We could not locate the page you were looking for.

Below we have generated a list of search results based on the page you were trying to reach.

404 Error
Technical Program Manager with over 20 years experience in the field as developer, development lead and program manager. I have experience presenting to large audiences, I enjoy technically challenging management roles, and I thrive in a fast paced culture focused on building high quality products. I started my career in US in 1998 working on Windows 2000 as a developer. I have shipped Windows 2000, Windows XP, Windows 2003 and Windows 7, working on various technologies from Print Spooler Service, Active Directory, 64bit Interop and RPC, TCP/IP and USB device connectivity, XPS document printing, Winb32 and .Net System APIs. I progressed from developer to development lead and I lead a team of 6 developers for 3 years. From 2009 to 2014 I have worked on Windows Phone as Senior Program Manager, driving multiple key projects from defining the process for Application certification of apps in the Windows Phone Store to Fast Application Switching and Fast Application Resume, app pre-compilation, deployment of phone apps in the Windows Store, multitasking for Location apps, VOIP, Audio, etc. As a Principal Program Manager Lead, I've lead a team of 5 Program Managers, driving the feature definition for the Windows Phone Execution Model and App Lifecycle, Resource Management, Multitasking, App-to-App and Page Navigation Model. From 2014 to 2018 I have worked on Speech Recognition for Cortana on PC, Windows Virtual Reality and Hololens experiences. I have worked on improving speech recognition accuracy via personalization of user's language models and acoustic models. I have been on point for enabling 3rd party skills for Cortana via voice commands and for enabling voice input for chat bots built with Microsoft Bot Framework. Since January 2019 I am working on Artificial Intelligence for Cognitive Services and Computer Vision at Microsoft, driving product requirements and working with Engineering and Research teams through the product lifecycle. I drove the product requirements and the engineering release for Computer Vision for Spatial Analysis. For more on Spatial Analysis and Azure Cognitive Services see https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
There are few things I love more than cuddling up with an exciting new book. There are always more things I want to learn than time I have in the day, and I think books are such a fun, long-form way of engaging (one where I won’t be tempted to check Twitter partway through). This book roundup is a selection from the last few years of TWIML guests, counting only the ones related to ML/AI published in the past 10 years. We hope that some of their insights are useful to you! If you liked their book or want to hear more about them before taking the leap into longform writing, check out the accompanying podcast episode (linked on the guest’s name). (Note: These links are affiliate links, which means that ordering through them helps support our show!) Adversarial ML Generative Adversarial Learning: Architectures and Applications (2022), Jürgen Schmidhuber AI Ethics Sex, Race, and Robots: How to Be Human in the Age of AI (2019), Ayanna Howard Ethics and Data Science (2018), Hilary Mason AI Sci-Fi AI 2041: Ten Visions for Our Future (2021), Kai-Fu Lee AI Analysis AI Superpowers: China, Silicon Valley, And The New World Order (2018), Kai-Fu Lee Rebooting AI: Building Artificial Intelligence We Can Trust (2019), Gary Marcus Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) (2019), Meredith Broussard Complexity: A Guided Tour (2011), Melanie Mitchell Artificial Intelligence: A Guide for Thinking Humans (2019), Melanie Mitchell Career Insights My Journey into AI (2018), Kai-Fu Lee Build a Career in Data Science (2020), Jacqueline Nolis Computational Neuroscience The Computational Brain (2016), Terrence Sejnowski Computer Vision Large-Scale Visual Geo-Localization (Advances in Computer Vision and Pattern Recognition) (2016), Amir Zamir Image Understanding using Sparse Representations (2014), Pavan Turaga Visual Attributes (Advances in Computer Vision and Pattern Recognition) (2017), Devi Parikh Crowdsourcing in Computer Vision (Foundations and Trends(r) in Computer Graphics and Vision) (2016), Adriana Kovashka Riemannian Computing in Computer Vision (2015), Pavan Turaga Databases Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (2021), Xin Luna Dong Big Data Integration (Synthesis Lectures on Data Management) (2015), Xin Luna Dong Deep Learning The Deep Learning Revolution (2016), Terrence Sejnowski Dive into Deep Learning (2021), Zachary Lipton Introduction to Machine Learning A Course in Machine Learning (2020), Hal Daume III Approaching (Almost) Any Machine Learning Problem (2020), Abhishek Thakur Building Machine Learning Powered Applications: Going from Idea to Product (2020), Emmanuel Ameisen ML Organization Data Driven (2015), Hilary Mason The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019), David Carmona MLOps Effective Data Science Infrastructure: How to make data scientists productive (2022), Ville Tuulos Model Specifics An Introduction to Variational Autoencoders (Foundations and Trends(r) in Machine Learning) (2019), Max Welling NLP Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics (2013), Emily M. Bender Robotics What to Expect When You’re Expecting Robots (2021), Julie Shah The New Breed: What Our History with Animals Reveals about Our Future with Robots (2021), Kate Darling Software How To Kernel-based Approximation Methods Using Matlab (2015), Michael McCourt
