Last week, the WannaCry ransomware attack crippled their network -- one report suggested people with life-threatening injuries were told not to come to the hospital. In the future, security systems could use artificial intelligence to monitor user behavior, track activity, suggest when there may be a danger and even mount an attack against the ransomware purveyors, effectively rendering the deadly malware client inoperable. Raja Mukerji, the cofounder and Chief Customer Officer at ExtraHop Networks, equates how an AI can block ransomware to how airport security stops people from using water bottles. A new technique using AI in airport security would not block all water bottles.

More overlooked machine learning and/or machine learning-related projects? OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. From Intel comes a(nother) deep learning framework, optimized for distribution over Apache Spark. BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

Such softwares reduce or totally eliminate the need for human intervention in the investing or trading process that is present in the traditional human discretional approach through fund managers and brokers. CPG is one of the few quant fund operators in Singapore today with total assets under management at slightly below US$100mil (RM434mil). Locally, BIMB Investment Management Bhd, which is a unit of Bank Islam Malaysia Bhd, recently launched its fund using AI technology. Whether or not quant funds outperform the traditional discretionary approach of investing is still very much open to debate.

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.

Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning -- as well as ensemble modeling, which uses a combination of approaches techniques, and semi-supervised learning, which combines supervised and unsupervised approaches. While it's not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields. By extracting high-level, complex abstractions as data representations through a hierarchical learning process, deep learning models yield results more quickly than standard machine learning approaches. Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach.

The biggest issue facing artificial intelligence right now is the question of'Why did the AI make a decision?' The problem we have now in research and academia is the lack of collaborative research concerning AI from multiple fields--science, engineering, medical, arts. We have a hard enough time telling people why the AI made a certain decision. Actually, what drives reverse engineering of the brain and the personalization of AI is not research in academia, it's more the lawyers coming in and asking'Why is the AI making these decisions?'

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. The resulting program, which the researchers named Deep Patient, was trained using data from about 700,000 individuals, and when tested on new records, it proved incredibly good at predicting disease. But it was not until the start of this decade, after several clever tweaks and refinements, that very large--or "deep"--neural networks demonstrated dramatic improvements in automated perception. Deep learning has transformed computer vision and dramatically improved machine translation.

Enjoying a surge in research and industry, due mainly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies - that is, neural network architectures with multiple hidden layers - to solve problems. Deep learning is a process, like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms. As defined above, deep learning is the process of applying deep neural network technologies to solve problems. Like data mining, deep learning refers to a process, which employs deep neural network architectures, which are particular types of machine learning algorithms.

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research.