"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Digital transformation is the key imperative in the corporate suite of most forward-thinking enterprises. IDC coined the term Digital Darwinism to reflect the impact of digital transformation on businesses of all sizes and across industries. According to IDC, organizations are moving away from business as usual and embracing digital transformation to become more competitive. Key components of enacting digital transformation are the applied sciences of artificial intelligence, machine learning, deep learning, and prescriptive analytics, the creation of computational systems that allow autonomous decision making. Through prescriptive analytics, organizations will redefine how business decisions are made.
This post is about implementing a – quite basic – Neural Network that is able to play the game Tic-Tac-Toe. For sure there is not really a need for any Neural Network or Machine Learning model to implement a good – well, basically perfect – computer player for this game. This could be easily achieved by using a brute-force approach. But as this is the author's first excursion into the world of Machine Learning, opting for something simple seems to be a good idea. The motivation to start working on this post and the related project can be comprised in one word: AlphaGo.
Unlike evaluating the accuracy of models that predict a continuous or discrete dependent variable like Linear Regression models, evaluating the accuracy of a classification model could be more complex and time-consuming. Before measuring the accuracy of classification models, an analyst would first measure its robustness with the help of metrics such as AIC-BIC, AUC-ROC, AUC- PR, Kolmogorov-Smirnov chart, etc. The next logical step is to measure its accuracy.
By using an artificially intelligent algorithm to predict patient mortality, a research team from Stanford University is hoping to improve the timing of end-of-life care for critically ill patients. In tests, the system proved eerily accurate, correctly predicting mortality outcomes in 90 percent of cases. But while the system is able to predict when a patient might die, it still cannot tell doctors how it came to its conclusion. Doctors must consider an array of complex factors, ranging from a patient's age and family history to their response to drugs and the nature of the affliction itself. To complicate matters, doctors have to contend with their own egos, biases, or an unconscious reluctance to assess a patient's prospects for what they are.
A recent MIT/Sloan survey of senior corporate executives showed they see artificial intelligence (AI) as the single most disruptive new technology, and nearly 70 percent said they already have AI investments underway. Looking at these results, you might feel insecure. You might worry that you're falling behind, and that your business will lose its competitive edge if you don't get with the program (literally). And it's tempting to march into your CEO's office and demand approval for an AI strategy now. You might want to sleep on that, though, because in the project management domain, the timing might not be quite right.
Today most healthcare data exists in silos. There is only limited sharing of healthcare data. There is massive, largely untapped potential for securely sharing healthcare data to improve the quality of patient care and reduce the cost of care. Healthcare is under increasing pressure to reduce the cost of care. Exacerbating this are the trends of aging populations and rampant chronic diseases.
Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration. Get the latest from CSO by signing up for our newsletters. Naturally, this has led to the belief that these intelligent security solutions will spot - and stop - the next WannaCry attack much faster than traditional, legacy tools.
Perhaps you heard recently about a new algorithm that can drive a car? Or scan a picture and find your face in a crowd? It seems as though every week companies are finding new uses for algorithms that adapt as they encounter new data. Last year Wired quoted an ex-Google employee as saying that "Everything in the company is really driven by machine learning." Machine learning has tremendous potential to transform companies, but in practice it's mostly far more mundane than robot drivers and chefs.
What we are looking at is a recommendation engine problem. Given the personal data you want to make the diet recommendation that are best suited for the person based on the data inserted. For creating this recommendation engine, you'll need data for personal details and appropriate recommendations. Based on this training data, you'll create the recommendation engine which will map the personal information to appropriate diet recommendations.
At Zendesk we are developing a series of machine learning products, the most recent of which is Answer Bot. It uses machine learning to interpret user questions and responds with relevant knowledge base articles. When a customer has a question, complaint or enquiry, they may submit their request online. Once their request is received, Answer Bot will analyse the request and suggest relevant articles which may best assist with the customer's request via email. Answer Bot uses a class of state-of-the-art machine learning algorithms known as deep learning to identify relevant articles.