If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Tellius, the AI-driven decision intelligence platform, announced a partnership with Looker, a data platform for data teams and data consumers. Through the partnership, the Tellius platform can natively connect to Looker, allowing customers to discover insights at scale and explore their data with natural language based on Looker's unified metrics. "The combination of Looker's developer friendly data platform and Tellius' insights capabilities makes for an easy, end-to-end analytics experience for all" Data-driven organizations continue to struggle to track key performance metrics at a granular level, identify the factors that cause metrics to change, and determine how to best act on information presented in business intelligence dashboards. To help mitigate these challenges, Looker enables organizations to build their own data platforms and power analytics experiences from diverse sources, eliminating the complexity of data models. Similarly, Tellius gives business users, analysts, and data experts a fast, simplified, and collaborative way to discover insights assisted by AI, visualize enterprise data using natural language, and automate machine learning across all of their business data.
To develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study. A secondary analysis on 40 participants (mean age, 51 years; age range, 30–67 years; 25 women) from the prospective GNC MRI study (2015–2016) was performed. Based on a proton density–weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep learning based landmark localization, bone segmentation of the femora and pelvis, and a shape model for annotation transfer. The centrum-collum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion were derived. Quantitative validation was provided by comparison with average manual assessments of radiologists in a cross-validation format. High agreement in mean Dice similarity coefficients was achieved (average of 97.52% 0.46 [standard deviation]). The subsequent morphometric analysis produced results with low mean MAD values, with the highest values of 3.34 (alpha 03:00 o'clock position) and 0.87 mm (HNO) and ICC values ranging between 0.288 (HNO ratio) and 0.858 (CE) compared with manual assessments. These values were in line with interreader agreements, which at most had MAD values of 4.02 (alpha 12:00 o'clock position) and 1.07 mm (HNO) and ICC values ranging between 0.218 (HNO ratio) and 0.777 (CE). Automatic extraction of geometric hip parameters from MRI is feasible using a morphometric analysis approach with deep learning.
People often confuse data science and data engineering, although this is not the case. Let us have a better understanding of this. Data science is a multi-disciplinary. It uses scientific techniques, procedures, algorithms, and technologies to extract information and insights from structured and unstructured figures. It then applies that knowledge and valuable insights across a variety of application areas.
Traditional finance is infamous for its slow and underwhelming processes. Loans used to take weeks of queuing, filling, and waiting. Investing and portfolio management used to take a dozen people to manage. But finance is taking a turn for the better. An OpenText survey of finance professionals cited by Business Insider reports that around 75% of big financial institutions use AI to strengthen their banking capabilities.
Despite warnings for more than a decade, most financial institutions are unable to manage the data at their disposal or extract actionable insights, leaving money and opportunities on the table. To compete with fintech, big tech and the largest banks, financial institutions of all sizes will need to harness the power of data, making insight-driven decisions and delivering the level of experiences consumers and businesses have come to expect from the firms with the highest levels of data analytics maturity, like Amazon, Google, Facebook, Apple and others. In research done on behalf of Deluxe by the Digital Banking Report, it was found that many organizations have the ability to extract insights from various data sources, supporting foundational marketing decisions and creating segmented marketing programs. Where most organizations fall short, however, is in using data and artificial intelligence (AI) to power real-time decision-making throughout every aspect of the customer journey. The lack of data analytics maturity also hampers the ability create instantaneous learnings from marketing initiatives, using tools like machine learning (ML), that can improve marketing performance over time.
How the board should understand and consider AI is, of course, highly dependent on the usage scenarios for AI within the organization. An organization might utilize AI purely as a decision-making support tool with the capability to better structure and analyze the available data. A more integrated approach could be to connect AI into the organization's operational processes. More advanced usage could be taking AI to customers as part of an existing product or offering. And its deepest role would emerge where an entire product is architected on top of AI capabilities.
SAN DIEGO, August 03, 2021--(BUSINESS WIRE)--LumenVox, a leading provider of speech and voice technology, today announced its next-generation Automatic Speech Recognition (ASR) engine with transcription. The new engine, built on a foundation of artificial intelligence (AI) and deep machine learning (ML), outpaces its competition in delivering the most accurate speech-enabled customer experiences. The new LumenVox ASR engine stands apart from the rest with its end-to-end Deep Neural Network (DNN) architecture and its state-of-the-art speech recognition processing capabilities. The new ASR engine not only accelerates the ability to add new languages and dialects but also provides a modern toolset to expand the language model to serve a more diverse base of users. "New demands have redefined the very meaning of Automated Speech Recognition," said Dan Miller, lead analyst at Opus Research.
Want to avoid an insurrection or genocide? Disconnect AI from centralized databases now! As the power of AI grows and the internet plays an ever greater role in our physical realities, we must act decisively to put the protection of user data at the forefront of any new developments in online products and services. The consequences of failing to protect personal privacy online could be another insurrection or genocide. Artificial Intelligence has a key role to play in securing online privacy, despite all the recent news stories about AI in conjunction with the misuse of private data.
Researchers from Israel have developed a neural network capable of generating'master' faces – facial images that are each capable of impersonating multiple IDs. The work suggests that it's possible to generate such'master keys' for more than 40% of the population using only 9 faces synthesized by the StyleGAN Generative Adversarial Network (GAN), via three leading face recognition systems. The paper is a collaboration between the Blavatnik School of Computer Science and the school of Electrical Engineering, both at Tel Aviv. Testing the system, the researchers found that a single generated face could unlock 20% of all identities in the University of Massachusetts' Labeled Faces in the Wild (LFW) open source database, a common repository used for development and testing of facial ID systems, and the benchmark database for the Israeli system. The Israeli system workflow, which uses the StyleGAN generator to iteratively seek out'master faces'. The new method improves on a similar recent paper from the University of Siena, which requires a privileged level of access to the machine learning framework.