The issue of bias in AI was the subject of much discussion in the AI community last week. The publication of PULSE, a machine learning model by Duke University researchers, sparked a great deal of it. PULSE proposes a new approach to the image super-resolution problem, i.e. generating a faithful higher-resolution version of a low-resolution image. In short, PULSE works by using a novel technique to efficiently search space of high-resolution artificial images generated using a GAN and identify ones that are downscale to the low-resolution image. This is in contrast to previous approaches to solving this problem, which work by incrementally upscaling the low-resolution images and which are typically trained in a supervised manner with low- and high-resolution image pairs. The images identified by PULSE are higher resolution and more realistic than those produced by previous approaches, and without the latter’s characteristic blurring of detailed areas. However, what the community quickly identified was that the PULSE method didn’t work so well on non-white input images. An example using a low res image of President Obama was one of the first to make the rounds, and Robert Ness used a photo of me to create this example: I’m going to skip a recounting of the unfortunate Twitter firestorm that ensued following the model’s release. For that background, Khari Johnson provides a thoughtful recap over at VentureBeat, as does Andrey Kurenkov over at The Gradient. Rather, I’m going to riff a bit on the idea of where bias comes from in AI systems. Specifically, in today’s episode of the podcast featuring my discussion with AI Ethics researcher Deb Raji I note, “I don’t fully get why it’s so important to some people to distinguish between algorithms being biased and data sets being biased.” Bias in AI systems is a complex topic, and the idea that more diverse data sets are the only answer is an oversimplification. Even in the case of image super-resolution, one can imagine an approach based on the same underlying dataset that exhibits behavior that is less biased, such as by adding additional constraints to a loss or search function or otherwise weighing the types of errors we see here more heavily. See AI artist Mario Klingemann’s Twitter thread for his experiments in this direction. Not electing to consider robustness to dataset biases is a decision that the algorithm designer makes. All too often, the “decision” to trade accuracy with regards to a minority subgroup for better overall accuracy is an implicit one, made without sufficient consideration. But what if, as a community, our assessment of an AI system’s performance was expanded to consider notions of bias as a matter of course? Some in the research community choose to abdicate this responsibility, by taking the position that there is no inherent bias in AI algorithms and that it is the responsibility of the engineers who use these algorithms to collect better data. However, as a community, each of us, and especially those with influence, has a responsibility to ensure that technology is created mindfully, with an awareness of its impact. On this note, it’s important to ask the more fundamental question of whether a less biased version of a system like PULSE should even exist, and who might be harmed by its existence. See Meredith Whittaker’s tweet and my conversation with Abeba Birhane on Algorithmic Injustice and Relational Ethics for more on this. A full exploration of the many issues raised by the PULSE model is far beyond the scope of this article, but there are many great resources out there that might be helpful in better understanding these issues and confronting them in our work. First off there are the videos from the tutorial on Fairness Accountability Transparency and Ethics in Computer Vision presented by Timnit Gebru and Emily Denton. CVPR organizers regard this tutorial as “required viewing for us all.” Next, Rachel Thomas has composed a great list of AI ethics resources on the fast.ai blog. Check out her list and let us know what you find most helpful. Finally, there is our very own Ethics, Bias, and AI playlist of TWIML AI Podcast episodes. We’ll be adding my conversation with Deb to it, and it will continue to evolve as we explore these issues via the podcast. I'd love to hear your thoughts on this. (Thanks to Deb Raji for providing feedback and additional resources for this article!)
Bits & Bytes Microsoft open sources Bing vector search. The company published its vector search toolkit, Space Partition Tree and Graph (SPTAG) [Github], which provides tools for building, searching and serving large scale vector indexes. Intel makes progress toward optical neural networks. A new article on the Intel AI blog (which opens with a reference to TWIML Talk #267 guest Max Welling’s 2018 ICML keynote) describes research by Intel and UC Berkeley into new nanophotonic neural network architectures. A fault tolerant architecture is presented, which sacrifices accuracy to achieve greater robustness to manufacturing imprecision. Microsoft research demonstrates realistic speech with little labeled training data. Researchers have crafted an “almost unsupervised” text-to-speech model that can generate realistic speech using just 200 transcribed voice samples (about 20 minutes’ worth), together with additional unpaired speech and text data. Google deep learning model demonstrates promising results in detecting lung cancer. The system demonstrated the ability to detect lung cancer from low-dose chest computed tomography imagery, outperforming a panel of radiologists. Researchers trained the system on more than 42,000 CT scans. The resulting algorithms turned up 11% fewer false positives and 5% fewer false negatives than their human counterparts. Facebook open-sources Pythia for multimodal vision and language research. Pythia [Github] [arXiv] is a deep learning framework for vision and language multimodal research framework that helps researchers build, reproduce, and benchmark models. Pythia is built on PyTorch and designed for Visual Question Answering (VQA) research, and includes support for multitask learning and distributed training. Facebook unveils what secretive robotics division is working on. The company outlined some of the focus areas for its robotics research team, which include teaching robots to learn how to walk on their own, using curiosity to learn more effectively, and learning through tactile sensing. Dollars & Sense Algorithmia raises $25M Series B for its AI platform Icometrix, a provider of brain imaging AI solutions, has raised $18M Quadric, a startup developing a custom-designed chip and software suite for autonomous systems, has raised $15M in a funding Novi Labs, a developer of AI-driven unconventional well planning software, has raised $7M To receive the Bits & Bytes to your inbox, subscribe to our Newsletter.
