Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers.
FDA has released a number of documents that could help clarify its expectations for artificial intelligence, machine learning, and cybersecurity. These include Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, published in January 2021; Good Machine Learning Practice for Medical Device Development: Guiding Principles, published in October 2021; and the just-released draft guidance, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. The AI/ML action plan provides a "more tailored regulatory framework for AI/ML," explained Pavlovic. She referred to FDA's 2019 discussion paper, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback, which laid out a "total product lifecycle approach to AI/ML regulations with the understanding that AI/ML products can be iterated much more efficiently and quickly than a typical medical device implant product or something that isn't software based." This is "because there is an opportunity to add additional data to training sets on which the products were originally formulated," she said.
The following is the "100 most noteworthy artificial intelligence companies" compiled by the AI generation (tencentAI) (in alphabetical order by company name): Inspired by recent discoveries about the way the brain processes information, Cortical.io's Retina engine converts language into semantic fingerprints, and then compares the semantic relatedness of any two texts by comparing the degree of overlap of the fingerprints. CrowdFlower is a human intervention training platform for data science teams that helps clients generate high-quality custom training data. The CrowdFlower platform supports a range of use cases including self-driving cars, personal assistants, medical image tagging, content classification, social data analysis, CRM data improvement, product classification and search relevance, and more. Headquartered in San Francisco, CrowdFlower's clients include Fortune 500 and data-driven companies.
After the spread of the COVID-19 catastrophe, many societal and consumer behavioral changes ensued. With lockdowns put in place overnight, businesses and educational institutions were forced to continue their operations remotely. This phenomenon led to an inevitable surge in the adoption of technologies for routine tasks. As a result, the country witnessed an increased attempts and incidences of digital fraud. Since the beginning of the outbreak in March 2020, the attempts of fraudulent digital transactions rose by over 28% between March 2020 & 2021 compared to the previous year.
The relevance of the video is that the browser identified the application being used by the IAI as Google Earth and, according to the OSC 2006 report, the Arabic-language caption reads Islamic Army in Iraq/The Military Engineering Unit – Preparations for Rocket Attack, the video was recorded in 5/1/2006, we provide, in Appendix A, a reproduction of the screenshot picture made available in the OSC report. Now, prior to the release of this video demonstration of the use of Google Earth to plan attacks, in accordance with the OSC 2006 report, in the OSC-monitored online forums, discussions took place on the use of Google Earth as a GEOINT tool for terrorist planning. On August 5, 2005 the user "Al-Illiktrony" posted a message to the Islamic Renewal Organization forum titled A Gift for the Mujahidin, a Program To Enable You to Watch Cities of the World Via Satellite, in this post the author dedicated Google Earth to the mujahidin brothers and to Shaykh Muhammad al-Mas'ari, the post was replied in the forum by "Al-Mushtaq al-Jannah" warning that Google programs retain complete information about their users. This is a relevant issue, however, there are two caveats, given the amount of Google Earth users, it may be difficult for Google to flag a jihadist using the functionality in time to prevent an attack plan, one possible solution would be for Google to flag computers based on searched websites and locations, for instance to flag computers that visit certain critical sites, but this is a problem when landmarks are used, furthermore, and this is the second caveat, one may not use one's own computer to produce the search or even mask the IP address. On October 3, 2005, as described in the OSC 2006 report, in a reply to a posting by Saddam Al-Arab on the Baghdad al-Rashid forum requesting the identification of a roughly sketched map, "Almuhannad" posted a link to a site that provided a free download of Google Earth, suggesting that the satellite imagery from Google's service could help identify the sketch.
This report from the Montreal AI Ethics Institute (MAIEI) covers the most salient progress in research and reporting over the second half of 2021 in the field of AI ethics. Particular emphasis is placed on an "Analysis of the AI Ecosystem", "Privacy", "Bias", "Social Media and Problematic Information", "AI Design and Governance", "Laws and Regulations", "Trends", and other areas covered in the "Outside the Boxes" section. The two AI spotlights feature application pieces on "Constructing and Deconstructing Gender with AI-Generated Art" as well as "Will an Artificial Intellichef be Cooking Your Next Meal at a Michelin Star Restaurant?". Given MAIEI's mission to democratize AI, submissions from external collaborators have featured, such as pieces on the "Challenges of AI Development in Vietnam: Funding, Talent and Ethics" and using "Representation and Imagination for Preventing AI Harms". The report is a comprehensive overview of what the key issues in the field of AI ethics were in 2021, what trends are emergent, what gaps exist, and a peek into what to expect from the field of AI ethics in 2022. It is a resource for researchers and practitioners alike in the field to set their research and development agendas to make contributions to the field of AI ethics.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance. The proposed LiteLSTM can be significant for learning big data where time-consumption is crucial such as the security of IoT devices and medical data. Moreover, it helps to reduce the CO2 footprint. The proposed model was evaluated and tested empirically on two different datasets from computer vision and cybersecurity domains.
A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. When deploying these systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in uncertain, complex, and unpredictable real-world contexts. We are also identifying a number of urgent systems challenges for trustworthy embodied systems, including robust and human-centric AI, cognitive architectures, uncertainty quantification, trustworthy self-integration, and continual analysis and assurance.
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.