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How Big Data and Artificial Intelligence Can Create New Possibilities

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By combining artificial intelligence (AI) and big data, organizations can see and predict upcoming trends in key sectors including business, technology, finance and healthcare. AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make'intelligent' machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing the data, and then analyzing data to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves.


Synthetic data platform Mostly AI lands $25M

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Did you miss a session from the Future of Work Summit? Austria-based Mostly AI, a startup that simulates synthetic data for AI model training and testing, today announced it has raised $25 million in a series B round from Molten Ventures. The company plans to use the investment to accelerate its work in setting the groundwork for responsible and unbiased AI, hiring fresh talent, and strengthening its presence across Europe and North America. For any modern-day enterprise, the biggest challenge associated with leveraging data for AI/ML is ensuring the privacy of its consumers -- the original source of the data -- and eliminating the possibility of any sort of bias due to historical or social inequities in that data. Organizations often find a hard time dealing with the two problems and either end up facing fines for privacy violations (under regulations such as GDPR) or train a model which is unfair on one or more parameters.


Tactile Based Fabric Classification via Robotic Sliding

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Tactile sensing endows the robots to perceive certain physical properties (which are not directly viable to visual and acoustic sensors) of the object in contact. Robots with tactile perception are able to identify different textures of the object touched. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors, can also be identified through exploratory robotic movements like sliding and rubbing. To study the problem of fine texture classification via robotic sliding, we design a robotic sliding experiment using daily fabrics (as fabrics are likely to be the most common materials of fine textures). We propose a feature extraction process to encode the acquired tactile signals (in the form of time series) into a low dimensional (<= 7D) feature vector. The vector captures the frequency signature of a fabric texture such that distinctive fabrics can be classified by their correspondent feature vectors. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, for the investigation into the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. For our specific sensor used in the experiments, there exists a sweet spot of pressure for the fabric classification task. Adversely, variation of sliding speed shows no apparent impact on the performance of the feature ext...


Computer vision-based anomaly detection using Amazon Lookout for Vision and AWS Panorama

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This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).


AI, machine learning, analytics, big data – and much more besides

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of data and machine learning. We are looking to train people who have deep knowledge in specific disciplines so that they know how to address …


Global Big Data Conference

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San Francisco-based Databricks, a company that offers the capabilities of a data warehouse and data lake in a single "lakehouse" architecture, today announced its first industry-specific offering: Lakehouse for Retail. Designed for enterprises dealing in the retail and consumer goods vertical, Databricks says Lakehouse for Retail is a fully integrated platform that aims to solve the most critical challenges retailers and their partners face while trying to leverage surging data volumes for AI and analytics projects. The solution, which is generally available as of today, has already seen early adoption from major retail enterprises including Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51, Co-Op Food, Gousto, and Acosta. "With hundreds of millions of prescriptions processed by Walgreens each year, Databricks' Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads," said Luigi Guadagno, the VP of pharmacy and healthcare platform at Walgreens. "By eliminating complex and costly legacy data silos, we've enabled cross-domain collaboration with an intelligent, unified data platform that gives us the flexibility to adapt, scale and better serve our customers and patients," Guadagno said.


Six Key Applications of Data Science in Healthcare

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The healthcare sector is no different--particularly in the wake of the global pandemic, during which rapid and remote healthcare practices have had to take shape almost overnight. Healthcare software development services and data science solutions have become an integral part of the industry today. In fact, data science in healthcare represents arguably one of the most critical and long-overdue sector revolutions of modern times. With data science, healthcare institutions can harness analytics to bring about faster and far more accurate diagnoses while providing treatments that carry a higher efficacy and lower risk to patients' health. And with over a billion clinical documents being produced every year in the US alone, there's a deep mine of healthcare data out there to be drilled.


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Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It's a science that's not new – but one that has gained fresh momentum.


Databricks targets retail vertical with its first industry-specific lakehouse

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Did you miss a session from the Future of Work Summit? San Francisco headquartered Databricks, a company that offers the capabilities of a data warehouse and data lake in a single "lakehouse" architecture, today announced its first industry-specific offering: Lakehouse for Retail. Designed specifically for enterprises dealing in the retail and consumer goods vertical, Databricks says Lakehouse for Retail is a fully integrated platform that aims to solve the most critical challenges retailers and their partners face while trying to leverage surging data volumes for AI and analytics projects. The solution, which is generally available as of today, has already seen early adoption from major retail enterprises including Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51, Co-Op Food, Gousto, and Acosta. "With hundreds of millions of prescriptions processed by Walgreens each year, Databricks' Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads," said Luigi Guadagno, the VP of pharmacy and healthcare platform at Walgreens.


Top Data Science Tools That You Should Learn in 2022

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We live in a time where data is supreme. Our private details, financial arrangements, careers, and amusement have been digitized and stored as data. Due to the greater volume of data generated, there is a more significant need to research and retain it. If you're conscious of the current market environment, you've probably noticed that the data science field is flourishing. Data Science signifies generated value from data, and it all comes down to comprehending the data and processing it to obtain actionable & insightful value from it.