PLEASE WELCOME VP DATABASE
ANALYTICS AND MACHINE LEARNING SWAMI SIVASUBRAMANIAN.
THANK YOU ALL SO MUCH FOR BEING HERE TODAY AND GIVING ME A
CHANCE TO SPEAK WITH YOU. A LOT HAS HAPPENED IN THE LAST YEAR
AND WITH ALL THIS CHANGE WE HAVE AN OPPORTUNITY TO REEVALUATE AND
ENHANCE AND OPTIMIZE THE TECHNOLOGY THAT SUPPORTS OUR
SUCCESS. I ALSO BELIEVE IT'S A TIME FOR NET NEW IDEAS. THESE
ARE THE SPARKS OF INVENTION THAT ENABLE US TO SOLVE REALLY
COMPLEX PROBLEMS AND REIMAGINE HOW WE DO THINGS. AND WHEN YOU
ARE BACKED BY THE MOST TRANSFORMATIVE, INNOVATIVE
TECHNOLOGY SIMS, YOU CAN BRING THESE AMAZING IDEAS TO LIFE. ONE
OF THOSE TRANSFORMATIVE TECHNOLOGIES THAT'S GAINING A
LOT OF TRACTION TODAY IS GENERATIVE AI GENERATIVE AI. AS
CAPTURED, OUR IMAGINATIONS FOR ITS ABILITY TO CREATE IMAGES AND
VIDEOS RIGHT STORIES AND EVEN GENERATE CODE. I BELIEVE IT WILL
TRANSFORM EVERY APPLICATION INDUSTRY AND BUSINESS. SO YOU
MIGHT BE WONDERING, WITH SO MUCH POTENTIAL, WHY IS THIS
TECHNOLOGY THAT HAS BEEN PERCOLATING FOR MANY DECADES
SUDDENLY SEEING SO MUCH TRACTION ONLY JUST NOW? THAT IS BECAUSE THIS
TECHNOLOGY HAS REACHED ITS TIPPING POINT AT THE CONVERGENCE
OF TECHNOLOGICAL PROGRESS AND THE VALUE OF WHAT IT CAN
ACCOMPLISH TODAY. THAT'S BECAUSE WE HAVE MASSIVE PROLIFERATION OF
DATA AND THE AVAILABILITY OF EXTREMELY SCALABLE COMPUTE
INFRASTRUCTURE AND THE ADVANCEMENT OF ML TECHNOLOGIES.
OVER TIME. GENERATIVE AI IS FINALLY TAKING SHAPE. IN
PARTICULAR FOR THESE INNOVATIONS HAVE MADE THE CAPABILITIES OF AI
POSSIBLE JUST WITHIN THE LAST MONTH. LET ME GIVE SOME
BACKGROUND FOR EXAMPLE, TRADITIONAL FORMS OF MACHINE
LEARNING ALLOWED US TO TAKE SIMPLE INPUTS LIKE NUMERICAL
VALUES AND MAP THEM TO SIMPLE OUTPUTS LIKE PREDICTOR VALUES
AND THEN DEEP LEARNING CAME ALONG WHERE WE COULD TAKE
COMPLICATED INPUTS LIKE VIDEOS OR IMAGES AND MAP THEM TO
RELATIVELY SIMPLE OUTPUTS. AND NOW WITH GENERATIVE AI, WE CAN
LEVERAGE THIS MASSIVE AMOUNTS OF COMPLEX DATA TO CAPTURE AND
PRESENT KNOWLEDGE IN MORE ADVANCED WAYS. MAPPING
COMPLICATED INPUTS TO COMPLICATED OUTPUTS IS THE STEP
WISE IMPROVEMENTS IN FUNCTIONALITY. WE WERE SUPPORTED
BY STEP WISE IMPROVEMENTS IN THE UNDERLYING ML MODELS THEMSELVES
THAT THAT'S BECAUSE TRADITIONAL ML MODELS USED ARCHITECTURES
THAT REQUIRE MONTHS OF COSTLY AND MANUAL DATA PREPARATION,
DATA LABELING AND MODEL TRAINING , ALL TO DO ONE SPECIFIC TASK.
THE LARGE MODELS THAT POWER THESE GENERATIVE AI APPLICATIONS
IS CALLED FOUNDATIONAL MODELS ARE DRIVEN BY THE TRANSFORMER
NEURAL NET ARCHITECTURE THAT SIGNIFICANTLY CUTS DOWN ON THIS
DEVELOPMENT PROCESS. WITH THIS ARCHITECTURE, MODELS CAN BE
TRAINED ON MASSIVE AMOUNTS OF UNLABELED DATA IN THE
PRE-TRAINING STAGE SO THAT THEY CAN BE USED OUT OF THE BOX FOR A
WIDE VARIETY OF GENERALIZED TASKS AND THEY CAN BE USED AND
EASILY ADAPTED FOR PARTICULAR DOMAINS OR APPLICATIONS WITH
RELATIVELY VERY SMALL AMOUNT OF UNLABELED DATA. THIS PROCESS OF
CUSTOMIZATION IS ALSO KNOWN AS FINE TUNING THE ABILITY TO
EASILY CUSTOMIZE A PRE-TRAINED MODEL THROUGH FINE TUNING IS AN
ABSOLUTE GAME CHANGER. IT IS SUBSTANTIAL FASTER. IT ALSO
REQUIRES A LOT LESS COMPUTATION. TIME AND A LOT LESS DATA FOR
FINE TUNING THAN SPENDING MONTHS OF CREATING A TASK SPECIFIC
MACHINE LEARNING MODEL. SO WHILE WHAT USED TO TAKE MONTHS OF
SCIENTISTS TO BUILD AN ML MODEL FOR ONE TASK CAN NOW BE DONE
RELATIVELY EASY WITH ONE BIG MODEL AND FINE TUNING TO
ACCOMPLISH THE SAME GOAL. SO TODAY, GENAI IS BEING APPLIED TO
USE CASES FROM ALL LINES OF BUSINESS, FROM ENGINEERING TO
CUSTOMER SERVICE TO FINANCE, YOU CAN IMPROVE CUSTOMER EXPERIENCES
THROUGH CAPABILITY, THINGS LIKE VIRTUAL CHATBOT, VIRTUAL
ASSISTANTS. YOU CAN BOOST YOUR EMPLOYEES PRODUCTIVITY WITH TEXT
SUMMARIZATION AND CODE GENERATION JSON YOU CAN
TURBOCHARGE PRODUCTION OF ALL TYPES OF CONTENT LIKE ART, MUSIC
OR ANIMATIONS, AND YOU CAN USE GENERATIVE AI TO IMPROVE
BUSINESS OPERATIONS. SO WHAT DO CUSTOMERS NEED TO DO TO UNLOCK THE VALUE OF
GENERATIVE AI FOR THEIR USE CASES? THE FIRST THING IS YOU
NEED ACCESS TO BEST IN CLASS FOUNDATIONAL MODELS. WE KNOW
MODEL CHOICES PARAMOUNT BECAUSE THERE IS GOING TO BE NO ONE
MODEL TO RULE THEM ALL. IT'S ABOUT CHOOSING THE RIGHT MODEL
FOR THE RIGHT JOB. THEN THE CUSTOMERS NEED THE ABILITY TO
SECURELY CUSTOMIZE THESE MODELS WITH THEIR DATA AND THEN THEY
NEED EASY TO USE TOOLS TO DEMOCRATIZE GENERATIVE AI WITHIN
THEIR ORGANIZATIONS AND IMPROVE EMPLOYEE PRODUCTIVITY AND
UNDERPINNING ALL OF THIS IS YOU NEED TO KEEP YOUR COSTS AND
LATENCY LOW WITH PURPOSE BUILT. ML INFRASTRUCTURE. WE ARE
DELIVERING ALL OF THIS TO OUR CUSTOMERS THROUGH AMAZON BEDROCK
WITH BEDROCK. CUSTOMERS CAN EASILY BUILD AND SCALE GENAI
APPLICATIONS WITH A SELECTION OF INDUSTRY LEADING FMS ALL WITH A
SIMPLE API WITHOUT MANAGING ANY INFRASTRUCTURE. AMAZON BEDROCK
MAKES IT EASY TO CUSTOMIZE THESE FOUNDATIONAL MODELS WITH YOUR
DATA. WITH JUST A FEW LABELED EXAMPLES IN AMAZON S3 ALL ALL
DATA IN BEDROCK IS ENCRYPTED AND YOUR DATA IS NEVER USED TO TRAIN
THE ORIGINAL BASE MODEL. YOU CAN CONFIGURE YOUR VPC SETTINGS TO
ACCESS BEDROCK APIS AND PROVIDE FINE TUNING MODEL DATA IN A
SECURE MANNER AND YOU ARE GOOD TO GO. WE BELIEVE IN OFFERING
THE BEST IN CLASS FOUNDATIONAL MODELS. TO THAT END, WE HAVE
AMAZON TITAN MODEL, INCLUDING TITAN TEXT MODEL FOR TEXT
SUMMARIES, CREATION AND GENERATION AND TITAN EMBEDDINGS
MODEL FOR PERSONALIZATION AND RECOMMENDATION. WE HAVE
ANTHROPIC CLAUDE THAT IS BUILT WITH THE LATEST RESEARCH ON
SAFETY AND NLP TO PERFORM CONVERSATIONS AND TEXT
PROCESSING. BEDROCK ALSO SUPPORTS STABILITY. AI'S LATEST
TEXT TO IMAGE AND VARIOUS IMAGE PROCESSING MODELS, AND WE OFFER
AI21LABS JURASSIC-2 MODEL NOW LET'S TAKE A CLOSER LOOK AT
TITAN OUR TITAN FMS ARE PRE-TRAINED ON DATASETS THAT
CONTAIN LARGE VOLUMES OF INFORMATION, OFTEN FROM DIVERSE
SOURCES, ENABLING CUSTOMERS TO BUILD FOR DIVERSE SET OF USE
CASES. THEY ALSO SUPPORT RESPONSE CIVIL USE OF AI BY
DETECTING INAPPROPRIATE CONTENT IN THE INPUT PROMPTS, BUT ALSO
FILTER ING IN THE OUTPUTS. ANY KIND OF INAPPROPRIATE CONTENT
LIKE HATE SPEECH, PROFANITY AND VIOLENCE. TITAN AND OUR BROAD
LIBRARY OF FMS AND BEDROCK HAVE ENABLED OUR CUSTOMERS TO START
INNOVATING WITH THE RIGHT TOOLS TODAY OUR CUSTOMERS ARE
DRIVING INNOVATION WITH BEDROCK FOR VARIOUS FOUNDATIONAL MODELS
FOR SELF-SERVICE, CUSTOMER CHAT, TEXT ANALYSIS, REPORT GENERATION
AND POST-CALL ANALYSIS. CUSTOMERS LIKE SUN LIFE, WHICH
USES BEDROCK TO EXPERIMENT WITH GENERATIVE AI APPLICATIONS THAT
ANALYZE MARKET DATA AND ASSESS EMPLOYEE PRODUCTIVITY.
