DHL&IBM-年度报告：物流中的人工智能（英文）-2018.12-45页.pdf

Powered by DHL Trend ResearchARTIFICIAL INTELLIGENCE IN LOGISTICSA collaborative report by DHL and IBM on implications and use cases for the logistics industry2018PUBLISHERDHL Customer Solutions&InnovationRepresented by Matthias HeutgerSenior Vice President,Global Head of InnovationDHL CSI,53844 Troisdorf,Germany PROJECT DIRECTOR Dr.Markus Kckelhaus Vice President,Innovation and Trend Research DHL Customer Solutions&Innovation Gina ChungGlobal Director,Innovation and Trend Research DHL Customer Solutions&Innovation PROJECT MANAGEMENT AND EDITORIAL OFFICEBen Gesing,Gianmarco Steinhauer,Michel HeckDHL Customer Solutions&Innovation IN COOPERATION WITH Keith Dierkx Global Industry Leader,Travel&TransportationIBM Industry AcademyDominic SchulzVice President,Hybrid Cloud Software DACH IBM Deutschland GmbH AUTHORS Ben GesingProject Manager,Innovation and Trend ResearchDHL Customer Solutions&InnovationSteve J.PetersonGlobal Thought Leader,Travel&TransportationIBM Institute for Business ValueDr.Dirk MichelsenManaging Consultant,Watson&AI Innovation DACHIBM Deutschland GmbHSPECIAL THANKS TOAll the experts at IBM,DHL,and Singapore Management University who contributed to make this story possible.Today we find ourselves in another transformational era in human history.Much like the agricultural and industrial revolutions before it,the digital revolution is redefining many aspects of modern life around the world.Artificial intelligence(AI)plays an increasingly central role in this transformation.In recent years,AI has come roaring out of research laboratories to become ubiquitous and ambient in our personal lives,so much so that many consumers do not realize they use products and applications that contain AI on a daily basis.AI stands to greatly benefit all industries,achieving adoption leaps from consumer segments to enterprises and onward to the industrial sector.Technological progress in the fields of big data,algorithmic develop-ment,connectivity,cloud computing and processing power have made the performance,accessibility,and costs of AI more favorable than ever before.Just as the relational database found its way into core business operations around the world providing better ways to store,retrieve,and organize information AI is now following a similar path.It is becoming an integral part of every future software system and soon we will no longer need to call it out as AI.Already today,AI is prevalent in consumer-facing applications,clerical enterprise functions,online and offline retail,autonomous mobility,and intelligent manufacturing.Logistics is beginning its journey to become an AI-driven industry,but the future is still rife with challenges to overcome and opportunities to exploit.With this in mind,experts from IBM and DHL have jointly written this report to help you answer the following key questions:What is AI,and what does it mean for my organization?What best practice examples from other industries can be applied to logistics?How can AI be used in logistics to reinvent back office,operational,and customer-facing activities?Looking ahead,we believe AI has the potential to signi-ficantly augment current logistics activities from end to end.As in other industries,AI will fundamentally extend human efficiency in terms of reach,quality,and speed by eliminating mundane and routine work.This will allow logistics workforces to focus on more meaningful and impactful work.We think there has never been a more exciting time for collaboration between logistics and technology professionals as they enable AI in logistics.We hope you will find this an insightful read,and we look forward to working together to bring AI into your organization.Yours sincerely,PREFACEKeith Dierkx Global Industry Leader,Travel&Transportation IBM Industry AcademyMatthias Heutger Senior Vice President,Global Head of Innovation DHL Customer Solutions&Innovation PREFACE .11 UNDERSTANDING ARTIFICIAL INTELLIGENCE.31.1 Origin&Definition of AI .3 1.2 How Machines Learn:Three Components of AI.6 1.3 Trends Accelerating AI .9 1.4 Challenges&Risks .13 1.5 Why Logistics?Why Now?.142 AI BEST PRACTICES FROM OTHER INDUSTRIES .16 2.1 Consumer AI:Ambient Assistance in Everday Life .16 2.2 Enterprise AI:Working Smarter&Harder on Behalf of Professionals .17 2.3 AI in Retail:Personalized Online Experiences&Self-Learning,Replenishing Spaces .19 2.4 Autonomous Transportation:AI Under the Hood .20 2.5 Engineering&Manufacturing:AI Shapes the Physical World .213 ARTIFICIAL INTELLIGENCE USE CASES IN LOGISTICS .22 3.1 Back Office AI.22 3.2 Predictive Logistics:The Next Operational Paradigm .25 3.3 Seeing,Speaking&Thinking Logistics Assets .27 3.4 AI-Powered Customer Experience .32 3.5 Getting Started with AI in Your Supply Chain .33CONCLUSION AND OUTLOOK .36SOURCES .37PICTORIAL SOURCES .39Table of Contents21.1 Origin&Definition of AIArtificial intelligence(AI)is not new.The term was coined in 1956 by John McCarthy,a Stanford computer science