In the final episode of our AI Rewind series, we're excited to have Siddha Ganju back on the show. Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.
Bits & Bytes ONNX Runtime for ML inference now in preview. Microsoft released a preview of the ONNX Runtime, a high-performance inference engine for Open Neural Network Exchange (ONNX) models. It is compatible with ONNX version 1.2 and comes in Python packages that support both CPU and GPU. Uber describes new platform for rapid Python ML development. Uber shared Michelangelo PyML, an extension to its Michelangelo platform providing for faster development and experimentation based on Docker containers. NYU and Facebook release cross-language NLU data set. As researchers look to increase the number of languages NLU systems can understand, gathering and annotating data in every language is a bottleneck. One alternative is to train a model on data in one language and then test that model in other languages. The Cross-Lingual Natural Language Inference (XNLI) data set advances this approach by providing that test data in languages. Malong researchers develop a technique to train deep neural networks. In this new paper, Malong introduces CurriculumNet, a training strategy leveraging curriculum learning to increase performance while decreasing noise when working on large sets of data. The code is now available on GitHub as well. Facebook launches Horizon reinforcement learning platform. Facebook has open-sourced Horizon, an end-to-end applied reinforcement learning platform. Unlike other open-source RL platforms focused on gameplay, Horizon targets real-world applications and is used at Facebook to optimize notifications, video streams, and chatbot suggestions. Google launches AdaNet for combining algorithms with AutoML. Google launched AdaNet, an open-source tool for automatically creating high-quality models based on neural architecture search and ensemble learning. Users can add their own model definitions to AdaNet using high-level TensorFlow APIs. Dollars & Sense People.ai announced that it has raised $30M in Series B funding led by Andreessen Horowitz DataRobot, a Boston-based automated ML company, raised $100M in Series D funding Syntiant Corp, an Irvine-based AI semiconductor company, raised $25M in Series B funding led by M12, Microsoft’s VC arm Oracle announced that it has acquired data management and AI solutions provider DataFox eSentire has acquired Seattle-based cybersecurity AI company Versive (formerly Context Relevant) AppZen, an AI auditing solutions provider, announced $35 million funding led by Lightspeed Venture Partners Validere, which provides an AI and IoT platform for oil and gas, raised $7m in seed funding Esperanto Technologies a hardware company focused on energy efficient systems for AI, ML, and DL, closed $58m Series B funding Conversica, offering conversational AI products for sales and marketing, announced it has secured a $31 million Series C funding Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Researchers develop AI to detect musical mood. Deezer researchers have developed a deep learning system which can identify the mood and intensity of songs based on audio and lyrics. Microsoft announces automated machine learning service. The new service aims to identify the best machine learning pipeline for the user’s labeled data. Automated ML is integrated with Azure Machine Learning and includes an SDK for integration with Python development environments including Visual Studio Code, PyCharm, Azure Databricks notebooks and Jupyter notebooks. Microsoft contributes $40 million for humanitarian AI. Microsoft launched a new $40-million program aimed at harnessing the power of AI for humanitarian action. The program targets causes such as disaster recovery, helping children, and protecting refugees. Microsoft features Shell industrial AI use cases. Early in his Ignite keynote, Microsoft CEO Satya Nadella highlighted work by Microsoft and Bonsai, which it acquired earlier this year, performed at Shell. The join work aims to enhance safety by applying AI and IoT in a number of areas including at retail gas stations. DeepMind outlines Technical AI Safety program. Interesting post by Google DeepMind researchers outlining the key tenets—specification, robustness, and assurance—of their AI safety research program. AI’s new muse: our sense of smell. Artificial neural networks have been loosely inspired by the brain, specifically the visual cortex. This article describes recent work being done to draw inspiration from our olfactory circuits as well. Dollars & Sense Netradyne, which applies AI to driver and fleet safety, has raised Series B funding of $21 million Olis Robotics, announced the acquisition of White Marsh Forests, a machine learning startup based in Seattle Slack, announced that it has acquired Astro, a messaging startup that applied AI to email Marketing agency Impact Group acquired ad platform startup Cluepto help connect retail brands to consumers Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes IBM launches tool aimed at detecting AI bias. IBM Research has launched the AI Fairness 360 Kit to scan for signs of AI bias and make recommendation adjustments. The open source Python package contains nine different algorithms, developed by the broader algorithmic fairness research community, to mitigate unwanted bias. Microsoft adds Tensorflow scoring to ML.Net. The company has added TensorFlow model scoring to version 0.5 of its ML.Net open source machine learning framework, enabling the use of existing TensorFlow models in ML.Net experiments. Tencent announces new AI services. The new Tencent Open AI Platform, called "AI.QQ.COM," aims to build a services ecosystem leveraging Tencent’s various AI capabilities. The platform makes more than 100 AI APIs available to industry. Accenture Introduces new healthcare bots. The virtual-assistant bots “Ella” and “Ethan” join the Accenture Intelligent Patient Platform to make intelligent recommendations for interactions between life sciences companies, patients, health care providers, and caregivers. Miso Robotics enhances AI platform to include frying skills. The cloud-connected Miso AI platform now enables Flippy, the company's autonomous robotic kitchen assistant, to perform frying tasks in addition to grilling. Dollars & Sense Ontario-based DarwinAI, which develops tools for optimizing deep neural nets, raised $3 million, led by Obvious Ventures and iNovia Capital Leena AI, an HR bot startup has raised $2 million in a seed funding round from a group of Silicon Valley investors Oxbotica, an Oxford, UK-based autonomous vehicle software company, completed a £14m funding round Orbital Insight announced its acquisition of a Boston-based “FeatureX,” which specializes in computer vision for satellite imagery Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our September 2018 Americas TWIML Online Meetup. In this month's community segment we discuss the upcoming topics for both the EMEA and Americas meetup groups, along with our recently started Fast.AI study group. We also briefly discuss episode #180 of the podcast, which featured Nick Bostrom, Professor and author of the book Superintelligence. Finally, Sam shares some interesting blog posts. In our presentation segment, David Clement leads us in a breakdown of the paper “DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills.” For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! https://youtu.be/RLa5XqH36c8 Paper: DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills The What-If Tool: Code-Free Probing of Machine Learning Models Help! I can’t reproduce a machine learning project! SQL Query Optimization Meets Deep Reinforcement Learning The Trinity Of Errors In Financial Models: An Introductory Analysis Using TensorFlow Probability
Bits & Bytes Executive change at Google Cloud AI. Google Cloud AI head Dr. Fei-Fei Li has left the organization and will be returning to her professorship at Stanford. Dr. Andrew Moore, Dean of the School of Computer Science at CMU, will replace her by year end. Cisco unveils server for Artificial Intelligence and Machine Learning. Cisco has launched new servers aimed at speeding up deep learning workloads. Facebook's 'Rosetta' can extract text from a billion images daily. Facebook has developed a machine learning system called Rosetta for contextual extraction of text in images. The system supports image search and will also help Facebook identify inappropriate or harmful content. NVIDIA launches new data center inference. NVIDIA launched “TensorRT Hyperscale” offering inference acceleration for voice, video, image and recommendation services. The new platform features NVIDIA Tesla T4 GPUs based on the Turing architecture. Facebook developed an AI-based debugging tool. The tool, called “SapFix,” aims to help programmers by finding and fixing software bugs automatically. Google open sources ‘What-If Tool’ for code-free ML Experiments. Google’s AI research team has developed What-If Tool, a new TensorBoard feature allowing users to analyze an ML model without writing code. The tool offers a visual interface for exploring different model results. Dollars & Sense Syllable.ai, which offers a healthcare chat platform, raises $13.7M Integrate.ai, a Toronto-based AI software company, secures $30 million in Series A funding Microsoft announced the acquisition of San Francisco based Lobe, whose slick demo of code-free deep learning swept the Twitters a few months ago Deloitte announced its acquisition of Magnetic Media Online's artificial intelligence platform business. Magnetic is a marketing technology company headquartered in New York City Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our September 2018 EMEA TWIML Online Meetup. In this month's community segment we briefly look at Remote Sensing and Auto Encoders for Weather Tracking. We also discuss the status of upcoming Fast.AI Study Groups, including groups looking to reengage in part 1 again, Starting Part 2, and the release up the updated Version 2 (Lesson 1). In our presentation segment, Kai Lichtenberg leads us in a breakdown of Geoffrey Hinton’s CapsNets paper. Topics covered include: - Whats wrong with CNNs - Why our brain is probably doing “Inverse Graphics.” - What is capsule? - CapsNet and Dynamic Routing. https://youtu.be/G7lvnt1eRjw For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! Paper: Dynamic Routing Between Capsules Paper: Matrix Capsules with EM Routing
Bits & Bytes Diffbot launches knowledge graph as-a-service. The startup, whose roots are in web scraping, applied machine learning, computer vision, and natural language processing to create a database of ‘all the knowledge of the Web,’ spanning over 10 billion entities and 1 trillion facts. Automatic transliteration helps Alexa find data across language barriers. Amazon researchers have developed a multilingual “named-entity transliteration system” to help Alexa overcome language barriers in multilingual environment. Oracle open sources GraphPipe for model deployment. Though Oracle has a strained relationship with open source, they recently released a new open source tool called GraphPipe, designed to simplify and standardize the deployment of machine learning models. Google turns datacenter cooling controls over to AI. Google was already using AI to optimize data center energy efficiency. Now they’ve handed over complete control of data center cooling to AI. Instead of humans implementing AI-generated recommendations, the system is now directly controlling data center cooling. IBM researchers propose ‘factsheets’ for AI transparency. Expanding on ideas like the Datasheets for Datasets paper I discussed previously, an IBM Research team has suggested a factsheet based approach for AI developers to ensure