Information Technology


Leveraging the immune response and machine learning to fight AMR

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A new platform that measures the body's immune-protein response, coupled with machine learning, can accurately distinguish between bacterial and viral infections within minutes – an effective tool in the fight against AMR. When a patient presents with fever, in many cases, the question comes down to whether it is a bacterial or viral infection, and if to treat, or not to treat, with antibiotics. Making this diagnosis can be challenging as bacterial and viral infections are frequently clinically indistinguishable. As a result, the disease causing pathogen is not clearly identified in as many as two out of three patients with acute infection, even when applying cutting edge microbiological tools.1–3 A complementary diagnostic paradigm has emerged in recent years that overcomes the limitations of direct pathogen detection, namely harnessing the body's immune-response to infection.


Sainsbury's taps Google Cloud for trends insights

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Sainsbury's commercial and technology teams are working with Accenture to implement machine learning processes that they say are providing the retailer with better insight into consumer behaviour. Using the Google Cloud Platform (GCP), the key aim of the collaboration is to generate new insights on what consumers want and the trends driving their eating habits. By tapping into data from multiple structured and unstructured sources, the supermarket chain has developed predictive analytics models that it uses to adjust inventory based on the trends it spots. According to Alan Coad, managing director of Google Cloud in the UK and Ireland, the platform can "ingest, clean and classify that data", while a custom-built front-end interface for staff can be used "to seamlessly navigate through a variety of filters and categories" to generate the relevant insights. Phil Jordan, group CIO of Sainsbury's, said: "The grocery market continues to change rapidly. "We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them.


Why is Sentiment Analysis important from a business perspective? - AYLIEN

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Sentiment essentially relates to feelings; attitudes, emotions and opinions. Sentiment Analysis refers to the practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. A person's opinion or feelings are for the most part subjective and not facts. Which means to accurately analyze an individual's opinion or mood from a piece of text can be extremely difficult. With Sentiment Analysis from a text analytics point of view, we are essentially looking to get an understanding of the attitude of a writer with respect to a topic in a piece of text and its polarity; whether it's positive, negative or neutral.


AI Talent in the European Labour Market

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Artificial Intelligence (AI) is a hotly debated topic, particularly in the context of its impact on the labour market and the workforce. These vital discussions are all too often based on assumptions and desktop projections rather than on concrete, objective data. New research from LinkedIn's Economic Graph uncovers novel, evidence-based insights into the state of AI talent development in the European Union (EU) labour market, and identifies emerging trends that can help inform policymaking in this area. The European Commission has clear ambitions and goals for AI, but right now Europe is lagging behind its peers in developing talent. The U.S. employs twice as many AI-skilled individuals than the EU, despite the American total labour force being just half the size.


Machine Learning Using Hardware and Software

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For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms.


The Age of Thinking Machines

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We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.


The future of resourcing work at local authorities with AI and automation

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In this roundtable write-up, hear from representatives of local government and the IT industry about the impact artificial intelligence and intelligent automation are having on councils across the country. Topics examined by the expert panel include how AI and automation can help alleviate budget strain, and the effect technology could have on service levels or the range of offerings for citizens. The panel will also examine the key considerations organisations ought to make in regard to their existing human workforce, and what advice they would give to local authorities considering deploying virtual workers. This whitepaper is provided by Thoughtonomy - a Blue Prixm company. Thoughtonomy delivers an AI-driven intelligent automation platform that enables organisations and the people they employ to do more and achieve more.


How Google Predictive Search Answers What Internet Users Want

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GoogleBot continually gets even smarter when resolving both paid PPC advertising and when displaying earned search results. This article endeavors to demystify the concept of how Google predictive search works around user intent and positive search experiences. Digital marketers, SEO's, SEM, and AdWords professionals who have a working knowledge of these processes find the keys to offering meaningful user voice activated activity on the Internet. Search engine's core task is to point people to the best information. Since Internet users express one idea in many different ways, by using Google search predictions, you can reach web surfers of any age, anywhere, and any time of day. Wikipedia says, "Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. Every business wants to leverage their data for optimal ...


Salesforce to Use Amazon AI Technology to Improve Customer Service Apps - Jploft

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On 19th November 2019, the Business software provider- Salesforce said that it will use the AI (Artificial Intelligence) technology of Amazon to improve the customer service apps. Salesforce develops such software systems that companies use to collect customer information, such as what products or services the customer has purchased and how long they have been customers. Agents use this information to solve customer problems. Salesforce said it will use Amazon's web services technology to translate words spoken by a customer into text, where they can be translated into different languages or analyzed to determine if the customer is satisfied or not, all in real-time. It further said: "From there, Salesforce's software can read the text and make suggestions using data already in the business' Salesforce-based systems, such as recommending answers to the customer's questions".


fashionabc The Leading global fashion, AI, blockchain powered directory, searchengine, fashtech, innovation, IoT, 4IR, fashiontech

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We are building the default global directory for fashion businesses. From designers, brands, fashion houses to producers and factories, we include every brand and any independent fashion professional, that is either operating in the ethical arena or eager to make the change. We are using Blockchain and AI tools (main drivers of the 4IR - Fourth Industrial Revolution) in our directory, with the aim to create the most comprehensive search engine for the fashion industry. The uniqueness of our project is that we use blockchain and AI to endow brands and fashion professionals with a rating according to assessment metrics, based on the UN Sustainable Development Goals. The creation of this database, the biggest of its kind, is invaluable for the end goal of creating an extensive and useful data driven directory of the global players and brands of the industry and supply chain of fashion, using the trustworthy technology of blockchain, where users can find the best and more ethical/sustainable fashion players operating in the field.