fit the parameters of the most advanced models, it takes hundreds or even thousands of GPUs. PaLM, or the Pathway Language Model from Google, has 530 billion parameters -- and that's only about half of the largest, at more than 1 trillion parameters.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Artificial intelligence (AI) and machine learning (ML) are disrupting every industry. Their impacts and integrations will only continue to grow. And ultimately, the future of AI is in distributed computing, Ion Stoica, cofounder, executive chairman and president of Anyscale, told the audience this week at VenureBeat's Transform 2022 conference. Distributed computing allows components of software systems to be shared among multiple computers and run as one system, thus improving efficiency and performance.

Opaque, a new startup born out of Berkely's RISELabs, announced a $9.5 million seed round today to build a solution to access and work with sensitive data in the cloud in a secure way, even with multiple organizations involved. Intel Capital led today's investment with participation by Race Capital, The House Fund and FactoryHQ. The company helps customers work with secure data in the cloud while making sure the data they are working on is not being exposed to cloud providers, other research participants or anyone else, says company president Raluca Ada Popa. "What we do is we use this very exciting hardware mechanism called Enclave, which [operates] deep down in the processor -- it's a physical black box -- and only gets decrypted there. Company co-founder Ion Stoica, who was a co-founder at Databricks, says the startup's solution helps resolve two conflicting trends. On one hand, businesses increasingly want to make use of data, but at the same time are seeing a growing trend toward privacy. Opaque is designed to resolve this by giving customers access to their data in a safe and fully encrypted way. Data is the world's most valuable (and vulnerable) resource The company describes the solution as "a novel combination of two key technologies layered on top of state-of-the-art cloud security--secure hardware enclaves and cryptographic fortification." This enables customers to work with data -- for example to build machine learning models -- without exposing the data to others, yet while generating meaningful results. Popa says this could be helpful for hospitals working together on cancer research, who want to find better treatment options without exposing a given hospital's patient data to other hospitals, or banks looking for money laundering without exposing customer data to other banks, as a couple of examples. Investors were likely attracted to the pedigree of Popa, a computer security and applied crypto professor at UC Berkeley and Stoica, who is also a Berkeley professor and co-founded Databricks. Both helped found RISELabs at Berkeley where they developed the solution and spun it out as a company. Mark Rostick, vice president and senior managing director at lead investor Intel Capital says his firm has been working with the founders since the startup's earliest days, recognizing the potential of this solution to help companies find complex solutions even when there are multiple organizations involved sharing sensitive data. "Enterprises struggle to find value in data across silos due to confidentiality and other concerns.

Inside a 13th-floor boardroom in downtown San Francisco, the atmosphere was tense. It was November 2015, and Databricks, a two-year-old software company started by a group of seven Berkeley researchers, was long on buzz but short on revenue. The directors awkwardly broached subjects that had been rehashed time and again. The startup had been trying to raise funds for five months, but venture capitalists were keeping it at arm's length, wary of its paltry sales. Seeing no other option, NEA partner Pete Sonsini, an existing investor, raised his hand to save the company with an emergency $30 million injection. Founding CEO Ion Stoica had agreed to step aside and return to his professorship at the University of California, Berkeley. The obvious move was to bring in a seasoned Silicon Valley executive, which is exactly what Databricks' chief competitor Snowflake did twice on its way to a software-record $33 billion IPO in September 2020.

Supervised learning is a more commonly used form of machine learning than reinforcement learning in part because it's a faster, cheaper form of machine learning. With data sets, a supervised learning model can be mapped to inputs and outputs to create image recognition or machine translation models. A reinforcement learning algorithm, on the other hand, must observe, and that can take time, said UC Berkeley professor Ion Stoica. Stoica works on robotics and reinforcement learning at UC Berkeley's RISELab, and if you're a developer working today, then you've likely used or come across some of his work that has built part of the modern infrastructure for machine learning. He spoke today as part of Transform, an annual AI event VentureBeat holds that this year takes place online.

In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiring sophisticated a priori knowledge of environmental parameters which may be difficult to obtain in real-time scenarios. In this paper, we propose a novel hybrid model-driven and data-driven architecture that adapts to the ever changing environment and allows for adaptive unimodular waveform design. In particular, the approach lays the groundwork for developing extremely low-cost waveform design and processing frameworks for radar systems deployed in autonomous vehicles. The proposed model-based deep architecture imitates a well-known unimodular signal design algorithm in its structure, and can quickly infer statistical information from the environment using the observed data. Our numerical experiments portray the advantages of using the proposed method for efficient radar waveform design in time-varying environments.

Life has gotten more convenient since 2012, when breakthroughs in machine learning triggered the ongoing frenzy of investment in artificial intelligence. Speech recognition works most of the time, for example, and you can unlock the new iPhone with your face. People with the skills to build things such systems have reaped great benefits--they've become the most prized of tech workers. But a new report on the downsides of progress in AI warns they need to pay more attention to the heavy moral burdens created by their work. It calls for urgent and active discussion of how AI technology could be misused.

Over the past decade, big data analysis and applications have revolutionized practices in business and science. They enabled new businesses (e.g., Facebook, Netflix), to disrupt existing industries (e.g., Airbnb, Uber), and accelerated scientific discovery (genomics, astronomy, biology). Today, we are seeing glimpses of the next revolution in data and computation, driven by three trends. First, there is a rapidly growing segment of the economy (e.g., Apple, Facebook, GE) that collects vast amounts of consumer and industrial information and uses this information to provide new services. This trend is spreading widely via the increasing ubiquity of networked sensors in devices like cell phones, thermostats and cars.

NASA is investing in a fleet of robotic probes that could soon explore Jupiter, Saturn and other interstellar bodies made of gas by flying with momentum from the wind. NASA's Jet Propulsion Laboratory has been awarded $100,000 to complete a study that will explore the possibility of "windbots" buzzing through the atmospheres above Jupiter and Saturn. To work, each drone would need to be self-sufficient and able to recharge based on wind and changes in temperature. The news was first reported Thursday by Wired magazine. "One could imagine a network of windbots existing for quite a long time on Jupiter or Saturn, sending information about ever-changing weather patterns," Jet Propulsion Laboratory engineer Adrian Stoica told Wired.