transparency. Facebook and NYU researchers speed up MRI scans with AI. Facebook announced the fastMRI project, in collaboration with NYU, which aims to apply AI to accelerate MRI scans by up to 10 times. Google releases Dopamine reinforcement learning research framework. Google announced the new TensorFlow-based framework, which aims to provide flexibility, stability, and reproducibility for new and experienced RL researchers. Baidu launches EZDL, a coding-free deep learning platform. Chinese firm Baidu EZDL, an online tool enabling anyone to build, design, and deploy models without writing code. Dollars & Sense Canvass Analytics, a Toronto-based provider of AI-enabled predictive analytics for IIOT, raised $5M in funding. Cloudalize, a cloud platform for running GPU-accelerated applications, has secured a €5 million funding round. Intel announced that it is buying Vertex.ai, a startup developing a platform-agnostic model suite, for an undisclosed amount. Zscaler, announced that it has acquired AI and ML technology and the development team of stealth security startup TrustPath. New Knowledge, an Austin-based cybersecurity company that protects corporations from covert, coordinated disinformation campaigns, raised $11M in Series A funding. Phrasee, a London based marketing technology company that uses AI to generate optimized marketing copy, closed a $4m Series A funding round. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
This video is a recap of our July 2018 TWIML Online Meetup. In this month's community segment we look at the ongoing Fast.ai Study Group, the upcoming meetup presenter schedule, the recent Glow paper from the folks at OpenAI, and entity embeddings. In our presentation segment, Nicholas Teauge leads us in a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software that outperforms classical computers on machine learning tasks. For links to the papers mentioned above and more information on this and previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup! https://youtu.be/-ftniM7248I Paper: Quantum Machine Learning OpenAI Glow
In this episode I'm joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley. Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring "Taskonomy: Disentangling Task Transfer Learning." In this work, Amir and his coauthors explore the relationships between different types of visual tasks and use this structure to better understand the types of transfer learning that will be most effective for each, resulting in what they call a "computational taxonomic map for task transfer learning." In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning. Along the way Amir provides a ton of great examples and explains the various tools his team has created to illustrate these concepts.
Bits & Bytes IBM hosts first AI-Human debate. In a publicity stunt in the spirit of Deep Blue’s match with Garry Kasparov, or IBM Watson’s appearance on Jeopardy, IBM hosted the first ever live public debate between its Project Debater AI and a human in San Francisco last week. The company has published several datasets and technical papersoutlining various components of the system. Nvidia publishes “Super SloMo” for transforming standard video into slow motion At last week’s CVPR, NVIDIA researchers presented research into a deep learning model for interpolating between video frames to produce slow-motion video from a standard 30-frame-per-second video. Amazon SageMaker now supports PyTorch and TensorFlow 1.8: In a recent update, Amazon SageMaker has added support for PyTorch deep learning models. I’m now wondering if the fast.ai course and library can be completed on SageMaker. AWS is also now supporting the latest stable TensorFlow versions. Microsoft to acquire Bonsai, one of my favorite AI companies. Berkeley-based Bonsai, a client of mine and sponsor of last year’s Industrial AI series, offers a deep reinforcement learning platform for enterprise AI. I’m super excited for them and looking forward to seeing how things evolve now that they’ll be part of Microsoft. Tracking the state-of-the-art (SOTA) in NLP. Researcher Sebastian Ruder has put together an interesting project to track the SOTA of a variety of problems in natural language processing. Dollars & Sense AI-powered fitness startup Vi raises $20 million Falkonry, a provider of machine learning software for manufacturing, raised $4.6 million in Series A funding Prifender, whose software uses AI to map PII in enterprise data systems, raised a $5M seed round AI-as-a-service startup Noodle.ai announced a $35 million round led by Dell Technologies WalkMe announced that it has acquired DeepUI, whose ML models seek to understand any software at the GUI level, without the need for an API Twitter has agreed to buy San Francisco-based Smyte, which offers tools to stop online abuse, harassment, and spam, and protect user accounts PayPal announced today that it has agreed to acquire Simility, a leading fraud prevention and risk management platform provider for $120 million Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Intel open sources nGraph neural network compiler. The newly open-sourced compiler, originally announced last summer and discussed on TWIML Talk #31, provides support for multiple deep learning frameworks while optimizing models for different hardware solutions. It supports six deep learning frameworks: TensorFlow, MXNet, neon, PyTorch, CNTK, and Caffe2. Google unveils augmented reality microscope. The prototype, which can detect cancer in real-time, was unveiled at an event organized by the American Association for Cancer Research. The new tool relays its predictions directly into the field of view of the user and has the ability to be retrofitted into existing microscopes. Google extends semantic language capabilities. Building on the hierarchical vector models at the heart of Gmails's Smart Reply feature, the new work extends these ideas