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AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.

AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.

AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure regularities in data so that the algorithm requires a skill: The algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation1 is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.

AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection2 system with five hidden was almost impossible a few years ago.All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.

AI achieves incredible accuracy through deep neural networks--which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning--and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

  1. 反向传播算法 (Back propagation) 是目前用来训练人工神经网络 (Artificial Neural Network, ANN) 的最常用且最有效的算法。其主要思想是:将训练集数据输入到 ANN 的输入层,经过隐藏层,最后达到输出层并输出结果,这是 ANN 的前向传播过程;由于 ANN 的输出结果与实际结果有误差,则计算估计值与实际值之间的误差,将被从输出层向隐藏层反向传播,直至传播到输入层;在反向传播的过程中,根据误差调整各种参数的值:不断迭代上述过程,直至收敛。
  2. 反欺诈 (Fraud Detection) 是反欺诈中所用到的机器学习模型。反欺诈应用的机器模型算法,多为二分类算法:GBDT 梯度提升决策树 (Gradient Boosting Decision Tree, GBDT) 算法,该算法的性能高,且在各类数据挖掘中应用广泛,表现优秀,应用场景较多;logistic 回归又称 logistic 回归分析,是一种广义的线性回归分析模型,常用于数据挖掘、疾病自动诊断、经济预测等领域,在有标注样本下训练模型对不同的欺诈情况进行二元判别;非监督的异常检测的方法,主要是从数据中找出异常的点,这些异常往往与欺诈有关联。
Last modification:September