BRIDGEWATER ASSOCIATES A PREMIER ASSET MANAGEMENT FIRM THAT IS
BUILDING AND SCALING GENAI APPLICATIONS. LEVERAGING THESE
FOUNDATIONAL MODELS ON AMAZON BEDROCK AND PHILIPS WHO WILL USE
BEDROCK TO DEVELOP GENAI APPLICATIONS ACROSS ITS ENTIRE
PORTFOLIO TO SUPPORT EFFICIENT CLINICAL WORKFLOWS, ENHANCE
DIAGNOSTIC CAPABILITY FOR THEIR PATIENTS. OUR CUSTOMERS ARE ALSO
PUTTING GENAI INTO ACTION WITH THE HELP OF OUR GENAI INNOVATION
CENTER, A NEW PROGRAM TO HELP THEM ACCELERATE SUCCESS WITH
TOOLS LIKE AMAZON BEDROCK. THIS INCLUDES CUSTOMERS LIKE
LONELYPLANET AND RYANAIR WHO ARE WORKING WITH OUR TEAMS TO
DEVELOP NEW USE CASES. SO WE ARE EXCITED TO SEE ALL OF THIS
MOMENTUM AND WE WILL CONTINUE TO SUPPORT OUR CUSTOMERS. GENAI USE
CASES WITH ALL THESE BEST IN CLASS FOUNDATIONAL MODELS.
THAT'S WHY TODAY WE ARE OFFERING EVEN MORE CHOICE IN FOUNDATIONAL
MODELS. WE ARE EXCITED TO ANNOUNCE THE ADDITION OF TWO NEW
MODELS FROM COHERE COMMAND AND EMBED. SO COHERE COMMAND IS A TEXT GENERATION MODEL FOR BUSINESS
APPS LIKE SUMMARIZATION COPYWRITING, DIALOG AND Q&A AND
EMBED IS OUR TEXT UNDERSTANDING MODEL THAT CAN BE USED FOR
SEARCH CLUSTERING OVER 100 PLUS LANGUAGES. THESE TWO ADDITIONS
REINFORCE OUR COMMITMENT TO MAKE THE BEST IN CLASS FMS AVAILABLE
. IN ADDITION TO THIS NEW PROVIDER, WE ARE ALSO EXCITED TO
ANNOUNCE STABILITY. AI'S LATEST MODEL STABLE DIFFUSION. XL 1.0.
WE ARE ALSO EXCITED TO ANNOUNCE THE ADDITION OF ANTHROPIC CLAUDE
2 CLAUDE V2 NOW CAN TAKE UP TO 100,000 TOKENS IN EACH
CONVERSATIONAL PROMPT, MEANING IT CAN OVER HUNDREDS OF PAGES OR
EVEN AN ENTIRE BOOK. IT CAN ALSO WRITE LONGER DOCUMENTS THAN ITS
PREVIOUS VERSION. CLAUDE IS ONE OF OUR POPULAR FMS AND WE KNOW
CUSTOMERS WILL BE EXCITED TO TAKE ADVANTAGE OF THESE
FUNCTIONALITY. ALL OF THIS EARLY SUCCESS ON BEDROCK IS BUILT ON
YEARS OF EXPERIENCE OF MAKING THE BEST IN CLASS PRE-TRAINED FM
AVAILABLE ON SAGEMAKER JUMPSTART WHERE CUSTOMERS CAN GET THEIR
HANDS ON LATEST PRE-TRAINED PUBLICLY AVAILABLE MODELS FROM
ACADEMIA AND INDUSTRY JUMPSTART OFFERS ML PRACTITIONERS THESE
DEEP MODEL CUSTOMIZATION AND EVALUATION CAPABILITIES LIKE
SAGEMAKER STUDIO SAGEMAKER SDK AND CONSOLE USING WHICH THEY CAN
ACTUALLY GO DO ALL THESE CUSTOMIZATIONS AND ADD NEW
MODELS ARE ADDED ON A WEEKLY BASIS, LIKE THE LLAMA V2 WE
ADDED LAST WEEK. NOW I'D LIKE TO INTRODUCE ONE OF OUR CUSTOMERS
TO THE STAGE TO SHARE HOW THEY ARE LEVERAGING ML AND GENERATIVE
AI TO HELP BUSINESSES. SIMS IFY THE JOURNEY FROM UNIFIED DATA TO
PERSONALIZED CUSTOMER EXPERIENCES. PLEASE WELCOME
GABRIELLE TAO FROM SALESFORCE. THANK YOU SWAMI. IT IS A GREAT HONOR TO BE HERE WITH YOU
TODAY. SALESFORCE IS THE WORLD'S LEADING CRM PROVIDING A SUITE OF
APPLICATIONS ACROSS SALES SERVICE, MARKETING, COMMERCE,
ANALYTICS AND MORE, AND NOW DATA CLOUD. THAT ENABLE BUSINESSES TO
BETTER CONNECT WITH THEIR CUSTOMERS. I REPRESENT
SALESFORCE DATA CLOUD AND WE LEVERAGED AWS SERVICES TO CREATE
MACHINE LEARNING GENAI AND BUILD A SOLUTION THAT HELPS OUR
COMPANIES, WHO ARE OUR CUSTOMERS, USE ALL OF THOSE DATA IN A
HARMONIZED FASHION, CREATE INSIGHTS AND DELIVER
PERSONALIZED EXPERIENCES ALL AT HYPER SCALE. THAT IS CERTAINLY
NOT EASY TODAY ON OUR PLANET THERE ARE MORE DEVICES AND THERE
ARE HUMANS. AS MORE AND MORE DECISIONS AND DATA GROWS, IT
BECOMES HARDER AND HARDER TO CONNECT WITH THE CUSTOMERS. AS
SALESFORCE, WE RECENTLY DID SOME RESEARCH AND FOUND THAT 60 TO
70% OF THE RESPONDENTS EXPECT COMPANIES TO UNDERSTAND AND EVEN
ANTICIPATE THEIR NEEDS. AND YET A FULL 56% OF THEM FEEL THAT
MOST COMPANIES TREAT THEM JUST AS A NUMBER. HOW COULD THEY NOT?
AN AVERAGE COMPANY HAS 976 APPS LOCATIONS TO BETTER UNDERSTAND
CUSTOMERS NEEDS, THERE REALLY NEEDS TO BE A MORE UNIFIED
UNDERSTANDING OF THE CUSTOMERS. SO SALESFORCE HAS BEEN ON THIS
JOURNEY. LAST YEAR WE ANNOUNCED SALESFORCE DATA CLOUD TO HELP
YOU CREATE A SINGLE SOURCE OF TRUTH OF YOUR DATA WITH AN
OPENNESS THAT IS UNPRECEDENTED IN A DATA PLATFORM, A ZERO-ETL
FRAMEWORK MERCK ACCESSING YOUR DATA WHERE THEY ARE SIDE BY SIDE
AND DEEPLY MERGED WITH THE CRM PLATFORM. IT IS A FOUNDATION
THAT UNDERPINS FUTURE INNOVATIONS AT SALESFORCE. IT IS
BUILT ON OUR HYPERFORCE INFRASTRUCTURE AND BACKED BY
QUITE A FEW AWS SERVICES EKS, EMR AND MANY OTHERS BACKED BY
THESE WONDERFUL BACKEND SERVICES. WE BUILT A LOT OF NO
CODE APPLICATION FEATURES, INCLUDING TRANSFORM IDENTITY
INSIGHTS SEGMENTS AND MORE CATERING, NOT JUST TO THE DATA
PERSONA BUT THE ANALYSTS AND BUSINESS PERSONAS AS WELL.
DEMOCRATIZING BIG. DATA NOW, WHILE SALESFORCE DATA CLOUD CAN
HANDLE THIS ENTIRE DATA PIPELINE, WE REALLY BELIEVE THAT
THE LATTER HALF OF THIS IS MOST IMPORTANT BECAUSE PERSONALIZED
EXPERIENCES ARE THE MOST IMPACTFUL WHEN DELIVERED AT THE
MOMENT. THAT MATTERS TO MOST, TO THE CUSTOMER. AND SO THIS LAST
STEP HERE ACT IS CRITICAL. AND AS SUCH, WE'VE CREATED THE
ABILITY TO PROACTIVELY TRIGGER OUR ACTIONS BASED ON ANY AND ALL
DATA AND INSIGHT CHANGES. AS A CUSTOMER STATUS, CHANGES AS A
PROPENSITY SCORE CHANGES, FOR EXAMPLE, IN THE MOMENT WE
TRIGGER THESE DATA SIGNALS OUT EVERYWHERE TO ALL THE
APPLICATION SYSTEMS, INCLUDING SALESFORCE, SES OVER THE LAST
YEAR WE'VE WORKED WITH AMAZON SAGEMAKER TO BRING THIS BRING
YOUR OWN AI CAPABILITY, WHICH DRASTICALLY REDUCES THE
OPERATIONAL BURDEN OF MAINTAINING AND USING AI. WE
WORK WITH AMAZON SAGEMAKER BECAUSE OUR CUSTOMERS LOVE IT
AND BECAUSE IT'S EASY TO CREATE A SEAMLESS EXPERIENCE FOR OUR
JOINT USERS. WITH OUR INTEGRATION, YOU CAN ACCESS
DATA, CLOUD DATA, ZERO-ETL STYLE BUILD AI MODELS IN SAGEMAKER AND
THEN ONLY INVOKE THOSE MODELS INFERENCES AS NEEDED AND IN REAL
TIME FROM DATA CLOUD. FOR EXAMPLE, IF YOU HAVE 50 MILLION
CUSTOMERS IN 20 PRODUCT CATEGORIES, FIVE AI MODELS, SO 5
BILLION SCORES IN TOTAL TRADITIONAL AI, ML OPS PROBABLY
HAVE YOU BATCH SCORING ALL OF THESE 5 BILLION COMBINATIONS
ACROSS THE BOARD EVERY SINGLE DAY AND THEN COPYING THEM
EVERYWHERE TO DIFFERENT SYSTEMS JUST IN CASE ANY ONE OF THOSE
SCORES NEEDS TO BE USED IN ANY ONE OF THOSE SYSTEMS, ANY GIVEN
DAY. WITH OUR INTEGRATION. YOU DON'T HAVE TO DO THAT. LET'S
TAKE A LOOK AT IT IN ACTION IN THE FIRST TWO STEPS HERE, OUR
USERS ACCESS AND EXPLORE DATA CLOUD DATA AND INSIGHTS WITHIN
DATA WRANGLER, WHERE THEY CAN DO FURTHER AI FEATURE ENGINEERING
AS NEEDED. THIRD STEP DATA SCIENTISTS TRAIN AND BUILD AI
MODELS IN SAGEMAKER STUDIO. WHEN A MODEL IS READY FOR PRODUCTION,
A PROJECT TEMPLATE AUTOMATES THE DEPLOYMENT OF AN INVOCATION
ENDPOINT, WHICH CAN THEN BE REGISTERED IN DATA CLOUD. LAST
STEP THERE. NOW WE HAVE SEEN MANY WONDERFUL REAL WORLD
APPLICATIONS OF THIS FEATURE. IN ONE CASE, A COMPANY STREAMS OR
DEVICE DATA INTO DATA CLOUD, WHICH THEN ENRICHES THEM WITH
UNIFIED PROFILE DATA. CALCULATE SOME REAL TIME INSIGHTS, THEN
DECIDES TO INVOKE A RETENTION CHURN MODEL INSIDE AWS AND RUNS
A CAMPAIGN TO TARGET THOSE CUSTOMERS AT RISK OF CHURN, AS
WELL AS EXPOSING ALL OF THOSE ENGAGEMENT EVENTS, TIMELINE
INSIGHTS IMMEDIATELY TO THE SALES AND SERVICE REPS IN THE
SAME WAY WE'VE AUTOMATED PREDICTION BASED TRIGGERS, WE'RE
NOW LOOKING AT GENAI SO THAT THE RIGHT ACTION AT THE RIGHT MOMENT
CAN BE COMBINED WITH THE RIGHT CONTENT. EARLIER THIS YEAR, OUR
COMPANY ANNOUNCED A FORAY INTO THE WORLD OF GENAI WITH EINSTEIN
, GPT. NOW THIS BRING YOUR OWN AI CAPABILITY. CAN INCORPORATE
ACCESS TO A WIDE RANGE OF LARGE LANGUAGE MODELS FROM AMAZON
BEDROCK INCLUDING AMAZON TITAN. WITHOUT YOU HAVING TO MANAGE
INFRASTRUCTURE YOURSELF AS YOU SAW EARLIER, YOU CAN EASILY AND
SECURELY ACCESS DATA CLOUD DATA FROM YOUR AWS. NOW NOW JUST AS
EASILY AND SECURELY. YOU CAN USE THOSE DATA TO FINE TUNE MODELS
WITHIN BEDROCK CUSTOMIZED MODELS OF YOUR CHOICE INSIDE BEDROCK
CAN THEN EASILY BE REGISTERED AS INFERENCE POINTS IN DATA CLOUD.