by creating vectors for larger chunks of language such as full sentences and small paragraphs. The company published a paper on its Universal Sentence Encoder and launched the Semantic Experiences demonstration site. A pre-trained TensorFlow model was also released. IBM releases Adversarial Robustness Toolbox. The open-source software library aims to support researchers and developers in defending deep neural nets against adversarial attacks. The software, which currently works with TensorFlow and Keras, can assess a DNNs robustness, increase robustness as needed, and offer runtime detection of potential threats. MATLAB 2018a adds deep learning features. Many self-taught data scientists were initially exposed to MATLAB via Octave, the open source clone Andrew Ng used in his original Stanford machine learning online course. Well, the commercial software continues to evolve, with its latest version adding a host of new deep-learning related features including support for regression and bidirectional LSTMs, automatic validation of custom layers, and improved hardware support. Dollars & Sense Sword Health, a Portuguese medtech company, raises $4.6 million LawGeex, a contract review automation business, raises $12 million XpertSea, applying computer vision to aquaculture, raises $10 million Konux, a sensor and AI analytics startup, raises $20 million Citrine, materials data and AI platform, raises $8 million Eightfold.ai launches talent intelligence platform, closes $18 million round Voicera, the AI-powered productivity service, announces acquisition of Wrappup Adobe announces acquisition of voice technology business, Sayspring Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Google develops AI that can pick out voices in a crowd. It is a deep learning audio-visual based model that uses both audio and video to isolate and enhance the targeted speaker while suppressing other sounds. The tech could be used in a wide range of applications from hearing aids to video conferencing. Microsoft halts sale of some enterprise AI tools over abuse fears. The tech giant is currently working with its internal Aether (AI and Ethics in Engineering and Research) Committee to review how AI tech could be used by its customers. There aren’t details on which applications have been ruled out but they've provided some insights into what issues they are prioritizing. Qualcomm’s launched two new chips to provide onboard AI processing to camera systems. Competing with AI inference silicon solutions like Intel Movidius and others, the AI edge system could be used in products like security cameras, drones, and robotics. Atos advances Quantum Learning Machine. The researchers have been able to successfully model quantum noise creating more realistic simulations. Not necessarily AI related but an interesting adjacent area. DimensionalMechanics updates NeoPulse framework. The new version includes updates to its NML modeling language for AI and new hyperparameter optimization features in its AI Studio. The company also raised an additional $1.25 million in an A-2 financing round. Dollars & Sense Juro, an AI startup for sales contracts, raises $2M Ocrolus, an AI company that analyzes financial documents, raises $4M Geoblink, a Spanish location intelligence startup, raises $6 million Mapillary, a startup developing a mapping system for autonomous vehicles, raises $15 million Xeeva, the procurement and sourcing software company, raises $40 million SleepScore Labs, a company providing sleep improvement systems, acquires Sleep.ai Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Amazon text-to-speech service, Polly releases new Breath feature. The new feature more closely mirrors human speech patterns by adding in pauses and breaths. The breaths can be added both manually and via an automated algorithm. IBM sets 46x faster benchmark record with POWER9 and NVIDIA GPUs. The test was done using IBM’s SnapML AI software. They were able to train a logistic regression classifier model in just 1.53 seconds with the fastest previous time for the model and dataset being 70 minutes on Google Cloud with Tensorflow. Apple partners with IBM to add Watson Visual Recognition to CoreML. Apple, which has been weak in its cloud services offerings, is likely trying to keep up with the new AI demands of mobile developers that may be swayed by Google’s robust suite of AI tools for the Android platform. (I'm quoted to this effect in the linked article.) Paperspace launches Gradient, an AI PaaS. The platform offers fully configured ML environments compatible with a large suite of leading AI development tools. IBM Launches Watson Data Kits to help accelerate enterprise AI adoption. The kits will provide companies with pre-enriched industry-specific data that can be used to scale AI across their business. The service will initially only be available for travel and transportation and food industries. Dollars & Sense Mythic, an Austin-based AI hardware startup, raised $40 million. Skyline AI, a real-estate investment service powered by AI, raised $3 million. Beijing Infervision, a company that develops AI medical imaging tech, raised $47 million. Sift Science, a cybersecurity company, has raised $53 million. AllyO, a provider of AI recruiting technology, raised $14m. Vision Critical, a customer relationship intelligence software company, has bought assets of AI-startup Aida Software. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes Google’s AI is being used on US military drone footage. The project provides the Department of Defense (DoD) with Tensorflow APIs to help flag and classify 1000s of hours of drone footage. The partnership has drawn criticism of both Google and the DoD. Microsoft partners with Esri to launch Geospatial AI on Azure. For geospatial analytics professionals, this product provides AI and predictive analytics capabilities including deep learning and machine learning algorithms. Microsoft announces Windows ML for efficient use of hardware with AI workloads. The software works across multiple hardware types including Intel Vision Processing Units. It allows for seamless integration of on system AI, in the form of personal assistants, enhanced biometric security, smart music, and photo search and recognition. China makes open-source platform to boost AI. This comes as part of a wider initiative to position China as a leader in AI technology by 2030. We’ve covered this in previous newsletters when it was announced China was building an AI-dedicated business park. Cloudera unveils enterprise ML and analytics Platform-as-a-Service. The platform aims to tackle the issue of “big data analytics cloud sprawl” (that’s a mouthful!) in enterprises using machine learning. Google’s Quantum AI Lab announces new ‘Bristlecone’ processor. The quantum processor will be used to research system error rates and scalability of google’s qubit technology as well as applications in quantum simulation, optimization, and machine learning. Dollar & Sense ELSA, an English language-learning tool, raises $3.2M for its A.I.-powered pronunciation assistant Medial Earlysign, an AI-powered health tech company secures $30 million Atomwise, which uses AI to improve drug discovery, raises $45M Series A Voicera, which offers an AI-powered transcribing assistant, raises $14.5 million for AI that draws insights from meeting notes GameSparks recently (leave) picked up by Amazon for $10M to build out its gaming muscle Kngine, an AI search engine startup is acquired by Samsung Kensho, a leader in AI analytics is acquired by S&P for $550 million
Bits & Bytes Google AI to predict heart disease with eye scans. The tech is being developed by Google’s health subsidiary Verily. It works by scanning the back of a patient’s eye, it then uses that image to deduce patient age, blood pressure, smoking status, and their risk of heart attack. It’s still in its early stages, though, and is not ready for clinical use. Google debuts ‘auto ads’ for intelligently ad placement. While Google has long used machine learning to determine the best ads to show on a web page, this new feature reads the target page and selects the best ad placement on the page. Google claims that participating publishers saw ad revenue increases of 10-15%, however, some beta users were not happy about the number of ads being placed on their pages. IBM partners with game dev platform Unity to create IBM Watson Unity SDK. I’ve had my eye on Unity since my interview with Danny Lange, their VP for ML and AI. The new SDK is being launchedon the Unity Asset Store and will allow developers to integrate visual recognition, speech text, and language classification features into their games and AR/VR applications more easily. Qualcomm adds AI engine to Snapdragon mobile platform. The Qualcomm AI Engine consists of software, hardware and APIs meant to support efficient neural network inference on client devicesrunning Snapdragon processors. Accenture launches AI testing service. Accenture’s taking a “Teach and Test” approach to the service, with the former focused on the choice of data, models, and algorithms used to train ML models, and the latter on up-front and ongoing evaluation of model performance, explainability and bias. MindBridge adds NLP to its AI-powered auditing software. The update allows audit professionals to naturally ask query transactional data and gain insight into potential errors and risky transactions. Dollars & Sense Vectra, a cybersecurity startup, raises $36M for global expansion of its AI-Based Security Platform SparkCognition, an AI solutions startup, raises $56.5 million Series B For International Expansion StatusToday, an employee productivity startup, raises $3.91 million to improve employee productivity with AI Prophesee, a machine vision startup, raises $19 million for its machine vision technology Agent IQ, an AI customer service bot startup, raises $6.3M Benevolentai acquires Cambridge research facility to accelerate AI-enabled drug development Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Interesting tidbits from recent news: Microsoft develops AI powered sketch artist. The new bot, based on recent GAN research, is capable of generating “drawings” from caption-like text descriptions. Applications for this technology include the arts, design, and perhaps at some point, police sketches. Overall very cool. IBM and Salesforce announce Watson + Einstein collaboration. The two tech giants are teaming up to integrate their two eponymously named, over-marketed, poorly understood machine learning products. Oh boy! Although it’s not immediately obvious in what ways Watson and Einstein are “combining”, Salesforce and IBM are making it clear that they are prioritizing AI and fleshing out their offerings. #SnarkLevelHigh Baidu grows AI research team. The new hires are Dr. Kenneth Church a pioneer in Natural Language Pioneering, Dr. Jun Huan a big data and data mining expert and Dr. Hui Xiong who specializes in data and knowledge engineering. Dating services firm Lunch Actually to launch ICO for Viola.AI. The dating service aims to not only match couples but also track their relationships, suggest date venues, remind them of new dates and advise them on relationship problems. Potentially a very interesting AI application, but one with tons of potential privacy implications. UC Berkeley & Facebook introduce House3D for reinforcement learning. The two teamed up to enable more robust intelligent agents by publishing a new dataset called “House3D”. House3D contains 45,622 3D scenes of houses, ranging from single-room studios to multi-storeyed houses equipped fully labeled 3D objects. In doing so, the groups aim to push RL research away towards focusing on tasks that more easily applicable to the real world. App claims to predict if an image will “go viral.” ParallelDots