Fix graph
Browse files- knowledge_graph.json +412 -2
knowledge_graph.json
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{"Amazon": [["Is", "Company"], ["Has url", "Https://aws.amazon.com/solutions/case-studies/tymex/?did=cr_card&trk=cr_card"], ["Provides", "Aws"], ["Is", "Cloud service provider"], ["Provides", "Aws solutions"]], "Tymex": [["Is", "Digital banking builder"], ["Builds", "Scalable digital banks"], ["Serves", "Underserved populations"], ["Operates in", "South africa"], ["Operates in", "Philippines"], ["Uses", "Amazon codewhisperer"], ["Uses", "Amazon q"], ["Uses", "Amazon bedrock"], ["Improves", "Developer productivity"], ["Enhances", "Code quality"], ["Develops", "Generative ai applications"], ["Mission", "Become serial bank builder"], ["Has", "12 million customers"], ["Collaborated with", "Aws enterprise support"], ["Honed in on", "Automating recurring sdlc aspects"], ["Implemented", "Amazon q"], ["Built", "Internal ai chatbot tymee"]], "Amazon codewhisperer": [["Is", "Ai-powered productivity tool"], ["Used by", "Over 200 developers"], ["Generates", "Code recommendations from natural-language questions"], ["Completes", "Boilerplate code"], ["Suggests", "Functions or blocks from comments"], ["Detects", "Security issues"]], "Amazon q": [["Supports", "Developers with coding questions and aws best practices"], ["Generates", "Code"]], "Tymee": [["Answers", "Questions about company directories"], ["Promoted through", "Slack"], ["Used in", "Human resources to screen curricula vitae"]], "Jefferies": [["Is", "Investment bank"], ["Started", "Modernize infrastructure on aws"], ["Builds on", "Aws"], ["Uses", "Amazon ec2"], ["Uses", "Amazon s3"], ["Uses", "Aws backup"], ["Enables", "Salesforce to deliver insights"], ["Enables", "Traders to deliver advice"]], "Vikram dewan": [["Is", "Chief information officer"]], "Amazon sagemaker": [["Is", "Fully managed service"], ["Builds", "Real-world ml applications"], ["Trains", "And deploys ml models"], ["Used for", "Build train deploy ml models"], ["Provides", "Computational power"], ["Provided", "Computational power"], ["Deploys", "Open source model llava"], ["Reduces", "Hardware maintenance cost"]], "Amazon bedrock": [["Is", "Fully managed service"], ["Offers", "Foundation models from ai companies"], ["Is", "Managed service"], ["Allows", "Platform as api"], ["Offers", "High-performance foundation models"], ["Offers", "High-performing foundation models"], ["Integrated into", "Open arena"], ["Provides", "Choice of foundation models"], ["Is", "Fully managed service for generative ai applications"], ["Processes", "Textual data from utility bills"], ["Offers", "Large language model"], ["Processes", "Data"], ["Extracts", "Data points"], ["Offers", "Choice of foundation models"], ["Offers", "High-performing foundation models from ai companies"], ["Brought", "Versatility"], ["Is used by", "Toyota connected"], ["Enables", "Experiment quickly with large language models"], ["Includes", "Models from ai21 labs"], ["Includes", "Models from anthropic"], ["Includes", "Models from cohere"], ["Includes", "Models from meta"], ["Includes", "Models from mistral ai"], ["Includes", "Models from stability ai"], ["Provides", "Single api"], ["Runs", "Large language models"], ["Reduces", "Memory consumption"], ["Reduces", "Cpu consumption"], ["Reduces", "Energy consumption"], ["Is", "Ai service"]], "Amazon rds custom": [["Is", "Managed database service"]], "Aws": [["Offers", "Ai and generative ai services"], ["Provides", "Cloud services"], ["Supports", "App development"], ["Provided", "Guidance"], ["Partnered through", "Aws generative ai innovation center"], ["Means", "Innovation"], ["Means", "Excitement"], ["Helps", "Organizations transform"], ["Supports", "Organizations of all sizes"], ["Provides", "Amazon bedrock"], ["Enables", "Extension of service globally"], ["Offers", "Generative ai services"], ["Used by", "Mercedes-benz consulting"], ["Accelerated", "Time to market"], ["Helped improve", "Answer accuracy"], ["Contributed to", "Staff productivity"], ["Offers", "One-month trial"], ["Provided", "Generative ai capabilities"], ["Offers", "Managed services"], ["Helps", "Organizations transform businesses"], ["Offers", "Solutions"], ["Enables", "Customer success