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A key requirement of machine learning is to label the data correctly to ensure the best results, but the process is long and time-consuming. This also brings about an issue when dealing with extremely large data sets in unsupervised or semi-supervised learning. The saviour here is active learning with strategies that assist developers in prioritising the data and selecting the most useful samples to label to have the highest training impact. Furthermore, it promises to reduce the samples needed by choosing the right examples.

Various strategies can be used depending on the applications and needs of the model. However, when it comes to learning active learning, the practice is generally a part of bigger machine learning modules, which is why we have created a one-stop guide to mastering active learning online through resources varying from online video tutorials to blog posts and academic papers.

YouTube

Computerphile is a popular YouTube channel that discusses computer science-related topics. Their tutorial on active learning is taught by Dr Michel Valstar, who holds a PhD in Computing and is currently a professor at the University of Nottingham. The tutorial is a foundational element for the basics of active learning, taught through diagrams and illustrations of the concepts.

ICML, the International Conference on Machine Learning, is one of the fastest-growing AI conferences that discuss the latest academic papers. During their 2019 conference, Robert Nowak and Steve Hanneke taught the basics of active learning theory and the popular algorithms to apply (the video is now available online). In addition, the tutorial focuses on sound active learning algorithms and how they can be used to reduce the labels on training data. Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison. Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute in Chicago, specialising in AI and ML.

Applied AI is a great resource for learning AI/ML online through core concepts and real-life applications. The channels collective views cross 12 million and are popular for the basic concepts thorough teachings. Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations.

PyData is an educational program of NumFOCUS, a US-based not for profit organisation that provides a forum for the international community of data science to share their ideas through conferences. Speaking at one of their events is Jan Freyberg, a machine learning software engineer at Google Health. In a detailed talk, Freyberg discusses active learning in the interactive Python environment, given the ease and comfort in the ecosystem.

Devansh is a Computer Science and Computational Math Double Major at the Rochester Institute of Technology. Through this YouTube tutorial, he comprehensively discusses the basics of active learning, its works and compares it to SSL and GANs. He further explains the concept in detail regarding its use and active learnings acquisition function.

Ranji Raj, holding a masters degree in data science, takes on Youtube to publish tutorials and classwork related to machine learning. His video on active learning gives an in-depth introduction to the subject while discussing important concepts through diagrams and demonstrations. Raj also has consequent coursework on his GitHub page for data scientists interested in learning further.

Scaleway is a French cloud computing company that creates Youtube videos consisting of short machine learning tutorials and real-world applications. In their webinar on active learning, the company collaborated with Kairntech, an AI modelling and dataset creation platform, to discuss the various applications of active learning. The video discusses training datasets and how active learning can be applied for classification. It also glossed over common issues and how to overcome them.

Blog tutorials

Ori Cohen is a PhD holder in CS, currently working as a senior director of data science at New Relic. His Towards Data Science blog post on active learning is an extensive tutorial that discusses the various scenarios possible while using active learning, the algorithms that can be used, the sample selection methods and the codings used for all.

A blog post on Data Camp, an online interactive learning platform, explains in depth the A-Zs of active learning in a moderate level of difficulty. The tutorial discusses the concept in detail with definitions, examples and visuals, and teaches how one can apply active learning on their datasets through a particular example.

Written by a CS and EE student at IIT, India, this post is an in-depth tutorial on using active learning with Python. The tutorial is technical, explaining the code and its concepts through codes and steps. In addition, the post discusses various inputs, outputs, and the Python codes needed to apply active learning correctly.

Alexandre Abraham, a senior research scientist at Dataiku and a Ph D holder in computer science, has written an extensive tutorial on active learning packages on his Medium blog post. The blog post analyses the active learning packages available through a feature comparison, their covered approaches, and their coding aspects. There are three main packages and different methods that data scientists can leverage.

Papers

The paper in discussion is written by Kai Wei, an assistant professor at UCLA, Rishabh Iyer, an assistant professor at the University of Texas, and Jeff Bilmes, a professor at the University of Washington. Their paper studies the problem of selecting a subset of data to train a classifier and how individuals can apply the active learning framework to mitigate the issue.

Online courses

The DeepLearning.AI course in ML data lifecycle has a fourth module, tagged Advanced Labeling, Augmentation and Data Preprocessing, that focuses on semi-supervised learning, dataset labelling, and the role played by active learning within. The instructor, Robert Crowe, works at TensorFlow by Google and has multiple degrees in AI, ML and data science.

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Top online resources to learn Active Learning - Analytics India Magazine

- 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