JUST THE SAME FOR USE IN SALESFORCE. WHAT MIGHT IT LOOK
LIKE IN ACTION? SUPPOSE RIGHT NOW YOU HAVE A CUSTOMER
IMPORTANT CUSTOMER BROWSING ON YOUR WEBSITE FOR ONE OF YOUR
COMPANY'S PRODUCT INFO. IT TRIGGERS REAL TIME INSIGHTS AND
DECISIONING IN DATA CLOUD, WHICH THEN DECIDES TO INVOKE THE
PROPENSITY TO BUY MODEL, THIS TIME IN AWS, THE RETRIEVED
PROPENSITY SCORE ALONG WITH THE PRODUCT INFO ITSELF, ARE BOTH
FED INTO A GENAI PROMPTS AND A CALL TO AMAZON. BEDROCK IS MADE
WHERE A FINE TUNED MODEL TAILORED FOR YOUR COMPANY'S
NEEDS IS WAITING AND IS USED TO GENERATE AMAZING CONTENT. THIS
ENTIRE FLOW IS AUTOMATED AND AT THE END A SALES REP GETS
NOTIFIED AND SEES A GENAI SUGGESTED EMAIL ALONG WITH ALL
THE ENGAGEMENT EVENTS TIMELINE INSIGHTS SO THEY KNOW WHAT LED
TO THIS MOMENT AND WHY THEY'RE GETTING THE EMAIL WHICH THEY CAN
THEN USE TO CONTACT THE CUSTOMER AT EXACTLY THE RIGHT TIME WITH
THE MOST RELEVANT CONTENT AT. WE BELIEVE THAT GENAI CAN BE A
GENERATIONAL OPPORTUNITY TO CREATE THE MOST INCREDIBLE
CUSTOMER EXPERIENCES AND AN AI IS GOING TO NEED GREAT DATA AND
AN ACTION SYSTEM TO DELIVER THOSE TIMELY, RELEVANT MOMENTS.
LOOKING FORWARD, SALESFORCE DATA CLOUD WILL POWER EINSTEIN GPT
GROUNDED WITH REAL TIME AND HARMONIZED DATA ALONG WITH THE
ABILITY TO BRING YOUR OWN AI SUCH AS THAT FROM SAGEMAKER, OR ACCESS LLM SUCH AS THAT FROM BEDROCK AWS IS REALLY HELPING US SEIZE THIS OPPORTUNITY TO CREATE A SOLUTION THAT HELPS COMPANIES
UNIFY THEIR DATA AND DRIVE AI OPTIMIZED INTERACTIONS THAT SCALE
IN A MEANINGFUL MANNER THANK YOU, THANK YOU VERY MUCH. THANK YOU GABRIELLE. I SHOULD START WITH SALESFORCE. FINE TUNING IS ONE WAY TO CUSTOMIZE
THESE FOUNDATION MODELS FOR YOUR USE CASES AND CREATE
INNOVATIVE CUSTOMER EXPERIENCES. WHILE THESE FOUNDATION MODELS ARE
INCREDIBLY POWERFUL AND HAVE AND HAVE A ROBUST UNDERSTANDING OF NATURAL LANGUAGE THEY STILL REQUIRE A LOT OF MANUAL
PROGRAMMING TO COMPLETE COMPLEX TASKS. LIKE BOOKING A FLIGHT, OR PROCESSING AN
INSURANCE CLAIM. THATS BECAUSE, OUT OF BOX FMS ARE NOT ABLE TO ACCESS
UP TO DATE KNOWLEDGE LIKE RECENT COMPANY SPECIFIC DATA. THEY ARE ALSO UNABLE TO TAKE SPECIFIC
ACTIONS TO FULFILL YOUR CUSTOMER REQUEST. TO MAKE THIS HAPPEN, DEVELOPERS NEED TO FOLLOW
A NUMBER OF RESOURCE INTENSIVE STEPS. LET ME WALK THROUGH A SPECIFIC EXAMPLE. IN THIS SCENARIO, A CUSTOMER WANTS TO EXCHANGE BLACK SHOES FOR BROWN SHOES PURCHASED
THROUGH AN ONLINE RETAILER. THEY USE THE SITE'S CUSTOMER SERVICE
CHAT INTERFACE TO COMMUNICATE THEIR REQUESTS. CONFIRM THE
AVAILABILITY OF THEIR DESIRED COLOR AND SHOE SIZE AND RECEIVE
THE EXCHANGE. THESE REQUESTS SOUND PRETTY REASONABLE, BUT TO
CODE IT UP ON A REGULAR SOFTWARE, IT WILL TAKE MONTHS.
BUT FOUNDATIONAL MODELS IT CAN BE ACTUALLY DONE FASTER. SO YOU
WOULD THINK IT'S PRETTY SIMPLE, RIGHT? WELL, THIS LOOKS LIKE AN
EASY EXCHANGE FOR FOUNDATIONAL MODELS. DEVELOPERS NEED TO
FOLLOW A SERIES OF TIME CONSUMING TASKS LIKE DEFINING
INSTRUCTIONS AND ORCHESTRATION, CONFIGURING THESE FOUNDATIONAL
MODELS TO ACCESS COMPANY SPECIFIC DATA SOURCES AND
WRITING CUSTOM CODE TO EXECUTE VARIOUS ACTIONS THROUGH A SERIES
OF API CALLS. AND FINALLY, DEVELOPERS MUST SET UP CLOUD
POLICIES AND HOSTING AND SECURITY CONTROLS. ALL OF THESE
STEPS CAN TAKE WEEKS, IF NOT MORE. LET'S TAKE A LOOK AT EACH
OF THEM MORE IN DETAIL. FOR THE FIRST STEP, DEVELOPER NEEDS TO
ORCHESTRATE A COMPLEX SYSTEM BETWEEN THE FOUNDATION MODELS,
SOFTWARE SYSTEMS AND THE END USERS. TO COMPLETE THIS TASK,
THEY NEED TO ENABLE THE FOUNDATION MODEL TO BREAK DOWN
THEM INTO MULTIPLE STEPS AND GIVE INSTRUCTIONS LIKE YOU ARE A
SHOE RETURN AGENT AS WELL AS CONTEXTUAL HISTORY FROM
DIFFERENT CHAT SESSIONS. WHILE THIS IS CRITICAL FOR FM TO
PRODUCE A BEST RESPONSE, THE PROCESS OF CRAFTING AND REFINING
CAN TAKE WEEKS. THAT'S BECAUSE THIS IS VERY MANUAL REQUIRES A
LOT OF TRIAL AND ERROR AND EACH FM REQUIRES A DIFFERENT FORMAT.
THEN YOU NEED TO MAKE SURE FMS HAVE THE RIGHT DATA TO FULFILL
THE REQUESTS. DEVELOPERS NEED TO CONFIGURE THE FM TO HAVE ACCESS
TO UP TO DATE KNOWLEDGE SOURCES WHICH IS IN THIS CASE IS THE
ONLINE RETAILER WHOSE MOST RECENT RETURN POLICY AND
CUSTOMER HISTORY. THIS PROCESS IS KNOWN AS RETRIEVAL AUGMENTED
GENERATION, ALSO KNOWN AS RAG RAG ENABLES FOUNDATIONAL MODELS
TO LEVERAGE THE MOST RECENT DATA, CONVERT THE DATA INTO
MACHINE READABLE FORMAT AND THEN PROGRAM THE FM TO QUERY THE DATA
SOURCE FOR A MORE ACCURATE RESPONSE. THEN TO COMPLETE THE
TASK AND EXCHANGE THE SHOES, DEVELOPERS NEED TO WRITE CODE SO
THAT THE FMS CAN TAKE ACTION AND MAKE SERIES OF API CALLS LIKE
PLACING A CUSTOMER ORDER CONFIRMING THE ORDER AND CALLING
TO SEND A CONFIRMATION EMAIL. ALL OF THESE NEEDS TO BE DONE IN
THE RIGHT SERIES. FINALLY, DEVELOPERS MUST HOST ALL OF
THESE AGENTS IN AND THEIR GENERATIVE AI APPS SET UP THE
RIGHT POLICIES, SECURITY SETTINGS, ALERTS, NETWORKING,
ALL THESE THINGS. SO WHAT STARTED OUT LOOKING LIKE A
PRETTY SIMPLE TASK WITH THESE FOUNDATIONAL MODELS IS ACTUALLY
PRETTY COMPLICATED. WHO WANTS TO DO THAT FOR EVERY GENERATIVE AI
POWERED TASK? WE BELIEVE THIS SHOULD BE A LOT EASIER FOR
DEVELOPERS TO DO. THAT'S WHY TODAY I'M VERY EXCITED TO
ANNOUNCE THE PREVIEW OF AGENTS FOR AMAZON BEDROCK. THIS IS A
NET NEW CAPABILITY FOR DEVELOPERS TO ENABLE GENERATIVE
AI APPLICATIONS TO COMPLETE TASKS IN JUST A FEW CLICKS WITH
A FEW CLICKS. AGENTS FOR BETTER OR CONFIGURE YOUR FOUNDATIONAL
MODELS TO AUTOMATIC BREAK DOWN AND ORCHESTRATE THESE TASKS
WITHOUT HAVING TO WRITE ANY CODE. THE AGENT SECURELY
CONNECTS YOUR FM TO THE RIGHT DATA SOURCE THROUGH A SIMPLE
API. AUTOMATICALLY CONVERTS YOUR DATA INTO MACHINE READABLE
FORMAT AND AUGMENTS THE USER'S REQUEST WITH RELEVANT
INFORMATION TO GENERATE A MORE ACCURATE RESPONSE. AND AGENTS IN
BEDROCK CAN TAKE ACTION BY AUTOMATICALLY MAKING API CALLS
ON YOUR BEHALF AND YOU DO NOT HAVE TO WORRY ABOUT COMPLEX
SYSTEMS AND HOSTING THEM BECAUSE IT IS FULLY MANAGED. NOW LET'S
TAKE A LOOK AT THE SAME EXAMPLE NOW WITH THE AGENTS FOR BEDROCK
. WITH THIS FUNCTIONALITY, THE DEVELOPER ONLY NEEDS TO FOLLOW A
REALLY FEW SIMPLE STEPS. FIRST, THEY USE THE BEDROCK CONSOLE TO
SELECT THE DESIRED FM, THEN TO DEFINE INSTRUCTIONS OR
ORCHESTRATION. THEY USE A SETUP WIZARD AND GIVE BASIC
INSTRUCTIONS LIKE YOUR APPLEID CUSTOMER SERVICE AGENT AND
UPDATE INVENTORY LEVELS. THEN THE DEVELOPER WILL SELECT DATA
SOURCES LIKE RETURN POLICIES WITHOUT WRITING ANY CODE THAT
ENABLES THE FM TO PREVENT INFORMATION. AND THEN FINALLY,
THE DEVELOPER SIMPLY SPECIFY THE FUNCTIONS TO EXECUTE THESE API
CALLS WITH THESE SIMPLE BASIC STEPS. DEVELOPER IS ABLE TO
QUICKLY FULFILL THE CUSTOMER REQUIREMENT WITHOUT WEEKS OF
MANUAL CODING. I AM EXCITED FOR OUR CUSTOMERS TO DRIVE
INNOVATION WITH THIS NEW CAPABILITY WHILE ALSO REDUCING
THE HEAVY LIFTING ASSOCIATED FOR DEVELOPMENT TEAMS. BUT THERE IS
ANOTHER CRITICAL ELEMENT UNDERPINNING ALL OF THIS
INNOVATION THAT WILL ALLOW YOU TO DERIVE EVEN MORE VALUE WITH
GENAI. THAT IS YOUR DATA. AS WE SAW WITH BEDROCK AGENTS AND
SALESFORCE SOURCE, WHILE FMS ARE INCREDIBLY POWERFUL OUT OF THE
BOX TO BE TRULY USEFUL TO YOUR ORGANIZATION, THEY NEED ACCESS
TO THE RIGHT DATA SOURCE. YOUR DATA IS YOUR DIFFERENTIATOR FOR
GENAI AND TO ENSURE YOU HAVE RIGHT RELEVANT HIGH QUALITY DATA
TO TRAIN YOUR MODELS OR CUSTOMIZE THESE FMS FOR YOUR USE
CASES, YOU NEED A STRONG DATA FOUNDATION. TO BUILD A STRONG
DATA FOUNDATION, YOU NEED ACCESS TO COMPRESS SENSITIVE SET OF
DATA SERVICES THAT ACCOUNT FOR THE SCALE VOLUME AND OF YOUR USE
CASES. THIS IS FAIR OFFERS A BROAD SET OF DATA CAPABILITIES
THAT SUPPORT YOUR END TO END DATA JOURNEY FROM STORING,
QUERYING AND ANALYZING DATA TO PUTTING YOUR DATA INTO WORK
THROUGH BUSINESS INTELLIGENCE, MACHINE LEARNING AND GENERATIVE
AI. WE ALSO HAVE SERVICES THAT HELP YOU EASILY INTEGRATE AND
GOVERN YOUR DATA. THE GOOD NEWS IS THAT ALL THE INVESTMENTS YOU
HAVE MADE TO BUILD THIS STRONG FOUNDATION WILL SERVE YOU WELL
FOR GENAI. BUT THERE IS ONE TOOL YOU WILL LIKELY NEED TO ADD TO
YOUR DATA FOUNDATION. EVEN IF YOU HAVE NOT DONE THAT ALREADY.