stories"], ["Is used by", "Organizations of all sizes"], ["Helps", "Deliver on missions"], ["Provides", "Data controls"], ["Supports", "Data partition by geographical region"], ["Drives", "Innovation"], ["Trusted by", "Leading organizations in europe"], ["Is", "Cloud service provider"], ["Supports", "Iberdrola"], ["Is", "Solution"], ["Is", "Customer stories"], ["Speeds up", "End-to-end development"], ["Accelerates", "Time to value from generative ai"], ["Allows", "Rws to operate in multiple dimensions"], ["Maintains", "High levels of security"], ["Maintains", "High levels of throughput"], ["Is", "Provider of technology"], ["Drives", "Innovation in organizations"], ["Is", "Cloud services platform"]], "Fractal analytics": [["Used", "Generative ai"], ["Built", "Knowledge assist"], ["Employs", "Large language models"], ["Serves", "Fortune 500 companies"], ["Offers", "Ai solutions"], ["Deployed", "Chat solution"], ["Experimented with", "Multiple models"], ["Achieves", "Call time reduction"]], "Knowledge assist": [["Relies on", "Amazon bedrock"], ["Uses", "Amazon eks"], ["Uses", "Amazon opensearch service"], ["Protects data with", "Private endpoints"], ["Encrypts", "Data end-to-end"], ["Masks", "Personally identifiable information"], ["Reduces", "Data retrieval time for users"]], "Ritesh radhakrishnan": [["Is", "Client partner"]], "Philz": [["Is", "Coffee shop"], ["Founded in", "Berkeley"], ["Founded in", "1982"]], "Llms": [["Can be chosen from", "Amazon bedrock"]], "Fractal": [["Worked with", "Aws"]], "Amazon elastic container service": [["Used for", "Build connectors"]], "Amazon opensearch service": [["Used for", "Vector/semantic search"], ["Used to", "Search monitoring analysis data"], ["Creates", "Vectorized database"]], "Amazon elastic kubernetes service": [["Runs on", "Saas application layer"]], "Aws lambda": [["Provides", "Serverless compute"], ["Automates", "Data migration"], ["Is", "Serverless event-driven compute service"], ["Is", "Function"]], "Platform services": [["Ensure", "Scaling and stability"]], "Doordash": [["Built", "Generative ai contact center solution"], ["Collaborated with", "Aws"], ["Used", "Amazon bedrock"], ["Used", "Amazon connect"], ["Used", "Anthropic\u2019s claude"], ["Achieved", "50x increase in testing capacity"], ["Achieved", "Response latency of 2.5 seconds or less"], ["Handled", "100k calls per day"], ["Reduced", "50% in application development time"], ["Handles", "Hundreds of thousands of calls daily"], ["Guides through", "Self-service ivr experience"], ["Provided", "Voice-operated self-service ai contact center"], ["Needed to", "Make sure dashers spend minimal phone time"], ["Wanted", "Use generative ai for self-service"], ["Chose", "Amazon bedrock"], ["Used", "Anthropic's claude models"], ["Achieved", "Response latency 2.5 seconds or less"], ["Added", "Data from help center"], ["Used", "Retrieval-augmented generation (rag"], ["Used", "Knowledge bases for amazon bedrock"], ["Built", "Test and evaluation framework using amazon sagemaker"], ["Helps", "Complete thousands of automated tests per hour"], ["Increases", "Capacity 50x"], ["Evaluates responses against", "Ground-truth data"], ["Handles", "Common dasher inquiries"], ["Improves", "Self-service workflows"], ["Increases", "Issue resolution speeds"], ["Accelerates", "Delivery time"], ["Enhances", "Dasher productivity"], ["Enhances", "Dasher satisfaction"], ["Reduced", "Call volumes for support inquiries"], ["Reduced", "Escalations to live agents"], ["Reduced", "Live agent tasks"], ["Plans to expand", "Knowledge bases"], ["Uses", "Aws and anthropic\u2019s claude"]], "Ivr experience": [["Powered by", "Amazon lex"]], "Response latency": [["Became", "Key factor for phone solution"]], "Teams": [["Iterated over", "Design and implementation"]], "Amazon bedrock\u2013based solution": [["Reduced", "Ai application development time by 50 percent"]], "Claude": [["Mitigates", "Hallucinations prompt injection events"], ["Detects", "Abusive language"]], "Framework": [["Increased", "Testing capacity by 50x"]], "Druva": [["Uses", "Aws enterprise support"], ["Built on", "Amazon web services"], ["Launched within", "3 months"], ["Experienced", "80 percent reduction in resolution time"], ["Delivers", "Innovation"], ["Focuses