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- 3rd generation NeuPro AI/ML architecture offers scalable performance of 20 to 1,200 TOPS at SoC and Chiplet levels, lowers memory bandwidth by 6X

- Targets broad use of AI/ML in automotive, industrial, 5G networks and handsets, surveillance cameras, and Edge Compute

LAS VEGAS, Jan. 6, 2022 /PRNewswire/ -- Consumer Electronics Show CEVA, Inc. (NASDAQ: CEVA), the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions, today announced NeuPro-M, its latest generation processor architecture for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. An industry first, NeuPro-M supports both system-on-chip (SoC) as well as Heterogeneous SoC (HSoC) scalability to achieve up to 1,200 TOPS and offers optional robust secure boot and end-to-end data privacy.

NeuPro-M is the latest generation processor architecture from CEVA for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor.

NeuProM compliant processors initially include the following pre-configured cores:

NPM11 single NeuPro-M engine, up to 20 TOPS at 1.25GHz

NPM18 eight NeuPro-M engines, up to 160 TOPS at 1.25GHz

Illustrating its leading-edge performance, a single NPM11 core, when processing a ResNet50 convolutional neural network, achieves a 5X performance increase and 6X memory bandwidth reduction versus its predecessor, which results in exceptional power efficiency of up to 24 TOPS per watt.

Built on the success of its' predecessors, NeuPro-M is capable of processing all known neural network architectures, as well as integrated native support for next-generation networks like transformers, 3D convolution, self-attention and all types of recurrent neural networks. NeuPro-M has been optimized to process more than 250 neural networks, more than 450 AI kernels and more than 50 algorithms. The embedded vector processing unit (VPU) ensures future proof software-based support of new neural network topologies and new advances in AI workloads. Furthermore, the CDNN offline compression tool can increase the FPS/Watt of the NeuPro-M by a factor of 5-10X for common benchmarks, with very minimal impact on accuracy.

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Ran Snir, Vice President and General Manager of the Vision Business Unit at CEVA, commented: "The artificial intelligence and machine learning processing requirements of edge AI and edge compute are growing at an incredible rate, as more and more data is generated and sensor-related software workloads continue to migrate to neural networks for better performance and efficiencies. With the power budget remaining the same for these devices, we need to find new and innovative methods of utilizing AI at the edge in these increasingly sophisticated systems. NeuPro-M is designed on the back of our extensive experience deploying AI processors and accelerators in millions of devices, from drones to security cameras, smartphones and automotive systems. Its innovative, distributed architecture and shared memory system controllers reduces bandwidth and latency to an absolute minimum and provides superb overall utilization and power efficiency. With the ability to connect multiple NeuPro-M compliant cores in a SoC or Chiplet to address the most demanding AI workloads, our customers can take their smart edge processor designs to the next level."

The NeuPro-M heterogenic architecture is composed of function-specific co-processors and load balancing mechanisms that are the main contributors to the huge leap in performance and efficiency compared to its predecessor. By distributing control functions to local controllers and implementing local memory resources in a hierarchical manner, the NeuPro-M achieves data flow flexibility that result in more than 90% utilization and protects against data starvation of the different co-processors and accelerators at any given time. The optimal load balancing is obtained by practicing various data flow schemes that are adopted to the specific network, the desired bandwidth, the available memory and the target performance, by the CDNN framework.

NeuPro-M architecture highlights include:

Main grid array consisting of 4K MACs (Multiply And Accumulates), with mixed precision of 2-16 bits

Winograd transform engine for weights and activations, reducing convolution time by 2X and allowing 8-bit convolution processing with <0.5% precision degradation

Sparsity engine to avoid operations with zero-value weights or activations per layer, for up to 4X performance gain, while reducing memory bandwidth and power consumption

Fully programmable Vector Processing Unit, for handling new unsupported neural network architectures with all data types, from 32-bit Floating Point down to 2-bit Binary Neural Networks (BNN)

Configurable Weight and Data compression down to 2-bits while storing to memory, and real-time decompression upon reading, for reduced memory bandwidth

Dynamically configured two level memory architecture to minimize power consumption attributed to data transfers to and from an external SDRAM

To illustrate the benefit of these innovative features in the NeuPro-M architecture, concurrent use of the orthogonal mechanisms of Winograd transform, Sparsity engine, and low-resolution 4x4-bit activations, delivers more than a 3X reduction in cycle count of networks such as Resnet50 and Yolo V3.

As neural network Weights and Biases and the data set and network topology become key Intellectual Property of the owner, there is a strong need to protect these from unauthorized use. The NeuPro-M architecture supports secure access in the form of optional root of trust, authentication, and cryptographic accelerators.