WE TOOLS FOR STORING AND RETRIEVING YOUR VECTOR
EMBEDDINGS VECTOR EMBEDDINGS ARE PRODUCED BY FOUNDATION MODELS
AND USED IN GENAI APPLICATIONS TO PRODUCE MORE RELEVANT
RESPONSES TO YOUR END USERS. LET'S TAKE A QUICK LOOK AT HOW
THEY WORK. THINK OF VECTOR EMBEDDINGS AS NUMERICAL
REPRESENTATIONS FOR TEXT, IMAGE AUDIO, AND VIDEO DATA. WELL
HUMANS CAN UNDERSTAND THE MEANING AND CONTEXT OF WORDS,
AND MACHINES CAN ONLY UNDERSTAND NUMBERS. SO WE HAD TO TRANSLATE
THEM INTO A FORMAT THAT'S SUITABLE FOR ML BY ASSIGNING A
NUMBER TO THE DIFFERENT FEATURE OF EACH WORD. WE CAN VIEW
VECTORS IN A MULTI DIMENSIONAL SPACE AND MEASURE THE DISTANCE
BETWEEN THEM. WE WORDS THAT ARE RELATED IN CONTEXT WILL HAVE
VECTORS THAT ARE CLOSER TOGETHER, WHICH HELPS MACHINE
UNDERSTAND THE SIMILARITIES AND DIFFERENCES BETWEEN WORDS. FOR
INSTANCE, A CAT IS CLOSER TO A KITTEN, WHEREAS DOG IS CLOSER TO
A PUPPY. BY COMPARING EMBEDDINGS IN THIS WAY, THE MODEL WILL
PRODUCE MORE RELEVANT AND CONTEXT ABLE RESPONSES THAN WORD
MATCHING. WHILE EMBEDDINGS ARE NOT NEW FOR MACHINE LEARNING,
BASED APPS THAT IMPART IS GROWING FAST WITH THE
AVAILABILITY OF GENERATIVE AI AND NATURAL LANGUAGE PROCESSING
. FOR EXAMPLE, THEY CAN SUPERPOWERS SEMANTIC SEARCH FOR
USE CASES LIKE RICH MEDIA, SEARCH AND PRODUCT
RECOMMENDATION. IN THIS SCENARIO, YOU CAN SEE THAT
SEMANTIC SEARCH GREATLY ENHANCES THE ACCURACY OF THE OUTPUT QUERY
FOR BRIGHT COLORED GOLF SHOES. IN ADDITION TO SEMANTIC SEARCH
EMBEDDINGS CAN BE USED TO AUGMENT YOUR PROMPTS FOR MORE
ACCURATE RESULTS THROUGH RAG. AS WE SAW IN THE AGENT'S EXAMPLE.
BUT IN ORDER TO USE THEM, YOU WILL NEED TO STORE THEM IN A
DATABASE WITH VECTOR CAPABILITY . TODAY AWS OFFERS VECTOR
DATABASE CAPABILITIES FOR POPULAR SERVICES LIKE AMAZON
OPENSEARCH SERVICE, AURORA POSTGRES AND POSTGRES. CUSTOMERS
CAN USE THESE SERVICES TO STORE AND SEARCH EMBEDDINGS USED IN
THEIR ML AND GENAI APPS. CO-LOCATING YOUR VECTOR
EMBEDDINGS WITH YOUR DATA MAKES THE PROCESS OF WITH EMBEDDINGS A
LOT EASIER AND REDUCES DATA DUPLICATION. YOU ALSO DON'T NEED
TO WORRY ABOUT THE MAINTENANCE VERSION LEARNING AND LICENSING
OF A SEPARATE DATABASE, LET ALONE KEEPING THEM IN SYNC. SO
NOW LET'S DIVE A LITTLE DEEPER INTO HOW VECTORS WORK ON OUR
OPENSEARCH SERVICE. OPENSEARCH SERVICE IS A FULLY MANAGED
SERVICE FOR REAL TIME SEARCH MONITORING AND ANALYSIS OF
BUSINESS AND OPERATIONS DATA. TODAY, CUSTOMERS USE OPENSEARCH
TO STORE MULTIDIMENSIONAL VECTOR EMBEDDINGS TO POWER APPLICATIONS
LIKE MULTIMODAL SEARCH PERSONAL ASSISTANTS AND RECOMMENDATION
ENGINES. OPENSEARCH CUSTOMERS LIKE THAT, THEY CAN STORE THEIR
VECTORS WITH THEIR DATA ON A DAILY BASIS, BUT STORING
BILLIONS OF VECTOR EMBEDDINGS AND QUICKLY SEARCHING THOSE
EMBEDDINGS WITH OPENSEARCH SERVICE REQUIRES DEVELOPERS TO
CONFIGURE, MANAGE AND SCALE THESE CLUSTERS. THAT MEANS THAT
YOU REQUIRE DEDICATED RESOURCES OR EXPERTISE THAT NOT ALL
ORGANIZATIONS HAVE TO MAKE. GENERATIVE AI ACCESSIBLE TO MORE
BUILDERS. WE WANTED TO MAKE IT EASIER TO LEVERAGE THE POWERFUL
VECTOR CAPABILITIES IN OUR OPENSEARCH SEARCH. THAT'S WHY
TODAY I'M VERY EXCITED TO ANNOUNCE THE PREVIEW OF OUR
VECTOR ENGINE FOR OPENSEARCH SERVERLESS. SO THIS VECTOR
ENGINE OFFERS SIMPLE, SCALABLE AND HIGH PERFORMING VECTOR
STORAGE AND SEARCH WITHOUT HAVING TO MANAGE ANY
INFRASTRUCTURE. DEVELOPERS CAN STORE VECTORS ALONGSIDE BUSINESS
DATA AND TEXT MAKING IT REALLY EASY. EMBEDDINGS METADATA THE
ASSOCIATED DATA ALL FROM A SINGLE API CALL. AND BECAUSE
IT'S SERVERLESS, THE DEVELOPERS DON'T NEED TO MANAGE CLUSTERS OR
WORRY ABOUT PRODUCTION SCALE. SO NOW NOT ONLY ARE YOU GOING TO
NEED TOOLS LIKE VECTOR DATABASES AND A COMPREHENSIVE SET OF
SERVICES TO SUPPORT YOUR GENAI STRATEGY, BUT YOU ALSO WANT TO
MAKE SURE THAT THOSE DATA SERVICES AND DATA THEY STORE ARE
INTEGRATED. WHEN YOU CONNECT THE DOTS WITH YOUR DATA ACROSS YOUR
DEPARTMENTS, SERVICES OR ON PREM DATABASES AND THIRD PARTY
APPLICATIONS, YOU'RE ABLE TO POWER GENERATIVE AI TO CREATE
REMARKABLE EXPERIENCES AS WE HAVE BEEN WORKING ON THIS DATA
INTEGRATION PROBLEM FOR A WHILE NOW. TODAY WE OFFER A FEDERATE
AND QUERY CAPABILITY IN AMAZON REDSHIFT, OUR PETABYTE SCALE
DATA WAREHOUSE AND AMAZON. ATHENA SO YOU CAN RUN QUERIES
ACROSS THESE SERVICES AND A WIDE RANGE OF DATA SOURCES AND THIRD
PARTY APPS. WE HAVE INTEGRATED SAGEMAKER WITH REDSHIFT AND
AURORA, SO WITH A SIMPLE SQL PROMPT, YOU CAN MAKE PREDICTIONS
WITHOUT HAVING TO WRITE ANY CODE AND AURORA NOW SUPPORTS ZERO-ETL
INTEGRATE WITH REDSHIFT SO THAT YOU CAN BRING YOUR TRANSACTIONAL
DATA FROM AURORA TO DO REAL TIME ANALYTICS IN REDSHIFT IT. BUT
FOR SOME USE CASES, BRINGING DATA TOGETHER IS JUST A PART OF
SOLUTION. FOR EXAMPLE, YOU MAY NEED TO PERFORM ADDITIONAL WORK
TO MATCH AND LINK RELATED DATA RECORDS. THIS IS A COMMON
PROBLEM WE SEE ACROSS FINANCIAL SERVICES, RETAIL AND ADVERTISING
WHERE RECORDS ARE OFTEN SILOED ACROSS APPLICATIONS, CHANNELS
AND DATA STORES. SO IMAGINE I RUN A CUSTOMER EXPERIENCE AT AN
AIRLINE AND I WANT TO DELIVER MORE RECENT RELEVANT TRIP
RECOMMENDATIONS TO MY CUSTOMERS . I WILL NEED TO INCORPORATE
RECENT INTERACTIONS FROM THE LOUNGE EXPERIENCES, LOYALTY
PROGRAMS AND CUSTOMER SUPPORT INTO A UNIFIED PROFILE TO
DELIVER THE BEST TRIP OPTIONS TO EACH CUSTOMER. HOWEVER FOR THESE
DISPARATE RECORDS, WHICH OFTEN CONTAIN INCOMPLETE OR
CONFLICTING INFORMATION WHICH CREATES THE PROBLEM OF MATCHING
REALLY DIFFICULT TO UTTERS, THESE COMPANIES SPEND MONTHS OR
DEVELOPMENT TIME TO BUILD COMPLEX MATCHING RULES AND
SYSTEMS THAT ARE TEND TO BE VERY FRAGILE. SO WE WANTED TO MAKE
THIS PROCESS EASIER. SO TODAY I'M EXCITED TO ANNOUNCE THE
GENERAL AVAILABILITY OF ENTITY RESOLUTION IN. ML POWERED
SERVICE THAT HELPS COMPANIES EASILY MATCH AND LINK RELATED
DATA RECORDS. WITH THIS NEW SERVICE, YOU CAN NOW SET UP EASY
TO USE ENTITY RESOLUTION WORKFLOWS IN JUST MINUTES
INSTEAD OF WEEKS. ENTITY RESOLUTION USES MATCHING
TECHNIQUES LIKE RULE BASED MATCHING TO QUICKLY LINK RELATED
INFORMATION INTO A UNIFIED ID. YOU CAN ALSO APPLY A POWERFUL ML
MODEL TO MATCH RELATED DATA SETS WHEN THERE ARE INCOMPLETE OR
CONFLICTING INFORMATION. ENTITY RESOLUTION READS RECORDS
DIRECTLY FROM S3 PROTECTING YOUR DATA BY MINIMIZING DATA,
DUPLICATION AND SOON WE WILL ADD PARTNER INTEGRATIONS WITH
LIVERAMP AND TRANSUNION AND AN INTEGRATION WITH OPEN SOURCE
ADVERTISING FRAMEWORK LIKE UNIFIED ID 2.0 TO TRANSLATE OR
ENRICH DATA RECORDS WITH COMMON INDUSTRY IDENTIFIERS. WHEN YOU
COMBINE THE POWER OF ML WITH YOUR DATA, YOU ARE ABLE TO
ACCOMPLISH SOME PRETTY AMAZING THINGS. NOW I'D LIKE TO WELCOME
ANOTHER CUSTOMER TO THE STAGE THAT IS USING THE DATA TO BUILD
AN AI POWERED INFRASTRUCTURE AND BUILD INNOVATIVE GENAI
APPLICATIONS. PLEASE WELCOME LINDSAY SILVER FROM FOX.