on", "Innovation"], ["Benefits from", "Support related issue resolution"]], "Aws enterprise support": [["Provides", "Concierge-like service"]], "David gildea": [["Is", "Vice president of product\u2013generative ai"]], "Dru": [["Is", "Ai product"]], "Organizations": [["Use", "Aws to transform"], ["Transforming", "Businesses using aws"], ["Are transforming", "Businesses using aws"], ["Transform", "Businesses"], ["Build on", "Aws"], ["Deliver", "Missions"]], "Ryanair": [["Has 3", "300 daily flights"]], "Thomson reuters": [["Seeks to", "Stay at forefront responsible innovation"], ["Developed", "Open arena"], ["Expanded", "Solution\u2019s offering models use-cases"], ["Reduced", "Ai model deployment time"], ["Streamlined", "Testing innovation"], ["Increased", "Accessibility generative ai tools"], ["Simplified", "User experience"], ["Developed", "Generative ai platform"], ["Built", "Checkpoint edge with cocounsel"], ["Used", "Amazon bedrock"], ["Accelerated", "Ai model deployment times"], ["Helps", "Abstract complexity for users"], ["Exploring", "New ai applications"], ["Expanded access to", "Generative ai models"], ["Evolved", "Generative ai platform"], ["Streamlined", "Testing and innovation"], ["Increased", "Accessibility to generative ai tools"], ["Serves", "Legal tax accounting compliance government media professionals"], ["Uses", "Ai natural language processing solutions"], ["Developed with", "Aws"], ["Created", "Web-based playground"], ["Can experiment with", "Ml tools powered by llms"], ["Chose", "Amazon bedrock"], ["Can customize", "Choice of models"], ["Uses", "Amazon bedrock"], ["Experiments", "Generative ai solutions"]], "Open arena": [["Is", "Self-service enterprise ai ml platform"]], "Thomson reuters labs": [["Has been pioneering", "Ai natural language processing solutions"]], "Platform": [["Is", "Web-based playground"]], "Employees": [["Can explore develop", "Solutions chat-based interface"]], "Checkpoint edge with cocounsel": [["Provides", "Responsive answers to customer queries"], ["Joins", "Checkpoint tax solutions"], ["Provides", "Tax resource citations"]], "Hron": [["Says", "Amazon bedrock allows finding right model for right job"]], "Thomson reuters employees": [["Experiment", "In open arena"]], "Generative ai": [["Democratized", "Accessibility"], ["Democratizes", "Accessibility"], ["Creates", "New use cases"]], "Aws solutions": [["Help", "Learn through doing"]], "Carrier": [["Is", "Global leader in climate energy solutions"], ["Strives to", "Advance sustainable efficient building environments"], ["Wants to", "Scale abound net zero management"], ["Harnessed", "Generative artificial intelligence on aws"], ["Deploys", "Abound net zero management at scale"], ["Deploys", "Abound net zero management"], ["Keeps", "Customer data secure"]], "Abound net zero management": [["Is designed to", "Optimize energy efficiency sustainability in buildings"], ["Can ingest", "Utility data"], ["Provides", "Analytics and insights"]], "Amazon textract": [["Is", "Machine learning service for extracting text from documents"], ["Extracts", "Textual information from bills"]], "Utility bills": [["Are uploaded using", "Amazon textract"]], "Large language model": [["Is offered by", "Amazon bedrock"]], "Customer insights": [["Help", "Reduce carbon footprints"]], "Aws ai services": [["Enable", "Global audience"]], "Solution": [["Available", "Globally"]], "Customer": [["Reduces", "Carbon footprints"], ["Gains", "Insights into energy usage"]], "Webel": [["Is", "Product ai lead"]], "Amazon s3": [["Is", "Object storage service"]], "Happyfox": [["Improved", "Performance of ai-powered customer support solutions"], ["Increased", "Support ticket resolution by 40%"], ["Increased", "Support agent productivity by 30%"], ["Reduced", "Latency by 20%"], ["Implemented", "Anthropic's claude in amazon bedrock"], ["Is", "Saas customer support platform"], ["Offers", "Help desk and support ticketing solutions"], ["Relies on", "External ai service for large language models"], ["Deployed", "Claude 2.1 on amazon bedrock"], ["No longer experiences", "Latency challenges"]], "Claude in amazon bedrock": [["Enhances", "Performance of customer support solutions"], ["Boosts", "Automated support ticket resolution"], ["Increases", "Support agent