For the automotive market, NeuPro-M cores and its CEVA Deep Neural Network (CDNN) deep learning compiler and software toolkit comply with Automotive ISO26262 ASIL-B functional safety standard and meets the stringent quality assurance standards IATF16949 and A-Spice.

Together with CEVA's multi award-winning neural network compiler CDNN and its robust software development environment, NeuPro-M provides a fully programmable hardware/software AI development environment for customers to maximize their AI performance. CDNN includes innovative software that can fully utilize the customers' NeuPro-M customized hardware to optimize power, performance & bandwidth. The CDNN software also includes a memory manager for memory reduction and optimal load balancing algorithms, and wide support of various network formats including ONNX, Caffe, TensorFlow, TensorFlow Lite, Pytorch and more. CDNN is compatible with common open-source frameworks, including Glow, tvm, Halide and TensorFlow and includes model optimization features like 'layer fusion' and 'post training quantization' all while using precision conservation methods.

NeuPro-M is available for licensing to lead customers today and for general licensing in Q2 this year. NeuPro-M customers can also benefit from Heterogenous SoC design services from CEVA to help integrate and support system design and chiplet development. For further information, visit https://www.ceva-dsp.com/product/ceva-neupro-m/.

About CEVA, Inc.CEVA is the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions for a smarter, safer, connected world. We provide Digital Signal Processors, AI engines, wireless platforms, cryptography cores and complementary software for sensor fusion, image enhancement, computer vision, voice input and artificial intelligence. These technologies are offered in combination with our Intrinsix IP integration services, helping our customers address their most complex and time-critical integrated circuit design projects. Leveraging our technologies and chip design skills, many of the world's leading semiconductors, system companies and OEMs create power-efficient, intelligent, secure and connected devices for a range of end markets, including mobile, consumer, automotive, robotics, industrial, aerospace & defense and IoT.

Our DSP-based solutions include platforms for 5G baseband processing in mobile, IoT and infrastructure, advanced imaging and computer vision for any camera-enabled device, audio/voice/speech and ultra-low-power always-on/sensing applications for multiple IoT markets. For sensor fusion, our Hillcrest Labs sensor processing technologies provide a broad range of sensor fusion software and inertial measurement unit ("IMU") solutions for markets including hearables, wearables, AR/VR, PC, robotics, remote controls and IoT. For wireless IoT, our platforms for Bluetooth (low energy and dual mode), Wi-Fi 4/5/6/6e (802.11n/ac/ax), Ultra-wideband (UWB), NB-IoT and GNSS are the most broadly licensed connectivity platforms in the industry.

Visit us at http://www.ceva-dsp.com and follow us on Twitter, YouTube, Facebook, LinkedIn and Instagram.

CEVA - a global leader in signal processing IP for everything smart and connected. (PRNewsFoto/CEVA, Inc.)

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CEVA Redefines High Performance AI/ML Processing for Edge AI and Edge Compute Devices with its NeuPro-M Heterogeneous and Secure Processor...

SEATTLE--(BUSINESS WIRE)--The Cloud Security Alliance (CSA), the worlds leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, today released Artificial Intelligence (AI) in Healthcare. Drafted by the Health Information Management Working Group, the report provides an overview of the ways in which AI and machine learning (ML) can be used to bring about major transformations in healthcare while addressing the challenges their use presents, and offering guidance for how to best incorporate them into healthcare systems now and in the future.

The document shares examples, use cases, and treatment methods for how AI, machine learning, and data mining can be effectively utilized throughout a healthcare system, including in research, diagnosis, and treatment. It also addresses ethical and legal challenges, bias in AI, and how it relates to telehealth, big data, and cloud computing in healthcare.

This is the time when healthcare leaders should be accelerating their use of AI, which when used with cloud computing has the potential for drastically improving patient outcomes. But, as with any new technology entering the healthcare arena, there are several challenges, among them a lack of data exchange, regulatory compliance requirements, and patient and provider adoption. This paper offers a summary of the areas in which healthcare can benefit, while providing healthcare delivery organizations guidance on how to best address the challenges their use brings, said Dr. James Angle, the papers lead author and co-chair of the Health Information Management Working Group.

The emergence of AI as a tool for better healthcare offers opportunities to improve patient and clinical outcomes and reduce costs. The ever-increasing volume and complexity of healthcare data provide an ideal environment for the application of both AI and ML, and there are several applications where these technologies can deliver an incredible value. Even so, healthcare delivery organizations must evaluate each to determine if and how they can be adopted, said Michael Roza, a contributor to the paper.

