INTRODUCTION TO MACHINE LEARNING:
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Some machine learning methods:
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training — typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
How Amazon uses AI & Algorithms:
Amazon has a very low key approach in leveraging algorithms, machine learning and AI in contrast to Alphabet/Google, Facebook, Uber or Apple.
The reality is that Amazon has been an early adopter of algorithms and automation for many years giving them an edge in using AI to improve efficiencies, reduce cost and improve customer experience. It has been heavily focused internally.
The Economist has a great read (paywall) on how Amazon is exploiting AI to gain significant business advantage and differentiation. Here are the top 3 areas driven by AI at Amazon:
Fulfillment Centers: Robots shuffle ‘pods’, algorithmically, inside Amazon’s giant warehouses — in fenced off areas. Amazon associates interact with the robots/pods in specially created gaps on the fence.
Some pick items out of pods brought to them by a robot; others pack items into empty pods, to be whirred away and stored. Whenever they pick or place an item, they scan the product and the relevant shelf with a bar-code reader, so that the software can keep track.
AI & algorithms work relentlessly to optimize the delays and efficiencies of product movement using the combination of Robots, Pods and Associates towards the goal of speedier product delivery. It was a pioneer in using robotics for its warehouses when it acquired Kiva back in 2012.
Amazon Web Services (AWS): Recently AWS has made a huge push (this on-demand conference is worth reviewing) in offering machine learning tools, solutions and services to its customers. AWS is using machine learning primarily to forecast demand for computation.
“We can’t say we’re out of stock,” says Andy Jassy, AWS’s boss. To ensure they never have to, Mr Jassy’s team crunches customer data. Amazon cannot see what is hosted on its servers, but it can monitor how much traffic each of its customers gets, how long the connections last and how solid they are. As in its fulfilment centres, these metadata feed machine- learning models which predict when and where aws is going to see demand.
Amazon Go: Go is Amazon’s cashierless grocery and it’s latest algorithmic venture. To figure out what items the shopper is picking up they are relying a bank of video cameras (an alternative may have been to use barcodes, RFID on items to figure out what is being shopped) — to build a profile (3D). This helps in tracking arms and hands as they are being used to handle and pick products from the shelves, and bill customers when they walk with their selections from the store.
This technology is being also leveraged by fulfillment centers in lieu of using barcode readers to identify products by the Associates.
It won’t be surprising, in future, that just like AWS, some of these use cases may be offered as products & solutions in the future. Amazon will continue to leverage technology to diversify its market presence and improve the customer experience.
Explore machine learning services that fit your business needs, and learn how to get started ⬇⬇
AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. Named a leader in Gartner’s Cloud AI Developer services’ Magic Quadrant, AWS is helping tens of thousands of customers accelerate their machine learning journey.