productivity"], ["Provides", "Generative ai capabilities"]], "Happyfox cto": [["Stated", "Multi-turn interactions had difficulty retaining context"]], "Claude 2.1": [["Hosted within", "Aws cloud infrastructure"], ["Implemented on", "Amazon bedrock"]], "Pradeek": [["Says", "We avoided sharing data with external vendor"]], "All data": [["Hosted within", "Aws"]], "Response times": [["Ranged between", "15\u201220 seconds"]], "Streaming support": [["Reduces", "Response times"]], "Claude in bedrock": [["Provides", "Better performance and accuracy"]], "Claude 2.0 and 2.1": [["Improve", "Agent productivity"]], "Ai agent copilot": [["Analyzes", "Support tickets"]], "Support agent": [["Requests", "Quick summary"]], "Automated q&a features": [["Develop", "Future customer experience"]], "Batch api": [["Designed for", "Aggregating and analyzing responses"]], "Empolis": [["Developed", "Virtual assistant"], ["Uses", "Amazon bedrock"], ["Specializes in", "Ai use cases"], ["Provides", "Cloud-based saas solutions"], ["Collaborated with", "Kuka"], ["Formulated", "Proof of concept"], ["Combined", "Knowledge-graph technology with aws ai services"]], "Kuka": [["Is", "Global supplier of automation solutions"]], "Empolis buddy": [["Can access", "Millions of documents"], ["Helps", "Troubleshoot problems"], ["Has", "Chat-based component"]], "Mercedes-benz consulting": [["Created", "Ai q&a system"], ["Collaborated with", "Aws professional services"], ["Developed", "Digital assistant"], ["Built", "User interface elements"], ["Created", "Applications"], ["Built data foundation", "Serverless-first approach"], ["Uses", "Amazon sagemaker"], ["Uses", "Aws lambda"], ["Can use", "Amazon sagemaker to integrate open-source ml models from hugging face"], ["Develops", "Innovative solutions for internal clients"]], "Millions of technical documents": [["Made searchable", "Using ai"]], "Aws glue": [["Processes", "And cleans data"], ["Is", "Serverless data integration service"]], "Data": [["Goes through", "Extract transform load process"], ["Processed and cleaned by", "Aws glue"]], "Text": [["Converted to", "Numbers using embeddings"]], "Embeddings": [["Accessed by", "Amazon opensearch service"]], "Ai solution": [["Improves", "Staff productivity"]], "Top three query results": [["Contain", "All relevant information"]], "Solution development": [["Supported by", "Aws professional services"]], "Perplexity": [["Accelerates", "Foundation model training"], ["Uses", "Amazon sagemaker hyperpod"], ["Builds", "Conversational answer engine"], ["Integrates", "Large language models"], ["Powered by", "Amazon ec2"], ["Reduces", "Model training time up to 40%"], ["Aims", "Improve user experience"], ["Built", "First conversational answer engine"], ["Gained", "Flexibility in resource allocation"], ["Uses", "Amazon ec2 instance types"], ["Uses", "Gpus"], ["Requires", "Large memory"], ["Chose", "Amazon ec2 p4de instances"], ["Hosts", "Llama 2 model"], ["Fine-tunes", "Open-source model using hyperpod"], ["Adopts", "Amazon bedrock"], ["Begins to use", "Claude 2 through amazon bedrock"], ["Launched", "Api"], ["Adopted", "Amazon bedrock"], ["Uses", "Claude 2"]], "Amazon sagemaker hyperpod": [["Preconfigured with", "Distributed training libraries"], ["Prevents", "Interruptions during training"], ["Detects and repairs", "Hardware failures"], ["Transfers", "Data among gpus faster"], ["Transfers data faster among", "Gpus"], ["Helps optimize", "Training time"], ["Reduces", "Training time"]], "Amazon ec2 p4de instances": [["Provide", "High performance for ml training"], ["Provide", "Highest performance for ml training"], ["Powered by", "8 nvidia a100 gpus"]], "Srinivas": [["Says", "Hyperpod doubles training throughput"], ["Says", "Power is in the hands of the customer"]], "Api": [["Provides", "Access to large language models"], ["Runs on", "Aws"], ["Optimized by", "Amazon sagemaker hyperpod"]], "Claude 2": [["Incorporated into", "Amazon bedrock"]], "There": [["Are no requirements", "Regarding services used"]], "Aravind srinivas": [["Is", "Ceo and cofounder of perplexity"]], "Amazon ec2 p5 instances": [["Deliver", "High performance for dl and hpc"]], "Availity": [["Is", "Largest real-time health information network"], ["Increased", "Productivity"], ["Accelerated", "Code