In the absence of a national data privacy law in the U.S., California has been more active than any other state in efforts to fill the gap on a state level. The state enacted one of the nation's first data privacy laws, the California Privacy Rights Act (Proposition 24) in 2020, and an additional law will take effect in 2023. A new state agency created by the law, the California Privacy Protection Agency, recently issued an invitation for public comment on the many open questions surrounding the law's implementation. Our team of Stanford researchers, graduate students, and undergraduates examined the proposed law and have concluded that data privacy can be a useful tool in regulating AI, but California's new law must be more narrowly tailored to prevent overreach, focus more on AI model transparency, and ensure people's rights to delete their personal information are not usurped by the use of AI. Additionally, we suggest that the regulation's proposed transparency provision requiring companies to explain to consumers the logic underlying their "automated decision making" processes could be more powerful if it instead focused on providing greater transparency about the data used to enable such processes. Finally, we argue that the data embedded in machine-learning models must be explicitly included when considering consumers' rights to delete, know, and correct their data.
Headquartered in Palo Alto, California and originally incubated out of IIT-Bombay, Safe Security helps organizations measure and mitigate enterprise-wide cyber risk in real-time using its machine learning-enabled API-First platform. The company claims a 400% business growth in the last one year due to the growing significance of cybersecurity.
Prisma Cloud from Palo Alto Networks is sponsoring our coverage of AWS re:Invent 2021. Launched at the company's re:Invent 2021 user conference earlier this month, Amazon Web Services' Amazon SageMaker Serverless Inference is a new inference option to deploy machine learning models without configuring and managing the compute infrastructure. It brings some of the attributes of serverless computing, such as scale-to-zero and consumption-based pricing. With serverless inference, SageMaker decides to launch additional instances based on the concurrency and the utilization of existing compute resources. The fundamental difference between the other mechanisms and serverless inference is how the compute infrastructure is provisioned, scaled, and managed. You don't even need to choose an instance type or define the minimum and maximum capacity.
Sagawa, Shiori, Koh, Pang Wei, Lee, Tony, Gao, Irena, Xie, Sang Michael, Shen, Kendrick, Kumar, Ananya, Hu, Weihua, Yasunaga, Michihiro, Marklund, Henrik, Beery, Sara, David, Etienne, Stavness, Ian, Guo, Wei, Leskovec, Jure, Saenko, Kate, Hashimoto, Tatsunori, Levine, Sergey, Finn, Chelsea, Liang, Percy
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data. However, existing distribution shift benchmarks for unlabeled data do not reflect the breadth of scenarios that arise in real-world applications. In this work, we present the WILDS 2.0 update, which extends 8 of the 10 datasets in the WILDS benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment. To maintain consistency, the labeled training, validation, and test sets, as well as the evaluation metrics, are exactly the same as in the original WILDS benchmark. These datasets span a wide range of applications (from histology to wildlife conservation), tasks (classification, regression, and detection), and modalities (photos, satellite images, microscope slides, text, molecular graphs). We systematically benchmark state-of-the-art methods that leverage unlabeled data, including domain-invariant, self-training, and self-supervised methods, and show that their success on WILDS 2.0 is limited. To facilitate method development and evaluation, we provide an open-source package that automates data loading and contains all of the model architectures and methods used in this paper. Code and leaderboards are available at https://wilds.stanford.edu.
Palo Alto Networks unveiled new security features for its Prisma Cloud product that will give developers and DevOps teams access to container image sandboxing. The tool will also now run a third-party container image in an isolated environment, leveraging machine learning to perform an inspection of processes, file systems and networking activity pre-deployment. "Today's announcement delivers a leap in what's possible for container security, taking our incredible machine learning and applying it to third party, or any, image, regardless of its provenance -- enabling customers to run these in a pre-deployment sandbox," Palo Alto Networks said in a statement. "Automatically, Prisma Cloud analyzes the actual runtime for dynamic threats, learning all the processes that will be run, the network activity for the image, and all filesystem access to build an in-depth model of what the image will do." The update includes protection for virtual machines on Azure and Google Cloud as well as Windows support, service mesh support and improved API telemetry.
Li, Chengshu, Xia, Fei, Martín-Martín, Roberto, Lingelbach, Michael, Srivastava, Sanjana, Shen, Bokui, Vainio, Kent, Gokmen, Cem, Dharan, Gokul, Jain, Tanish, Kurenkov, Andrey, Liu, C. Karen, Gweon, Hyowon, Wu, Jiajun, Fei-Fei, Li, Savarese, Silvio
Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simulate: motion and physical contact. We present iGibson 2.0, an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations. As a result, we can collect demonstrations from humans on these new types of tasks, and use them for imitation learning. We evaluate the new capabilities of iGibson 2.0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI. iGibson 2.0 and its new dataset will be publicly available at http://svl.stanford.edu/igibson/.
SambaNova says just one quarter of a rack's worth of its DataScale computer can replace 64 separate Nvidia DGX-2 machines taking up multiple racks of equipment, when crunching various deep learning tasks such as natural language processing tasks on neural networks with billions of parameters such as Google's BERT-Large. The still very young market for artificial intelligence computers is spawning interesting business models. On Wednesday, SambaNova Systems, the Palo Alto-based startup that has received almost half a billion dollars in venture capital money, announced general availability of its dedicated AI computer, the DataScale and also announced an as-a-service offering where you can have the machine placed in your data center and rent its capacity for $10,000 a month. "What this is, is a way for people to gain quick and easy access at an entry price of $10,000 per month, and consume DataScale product as a service," said Marshall Choy, Vice President of product at SambaNova, in an interview with ZDNet via video. "I'll roll a rack, or many racks, into their data center, I'll own and manage and support the hardware for them, so they truly can just consume this product as a service offering."
PALO ALTO, Calif., July 22, 2020 – QC Ware, provider of enterprise software and services for quantum computing, announced a significant breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. QC Ware's algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware's Forgecloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators. "QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines."
Palo Alto Networks has released next-generation firewall (NGFW) software that integrates machine learning to help protect enterprise traffic to and from hybrid clouds, IoT devices and the growing numbers of remote workers. The machine learning is built into the latest version of Palo Alto's firewall operating system – PAN 10.0 – to prevent real-time signatureless attacks and to quickly identify new devices – in particular IoT products – with behavior-based identification. NGFWs include traditional firewall protections like stateful packet inspection but add advanced security judgments based on application, user and content. "Security attacks are continually morphing at rapid pace and traditional signature-based security approaches cannot keep up with the millions of new devices, running a variety of operating systems and software stacks coming on the network," said Anand Oswal senior vice president and GM at Palo Alto. "IoT devices, which are growing exponentially, exacerbated that issue because they have so many of their own different agents, patches and OS's it's impossible to set security policies around them." Oswal said the ML in its new NGFW uses inline machine-learning models to identify variants of known attacks as well as many unknown cyberthreats to prevent up to 95% of zero-day malware in real time.
PALO ALTO, Calif., May 6, 2020 – Cloudera (NYSE: CLDR), the enterprise data cloud company, today announced an expanded set of production machine learning capabilities for MLOps is now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML's new MLOps features and Cloudera SDX for models. Data scientists, machine learning engineers, and operators can collaborate in a single unified solution, drastically reducing time to value and minimizing business risk for production machine learning models. "Companies past the piloting phase of machine learning adoption are looking to scale deployments in production to hundreds or even thousands of ML models across their entire business," said Andrew Brust, Founder and CEO of Blue Badge Insights. "Managing, monitoring and governing models at this scale can't be a bespoke process. With a true ML operations platform, companies can make AI a mission-critical component of their digitally transformed business."