THANK YOU ALL FOR BEING HERE. THANK YOU ALL FOR BEING HERE AND JOINING ME TODAY TO TALK ABOUT AI ON ONE OF THE MOST IMPORTANT DAYS, ONE OF THE MOST IMPORTANT
YEARS, I THINK WE CAN SAY WE'VE HAD IN THE AI SPACE. I'M LINDSAY
SILVER, THE HEAD OF DATA AT FOX AND I'M REALLY EXCITED TO TALK
TO YOU TODAY ABOUT HOW WE'RE USING AI TO CHANGE THE FACE OF
MEDIA AND GIVE SUPERPOWERS TO OUR BROADCASTERS, ADVERTISERS
AND PRODUCTS. IF YOU DON'T KNOW FOX FOX IS ONE OF THE US'S
LARGEST MEDIA COMPANIES. WE HAVE BRANDS THAT SPAN ENTERTAINMENT,
SPORTS, NEWS AND NOW A TO BE AN INCREDIBLE PLATFORM FOR FREE
CONTENT ON THE WEB. SO I INVITE YOU TO TRY IT OUT. IT'S GREAT.
WE HAVE SOME NEW MOVIES ON THIS YEAR THAT ARE REALLY COOL. WE
HAVE A HUGE FOOTPRINT. WE HAVE OVER 300 MILLION MONTHLY ACTIVE
USERS. THAT MEANS 300 MILLION PEOPLE SEE ONE CONTENT ON FOX
EVERY MONTH. WE HAVE OVER 600 OR 6 MILLION PEOPLE WHO SEE OUR NFL
BROADCASTS EACH GAME IN THE FALL, AND WE COLLECT OVER 50,000
DATA POINTS PER SECOND ABOUT OUR AUDIENCES, ABOUT OUR CONTENT,
ABOUT OUR BUSINESS AND OUR OPERATIONS. THAT'S A MASSIVE
FOOTPRINT. BUT WE'RE NOT HERE TO TALK ABOUT DATA AS AN INGESTION
POINT. WE'RE HERE TO TALK ABOUT HOW TO LEARN FROM DATA AND HOW
TO USE AI TO MAKE A BUSINESS BETTER. AT FOX, I LIKE TO THINK
ABOUT LEARNING AS A HUMAN PROCESS. SO I'D LIKE TO JUMP
BACK TO THE VERY START OF TIME WHEN HUMANS STARTED DOCUMENTING
CROP CYCLES, DOCUMENTING TAX ROLLS IN AN EFFORT TO AUTOMATE
THE PROCESS OF LEARNING IN SOCIETY. WE'VE BEEN DOING THIS
AS HUMANS FOREVER. BEAR IN 1923, NIELSEN CAME AROUND AND IN THE
MEDIA INDUSTRY THEY STARTED DOCUMENTING HOW MEDIA WAS
PERFORMED IN AN EFFORT TO HELP ADVERTISERS AND MEDIA COMPANIES
LEARN AND GET FEEDBACK ON THEIR ON DATA IN REAL TIME. THIS WAS A
HUMAN PROCESS. IT WAS SLOW. IT WAS BLOCKY. IT WASN'T UNTIL THE
80S THAT WE STARTED AUTOMATE ING THESE STEPS. WE STARTED GETTING
BETTER AT OBSERVATION PRODUCTS. WE STARTED GETTING BETTER AT
SYNTHESIZING DATA WITH FORECASTING, WITH METEOROLOGY
AND FINANCE. WE STARTED GETTING BETTER AT MAKING AUTOMATED
DECISIONS IN ADVERTISING, AND THEN WE STARTED ACTING AGAIN.
WE'RE TURNING A CORNER IN THE LAST COUPLE OF YEARS FOR THE
FIRST TIME EVER, LARGE LANGUAGE MODELS AND GANS HAVE ALLOWED US
TO TAKE DATA AND GO STRAIGHT FROM OBSERVATION BACK TO A
PRODUCT THAT WE CAN ACT ON. IT MEANS GENERATING CONTENT
GENERATING RESPONSE PIECES IN CHAT, GENERATING IMAGES THAT WE
CAN THAT WE CAN TAKE DIRECTLY BACK AND ACT ON AT. THIS IS A
HUGE BOON TO OUR BUSINESS, BUT WE VIEW IT DIFFERENTLY THAN SOME
MIGHT THINK AS A WAY TO SUPERCHARGE HOW OUR PRODUCERS
AND OUR EDITORS AND OUR BROADCASTERS AND ADVERTISERS
WORK WITH US IN ORDER TO DO THAT, WE NEED FOUNDATIONS. WE
NEED A PLATFORM FOR DRIVING THIS CHANGE. AND THAT STARTS WITH OUR
DATA SOURCES. FOX TAKES IN HUNDREDS OF DIFFERENT DATA
SOURCES ACROSS OUR BRANDS, ACROSS OUR OPERATIONS AND FEEDS
THEM INTO AN INFRASTRUCTURE LARGELY BUILT ON AMAZON. AND
WE'RE EXTREMELY PROUD OF THE INFRASTRUCTURE WE'VE BUILT. BUT
ALSO PROUD OF THE RELATIONSHIP AND THE TECHNOLOGIES THAT AMAZON
PROVIDES US WITH FROM DATA INFRASTRUCTURE THROUGH TO OUR AI
AND ML TOOLCHAIN, AND THEN ALL THE WAY UP TO OUR APPLICATION
STACKS. THESE LET US DO THINGS THAT OTHERWISE WOULD NEVER BE
POSSIBLE AT A COMPANY LIKE FOX, WE USE THIS INFRASTRUCTURE AS
PART OF A LARGER PLATFORM THAT INCLUDES GROWING AI CATALOG OF
PROPRIETARY FOX BUILT MODELS THAT SPAN SPORTS CONTENT
AUDIENCE AND NOW GENERATIVE AI WE PLUG THOSE IN THROUGH
PREDICTIVE APIS, TWO TECHNOLOGIES THAT WE'VE BUILT
THAT SPAN OUR OPERATION SPAN, OUR ADVERTISING BUSINESS, AND
ALSO OUR CONSUMER PRODUCTS. AND THEN WE BRING THOSE TO OUR
CONSUMER INTERFACES THROUGH DYNAMIC RECOMMENDATION ACTIONS,
THROUGH SPORTSCASTING APPLICATIONS AND OPERATIONAL
CHANGES. IT'S A POWERFUL SYSTEM THAT THEN FEEDS BACK INTO OUR
CORE DATA SETS AND THAT FEEDBACK LOOP BECOMES THE CORE OF HOW FOX
USES DATA, AND POWERS AI. SO LET’S SEE WHAT WE CAN DO WITH THIS
PLATFORM. FORESIGHT. FORESIGHT HAS BEEN A
PRODUCT THAT'S BEEN IN THE WORKS AT FOX FOR SEVERAL YEARS, IS NOW
POWERING SPORTSCASTERS ACROSS MULTIPLE SPORTS THAT FOX
BROADCASTS. IT'S A HEADS UP INTERFACE THAT ALLOWS US TO USE
AI TO PROVIDE DEEP KNOWLEDGE AND INSIGHT TO OUR SPORTS CASTERS
AND TO CREATE GRAPHICS ON THE FLY. THAT OVERLAY THE GAME WITH
IMPORTANT CONTEXT ABOUT THE PLAYERS, ABOUT THE REFEREES AND
THE MATCHUPS. IT'S REALLY EXCITING PRODUCT AS WE GET INTO
OUR NEXT GENERATION OF FORESIGHT, WE'RE LOOKING TOWARD
LMS AND GANS TO HELP US CREATE EVEN MORE DYNAMIC CONTENT WITH
THIS PRODUCT. FOX IS A MEDIA COMPANY. WOULDN'T BE ANYTHING
WITHOUT OUR ADVERTISERS. WE TRY TO CREATE EXPERIENCES THAT ARE
THE BEST, BOTH FOR OUR END USERS AND CONSUMERS, AS WELL AS OUR
ADVERTISING CLIENTS. FOX ATLAS ALLOWS US TO DO THAT. ATLAS
TAKES THE DATA THAT WE KNOW ABOUT OUR VIDEOS AT ANY GIVEN
POINT IN TIME, MAKES IT ACCESSIBLE TO ADVERTISERS SO
THAT THEY CAN TARGET VERY SPECIFIC POINTS IN OUR VIDEO
WHERE THEIR CONTENT WILL ALIGN. WELL, THIS COULD BE ANYTHING
FROM A MOMENT IN THE SUPER BOWL WHERE WE HAVE A WHERE WE HAVE A
TOUCHDOWN TO A MOMENT IN A SHOW OR A PROGRAM THAT MENTIONS THEIR
PRODUCT. IT'S AN EXTREMELY EXCITING PRODUCT. IT'S IN
PRODUCTION NOW AND CONTINUALLY EVOLVING AS WE GO. LASTLY ONE OF
MY FAVORITES AND ONE I'M EXTREMELY PROUD OF ARE CATCH UP
WITH HIGHLIGHTS PRODUCT. THIS IS A DIRECT TO CONSUMER PRODUCT
