Powered by DHL Trend Research ARTIFICIAL INTELLIGENCE IN LOGISTICS A collaborative report by DHL and IBM on implications and use cases for the logistics industry 2018PUBLISHER DHL Customer Solutions 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 INTELLIGENCE Understanding Artificial Intelligence 3 that 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 born Focus on specific intelligence Focus on specific problems The Turing Test Dartmouth College conference Information theory-digital signals Symbolic reasoning Expert systems Source: Nvidia 1950s 1960s 1990s 2010s 2000s 1980s 1970s AI, MACHINE LEARNING Source: Getty Images Sedols 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. 1 Current 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 Intelligence 5 Figure 4: A diagram of a neural network with six inputs, seven tuning parameters, and a single output; Source: Nielsen, M. DIAGRAM OF A NEURAL NETWORK INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Problem Type Image Recognition Loan Approval Online Ad Placement Inputs Picture(s) Loan application Social media profile, browsing history Hidden Layers Person? Face? Gender? Age? Hair 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 humanity's 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: Stanford Instance Segmentation Object Detection Classification + Localization Classification Single Objects Multiple Objects