released the app with an open API that allows user to upload images then receive a “virality” score. It’s no secret that viral sharing is the dream of many marketers, so it’ll be interesting to see if this type of service could provide beneficial insights when planning ad campaigns. Amazon launched SageMaker BlazingText. BlazingText is an unsupervised learning algorithm for generating word2vec (see TT # 48) embeddings and is the latest addition to Amazon SageMaker’s suite of built-in algorithms. Deal Flow There seemed to be an abundance of deals last week: Smartphone-maker Coolpad has raised $300 million from Chinese property mogul Chen Hua-backed Power Sun Ventures to enhance its artificial intelligence capabilities. Understand.ai, a Karlsruhe, Germany-based machine learning startup for training and validation data in autonomous vehicles, raised $2.8 million in seed funding. C3 IoT, a provider whose software offerings include AI-for-IoT tools, announced a $100 million new round of financing. Data Nerds, a Canada-based developer of data products, raised $3m in Series A funding. Techcyte, Inc. closed a $4.3 million funding round to commercialize its digital pathology platform. Babblabs, a fresh start-up in advanced speech processing, announced today a Series Seed investment of $4 million. Owkin, a NYC-based predictive analytics company that utilizes transfer learning to accelerate drug discovery and development, raised $11m in Series A funding. Pony.ai, a year-old California-based self-driving car startup, announced it recently completed a $112 million Series A funding round. Smartsheet, that builds software for corporate process management, acquires business automation chatbot startup Converse.AI. Workday, the cloud HR and financials SaaS provider, buys SkipFlag to bolster machine learning capabilities. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits and Bytes Microsoft and Adaptive Biotechnologies want to decode the human immune system. The partners aim to create individual disease diagnostics, and ultimately a universal diagnostic, from a simple blood test using immunosequencing and machine learning. In other Microsoft news, the company is launching a $33 million AI hub in Taiwan. Microsoft will collaborate on AI research with a range of Taiwanese entities including government agencies, private sector, and academia. Intel brings AI tech to the Ferrari Challenge. Intel unveiled this deep computer vision application at CES, which uses fine-grained object detection to enable the personalization of race video streams. Stay tuned for my interview with the team’s lead data scientist in our upcoming CES coverage. Volkswagen and NVIDIA partner on autonomous vehicles. At the opposite end of the automotive and chip architecture spectra, the two companies announced plans to bring autonomous driving and AI-powered safety features to future cars, and unveiled the new I.D. Buzz concept which brings AI technology to the iconic VW MetroBus design. MediaTek launches cross-platform AI tech for consumer devices. Not to be left out, system-on-chip provider MediaTek is building out its NeuroPilot AI platform targeting consumer device manufacturers like Amazon, Belkin and Sony. The company also recently rolled out the Sensio 6-in-1 biosensor module that can track heart rate, blood pressure, peripheral oxygen saturation levels and more. DeepAR algorithm gives Amazon SageMaker new time-series capabilities. AWS added DeepAR support to its recently released SageMaker platform. The DeepAR algorithm is a supervised machine learning algorithm for forecasting using time-series data using recurrent neural networks (RNNs). Uber and Google explore “doubt” in deep AI systems. Interesting article the new crop of deep probabilistic programming tools including Uber’s Pyro and Columbia’s Edward. Unbabel nabs $23 million investment from Microsoft, Salesforce, Samsung for its translation software. Unbabel utilizes natural language processing, neural machine translation and quality estimation algorithms to bring greater accuracy to their translations. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.
Bits & Bytes A few interesting ML and AI related tidbits from around the web over the past week or so: China is building a huge AI-focused research and business park. The state-backed $2.1 billion technology park is part of China’s wider initiative to position themselves at the forefront of emerging markets. The 55 hectare park is expected to house 400 businesses and generate nearly $8 billion a year. Richmond-based AI startup, Notch, acquired by Capital One. Fifteen months after Capital One created an in-house “Center for Machine Learning,” the company has reportedly acquired Notch, a data engineering and machine learning consulting firm. LG distributes in house AI development tools throughout company. A few weeks after LG introduced ThinQ, the umbrella brand for the company’s smart home products, the company has announced availability of an in-house deep learning platform, called DeepThinQ, which is meant to facilitate AI technology development across platforms within the company. Google releases preemptible GPUs with a 50% discount incentive. Preemptible GPUs work well with large machine learning tasks and other batch computational jobs, and Google is making them cheaperfor customers. CEVA unveils new family of AI-processors designed for deep learning at the edge. As I mentioned previously, I’ll be keeping an eye on AI acceleration hardware products this year. CEVA's new line is one such offering. A new derivative-free optimization toolbox for deep learning. ZOOpt/ZOOjl is an interesting toolset that allows for optimization across data that is inconsistent or non-continuous, where standard algorithms like gradient descent which require differentiability would fall short. Sign up for our Newsletter to receive the Bits & Bytes weekly to your inbox.