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DeepTech Innovation - Network Science

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DeepTech Innovation - Network Science

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  1. Unleashing MachineLearning MachineLearning (ML) TheRationaleBehindSmartPredictions Artificial Intelligence (AI) https://www.networkscience.ai/

  2. MachineLearning (ML) & ArtificialIntelligence (AI) albeitrecenttechnologies, arebynomeans new. Historically, machineshavebeenusedfor severaltasksandfunctionsthatwereotherwise deemedtrivialanddirtyforhumans. Overtime, thesemachineshaveevolvedtotakeonmore complex & sophisticatedactivitiesincluding decisionmakingandstrategyformulation. However, ascomplexitiesincrease, thereisa growingneedforpeopletotrustinthepowerof thesemachinesratherthanquestiontheir abilities. Artificial Intelligence Machine Learning

  3. ArecentstudybyAccentureLabshasrevealedthat MachineLearning, especiallydeeplearning, isquickly seeinganupsurgeinitsadoptioninworkplacesacross industries. Inhealthcare, forinstance, hundredsof companiesareusingMachineLearningalgorithmsand predictiveanalyticstoreducedrugdevelopmenttime anddiagnoseailmentsfrommedicalimages. Similarly, inthetransportationsector, self-drivingcarsusingML areexpectedtobecomeanormwithinthenextcouple ofyears, withcommercialapplicationsofthese automobilesbeingclosebehind. https://www.networkscience.ai/

  4. WhatisMachine Learning?

  5. Theseintelligentsystemstakeonlow-levelpatternrecognitiontaskslikeimagerecognition,Theseintelligentsystemstakeonlow-levelpatternrecognitiontaskslikeimagerecognition, speechrecognitionandnaturallanguageprocessingtohelpcompanieschurnlargevolumesof dataformakingspecificrecommendations. MLallowssoftwaresystemstoprovideuserswith accuratepredictionswithminimaluncertainty. Theinternalalgorithmsinvolvedinthisdecision-makingprocess, however, areoftennotvisibletocompanypersonnel, makingML systemsoperateasostracized “blackboxes”. Thismakes organizationsunwillingtoallocatecorecompetenciestomachines duetohigherrisksofpoordecision-makingandrelatedcosts. Researchindicatesthatintheupcomingyears, machineswillbecompelledtoexplaintheir reasoningandrecommendationsinadeepermanner. Asthenextstageofhumanaugmentation bymachines, thisinteractionwillenablepeopletounderstandandactresponsibly. Itwillwork towardscreatinganeffectiveteambetweenhumansandmachines.

  6. 61%

  7. https://www.networkscience.ai/ MachineLearningSynergy

  8. IntelligentsystemspoweredbyMLarenowheretowork alongsidetheirhumancounterparts. Byutilizingsmart machinesforresponsibility, fairness, andtransparency, organizationscanenforcecollaboration & efficiencywithin theirworkplaces. Theseadvancedintelligentsystemsofthe future, however, willnotreplacepeople. Theywill complementandsupporthumansinamannerthatallows businessestomakesmarter, better, andmoreaccurate decisions.

  9. ThereAre3MainMarketDrivers forAdvancedMl-LedSystems. First, thegrowingneedfortransparency, asrequiredbylawssuchastheEU’sGDPR, makesit essentialforcompaniestodisclosehowpersonaldataisbeingusedforselectionandother decision-making. Second, agrowingneedfortrustbetweenAIandhumanbeingsmandatesthat systemsareabletoeffectivelyexplaintherationalebehindtheirdecision-making. Third, istheneed forbettermachine-humansynergy. Withmachinesbeingbetteratrecognizingminutepatternsin largevolumesofdataandpeoplebeingmoreefficientatconnectingthedotsamonghigh-level patterns, thebusinessesoftomorrowaregoingtoincreasinglyneedbothresourcesworkinghand- in-hand.

  10. Data-Level Explanation Throughthismethod, ML-based systemscanprovideevidenceof themodelinganditsresultsusing comparisonsmadewithother examples. Thisallowsthesystem tojustifythedecisiontaken aroundanyparticularissueor targetedprediction.

  11. Model-Level Explanation Thisapproachfocusesmoreon MachineLearningalgorithms. Throughthismethod, the explanationprovidedmakesthe logicmoreunderstandableto humansbyaddingalayerof domainknowledgeontop. Comparedtotheothermethods, model-levelexplanationabstracts mostfromthedatathroughrules orbycombiningitwithsemantics.

  12. Hybrid-Level Explanation Thisapproachworksthebestandis mostusefulifthedatabeingstudied isparticularlylarge, complex, or packed. Themethodusesahigh levelofabstractionbyrefactoring dataatametadatalevel. Ratherthan usingthedataasapieceofevidence asinthecaseofothermethods, the hybrid-levelexplanationoffersan explanationforeveryfeature atametadatalevel.

  13. EnhancedMlWillAllow SophisticatedSystemsTo... Explainthereasoningbehindtheirresultsandhowtheyarrivedatthem Characterizethesystem’sstrengths & weaknesses Comparetheirperformance & outputwiththoseofotherintelligentmachines Conveyresultsinacomprehensivemannerthatshowcasesthepotentialoffuture technologies Makethedecision-makingprocessinbusinessessmarter

  14. ContactUs MakingBusinessThrive ThankyouforshowinginterestinNetworkScience! WeatNetworkScienceareexcitedtobeingin DeepTechtransformationssolutionstoyou. 24, AroraTower, 2 https://www.networkscience.ai/ +442081339019 contact@networkscience.ai WaterviewDrive, London SE100TW, UnitedKingdom

  15. https://www.networkscience.ai/ ThanksForWatching contact@networkscience.ai contact@networkscience.ai contact@networkscience.ai

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