professor who organized an academic conference on the topic at Dartmouth College in the summer of that year.The field of AI has gone through a series of boom-bust cycles since then,characterized by technological break-throughs that stirred activity and excitement about the topic,followed by subsequent periods of disillusionment and disinterest known as AI Winters as technical limita-tions were discovered.As you can see in figure 1,today we are once again in an AI Spring.Artificial intelligence can be defined as human intelligence exhibited by machines;systems that approximate,mimic,replicate,automate,and eventually improve on human thinking.Throughout the past half-century a few key com-ponents of AI were established as essential:the ability to perceive,understand,learn,problem solve,and reason.Countless working definitions of AI have been proposed over the years but the unifying thread in all of them is 1 UNDERSTANDING ARTIFICIAL INTELLIGENCEUnderstanding Artificial Intelligence3that computers with the right software can be used to solve the kind of problems that humans solve,interact with humans and the world as humans do,and create ideas like humans.In other words,while the mechanisms that give rise to AI are artificial,the intelligence to which AI is intended to approximate is indistinguishable from human intelligence.In the early days of the science,pro-cessing inputs from the outside world required extensive programming,which limited early AI systems to a very narrow set of inputs and conditions.However since then,computer science has worked to advance the capability of AI-enabled computing systems.Board games have long been a proving ground for AI research,as they typically involve a finite number of players,rules,objectives,and possible moves.This essen-tially means that games one by one,including checkers,backgammon,and even Jeopardy!to name a few have been taken over by AI.Most famously,in 1997 IBMs Deep Blue defeated Garry Kasparov,the then reigning world champion of chess.This trajectory persists with the ancient Chinese game of Go,and the defeat of reigning world champion Lee Sedol by DeepMinds AlphaGo in March 2016.Figure 1:An AI timeline;Source:Lavenda,D./Marsden,P.AI is bornFocus on specific intelligenceFocus on specific problemsThe Turing TestDartmouth College conferenceInformation theory-digital signalsSymbolic reasoningExpert systems&knowledgeNeural networks conceptualizedOptical character recognitionSpeech recognitionMachine learningDeep learning:pattern analysis&classificationBig data:large databasesFast processors to crunch dataHigh-speed networks and connectivity AI Winter IAI Winter II 1964 Eliza,the first chatbot is developed by Joseph Weizenbaum at MIT1997IBMs Deep Blue defeats Garry Kasparov,the worlds reigning chess championEdward Feigenbaum develops the first Expert System,giving rebirth to AI1975 1982IBMs Watson Q&A machine wins Jeopardy!Apple integrates Siri,a personal voice assistant into the iPhone 20112016AlphaGo defeats Lee Sedol195019601990201020202000198019702014YouTube recognizes cats from videosDartmouth conference led by John McCarthy coins the term artificial intelligence 1956Real-world problems are complicated Facial recognition,translation Combinatorial explosionLimited computer processing power Limited database storage capacityLimited network abilityDisappointing results:failure to achieve scaleCollapse of dedicated hardware vendorsTHE RISE OF AIUnderstanding Artificial Intelligence4usually within a specific domain,and learn from whatthey have been given.These systems draw on the abilityto evaluate and categorize received data,and then drawinferences from this.The output of this process is an insight,decision,or conclusion.Figure 4 shows three applications based on machine learning.For example,when the input is an image of you uploaded to a social media platform,image recognition software analyzes the content of the image pixels forknown patterns using machine learning algorithms inthe hidden layers,and produces an output in the formof an automatic tag of your name in the uploaded photo.It is able to do this based on statistical probability that characteristics of the image resemble existing images you have uploaded previously.1 Moyer,C.(2016).ARTIFICIAL INTELLIGENCEMACHINE LEARNINGDEEP LEARNINGEarly artificial intelligencestirs excitement.Machine learning begins to flourish.Deep learning breakthroughs drive AI boom.Figure 3:A visual representation of AI,machine learning,and deep learning;Source:Nvidia1950s1960s1990s2010s2000s1980s1970sAI,MACHINE LEARNING&DEEP LEARNINGFigure 2:Lee Sedol is defeated by DeepMinds AlphaGo in the ancient Chinese game of Go;Source:Getty ImagesSedols defeat was a watershed moment for the prowess of AI technology.Previous successes had depended on what could be called a brute force approach;systems learned well-structured rules of the game,mastered all possible moves,and then programmatically decided the best move at machine speed,which is considerably