The CSA Health Information Management Working Group aims to provide a direct influence on how health information service providers deliver secure cloud solutions (services, transport, applications, and storage) to their clients, and to foster cloud awareness within all aspects of healthcare and related industries. Individuals interested in becoming involved in Health Information Management future research and initiatives are invited to join the working group.

Download Artificial Intelligence in Healthcare.

About Cloud Security Alliance

The Cloud Security Alliance (CSA) is the worlds leading organization dedicated to defining and raising awareness of best practices to help ensure a secure cloud computing environment. CSA harnesses the subject matter expertise of industry practitioners, associations, governments, and its corporate and individual members to offer cloud security-specific research, education, training, certification, events, and products. CSA's activities, knowledge, and extensive network benefit the entire community impacted by cloud from providers and customers to governments, entrepreneurs, and the assurance industry and provide a forum through which different parties can work together to create and maintain a trusted cloud ecosystem. For further information, visit us at http://www.cloudsecurityalliance.org, and follow us on Twitter @cloudsa.

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Cloud Security Alliance Releases Guidance on Use of Artificial Intelligence (AI) in Healthcare - Business Wire

Luis Voloch is the CTO and co-founder of Immunai. He was previously Israel Tech Challenges head of data science, worked on varied machine learning efforts at Palantir and led the machine learning initiatives for ML modeling of DNA data at MyHeritage.

What do deep neural networks and cancer immunotherapy have in common?

While both are among the most transformational areas of modern science, 30 years ago, these fields were all but ridiculed by the scientific community. As a result, progress in each happened at the sidelines of academia for decades.

Between the 1970s and 1990s, some of the most prominent computer scientists, including Marvin Minsky, in his book Perceptrons, argued that neural networks (the backbone of most modern AI) would never work for most applications. He exposed flaws in the early conceptions of neural networks and argued that the whole approach was ineffective.

Meanwhile, during the 1980s through the 2000s, neural network pioneers and believers Geoffrey Hinton, Yoshua Bengio, and Yann LeCun continued their efforts and pursued their intuition that neural networks would succeed. These researchers found that most of the original ideas were correct but simply needed more data (think of ImageNET), computational power and further modeling tweaks to be effective.

Hinton, Bengio and LeCun were awarded the Turing Award in 2018 (the computer science equivalent of a Nobel prize) for their work. Today, their revelations have made neural networks the most vibrant area of computer science and have revolutionized fields such as computer vision and natural language processing.

Cancer immunology faced similar obstacles. Treatment with IL-2 cytokine, one of the first immunomodulatory drugs, failed to meet expectations. These outcomes slowed further research, and for decades, cancer immunology wasnt taken seriously by many cancer biologists. With the effort and intuition of some, however, it was discovered decades later that the concept of boosting the immune system to fight cancer had objective validity. It turned out that we just needed better drug targets and combinations, and eventually, researchers demonstrated that the immune system is the best tool in our fight against cancer.

James P. Allison and Tasuku Honjo, who pioneered the class of cancer immunotherapy drugs known as checkpoint inhibitors, were awarded the Nobel Prize in 2018.

Though widely accepted now, it took decades for the scientific establishment to accept these novel approaches as valid.

Machine learning and immunotherapy have more in common than historical similarities. The beauty of immunotherapy is that it leverages the versatility and flexibility of the immune system to fight different types of cancers. While the first immunotherapies showed results in a few cancers, they were later shown to work in many other cancer types. AI, similarly, utilizes flexible tools to solve a wide range of problems across applications via transfer and multitask learning. These processes are made possible through access to large-scale data.

Heres something to remember: The resurgence of neural networks started in 2012 after the AlexNet architecture demonstrated 84.7% accuracy in the ImageNET competition. This level of performance was revolutionary at the time, with the second-best model achieving 73.8% accuracy. The ImageNET dataset, started by Fei-Fei Li, is robust, well labeled and high quality. As a result, it has been integral to how far neural networks have brought computer vision today.

Interestingly, similar developments are happening now in biology. Life sciences companies and labs are building large-scale datasets with tens of millions of immune cells labeled consistently to ensure the validity of the underlying data. These datasets are the analogs of ImageNET in biology.

Were already seeing these large, high-quality datasets giving rise to experimentation at a rate and scale that was impossible before. For example, machine learning is being used to identify immune cell types in different parts of the body and their involvement in various diseases. After identifying patterns, algorithms can map or predict different immune trajectories, which can then be used to interpret, for example, why some cancer immunotherapies work on particular cancer types and some dont. The datasets act as the Google Maps of the immune system.