THAT WE LAUNCHED LAST YEAR AT THE WORLD CUP AND IT'S RUNNING
AGAIN AT THE WOMEN'S WORLD CUP. NOW THE CHALLENGE WITH ANY SORT
OF SPORT IS THAT PEOPLE OFTEN TUNE IN MID GAME. HERETOFORE,
OUR EDITORS WOULD HAVE TO PRODUCE SUMMARIZED VERSIONS OF
THAT GAME AND CATCH UP MOMENTS ALL THROUGHOUT THE GAME. AND IT
WAS AN EXTREMELY LABORIOUS PROCESS. NOW, USING AI THAT
SPANS BOTH SPORTS AND COMPUTER VISION, WE'RE TAKING AN
AUTOMATICALLY COMPRESSING OUR GAMES AT ANY GIVEN MOMENT IN
TIME TO THE MOST IMPORTANT POINTS, ALLOWING OUR CONSUMERS
TO CONSUME A SHORT SUMMARY OF THE GAME BEFORE THEY TUNE IN
ACROSS ALL OF OUR APPLICATIONS. SO NEXT TIME YOU WATCH A GAME ON
THE WOMEN'S WORLD CUP, IF YOU TUNE IN LATE, THIS WILL BE THE
FIRST THING YOU SEE. AND IT'S AN EXTREME, EXCITING MOMENT FOR US
ON OUR DATA TEAMS AND OUR USING THE AI. SO WHAT'S NEXT ABOUT
GENERATIVE? AI HAS ALLOWED US TO START THINKING ABOUT CONTEXT
MORE DEEPLY. FOX CARES ABOUT MOMENTS. WE CARE ABOUT CREATING
CONTENT THAT REALLY MATTERS TO PEOPLE. AND WE UNDERSTAND THAT
THAT CONTENT IS EXTREMELY RICH. THE END OF THE ARGENTINA -
FRANCE GAME LAST YEAR AT THE WORLD CUP WAS ACTUALLY
THE CULMINATION OF A HUGE AMOUNT OF CONTEXT. EVERYONE WAS ASKING,
WHAT WOULD MESSI DO NEXT? EVERYONE WAS SAYING, WOW, THAT
WAS ONE OF THE GREATEST SHOOTOUTS IN HISTORY. WHEN WAS
THE LAST TIME THAT WE HAD A WORLD CUP END? THAT WAY EVERYONE
WAS WONDERING ABOUT THE REFEREES WAS ABOUT THE STADIUM. THERE WAS
A HUGE AMOUNT OF CONTEXT THERE. THAT'S CONTEXT THAT OUR TEAMS
REALLY WANT TO COVER. THEY WANT TO BRING THAT TO TWO FOUR, BUT
IT'S EXTREMELY LABORIOUS TO DO THAT USING GENERATIVE AI FOR THE
FIRST TIME, WE'RE ABLE TO START CREATING CONTENT ABOUT THOSE
THAT CONTEXT IN REAL TIME. AS MOMENTS HAPPEN SO THAT OUR
EDITORS, OUR PRODUCERS AND OUR PRODUCTION TEAMS CAN FOCUS ON
THAT CORE MOMENT WHILE HAVING THAT THAT SUPER POWER OF
CREATING ADDITIONAL CONTENT. IT'S EXTREMELY COOL. AND WE'RE
ROLLING THIS OUT ACROSS OUR BRANDS, ACROSS OUR SPORTS, AND
YOU'LL SEE THESE PERCOLATE IN NOT NOT AS A REPLACEMENT TO
CONTENT. WE'RE REALLY FOCUSED ON CONTINUING TO UP THE QUALITY AND
THE SCALE OF THE CONTENT. WE CAN PRODUCE. BUT AS A COMPLEMENT TO
THAT CONTENT AND YOU'LL SEE THIS THROUGHOUT EVERYTHING THAT WE DO
IN THE FUTURE, WE'RE REALLY EXCITED ABOUT IT. WE'RE REALLY
EXCITED ABOUT THE TECHNOLOGIES THAT SWAMI AND THE TEAMS HAVE
TALKED ABOUT TODAY. WE CAN'T GET ENOUGH OF IT AND WE LOOK FORWARD
TO THE FUTURE. THANK YOU SO MUCH FOR HAVING ME.
THANK YOU. LINDSAY IT'S GREAT TO SEE HOW CUSTOMERS LIKE FOX CAN
LEVERAGE A STRONG DATA FOUNDATION TO BUILD AI APPS. SO
FAR WE HAVE EXPLORED HOW YOU CAN CUSTOMIZE THE FOUNDATIONAL MODEL
IN BEDROCK AND LEVERAGE YOUR DATA FOUNDATION TO BUILD
POWERFUL NEW CUSTOMER EXPERIENCES. BUT MANY OF OUR
CUSTOMERS WILL GET VALUE FROM GENERATIVE AI WHEN IT IS BUILT
DIRECTLY INSIDE OUR SERVICES AND APPLICATIONS. BY BUILDING THIS
TECHNOLOGY INTO MORE OF OUR SERVICES, WE ARE MAKING THEM
EVEN EASIER TO USE. INCREASING PRODUCTIVITY ACROSS YOUR
ORGANIZATION, FOR EXAMPLE, GENAI HAS ENORMOUS POTENTIAL FOR
ANALYSTS AND BUSINESS USERS THAT WANT TO QUICKLY ACCESS THAT DATA
FOR MORE STRATEGIC DECISION MAKING. EVERY ORGANIZATION I
WANT I MEET WITH WANTS TO BECOME MORE DATA DRIVEN COMPANIES THAT
CAN TAP THE VALUE OF THEIR DATA CAN BUILD AND INNOVATE FASTER.
FOR EXAMPLE, WHEN A SALES TEAM CAN BETTER UNDERSTAND CONVERSION
RATES FROM FREE TIER TO PAYING ACCOUNTS, THEY CAN OPTIMIZE
MARKETING AND SALES PROGRAM SIMS CUSTOMERS HAVE TOLD US THAT
GETTING THESE TYPE OF INSIGHTS FROM THEIR DATA CAN BE REALLY
DIFFICULT, EVEN AFTER HUGE AMOUNT OF INVESTMENT IN THEIR
ANALYTICS INFRASTRUCTURE. WE BELIEVE IT SHOULD BE EASIER FOR
ORGANIZED OCEANS TO EXTRACT INSIGHTS AND SHARE THEM ACROSS
THEIR ORGANIZATION. THAT'S WHY WE BUILT AMAZON QUICKSIGHT
QUICKSIGHT IS OUR UNIFIED BUSINESS INTELLIGENCE SERVICE
THAT ALLOWS INSIGHTS TO BE SHARED ACROSS THE ORGANIZATION
FOR DATA DRIVEN DECISION MAKING . IT HELPS COMPANIES QUICKLY
ANALYZE THEIR DATA THROUGH TOOLS LIKE DASHBOARDS. PAGINATED
REPORTS AND EMBEDDED ANALYTICS. ONE OF THE MOST POWERFUL
CAPABILITIES OF QUICKSIGHT IS Q . Q ENABLES USERS TO ASK ANY
QUESTIONS OF THEIR DATA USING NATURAL LANGUAGE AND GET ANSWERS
IN JUST SECONDS WITHOUT HAVING TO WRITE SQL QUERIES OR LEARNING
BI TOOLS. WE HAVE BEEN USING GENERATIVE AI MODELS TO POWER Q
SINCE ITS LAUNCH IN 2020. SINCE THEN, WE HAVE LEARNED QUITE A
BIT ON HOW USERS WOULD LIKE TO USE NATURAL LANGUAGE TO GET
VALUE FROM THAT DATA, AND ALSO THESE FOUNDATIONAL MODELS HAVE
BECOME MORE AND MORE POWERFUL. THESE TWO THINGS MADE US ASK, SO
WITH ALL THE INNOVATION IN GENERATIVE AI, HOW WOULD WE
REINVENT EVERY ASPECT OF A BI SYSTEM RIGHT FROM DATA
PREPARATION TO DATA ANALYSIS TO DASHBOARD AUTHORING TO CREATING
THESE DATA, PRESENTATION AND STORY SHOWS, HOW CAN WE REINVENT
BI? THAT'S WHY TODAY I'M EXCITED TO ANNOUNCE S NEW GENERATIVE BI
CAPABILITIES AND AMAZON QUICKSIGHT. NOW LET'S TAKE A
QUICK LOOK AT A COUPLE OF EXAMPLES OF HOW GENERATIVE BI
SIMPLIFIES AND ACCELERATES GETTING INSIGHTS FROM YOUR DATA
FOR ANYONE WHO HAS USED BI TOOLS, YOU KNOW, DASHBOARDS ARE
EXTREMELY POWERFUL TO SHARE DATA INSIGHTS, BUT BUSINESS ANALYSTS
SPEND A LOT OF TIME AND STRUGGLE TO DEVELOP THE RIGHT DATA
VISUALS. THEY SPEND SIGNIFICANT AMOUNT OF TIME EXPLORING DATA
AND IDENTIFYING THE RIGHT FIELDS AND FILTERS TO CREATE VISUALS.