development"], ["Faced difficulties accessing", "Enterprise data"], ["Turned to", "Amazon q services"], ["Implemented", "Ai solutions with amazon q"], ["Created", "Team-wide q&a assistant"], ["Integrated", "Amazon q developer"], ["Used", "Amazon q in quicksight for reports"], ["Deployed", "General purpose chatbot"], ["Created", "Team-wide q&a conversational assistant"], ["Added", "Amazon q in quicksight"], ["Used", "Amazon q business"], ["Used", "Amazon q developer"], ["Used", "Amazon q in quicksight"], ["Improved", "Data research time"], ["Reduced", "Review meeting duration"], ["Creates", "Data stories"]], "Amazon q business": [["Offers", "Conversational ai assistance"], ["Enhances", "Release-management process"]], "Amazon q developer": [["Accelerates", "Software development lifecycle"], ["Supports", "Coding activities"]], "Amazon q in quicksight": [["Delivers", "Business intelligence capabilities"], ["Generates", "Insights from enterprise data"], ["Delivers", "Generative business intelligence"]], "Cloud services": [["Help", "Streamline operations"]], "Availity\u2019s developers": [["Focus", "Creative work"]], "Condor": [["Develops", "Generative ai prototype"], ["Uses", "Amazon bedrock"], ["Is", "Brazilian company"], ["Founded in", "1929"], ["Exports to", "Over thirty countries"]], "Madeinweb": [["Is", "Aws partner"]], "Charla": [["Is", "Generative platform"], ["Built on", "Aws"]], "Amazon simple storage service": [["Provides", "Secure repository"]], "Source": [["Is", "File path"]], "Charla platform": [["Uses", "Foundational models"]], "Anthropic's claude-2": [["Is", "Foundational model"]], "Claude-2": [["Includes", "Legal concepts"], ["Facilitates", "Control over hallucinations"]], "Architecture": [["Built using", "Aws services"]], "Aws services": [["Include", "Amazon api gateway"], ["Help", "Organizations transform"]], "Pereira": [["Explains", "Prototype completed"], ["Plans", "Expand ai tools in 2024"]], "Toyota": [["Uses", "Aws"], ["Produces", "Around 10 million vehicles"], ["Is", "Largest automobile manufacturer"], ["Established", "Toyota connected in 2016"], ["Fuels", "Innovation with data and ai"]], "Toyota connected": [["Develops", "Cloud-based mobility services platform"], ["Processes", "Petabytes of data"], ["Uses", "Sensors"], ["Leads", "Toyota mobility services platform"]], "Kursar": [["Is", "Senior vice president"], ["Is", "Chief technology officer"], ["Works at", "Toyota connected north america"]], "Parks": [["Is", "Senior devops engineer"]], "Kabam robotics": [["Enhances", "Robot intelligence with generative ai on aws"], ["Created", "Smart+"], ["Created", "Remi"], ["Represents", "Leap forward in capabilities"]], "Smart+": [["Is", "Cloud-based robot management platform"], ["Leverages", "Aws"]], "Remi": [["Equips", "Robots with large language models"], ["Is integrated using", "Amazon bedrock api"], ["Utilizes", "Amazon sagemaker"], ["Utilizes", "Amazon eks"], ["Utilizes", "Amazon polly"]], "Adobe": [["Is", "Software company"]], "Coca-cola andina": [["Is", "Beverage bottling company"], ["Migrated to", "Aws"], ["Built", "Data lake on aws"], ["Created", "Thanos application"], ["Provides", "Data visibility"], ["Uses", "Amazon s3"], ["Improved", "Customer satisfaction"], ["Uses", "Thanos"], ["Can view", "Status of distribution centers"], ["Tracks", "Employee names"], ["Discovers", "Causes of anomalies"], ["Uses", "Amazon rds"], ["Uses", "Aws lambda"], ["Improves", "Order fill rate by 1 percent"], ["Reduces", "Out-of-stock frequency by 0.2 percent"], ["Doubles", "Stock-keeping units"], ["Predicts", "Delivery failures with machine learning"], ["Builds", "Models on amazon sagemaker"]], "Thanos": [["Updates", "Data every 15 minutes"], ["Presents", "Online dashboard"]], "Online dashboard": [["Organizes", "Operations data"]], "Canva": [["Empowers", "Creatives"], ["Built with", "Amazon bedrock"], ["Uses", "Generative ai tools"], ["Built", "Generative ai tools"], ["Empowered by", "Amazon bedrock"], ["Uses", "Magic write"], ["Built", "Gen ai tools"], ["Empowered", "Users"], ["Used", "Amazon bedrock"]], "Volkswagen group of america": [["Leveraged", "Amazon q business"], ["Created", "Amazon q app"], ["Mapped", "4000 job descriptions"]], "Genentech": [["Developed", "Gred research