An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples. An artificial neural network consists of artificial neurons or processing elements and is organized in three interconnected layers: input, hidden that may include more than one layer, and output. The input layer contains input neurons that send information to the hidden layer. The hidden layer sends data to the output layer. Every neuron has weighted inputs (synapses), an activation function (defines the output given an input), and one output.
In May, Sundar Pichai, CEO of Google, discussed AI applications for digital pathology in his keynote speech to an audience of millions at Google's annual I/O event. Five weeks earlier, the FDA announced it had approved the first whole slide imaging system for primary diagnostic use in pathology. Both events point to the future of pathology and laboratory medicine: Software will soon dominate. Over the past 20 years, software has taken over the world. Retail was dominated by Amazon, Netflix put Blockbuster out of business, and Uber used software to take over the taxi industry.
Even though predictive analytics has been around for quite some time, interest around this topic has increased over the last couple of years. It is no longer enough for a company to accurately record what has happened. Today, an organization's success depends on its ability to reliably predict what will happen – be it predictions about what a customer is likely to buy next, an asset that could require maintenance, or the best action to take next in a business process. Predictive analytics uses (big) data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, enabling both optimization and innovation. Existing processes can be improved – for example by forecasting sales and spikes in demand and enabling the required adjustments to the production planning.
Cisco has revealed plans to acquire San Jose startup Perspica to bolster the firm's previous purchase of AppDynamics in the data analytics arena. On Thursday, Cisco said in a blog post that Perspica is "the first acquisition to support and accelerate the AppDynamics vision." Financial details were not disclosed. The network equipment maker snapped up AppDynamics in January this year in a deal valued at $3.7 billion. AppDynamics is the developer of an enterprise platform suitable for monitoring application performance and business metrics.
In Lecture 8 we discuss the use of different software packages for deep learning, focusing on TensorFlow and PyTorch. We also discuss some differences between CPUs and GPUs. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
I was rehearsing a speech for an AI conference recently when I happened to mention Amazon Alexa. At which point Alexa woke up and announced: "Playing Selena Gomez." I had to yell "Alexa, stop!" a few times before she even heard me. But Alexa was oblivious to my annoyance. Like the majority of virtual assistants and other technology out there, she's clueless about what we're feeling.
In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.
At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.
Nicolas Economou is the CEO of electronic discovery and information retrieval firm H5, a Senior Advisor to the AI Initiative of the Future Society at Harvard Kennedy School, and is an advocate of the application of scientific methods to electronic discovery. If asked whether entirely autonomous, artificially intelligent judges should ever have the power to send humans to jail, most of us would recoil in horror at the idea. Our answer would be a firm "Never!" But assume that AI judges, devoid of biases or prejudices, could make substantially more equitable, consistent and fair systemwide decisions than humans could, nearly eliminating errors and inequities. Would (should?) our answer be different?
Earlier this year, Cisco announced the acquisition of AppDynamics – uniquely positioning Cisco to enable enterprises to accelerate their digital transformations by actively monitoring, analyzing and optimizing complex application environments at scale. Today, we are excited to announce the intent to acquire Perspica, the first acquisition to support and accelerate the AppDynamics vision. In our experience working with the world's largest companies, we know that machine learning is only as good as the data it ingests; only as relevant as the data's timeliness; and only as valuable as the data's business context. Cisco's AppDynamics data sets span wherever the application components are deployed, and there is a massive opportunity to correlate this with user experience and business context. With the addition of Perspica to our AppDynamics capabilities, customers will be able to further take advantage of machine learning capabilities to analyze large amounts of application-related data, in real time and with business context, including when an application is deployed in a company's public, private and multiple cloud environments.