IF YOU HAD TO CREATE A CALCULATION ON SOMETHING LIKE
MONTHLY PERCENTAGE INCREASE IN SALES REVENUE, THEY NEED TO LOOK
UP THE SYNTAX AND IDENTIFY THE RIGHT DATA SETS AND TEST THEIR
CALCULATION. ONCE THEY BUILD THE DATA SET AND THEN BUILD THE
VISUAL THEY SPEND EVEN MORE TIME FINE TUNING THE FORMAT TO MATCH
THE DESIRED STYLING WITH A NEW GENERATIVE BI CAPABILITIES AND
QUICKSIGHT BUSINESS ANALYSTS CAN DO ALL OF IT IN NATURAL LANGUAGE
AND FINE TUNE VISUALS IN JUST SECONDS AND ADD THEM TO THEIR
DASHBOARDS IN THIS NEW AUTHORING EXPERIENCE ALSO ENABLES THESE
ANALYSTS TO CREATE CALCULATIONS FOR JUST SIMPLE NATURAL
LANGUAGE. JUST LIKE WHAT I SAID , MONTHLY PERCENTAGE INCREASE IN
SALES AND THEY DO NOT HAVE TO LEARN ANY SPECIFIC SYNTAX LIKE
CREATING A NEW DASHBOARD OR CALCULATION IS NOW AS SIMPLE AS
ASKING A FEW QUESTIONS. AND ALSO TO HELP OUR IDEAS REALLY TAKE
FLIGHT. WE NEED TO USE OUR DATA TO TELL STORIES. FOR INSTANCE, A
MARKETING MANAGER MAY NEED TO CREATE A DATA DRIVEN
PRESENTATION TO REQUEST ADDITIONAL BUDGET FOR HOSTING AN
EVENT IN NEW YORK TODAY. THEY WILL NEED TO SPEND A LOT OF TIME
EXTRACTING DATA SUMMARIZING RESULTS AND CREATING
PRESENTATIONS BECAUSE THEY ARE OFTEN EXPORTED INTO A PDF OR
POWERPOINT, THEY CAN QUICKLY BECOME OUTDATED AND THEN
RECREATING THEM AS A SLOW MANUAL PROCESS WITH THE NEW GENERATIVE
BI CAPABILITIES IN QUICKSIGHT, IT CAN HELP WITH THIS TO NOW
AGAIN BY BUSINESS USERS CAN AUTO AUTOMATICALLY GENERATE A STORY
OR A VISUAL PRESENTATION OF THEIR QUICKSIGHT DATA USING
NATURAL LANGUAGE PROMPTS SIMPLE ONLY TYPE THE DESCRIPTION OF A
STORY IN ENGLISH TO CREATE A COMPELLING VISUAL WITH DATA FROM
RELEVANT DASHBOARDS. AFTER THE STORY IS GENERATED, THEY CAN
MODIFY IT AS NEEDED AND SECURELY SHARE IT WITH THEIR BUSINESS
TEAMS. EXCITED TO BRING MORE GENERATIVE BI CAPABILITIES TO
QUICKSIGHT IN THE FUTURE. FOR NOW, WE TALKED ABOUT BI. HOW
ELSE CAN WE USE GENERATIVE AI ACROSS OUR SERVICES TO IMPROVE
PRODUCTIVITY? ONE EXAMPLE IS AMAZON, OUR AI CODING COMPANION
FOR DEVELOPERS CODEWHISPERER GENERATES CODE RECOMMENDATIONS
FROM NATURAL LANGUAGE BASED ON CONTEXTUAL INFORMATION SUCH AS
DEVELOPERS RECENTLY OPENED PRIOR CODE AND COMMENTS, AND IT IS THE
ONLY CODING COMPANION WITH BUILT IN SECURITY SCANNING FOR HARD TO
DETECT VULNERABILITIES AND A BUILT IN REFERENCE TRACKER THAT
DETECTS WHETHER A SOURCE CODE RECOMMENDATION MAY BE SIMILAR TO
A PARTICULAR TRAINING DATA. THIS MAKES IT EASIER FOR DEVELOPERS
TO DECIDE WHETHER TO USE THEIR PARTICULAR RECOMMENDATION IN
THEIR PROJECTS DURING PREVIEW, WE RAN A STUDY A PRODUCTIVITY
CHALLENGE, THAT SHOWED PARTICIPANTS WHO USE
CODEWHISPERER WERE 27% MORE LIKELY TO COMPLETE THEIR TASK
SUCCESSFULLY AND THEY DID IT 57% FASTER ON AVERAGE, CUSTOMERS AND
PARTNERS LIKE INFOSYS, PUBLICIS SAPIENT AND HCLTECH ARE
EMPOWERING THEIR DEVELOPERS TO DRIVE INNOVATION WITH
CODEWHISPERER AND PYTHON DEVELOPERS WITHIN OUR OWN AMAZON
ADS TEAM HAVE FOUND THAT CODE WHISPERER MAKES IT SEAMLESS FOR
THEM TO PICK UP NEW LANGUAGES LIKE JAVA, AND THEY DON'T HAVE
TO CONSTANTLY LOOK UP DOCUMENTATION AND SYNTAX NOW
BECAUSE WE KNOW HOW MUCH TIME OUR BUILDERS HAVE SPENDING AND
WRITING CODE. WE ARE ALSO LOOKING TO INTEGRATE
CODEWHISPERER INTO MANY OF OUR SERVICES. ONE OF THOSE SERVICES
IS AWS GLUE, WHICH MAKES IT EASY TO INTEGRATE DATA FROM MULTIPLE
SOURCES FROM ANALYTICS MACHINE LEARNING AND APPLICATION
DEVELOPMENT. GLUE PROVIDES MULTIPLE INTERFACES FOR
CUSTOMERS TO BUILD DATA INTEGRATION JOBS AND THE COMMON
INTERFACE IS GLUE STUDIO, WHICH ENABLES DATA ENGINEERS, ANALYSTS
TO BUILD DATA INTEGRATION JOBS. HOWEVER, WHEN YOU THINK ABOUT
AUTHORING THESE COMPLEX ETL PIPELINES, USERS STILL NEED TO
UNDERSTAND GLUE AND SEVERAL SERVICES, WHICH MEANS THEY SPEND
A LOT MORE TIME DOING ON THE APPROPRIATE SYNTAX AND BEST
PRACTICES AND LESS TIME ACTUALLY SOLVING THEIR PROBLEM TO HELP
USERS BUILD DATA INTEGRATION JOBS FASTER. I'M EXCITED TO
ANNOUNCE A CODEWHISPERER INTEGRATION WITH GLUE STUDIO.
THIS IS AN AI POWERED ETL CODING ASSISTANT FOR DATA ENGINEERS
ANALYSTS AND DEVELOPERS TO AUTHOR GLUE JOBS WITH THIS
INTEGRATION, SIMPLY WRITE CODE OR COMMENTS AND NATURAL LANGUAGE
IN GLUE STUDIO NOTES BOOKS TO RECEIVE CODE SUGGESTIONS AND
SYNTAX CORRECTIONS FROM CODEWHISPERER IN REAL TIME. YOU
CAN EASILY ACCEPT SUGGESTIONS OR DECLINE AND KEEP WRITING YOUR
CUSTOM CODE. AND THIS INTEGRATION IS OPTIMIZED FOR
DATA SOURCES LIKE S3, RDS AND REDSHIFT. IT IS THE BEST CODING
COMPANION TO BUILD APPLICATIONS ON AWS. SO WHILE GENERATIVE AI
IS ENHANCING PRODUCTIVITY FOR DEVELOPERS, DATA SCIENTISTS AND
ANALYSTS, THESE TECHNOLOGIES ARE ALSO BUSINESS PROCESS
IMPROVEMENTS FOR FRONT EMPLOYEES ACROSS A VARIETY OF INDUSTRIES,
FROM HEALTH CARE TO AUTOMOTIVE. LET'S DIVE DEEPER ON HEALTH CARE
FOR A MOMENT. AWS OFFERS A PORTFOLIO OF SERVICES THAT
EMPOWER OUR HEALTH CARE AND LIFE SCIENCES CUSTOMERS, INCLUDING
PROVIDERS, PAYERS AND IT VENDORS TO MANAGE AND TRANSFORM HEALTH
CARE DATA AT SCALE. THIS ALSO INCLUDES VENDOR THAT BUILT
HIGHLY CRITICAL HIGH PERFORMANCE APPLICATIONS FOR CLINICAL
SETTINGS AND TELEHEALTH APPS TO HELP STREAMLINE WORKFLOWS AND
REDUCE ADMINISTRATIVE TASKS FOR PHYSICIAN PATIENTS AND PROVIDERS
. WE HEAR THAT ONE OF THE MOST COMMON HEALTH CARE INDUSTRY PAIN
POINT IS THE AMOUNT OF TIME IT TAKES FOR CLINICIANS TO WRITE
DETAILED DOCUMENTATION FOR EACH PATIENT VISIT, WHICH OFTEN TAKES
AWAY THE TIME FROM THE FACE TO FACE INTERACTIONS WITH THE
PATIENT. TO SOLVE THIS PROBLEM, HEALTH CARE SOFTWARE VENDORS ARE
EXPLORING HOW TO LEVERAGE GENERATIVE AI TO AUTOMATICALLY
GENERATE THESE CLINICAL NOTES. BUT BUILDING THESE TYPE OF
APPLICATION REQUIRES SIGNIFICANT AMOUNT OF ENGINEERING RESOURCE
AND EXPERTISE TO TAKE THESE FOUNDATIONAL MODELS FOR MEDICAL
USE CASES ON TOP OF ALL OF THESE CHALLENGES ARE HEALTH CARE HAS
NEED TO MEET STRINGENT INDUSTRY SECURITY REQUIREMENTS. WE WANT
TO EASIER TO ADDRESS THIS PAIN POINT FOR CLINICIANS THAT IS WHY
TODAY I'M VERY EXCITED TO ANNOUNCE THE PREVIEW OF
HEALTHSCRIBE. THIS WILL SERVICE PROVIDES AUTOMATIC NOTE
GENERATION FOR CLINICAL APPLICATIONS. HEALTHSCRIBE
LEVERAGES AUTOMATIC SPEECH RECOGNITION AND GENERATIVE AI TO
AUTOMATICALLY ANALYZE CONSULTATION AUDIO IDENTIFY
SPEAKER ROLES FOR PATIENTS AND CLINICIANS, EXTRACT THE RELEVANT
MEDICAL TERMS AND GENERATE A PRELIMINARY CLINICAL NOTES WITH
HEALTHSCRIBE HEALTH CARE SOFTWARE VENDORS CAN CREATE
APPLICATIONS THAT REDUCE THE BURDEN OF CLINICAL DOCUMENT
STATION. IT SUPPORTS THE RESPONSIBLE USE OF AI BY
INCLUDING REFERENCES TO THE ORIGINAL TRANSCRIPTS FOR EVERY
SENTENCE IN THE AI GENERATED CLINICAL NOTES. THIS REALLY
MAKES IT EASIER TO VALIDATE THE ACCURACY OF IN THEIR APPLICATION
BEFORE THEY ENTER IT INTO THE EHR, SECURITY AND PRIVACY
FEATURES ARE ALSO BUILT IN TO ENSURE ANY AND OUTPUT TEXT ARE
NOT STORED IN HEALTHSCRIBE. CUSTOMERS LIKE 3M HEALTH
INFORMATION SYSTEMS ARE LEVERAGING HEALTHSCRIBE AS THE
FOUNDATION TO HELP EXPEDITE, REFINE AND SCALE THE DELIVERY OF
THEIR CLINICAL DOCUMENTATION AND VIRTUAL ASSISTANT SOLUTIONS. IT
IS CLEAR THAT GENERATIVE AI HAS THE POWER TO TRANSFORM HEALTH
CARE AND LIFE SCIENCES INDUSTRY IN MANY WAYS. LET'S SEE HOW
ANOTHER COMPANY MERCK AI TO SOLVE A COMMON PROBLEM IN THE
PHARMA INDUSTRY. OUR GLOBAL PHARMACEUTICAL COMPANY MERCK,
HAS BROUGHT HOPE TO HUMANITY THROUGH DEVELOPMENT OF IMPORTANT
MEDICINES AND VACCINES. AND TODAY WE'RE AT THE FOREFRONT OF
RESEARCH TO DELIVER INNOVATIVE HEALTH SOLUTIONS THAT ADVANCE
PREVENTION AND TREATMENT OF DISEASES IN PEOPLE AND ANIMALS.