agent"], ["Aims", "To automate drug discovery"], ["Aimed to save", "Nearly 5 years"], ["Built", "Generative ai system"]], "Gred research agent": [["Built using", "Amazon bedrock agents"], ["Built with", "Amazon bedrock agents"], ["Automates", "Analyzing scientific data"]], "Jacaranda health": [["Developed", "Prompts platform"], ["Developed", "Digital health platform prompts"]], "Apad": [["Built", "Custom machine learning models"], ["Analyze", "Satellite imagery"], ["Identify", "Pollution sources"], ["Use", "Amazon web services infrastructure"], ["Built", "Custom ml models"], ["Analyzed", "Satellite imagery"], ["Built", "Ml models"], ["Analyzes", "Satellite imagery"], ["Identifies", "Pollution sources"], ["Uses", "Amazon web services infrastructure"]], "Amazon web services": [["Store", "Dataset"], ["Process", "Dataset"], ["Store and process", "Dataset"], ["Stores and processes", "Dataset"], ["Helps", "European customers"]], "Organization": [["Create", "Pollution source map"], ["Give", "Communities evidence"], ["Create", "Comprehensive map of pollution sources"], ["Build", "Organizational innovation"], ["Creates", "Pollution map"]], "Communities": [["Make", "Targeted interventions"]], "Tmna": [["Aims", "Transform data landscape"], ["Create", "Unified lakehouses"], ["Focus on", "Breaking down data silos"], ["Enable", "Comprehensive data sharing"], ["Implement", "Generative bi tooling"], ["Seek", "Embed data-driven insights"], ["Aims to transform", "Data landscape"], ["Leverage", "Sagemaker unified studio"], ["Aims", "Create unified lakehouses"], ["Leverages", "Sagemaker unified studio"], ["Creates", "Unified lakehouses"], ["Seeks to create", "Governed data ecosystem"]], "Prompts": [["Uses", "Two-way sms exchange"]], "Strategic vision": [["Focus on", "Breaking down data silos"]], "Sagemaker unified studio": [["Embed", "Data-driven insights"], ["Embedded", "Data-driven insights"]], "Epic games": [["Create", "Smart cloud governance strategy"]], "Turintech": [["Specializes in", "Using generative artificial intelligence"], ["Uses", "Amazon bedrock"], ["Achieves", "Lower costs"], ["Enables", "Faster code deployment"], ["Promotes", "Greater sustainability"], ["Makes", "Code more efficient"], ["Focuses on", "Financial services and technology customers"]], "Dr. leslie kanthan": [["Is ceo of", "Turintech"]], "Air pollution asset-level detection": [["Built", "Custom ml models"]], "Pollution map": [["Provides", "Evidence to communities"]], "Lakehouses": [["Focus on", "Breaking down data silos"]], "Generative bi tooling": [["Supports", "Self-service analytics"]], "Complyadvantage": [["Helps", "Customers tackle fraud and financial crime"], ["Uses", "Amazon web services"], ["Manages", "Risk"], ["Helps", "Regulated organizations"], ["Meets", "Soc 2 compliance"], ["Meets", "Iso 27001 compliance"], ["Focuses on", "Data security"], ["Is a", "Software provider"]], "Mark watson": [["Is", "Chief technology officer"]], "Iberdrola": [["Is", "Energy company"], ["Founded in", "Unspecified"], ["Focuses on", "Sustainability"], ["Delivers", "Digital services"], ["Uses", "Machine learning models"], ["Reduces", "Customer energy consumption"], ["Lowers", "Energy costs"], ["Supports", "Energy transition"], ["Developed", "Energy management solution"], ["Keeps", "Customer data secure"], ["Develops", "New products"]], "Machine learning models": [["Deployed on", "Amazon sagemaker"]], "Rws": [["Accelerates", "Generative ai value using aws"], ["Provides", "Translation"], ["Provides", "Localization"], ["Provides", "Content services"], ["Is", "World leader in its field"]], "Ai": [["Is central to", "Rws"]], "Mihai vlad": [["Considers", "Aws \u201ca unique partner\u201d"]], "Firemind": [["Helps", "Companies"], ["Uses", "Aws"], ["Harnesses", "Generative ai"], ["Explores", "Amazon bedrock"], ["Is", "Aws advanced partner"]], "Charlie hudson": [["Is", "Managing director of firemind"]], "Customers": [["Develop", "New generative ai use cases"]]}
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|
| 1 |
+
{
|
| 2 |
+
"Generative ai": [
|
| 3 |
+
[
|
| 4 |
+
"Transforms",
|
| 5 |
+
"Music"
|
| 6 |
+
],
|
| 7 |
+
[
|
| 8 |
+
"Transforms",
|
| 9 |
+
"Literature"
|
| 10 |
+
],
|
| 11 |
+
[
|
| 12 |
+
"Transforms",
|
| 13 |
+
"Visual arts"
|
| 14 |