Mapping patterns of genes, proteins and cell interactions across diseases allows researchers to understand molecular pathways as the building blocks of disease. The presence or absence of a functional block helps interpret why some cancer immunotherapies work on particular cancer types but not others.

Mapping pathways of genes and proteins across diseases and phenotypes allows researchers to learn how they work together to activate specific pathways and fight multiple diseases. Genes can be part of numerous pathways, and they can cause distinct types of cells to behave differently.

Moreover, different cell types can share similar gene activities, and the same functional pathways can be found in various immune-related disorders. This makes a case for building machine learning models that perform effectively on specific tasks and transfer to other tasks.

Transfer learning works in deep learning models, for example, by taking simple patterns (in images, think of simple lines and curves) learned by early layers of a neural network and leveraging those layers for different problems. In biology, this allows us to transfer knowledge on how specific genes and pathways in one disease or cell type play a role in other contexts.

AI research that addresses the effects of genetic changes (perturbations) on immune cells and their impact on the cells and possible treatments is increasingly common in cancer immunology. This kind of research will enable us to understand these cells more quickly and lead to better drugs and treatments.

With large-scale data fueling further research in immunotherapy and AI, we are confident that more effective drugs to fight cancer will appear soon, thus giving hope to the over 18 million people who are diagnosed with cancer every year.

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The convergence of deep neural networks and immunotherapy - TechCrunch

A recent study by Gartner indicates that by 2025 the 10% of enterprises that establish Machine Learning (ML) or Artificial Intelligence (AI) engineering best practices will generate at least three times more value from their AI and ML efforts than the 90% of enterprises that don't. With such a high value estimated to be derived only from the adoption of ML/AI practices, it is difficult to not agree that the future of enterprises rests heavily on AI and ML technologies with other digital technologies.The pandemic has unveiled a world that embraced technology at a pace that would have otherwise taken ages to evolve.

Traditional practices that saw monolithic systems, lack of flexibility and manual processes were all blocking innovation.

Also Read:Artificial Intelligence: A Pathway to success for enterprises

However, mass new-age technology acceptance induced by the pandemic has helped enterprises overcome these challenges. Modern technologies like AI and ML are opening a new world of possibilities for organisations.

Seizing the early-mover advantage will particularly benefit organisations in taking important business decisions in a more informed, intuitive way.

The applicability of new-age technologies is growing every day. For example, marketers are starting to use ML-based tools to personalise offers to their customers and further measure their satisfaction levels through the successful implementation of ML algorithms into their operations.

This and there are more examples of how AI/ML algorithms are enabling organisations run their businesses smartly and make them profitable.Additionally, enterprises are recognising the benefits of cloud infrastructure and applications with ML and AI algorithms built in.

They allow companies to spend less time on manual work and management and instead focus on high-value jobs that drive business results. ML can result in efficiencies in workloads of enterprise IT and ultimately reduce IT infrastructure costs.

This stands especially true in India, where consulting firm Accenture estimates in one of its reports that the use of AI could add $957 billion to the Indian economy in 2035 provided the "right investments" are made in new-age technology. India, with its entrepreneurial spirit, abundance of talent and the right sources of education has mega potential to unleash AI's true capabilities - but they need the right partner.

The biggest limitation in using AI is that companies often run into implementation issues which could be anything from scarcity of data science expertise to making the platform perform in real-time.

As a result, there is slight reluctance in accepting AI among organisations, and this, in turn, is leading to inconsistencies and lack of results.

Also Read:Three ways AI can help transform businesses

However, with the right partner, India's true potential can be harnessed. As we move into an AI/ML led world, we need to lead the change by building the requisite skills.

While many companies don't have enough resources to marshal an army of data science PhDs, a more practical alternative is to build smaller and more focused "MLOps" teams - much like DevOps teams in application development.

Such teams could consist of not just data scientists, but also developers and other IT engineers whose mission would be to deploy, maintain, and constantly improve AI/ML models in a production environment. While there is a huge responsibility lying on IT professionals to develop an AI/ML led ecosystem in India, companies must also align resources to help them be successful. In due course, AI/ML will be the competitive advantage that companies will need to adopt in order to stay relevant and sustain businesses.

Forrester predicts that one in five organisations will double down on "AI inside" - which is AI and ML embedded in their systems and operational practices.

AI and ML are powerful technology tools that hold the key to achieving an organization's digital transformation goals.

(The author is Head-Technology Cloud, Oracle India.)

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How leveraging AI and machine learning can give companies a competitive edge - Business Today

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do reports MIT News. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the inverse of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process.Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects shape that it doesnt know.

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, were seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

Republished with permission ofMIT News. Read theoriginal article.

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Machines that see the world more like humans do - Big Think