A COMMON PROBLEM ACROSS PHARMACEUTIQUE INDUSTRY IS THE
OCCURRENCE OF FALSE REJECTS DURING DRUG INSPECTION PROCESS
TO INVESTIGATE THIS PROBLEM HOLISTICALLY, WE NEEDED TO
INGEST PRODUCT GENEALOGY DATA PROCESS DATA QUALITY DATA FROM
VARIOUS MANUFACTURING SYSTEMS AND REAL TIME DATA FROM
INSPECTION MACHINES, AND THEN CONTEXTUALIZE AND HARMONIZE THIS
DATA THAT'S WHY WE TURN TO AND TO SOLVE THIS CHALLENGE. WE USE
AWS GLUE AND KINESIS TO INGEST, TRANSFORM AND CONTEXTUALIZE
PROCESS AND REAL TIME DATA AND RUN ANALYTICS ON THAT DATA LOAD
THAT DATA INTO REDSHIFT, WHICH IS THEN USED BY OUR ANALYTICS
DASHBOARDS, OUR AI, ML PLATFORM IS BUILT ON AMAZON SAGEMAKER. WE
LEVERAGE AWS DATASYNC TO INGEST DEFECT IMAGE DATA FROM
INSPECTION MACHINES ACROSS SITES. OUR INFERENCE PIPELINES
LOOK UP OUR MODEL REGISTRY FOR ALL ML DEEP LEARNING MODELS FOR
THESE IMAGES RUNS THOSE SCALE CLASSIFIES THE IMAGES AND SAVES
THOSE INFERENCES INTO DYNAMODB, WHICH IS THEN SERVED UP BY OUR
CONSUMPTION APPS. WE USE GENERATIVE AI APPROACHES AND
GENERATIVE MODELS LIKE GANS AND VARIATIONAL AUTOENCODERS TO
DEVELOP SYNTHETIC DEFECT IMAGE DATA FOR COMPLEX DEFECTS WHERE
WE HAVE LIMITED TRAINING DATA. THE INSIGHTS GAINED HAVE HELPED
US TO UNDERSTAND THE ROOT OF REJECT, OPTIMIZE PROCESSES AND
REDUCE OVERALL FALSE REJECTS ACROSS VARIOUS PRODUCT LINES BY
MORE THAN 50. IT IS GRATIFYING AND MOTIVATING TO KNOW THAT THE
WORK WE ARE DOING HAS A DIRECT IMPACT ON PATIENT LIVES IN TERMS
OF IMPROVING THE AVAILABILITY OF LIFE SAVING MEDICINES AND
VACCINES. WHAT A GREAT STORY. WHETHER YOU'RE BUILDING
APPLICATIONS WITH FMS IN BEDROCK OR SAGEMAKER OR USING OUR ML
POWERED SERVICES, WE WILL CONTINUE TO INVEST IN MAKING
PLACE TO HARNESS THE POWER OF GENERATIVE AI AND ML FOR ALL
TYPES OF INDUSTRIES, BUT MAKING THE MOST OF AI AND ML REQUIRES
MORE THAN JUST TOOLS. A SUCCESSFUL GENAI STRATEGY
INCLUDES A STRONG INFRASTRUCTURE LAYER FOUNDATION THAT CAN
SUPPORT THE MASSIVE SCALE OF POWER, SECURITY AND RELIABILITY
NEEDS OF ENTERPRISE APPS. WHAT OUR CUSTOMERS ARE TRYING TO DO
WITH THESE FMS IF THEY ARE BUILDING THEM, CUSTOMIZING THEM,
THEY NEED THE MOST PERFORMANT, COST EFFECTIVE INFRASTRUCTURE.
THAT IS PURPOSE BUILT FOR MACHINE LEARNING. AWS HAS BEEN
INVESTING WITH OUR PARTNERS AND IN OUR SILICON FOR MORE THAN TEN
YEARS TO OFFER A BROAD CHOICE OF HIGH PERFORMANCE, LOW COST ML
INFRASTRUCTURE. THAT IS WHY AWS IS UNIQUELY POSITIONED TO HELP
OUR CUSTOMERS ACCELERATE THEIR INNOVATION WITH GENAI. I, FOR
EXAMPLE, WE HAVE A BROAD CHOICE OF ACCELERATORS FOR CUSTOMERS,
INCLUDING OUR GPU BASED SOLUTION . AWS WAS THE FIRST TO BRING
NVIDIA GPUS TO THE CLOUD MORE THAN 12 YEARS AGO AND WE ARE
COLLABORATING WITH THEM TO DELIVER LARGE SCALE, HIGH
PERFORMANCE GPU BASED SOLUTION FOR APPLICATIONS SUCH AS AI, ML
GRAPHICS, GAMING AND HPC COMPANIES HAVE BEEN USING THESE
GPU BASED INSTANCES TO SPEED UP ML AND THEY HAVE SCALED THEIR ML
TRAINING WORKLOADS UP TO 10,000 GPUS. BUT OUR CUSTOMERS ARE
CONTINUING TO PUSH THE BOUNDARIES OF THESE LARGE SCALE
, LARGE LANGUAGE MODELS. THEY ARE TRAINING AND DEPLOYING MORE
SOPHISTICATED MODELS. AS A RESULT, THEY ARE VERY EAGER FOR
THE MOST UP TO DATE SOLUTIONS THAT HELP THEM GAIN A STRATEGIC
EDGE TO MAKE THEIR MODEL TRAINING FASTER AND AT SCALE.
THAT'S WHY TODAY I'M EXCITED TO ANNOUNCE THE GENERAL
AVAILABILITY OF AMAZON EC2 P5 INSTANCES. THESE INSTANCES ARE
OPTIMIZED FOR GENERATIVE AI AND POWERED BY NVIDIA H100 TENSOR
CORE GPUS. THEY ARE IDEAL FOR TRAINING AND RUNNING INFERENCE
FOR THESE INCREASINGLY COMPLEX LLMS THAT HAVE MORE THAN ONE
HUNDREDS OF BILLIONS OF PARAMETERS. P5 INSTANCES WILL
PROVIDE THE HIGHEST PERFORMANCE IN OUR PORTFOLIO, ACCELERATING
PERFORMANCE BY UP TO SIX X AND REDUCING TRAINING COSTS BY UP TO
40% AS COMPARED TO EC2 P FOR INSTANCES. P5 INSTANCES ARE
DEPLOYED IN SECOND GENERATION EC2. ULTRACLUSTERS WHICH SCALE
UP TO 20,000 GPUS INTER CONNECTED WITH PETABYTE SCALE
NON-BLOCKING NETWORK. THIS ENABLES US TO DELIVER 20
EXAFLOPS OF AGGREGATE COMPUTE CAPABILITY. NOW, BEYOND OUR
COLLABORATION WITH NVIDIA, WE HAVE MORE THAN A DECADE OF
EXPERIENCE DESIGNING AND BUILDING SILICON AND WE HAVE
APPLIED OUR OWN LEARNINGS TO CREATE INNOVATIVE PURPOSE BUILT
SILICON FOR YOUR ML AND AI WORKLOADS. WE RECENTLY ANNOUNCED
GENERAL AVAILABILITY OF EC2, EC2 INSTANCES POWERED BY OUR
INFERENTIA CHIP, A CUSTOM BUILT CHIP FOR ML INFERENCE. THESE
INSTANCES DELIVER UP TO 40% BETTER INFERENCE PRICE
PERFORMANCE THAN COMPARABLE EC2 INSTANCES AND OUR TR AND ONE
INSTANCE DELIVERS UP TO 50% SAVINGS ON TRAINING COSTS WHERE
WE OFFER HIGH PERFORMANCE FOR DEEP LEARNING, TRAINING AND
INFERENCE WITH SINGLE NIFICANT COST SAVINGS. OUR CUSTOMERS ARE
SEEING IMPRESSIVE RESULTS AS THEY BUILD DEEP LEARNING MODELS
AND FOUNDATIONAL MODELS USING THESE INSTANCES FOR INSTANCE,
FINCHAI DEPLOYED THEIR TRANSLATE MODELS ON AWS INFERENTIA. AND
SAVED 80% ON COST WHILE MAINTAINING THE SAME THROUGHPUT
. OUR ABILITY TO DELIVER HIGH PERFORMANCE AT THE LOWEST COST
IN AMAZON EC2 IS WHY CUSTOMERS LIKE AIRBNB, SPRINKLR AND
AUTODESK USE OUR PURPOSE BUILT ACCELERATOR FOR THEIR MOST
DEMANDING ML WORKLOADS WITH THESE PURPOSE BUILT ACCELERATORS
AS WELL AS THE LATEST EC2 P5 INSTANCES, OUR CUSTOMERS HAVE A
HIGHLY PERFORMED AND DIFFERENTIATED SET OF TOOLS TO
ADDRESS THE LARGEST ML CHALLENGES. THESE INFRASTRUCTURE
INVESTMENTS WILL SUPPORT YOUR GENAI STRATEGY AS YOU EXPERIMENT
AND QUICKLY SCALE YOUR APPLICATIONS FOR A WIDE VARIETY
OF YOUR USE CASES. NOW OUR CUSTOMERS HAVE ACCELERATED THEIR
USE OF GENERATIVE AI IN JUST THE PAST FEW MONTHS, AND WE KNOW
MANY OF YOU ARE EAGER TO GET STARTED AS WELL. AND BECAUSE WE
KNOW THIS TECHNOLOGY IS NEW TO SO MANY, WE ARE ALSO MAKING IT
ACCESSIBLE THROUGH A VARIETY OF TRAINING OPPORTUNITIES. AWS IN
ADDITION TO OUR BROADER LIBRARY OF DIGITAL COURSES ON AWS SKILL
BUILDER AS WELL AS PROGRAMS LIKE AWS ACADEMY, WE RESTART AND
EDUCATE, WE OFFER NOW A NEW COLLECTION OF FREE AND LOW COST
TRAININGS TO HELP PEOPLE UNDERSTAND, IMPLEMENT AND BEGIN
USING GENERATIVE AI FOR EXAMPLE, WE RECENTLY LAUNCHED A TRAINING
ON COURSERA CALLED GENERATIVE AI WITH LARGE LANGUAGE MODELS, A
COURSE BUILT BY EXPERTS WITH DR. ANDREW NG FROM DEEPLEARNING.AI.
THIS IS A GREAT OPPORTUNITY TO LEARN ABOUT LMS AND GET HANDS ON
EXPERIENCE FROM SELECTING, TRAINING, FINE TUNING AND
DEPLOYING THESE FOR YOUR APPLICATIONS. WE ALSO OFFER FREE
COURSES USING CODEWHISPERER FOR EXECUTIVES WHO WANT TO ADDRESS
THEIR BUSINESS CHALLENGES AND FOR PARTNERS WHO WANT TO BUILD
GENAI APPS. NOW TO SUM IT ALL UP, WE HAVE COVERED A LOT OF
GROUND TODAY AND SHARED SEVERAL NEW TOOLS AND INNOVATIONS TO
HELP YOU START WORKING WITH GENERATIVE AI ON AWS, WITH A
WIDE SELECTION OF FOUNDATIONAL MODELS AND TOOLS AND AMAZON
BEDROCK NEW SET OF GENERATIVE AI POWERED SERVICES TO HELP DRIVE
PRODUCTIVITY ACROSS YOUR ORGANIZATION. PASSION AND
PURPOSE BUILT MACHINE LEARNING INFRASTRUCTURE AND GPUS FOR
BETTER PERFORMANCE AND LOWER COSTS AND ACCESS TO EDUCATION
AND TRAINING OPPORTUNITIES. ENTITIES WITH ALL OF THESE AWS
HAS EVER EVERYTHING YOU NEED TO ACCELERATE YOUR GENERATIVE AI
JOURNEY. THERE IS SO MUCH DESERT TO LEARN MORE AND EXPERIMENT
WITH THIS TECHNOLOGY. AND THIS IS JUST THE BEGINNING. WE HAVE A
LOT MORE COMING THIS YEAR AND I'M EXCITED TO SEE WHAT YOU CAN
DO TO REIMAGINE AND TRANSFORM MIME YOUR APPLICATIONS WITH
GENERATIVE AI ON AWS. THANK YOU FOR YOUR TIME TODAY.