faster than human decision making.In a traditional Go board of 19 by 19 lines,there are more possible combinations than the number of atoms on planet earth,meaning it is impossible for any computing system available today to master each move.DeepMinds AlphaGo effectively had to develop a sense of reasoning,strategy,and intuition to defeat Sedol;something that Go players have tirelessly tried to perfect for over 2,500 years yet DeepMind trained AlphaGo to do in a matter of months.The important outcome from Sedols defeat is not that DeepMinds AI can learn to conquer Go,but that by extension it can learn to conquer anything easier than Go which amounts to a vast number of things.1Current understanding of AI can quickly become convoluted with a dizzying array of complex technical terms and buzz-words common to mainstream media and publications on the topic today.Two terms in particular are important in understanding AI machine learning which is a subset of AI and deep learning which is a subset of machine learning,as depicted in figure 3.Whereas AI is a system or device intended to act with intelligence,machine learning is a more specific term that refers to systems that are designed to take in information,Understanding Artificial Intelligence5Figure 4:A diagram of a neural network with six inputs,seven tuning parameters,and a single output;Source:Nielsen,M.DIAGRAM OF A NEURAL NETWORKINPUT LAYERHIDDEN LAYERSOUTPUT LAYERProblemTypeImage RecognitionLoanApprovalOnline Ad PlacementInputsPicture(s)LoanapplicationSocial media profile,browsing historyHidden LayersPerson?Face?Gender?Age?Hair&eye color?Income?Credit history?Employment?Marital status?Demographics?Browsing history metadataOutputIs it you?(%)Will yourepay?(%)Will you click?(%)Deep learning takes the concept of machine learning a bit further.Deep learning is about learning continually;the intention of the system is to learn from the real world and adjust the learning model as it takes in new informa-tion and forms new insights.In simplified form,figure 5 depicts how deep learning algorithms can distinguish the content of an image,as well as where the elements of the image are in relation to one another,by analyzing pixel data alone.The human visual cortex is constantly doing this without our conscious awareness;however this perceptive ability in computers is truly novel.This is the type of system that is more useful in addressing real-world data challenges,which is why deep learning systems are the ones that have been directed at extremely large and fast-moving datasets typically found on social media platforms and in autonomous vehicles.Deep learning is typically done with neural networks.Neural networks are humanitys best attempt to mimic both the structure and function of the human brain.As new data is fed into a neural network,connections between nodes are established,strengthened,or diminished,in a similar fashion to how connections between neurons in the human brain grow stronger through recurring experiences.Furthermore,each connection in a neural network can be tuned,assign-ing greater or lesser importance to an attribute,to achieve the quality of the output.Figure 5:Deep learning goes beyond classifying an image to identify the content of images in relation to one another;Source:StanfordInstance SegmentationObject DetectionClassification+LocalizationClassificationSingle Objects Multiple ObjectsUnderstanding Artificial Intelligence61.2 How Machines Learn:Three Components of AIDespite the oversimplification that tends to define AI in the popular press,AI is not one single,unified technology.AI is actually a set of interrelated technology components that can be used in a wide variety of combinations depending on the problem it addresses.Generally,AI technology consists of sensing components,processing components,and learn-ing components(see figure 6).Sensing:The Fuel of AITo be able to understand or“sense”the real world,AI must take in information.As real-world information comes in many forms,AI must be able to digest text,capture images and video,take in sound,and eventually gather information about environmental conditions such as tem-perature,wind,humidity,etc.everything that is typically understood by humans through our sense of touch.One of the most mature AI sensing capabilities is text-based processing.While AI systems have been processing structured data from databases,spreadsheets,and the internet for many years,recent advances in deep learn-ing have improved AIs ability to process and understand unstructured data.Comments online,in social media,and even within apps are unstructured,so this critical capa-bility dramatically increases the amount and diversity of inputs that AI can leverage to understand the world.Putting it all togetherA FULL AI LEARNING CYCLEFigure 6:A full AI learning cycle;Source:IBM/DHL1.Training data2.Data gathered continuously from the environment,sensors and online behavior3.Data is aggregated and harmonized4.Machine learning frame