As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.
"Most of the talk about machine learning is a bunch of hokum," said Douglas Greenig, a Ph.D. in mathematics who started quantitative hedge fund Florin Court Capital. Man AHL's scientists and engineers developed and tested the technology on historical data, often expecting it to flop, and only overcame doubts as the strategy produced solid returns in trials. It has returned an annualized 7.1 percent in three years through September, compared with a 3.2 percent gain for the average hedge fund, and an 11 percent rise for the S&P 500 Index. "It's not about applying machine learning techniques to historical data and being satisfied if we rediscover what we know already," Ledford said.
Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours. For example, in May 2016 a millennial taskforce at McCann Japan developed the world's first artificial intelligence creative director, AI-CD ß. For instance, Mondelez asked a real life creative director to develop the creative direction for AI-CD ß's ad and to explain the product's benefits.
The milestone was enabled with the new Microsoft Cognitive Toolkit, the software that enables those speech recognition advances (as well as image recognition and search relevance). In addition to helping the researchers hit the 5.9 WER, the new Microsoft Cognitive Toolkit 2.0 helped the researchers enable what the company is calling "reinforcement learning." The company released the new Watson Data Platform (WDP), a cloud-based analytics development platform that allows programming teams including data scientists and engineers to build, iterate and deploy machine-learning applications. WDP runs on IBM's Bluemix cloud platform, integrates with Apache Spark, works with the IBM Watson Analytics service and will underpin the new IBM Data Science Experience (DSX), which is a "cloud-based, self-service social workspace that enables data scientists to consolidate their use of and collaborate across multiple open source tools such as Python, R and Spark," said IBM Big Data Evangelist James Kobielus in a blog post outlining last month's announcements at the company's World of Watson conference in Las Vegas.
The revenue forecast for enterprise AI changes depending on the source, but it's estimated to be at about $300-$350 million in 2016, and predicted to reach upwards of $30 billion by 2025 The technologies included in this focus are cognitive computing, natural language processing, image recognition, speech recognition, predictive APIs, deep learning, and machine learning. The process of creating a trailer for new horror movie "Morgan" involved using machine learning techniques and experimental APIs through IBM's Watson platform. The software uses the machine learning process of Natural Language Processing (NLP) to analyze thousands of movie plot summaries correlated to box office performance. Machine Learning is also helping entertainment providers recommend personalized content, based on the user's previous viewing activity and behavior.
It turns out that Mr. Khosla believes that AI will take away 80% of physicians' work, but not necessarily 80% of their jobs, leaving them more time to focus on the "human aspects of medical practice such as empathy and ethical choices." Sherpaa claims that 70% of members' health issues are delivered via virtual visits. Digital Trends reported on two U.K.-based companies who are developing AI chatbots designed specifically for health care, Your.MD and Babylon Health. The hardest part of using AI in health care may not be developing the AI, but in figuring out what the uniquely human role in providing health care is.
In contrast to k-nearest neighbors, a simple example of a parametric method would be logistic regression, a generalized linear model with a fixed number of model parameters: a weight coefficient for each feature variable in the dataset plus a bias (or intercept) unit. While the learning algorithm optimizes an objective function on the training set (with exception to lazy learners), hyperparameter optimization is yet another task on top of it; here, we typically want to optimize a performance metric such as classification accuracy or the area under a Receiver Operating Characteristic curve. Thinking back of our discussion about learning curves and pessimistic biases in Part II, we noted that a machine learning algorithm often benefits from more labeled data; the smaller the dataset, the higher the pessimistic bias and the variance -- the sensitivity of our model towards the way we partition the data. We start by splitting our dataset into three parts, a training set for model fitting, a validation set for model selection, and a test set for the final evaluation of the selected model.
We can use a machine learning algorithm and feed it input data (emails) and it will automatically discover rules that are powerful enough to distinguish spam emails. The most common preprocessing steps are: removing missing values, converting categorical data into shape suitable for machine learning algorithm and feature scaling. For example, size (small, medium, large), we can order these sizes large medium small. For example, a sample with "Red" color is now encoded as (Red 1, Green 0, Blue 0) Assume we have data with two features one on a scale from 1 to 10 and the other on a scale from 1 to 1000.
The Knights Landing Xeon Phi chips, which have been shipping in volume since June, deliver a peak performance of 3.46 teraflops at double precision and 6.92 teraflops at single precision, but do not support half precision math like the Pascal GPUs do. The Pascal chips, which run at 300 watts, would still deliver better performance per watt – specifically, 70.7 gigaflops per watt compared to the hypothetical Knights Mill chip based on Knights Landing we are talking about above, which would deliver 56 gigaflops per watt. The "Knights Corner" chip from 2013 was rated at a slightly more than 2 teraflops single precision, and the Knights Landing chip from this year is rated at 6.92 teraflops single precision. Thus, we have a strong feeling that the chart above is not to scale, or that Intel showed half precision for the Knights Mill part and single precision for the Knights Corner and Knights Landing parts.
Machine learning is changing the balance of labor between the decision-making role of humans, and the number-crunching roles of computers. The High Performance Computing (HPC) Center Lunch and Learn seminars are opportunities for students and professional developers to meet with HPC industry experts. Take the difficulty out of managing IoT development by using IoT cloud services from Microsoft Azure* with Intel IoT Technology. Intel Developer Zone experts, Intel Software Innovators, and Intel Black Belt Software Developers contribute hundreds of helpful articles and blog posts every month.