+
],
|
| 15 |
+
[
|
| 16 |
+
"Acts as",
|
| 17 |
+
"Catalyst for artistic innovation"
|
| 18 |
+
],
|
| 19 |
+
[
|
| 20 |
+
"Provides",
|
| 21 |
+
"Tools for artists"
|
| 22 |
+
],
|
| 23 |
+
[
|
| 24 |
+
"Assists in",
|
| 25 |
+
"Composing music"
|
| 26 |
+
],
|
| 27 |
+
[
|
| 28 |
+
"Assists in",
|
| 29 |
+
"Generating unique artworks"
|
| 30 |
+
],
|
| 31 |
+
[
|
| 32 |
+
"Assists in",
|
| 33 |
+
"Writing stories"
|
| 34 |
+
],
|
| 35 |
+
[
|
| 36 |
+
"Assists in",
|
| 37 |
+
"Writing scripts"
|
| 38 |
+
],
|
| 39 |
+
[
|
| 40 |
+
"Democratizes",
|
| 41 |
+
"Creative process"
|
| 42 |
+
],
|
| 43 |
+
[
|
| 44 |
+
"Raises",
|
| 45 |
+
"Ethical challenges"
|
| 46 |
+
],
|
| 47 |
+
[
|
| 48 |
+
"Is",
|
| 49 |
+
"Branch of artificial intelligence"
|
| 50 |
+
],
|
| 51 |
+
[
|
| 52 |
+
"Focuses on",
|
| 53 |
+
"Generating new data"
|
| 54 |
+
],
|
| 55 |
+
[
|
| 56 |
+
"Enables",
|
| 57 |
+
"Data retrieval"
|
| 58 |
+
],
|
| 59 |
+
[
|
| 60 |
+
"Enables",
|
| 61 |
+
"Data analysis"
|
| 62 |
+
],
|
| 63 |
+
[
|
| 64 |
+
"Enables",
|
| 65 |
+
"Content generation"
|
| 66 |
+
],
|
| 67 |
+
[
|
| 68 |
+
"Enables",
|
| 69 |
+
"Summarization"
|
| 70 |
+
],
|
| 71 |
+
[
|
| 72 |
+
"Applies to",
|
| 73 |
+
"Cybersecurity"
|
| 74 |
+
],
|
| 75 |
+
[
|
| 76 |
+
"Assists",
|
| 77 |
+
"Threat hunters"
|
| 78 |
+
],
|
| 79 |
+
[
|
| 80 |
+
"Provides",
|
| 81 |
+
"Real-time insights"
|
| 82 |
+
],
|
| 83 |
+
[
|
| 84 |
+
"Uses",
|
| 85 |
+
"Foundation models"
|
| 86 |
+
],
|
| 87 |
+
[
|
| 88 |
+
"Produces",
|
| 89 |
+
"Text"
|
| 90 |
+
],
|
| 91 |
+
[
|
| 92 |
+
"Produces",
|
| 93 |
+
"Audio"
|
| 94 |
+
],
|
| 95 |
+
[
|
| 96 |
+
"Produces",
|
| 97 |
+
"Code"
|
| 98 |
+
],
|
| 99 |
+
[
|
| 100 |
+
"Produces",
|
| 101 |
+
"Images"
|
| 102 |
+
],
|
| 103 |
+
[
|
| 104 |
+
"Produces",
|
| 105 |
+
"Videos"
|
| 106 |
+
],
|
| 107 |
+
[
|
| 108 |
+
"Improve",
|
| 109 |
+
"Efficiency"
|
| 110 |
+
],
|
| 111 |
+
[
|
| 112 |
+
"Reduce",
|
| 113 |
+
"Administrative burdens"
|
| 114 |
+
]
|
| 115 |
+
],
|
| 116 |
+
"Ethical challenges": [
|
| 117 |
+
[
|
| 118 |
+
"Include",
|
| 119 |
+
"Authorship rights"
|
| 120 |
+
],
|
| 121 |
+
[
|
| 122 |
+
"Include",
|
| 123 |
+
"Originality"
|
| 124 |
+
],
|
| 125 |
+
[
|
| 126 |
+
"Include",
|
| 127 |
+
"Impact on traditional creative roles"
|
| 128 |
+
]
|
| 129 |
+
],
|
| 130 |
+
"Foundation models": [
|
| 131 |
+
[
|
| 132 |
+
"Include",
|
| 133 |
+
"Large language models"
|
| 134 |
+
]
|
| 135 |
+
],
|
| 136 |
+
"Generative ai tools": [
|
| 137 |
+
[
|
| 138 |
+
"Help",
|
| 139 |
+
"Artists push boundaries of creativity"
|
| 140 |
+
]
|
| 141 |
+
],
|
| 142 |
+
"Integration of generative ai": [
|
| 143 |
+
[
|
| 144 |
+
"Creates",
|
| 145 |
+
"Synergy between human creativity and machine intelligence"
|
| 146 |
+
],
|
| 147 |
+
[
|
| 148 |
+
"Produces",
|
| 149 |
+
"Novel genres"
|
| 150 |
+
],
|
| 151 |
+
[
|
| 152 |
+
"Produces",
|
| 153 |
+
"New forms of art"
|
| 154 |
+
]
|
| 155 |
+
],
|
| 156 |
+
"Researchers": [
|
| 157 |
+
[
|
| 158 |
+
"Debate",
|
| 159 |
+
"Authenticity of ai-generated content"
|
| 160 |
+
],
|
| 161 |
+
[
|
| 162 |
+
"Debate",
|
| 163 |
+
"Emotional depth of ai-generated content"
|
| 164 |
+
]
|
| 165 |
+
],
|
| 166 |
+
"The book": [
|
| 167 |
+
[
|
| 168 |
+
"Features",
|
| 169 |
+
"Original manuscripts on generative technologies"
|
| 170 |
+
],
|
| 171 |
+
[
|
| 172 |
+
"Features",
|
| 173 |
+
"Practical applications"
|
| 174 |
+
],
|
| 175 |
+
[
|
| 176 |
+
"Features",
|
| 177 |
+
"Critical analysis of ethical implications"
|
| 178 |
+
],
|
| 179 |
+
[
|
| 180 |
+
"Features",
|
| 181 |
+
"Critical analysis of legal implications"
|
| 182 |
+
],
|
| 183 |
+
[
|
| 184 |
+
"Features",
|
| 185 |
+
"Critical analysis of cultural implications"
|
| 186 |
+
],
|
| 187 |
+
[
|
| 188 |
+
"Serves as",
|
| 189 |
+
"Resource for researchers"
|
| 190 |
+
],
|
| 191 |
+
[
|
| 192 |
+
"Serves as",
|
| 193 |
+
"Resource for artists"
|
| 194 |
+
],
|
| 195 |
+
[
|
| 196 |
+
"Serves as",
|
| 197 |
+
"Resource for practitioners"
|
| 198 |
+
]
|
| 199 |
+
],
|
| 200 |
+
"10": [
|
| 201 |
+
[
|
| 202 |
+
"Comes from",
|
| 203 |
+
"Machine learning"
|
| 204 |
+
]
|
| 205 |
+
],
|
| 206 |
+
"Machine learning": [
|
| 207 |
+
[
|
| 208 |
+
"Uses",
|
| 209 |
+
