Search Results

1836 items found

Blog Posts (16)

  • We sent our Founder, Simon Greenman, to Number 10 to Talk AI

    Very proud that our firm Best Practice AI was invited to participate in a roundtable discussion at Number 10 on AI and digital trade with George Hollingbery, Minister of State for Trade Policy.

  • Best Practice AI at World Economic Forum's Global AI Council Meeting with DCMS minster Jeremy Wright

    Honoured that Simon Greenman, Co-Founder and Partner at Best Practice AI, is on the Global AI Council of the World Economic Forum. Minister Jeremy Wright of the UK Department for Digital, Culture, Media and Sport chaired the council's meeting on Tuesday the 11th of June to discuss its directions and priorities.

  • Healthily and Best Practice AI publish world’s first AI Explainability Statement reviewed by the ICO

    LONDON, Fri 17th Sep, 2021 - One of the world’s leading AI smart symptom checkers has taken the groundbreaking decision to publish a statement explaining how it works. Healthily, supported by Best Practice AI together with Simmons & Simmons and Jacob Turner of Fountain Court Chambers today publish the first AI Explainability Statement to have been reviewed by the UK Information Commissioner’s Office (ICO). The Healthily AI Explainability Statement explains how Healthily uses AI in its app including why AI is being used, how the AI system was designed and how it operates. The statement, which can be viewed here, provides a non-technical explanation of the Healthily AI to its customers, regulators and the wider public. Around the world, there is a growing regulatory focus and consensus around the need for transparent and understandable AI. AI Explainability Statements are public-facing documents intended to provide transparency, particularly so as to comply with global best practices and AI ethical principles, as well as binding legislation. AI Explainability Statements such as this are intended to facilitate compliance with Articles 13, 14, 15 and 22 of the GDPR for organisations using AI to process personal data. The lack of such transparency has been at the heart of recent EU court cases and regulatory decisions, involving Uber and Ola in the Netherlands and Foodinho in Italy. Healthily, a leading consumer digital healthcare company, worked with a team from the AI advisory firm, Best Practice AI, the international law firm Simmons & Simmons, and Jacob Turner from Fountain Court Chambers to create the first AI Explainability Statement in the sector. They also engaged with the ICO. A spokesperson for the ICO confirmed: “In preparing its Explainability Statement, Healthily received feedback from the UK’s data protection regulator, the Information Commissioner’s Office (ICO) and the published Statement reflects that input. It is the first AI Explainability Statement which has had consideration from a regulator. The ICO has welcomed Healthily publication of its Explainability Statement as an example of how organisations can practically apply the guidance on Explaining Decisions Made With AI”. Matteo Berlucchi, CEO of Healthily said: “We are proud to continue our effort to be at the forefront of transparency and ethical AI use for our global consumer base. It was great to work with Best Practice AI on this valuable exercise.” Simon Greenman, Partner at Best Practice AI, said: “Businesses need to understand that AI Explainability Statements will be a critical part of rolling out AI systems that retain the necessary levels of public trust. We are proud to have worked with Healthily and the ICO to have started this journey.” To learn more about how Best Practice AI, Simmons & Simmons LLP, and Jacob Turner from Fountain Court Chambers built the AI Explainability Statement, please contact us below.

View All

Pages (1820)

  • AI Case Study | Wasco_water_treatment_plant_pilot_optimises_treatment_of_210K_gallons_of_water_a_day_for_recycling_using_AI

    < back AI Case Study Wasco water treatment plant pilot optimises treatment of 210K gallons of water a day for recycling using AI Wasco water treatment facility is implementing MembranePRO's AI system to reuse water used in oil refining. The system analyses the pollution content of the water and provides a recommendation on how to treat it. Initially 210K gallons of water per day will be recycled and reused. Industry Utilities Gas Water And Multi Utilities Project Overview According to Bakersfield Now, the water treatment plant uses technology from MembranePRO to turn "brown, oil-polluted wastewater, into clean, reusable water. AI algorithms and breakthrough membrane technology to take gallons of water, analyse how polluted it is, and determine the most effective treatment method... This strategically located water treatment facility provides significant environmental and economic benefits to the region. Local oil and gas producers now have a reliable, economical and sustainable option for converting 210,000 gallons per day of produced water into high quality clean water for reuse. Oil and gas producers, agriculture and industry throughout Kern County and Central Valley can now use this recycled water for their operations, reducing the need to utilize precious freshwater resources." Reported Results "The treatment plant will soon be recycling up to 420,000 gallons per day, fueled by growing demand by oil producers who see this as a cost-saving solution." (Bakersfield Now) Technol ogy Details undisclosed Function Operations General Operations Background According to Bakersfield Now: "For the first time in California, water treatment is leveraging artificial intelligence... One barrel of oil can produce up to 100 gallons of waste water, costing the oil and gas industry hundreds of millions of dollars to dispose of." Benefits ​ Data Details undisclosed

  • AI Case Study | University_of_Glasgow_researchers_predict_virus_reservoir_hosts_with_83.5%_accuracy_and_provide_hypotheses_about_unknown_viral_vectors