"Algorithms"
|
| 210 |
+
],
|
| 211 |
+
[
|
| 212 |
+
"Is domain of",
|
| 213 |
+
"Deep learning"
|
| 214 |
+
]
|
| 215 |
+
],
|
| 216 |
+
"Algorithms": [
|
| 217 |
+
[
|
| 218 |
+
"Learn from",
|
| 219 |
+
"Data patterns"
|
| 220 |
+
]
|
| 221 |
+
],
|
| 222 |
+
"Deep learning": [
|
| 223 |
+
[
|
| 224 |
+
"Uses",
|
| 225 |
+
"Neural networks"
|
| 226 |
+
]
|
| 227 |
+
],
|
| 228 |
+
"Neural networks": [
|
| 229 |
+
[
|
| 230 |
+
"Mimic",
|
| 231 |
+
"Brain neurons"
|
| 232 |
+
]
|
| 233 |
+
],
|
| 234 |
+
"Transformers": [
|
| 235 |
+
[
|
| 236 |
+
"Are type of",
|
| 237 |
+
"Neural network"
|
| 238 |
+
],
|
| 239 |
+
[
|
| 240 |
+
"Analyze",
|
| 241 |
+
"Data in parallel"
|
| 242 |
+
]
|
| 243 |
+
],
|
| 244 |
+
"Gpt": [
|
| 245 |
+
[
|
| 246 |
+
"Is",
|
| 247 |
+
"Transformer model"
|
| 248 |
+
],
|
| 249 |
+
[
|
| 250 |
+
"Pre-trained on",
|
| 251 |
+
"Large datasets"
|
| 252 |
+
],
|
| 253 |
+
[
|
| 254 |
+
"Generates",
|
| 255 |
+
"Human-like text"
|
| 256 |
+
]
|
| 257 |
+
],
|
| 258 |
+
"Large language models": [
|
| 259 |
+
[
|
| 260 |
+
"Trained on",
|
| 261 |
+
"Large volumes of data"
|
| 262 |
+
],
|
| 263 |
+
[
|
| 264 |
+
"Used for",
|
| 265 |
+
"Text generation"
|
| 266 |
+
],
|
| 267 |
+
[
|
| 268 |
+
"Classified into",
|
| 269 |
+
"Generative artificial intelligence"
|
| 270 |
+
]
|
| 271 |
+
],
|
| 272 |
+
"Gpt-3.5": [
|
| 273 |
+
[
|
| 274 |
+
"Is",
|
| 275 |
+
"Foundation model"
|
| 276 |
+
],
|
| 277 |
+
[
|
| 278 |
+
"Underpin",
|
| 279 |
+
"Chatgpt"
|
| 280 |
+
],
|
| 281 |
+
[
|
| 282 |
+
"Underpin",
|
| 283 |
+
"Bing chat"
|
| 284 |
+
]
|
| 285 |
+
],
|
| 286 |
+
"Foundation model": [
|
| 287 |
+
[
|
| 288 |
+
"Is",
|
| 289 |
+
"Gpt-4"
|
| 290 |
+
]
|
| 291 |
+
],
|
| 292 |
+
"Chatgpt": [
|
| 293 |
+
[
|
| 294 |
+
"Underpin",
|
| 295 |
+
"Gpt-4"
|
| 296 |
+
],
|
| 297 |
+
[
|
| 298 |
+
"Is",
|
| 299 |
+
"Chatbot"
|
| 300 |
+
]
|
| 301 |
+
],
|
| 302 |
+
"Bing chat": [
|
| 303 |
+
[
|
| 304 |
+
"Underpin",
|
| 305 |
+
"Gpt-4"
|
| 306 |
+
],
|
| 307 |
+
[
|
| 308 |
+
"Is",
|
| 309 |
+
"Chatbot"
|
| 310 |
+
]
|
| 311 |
+
],
|
| 312 |
+
"Report": [
|
| 313 |
+
[
|
| 314 |
+
"Explores",
|
| 315 |
+
"Opportunities of generative ai in education"
|
| 316 |
+
],
|
| 317 |
+
[
|
| 318 |
+
"Explores",
|
| 319 |
+
"Risks of generative ai in education"
|
| 320 |
+
],
|
| 321 |
+
[
|
| 322 |
+
"Covers",
|
| 323 |
+
"Adoption"
|
| 324 |
+
],
|
| 325 |
+
[
|
| 326 |
+
"Covers",
|
| 327 |
+
"Applications"
|
| 328 |
+
],
|
| 329 |
+
[
|
| 330 |
+
"Covers",
|
| 331 |
+
"Impact"
|
| 332 |
+
],
|
| 333 |
+
[
|
| 334 |
+
"Covers",
|
| 335 |
+
"Challenges"
|
| 336 |
+
],
|
| 337 |
+
[
|
| 338 |
+
"Covers",
|
| 339 |
+
"Recommendations"
|
| 340 |
+
]
|
| 341 |
+
],
|
| 342 |
+
"Generative artificial intelligence": [
|
| 343 |
+
[
|
| 344 |
+
"Transforms",
|
| 345 |
+
"Healthcare systems"
|
| 346 |
+
],
|
| 347 |
+
[
|
| 348 |
+
"Encompasses",
|
| 349 |
+
"Machine learning techniques"
|
| 350 |
+
],
|
| 351 |
+
[
|
| 352 |
+
"Classified into",
|
| 353 |
+
"Image-generating models"
|
| 354 |
+
]
|
| 355 |
+
],
|
| 356 |
+
"Image-generating models": [
|
| 357 |
+
[
|
| 358 |
+
"Create or enhance",
|
| 359 |
+
"Visual data"
|
| 360 |
+
]
|
| 361 |
+
],
|
| 362 |
+
"Generative ai models": [
|
| 363 |
+
[
|
| 364 |
+
"Applied in",
|
| 365 |
+
"Clinical practice"
|
| 366 |
+
],
|
| 367 |
+
[
|
| 368 |
+
"Applied in",
|
| 369 |
+
"Research"
|
| 370 |
+
],
|
| 371 |
+
[
|
| 372 |
+
"Assist in",
|
| 373 |
+
"Medical documentation"
|
| 374 |
+
],
|
| 375 |
+
[
|
| 376 |
+
"Assist in",
|
| 377 |
+
"Diagnostics"
|
| 378 |
+
],
|
| 379 |
+
[
|
| 380 |
+
"Assist in",
|
| 381 |
+
"Patient communication"
|
| 382 |
+
],
|
| 383 |
+
[
|
| 384 |
+
"Assist in",
|
| 385 |
+
"Drug discovery"
|
| 386 |
+
],
|
| 387 |
+
[
|
| 388 |
+
"Generate",
|
| 389 |
+
"Text messages"
|
| 390 |
+
],
|
| 391 |
+
[
|
| 392 |
+
"Answer",
|
| 393 |
+
"Clinical questions"
|
| 394 |
+
],
|
| 395 |
+
[
|
| 396 |
+
"Interpret",
|
| 397 |
+
"Ct scan images"
|
| 398 |
+
],
|
| 399 |
+
[
|
| 400 |
+
"Interpret",
|
| 401 |
+
"Mri images"
|
| 402 |
+
],
|
| 403 |
+
[
|
| 404 |
+
"Help in",
|
| 405 |
+
"Rare diagnoses"
|
| 406 |
+
],
|
| 407 |
+
[
|
| 408 |
+
"Discover",
|
| 409 |
+
"New molecules"
|
| 410 |
+
]
|
| 411 |
+
]
|
| 412 |
+
}
|