    < back AI Case Study University of Glasgow researchers predict virus reservoir hosts with 83.5% accuracy and provide hypotheses about unknown viral vectors Researchers at the Institute of Biodiversity, Animal Health and Comparative Medicine at the University of Glasgow use supervised learning to predict virus reservoir hosts with 83.5% accuracy. They then used the trained systems to hypothesise about potential virus vectors and hosts. This research helps lay groundwork for further discovery and provides early triggers for surveillance. Industry Healthcare Pharmaceuticals And Biotech Project Overview The researchers used gradient boosting machines (GBM) as the most effective classified for identifying the most useful genomic traits of the ecology of viruses and predicting associated hosts. They "created a machine learning framework that leverages traits from individual viruses with network-derived information from their relatives to predict: (i) the reservoir hosts of 12 key groups of RNA viruses, (ii) whether their transmission involves an arthropod vector, and (iii) the identity of that vector." After training their models, the researchers used them "to predict the natural epidemiology of viruses with previously unknown hosts (hereafter “orphan” viruses). As expected from the accuracy of our models on viruses with known hosts, model-projected reservoirs and vectors often matched those suspected from epidemiological investigations. For example, we predicted an artiodactyl reservoir for human enteric coronavirus 4408, a suspected spillover infection from cows into humans... For viruses without conjectured reservoirs or vectors, we generate candidates for prioritized surveillance. For example, Bas-Congo virus caused an outbreak of hemorrhagic fever in the Democratic Republic of the Congo and was detected in humans only (18). Our models predicted an artiodactyl reservoir, a high probability of arthropod-borne transmission, and midges as the likely vector of this emerging disease. Reported Results Gradient boosting machines were chosen as the most effective classified as by "combining selected genomic traits (SelGen) with viral PNs [phylogenetic neighborhoods] predicted reservoir hosts with up to 83.5% accuracy, distinguishing all 11 reservoir groups, including taxonomic divisions within the birds (i.e., Neoaves versus Galloanserae) and bats." Furthermore, the researchers used their methodology to identify potential vectors or reservoirs for viruses without identified ones. This can aid in discovery or prioritise surveillance for early detection. Technol ogy The researchers used "supervised machine learning, a class of statistical models that can integrate multiple traits that carry a weak signal in isolation but build a strong signal when optimally weighted (12). Gradient boosting machines (GBMs) (13) outperformed seven alternative classifiers in predicting host associations from viral genomic biases and identified the most informative genomic traits for each aspect of viral ecology... We trained two additional sets of models that focused on arthropod-borne transmission (6). The first nearly perfectly identified which viruses were transmitted by arthropod vectors. Combined GBMs were most accurate overall (bagged accuracy = 97.0%). Only 5 out of 527 viruses were misclassified by all three GBMs (PN, SelGen, and combined), potentially reflect- ing uncertainty in some currently accepted trans- mission routes (supplementary text). The second set of models distinguished transmission by all four vector classes (bagged accuracy = 90.8%). Ranking traits according to their predictive power showed that midge and sandfly vectors were identified predominately from genomic biases, whereas mosquito and tick vectors were strongly correlated with viral phylogeny." Function R And D Core Research And Development Background "Preventing emerging viral infections—including Ebola, SARS, and Zika—requires identi- fication of reservoir hosts and/or blood- feeding arthropod vectors that perpetuate viruses in nature. Current practice requires combining evidence from field surveillance, phylogenetics, laboratory experiments, and real- world interventions but is time consuming and often inconclusive. This creates prolonged periods of uncertainty that may amplify economic and health losses." Benefits ​ Data "We collected a single representative genome sequence per viral species or strain from 12 taxonomic groups (11 families and one order) of ssRNA viruses that can infect humans; that is, 80% of all human-infective groups. For each virus, we used extensive literature searches to determine currently accepted reservoir hosts (437 viruses, 11 reservoir groups), whether transmission involves an arthropod vector (527 viruses), and if so, the identity of arthropod vectors (98 viruses, four vector groups). To maximize predictive scope, reservoir and vector groups included the most- frequent sources of emerging human viruses as well as other common hosts in human-infective viral families (e.g., fish, plants, and insects)".

  • AI Case Study | Cisco_improves_its_online_conferencing_platform_using_accurate_voice_and_chat_assistants_

    < back AI Case Study Cisco improves its online conferencing platform using accurate voice and chat assistants Cisco's Webex platform for online conferences is now using MindMeld's conversational AI which Cisco acquired recently. Using conversational agent is enabling people to communicate easily with applications, devices and can also be used in authenticating participants. Industry Technology Software And It Services Project Overview "Cisco's flagship cloud-collaboration platform Cisco Webex, which claims to facilitate six billion meeting instances a month, is bringing together machine-learning algorithms in areas such as face and voice recognition, and sound mapping to create a new kind of meeting experience. It is Cisco Webex’s AI and machine-learning capabilities, however, which are allowing Cisco to blend physical meetings with virtual environments. As the world’s first enterprise-ready voice assistant for meetings, Webex Assistant uses natural-language understanding to enable users to join, start and end meetings with voice command. " Reported Results Pilot; Results not yet available. However, initial feedback suggests 82% users prefer having a voice assistant Technol ogy ​ Function R And D Product Development Background "With 130 million active users a month, Cisco Webex is one of the largest and most widely recognised enterprise cloud services in the world." Benefits ​ Data ​

View All