Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it's becoming increasingly important that you--yes, you--actually understand it. I don't think you should need to have a technical background to know what machine learning is or how it's done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field. This is for smart, ambitious people who want to know more about machine learning but who don't care about the esoteric statistical and computational details underlying the field. You don't need to know any math, statistics, or computer science to read and understand it.

Domino's Pizza has been a company willing to embrace technological change over the past few years. In 2015, it announced that customers could order pizza just by texting a pizza emoji to Domino's. Last November, it delivered a pizza by drone to a couple in New Zealand, claiming that it will be faster and safer as a drone delivery would not have to worry about traffic. Now Domino's is taking another step towards the future as it announced the DRU platform, which ZDNet describes as "an artificial intelligence (AI)-based technology that will allow customers to order a pizza using their voice." Customers could use their phone, computers, or virtual assistants like Amazon Alexa to order pizza, find opening hours, and browse through a menu. Domino's has been developing the DRU platform for years, and most companies obviously do not have the resources to make their own virtual assistants.

Human error is a troubling problem in healthcare for hospitals, healthcare providers, and for patients and their families. A report published by The BMJ last year noted that medical errors claim 250,000 lives each year, making it the third leading cause of death in the U.S. The need to address this issue in a meaningful way has led to the development of numerous clinical decision support tools and efforts to improve care team communication. Artificial intelligence can play a critical role in improving patient safety as well. In a talk at SXSW in Austin this week on the future of AI, Eric Horvitz, Microsoft Research Laboratory Technical Fellow and managing director, highlighted a few examples of how AI can be used to learn from adverse events and prevent them. Horvitz said he was excited by the potential applications from AI's adoption in healthcare, especially applications where it could protect and assist physicians, much like a safety net.

Adobe, one of the world's largest and most powerful software companies, is trying something new: It's applying machine learning and image recognition to graphic and web design. In an unnamed project, the company has created tools that automate designers' tasks, like cropping photos and designing web pages. The new project, which uses Adobe's AI and machine learning program Sensei and integrates into the Adobe Experience Manager CMS, will debut at the company's Sneaks competition later in March. While Adobe hasn't committed to integrating it into any of its products, it's one of the most ambitious attempts to marry machine learning and graphic design to date. There have been efforts to use AI in the design world before--for instance, Wix's Advance Design Intelligence and automated projects like Mark Maker, but Adobe's is notable because of the company's sheer reach in the design world.

In the post How to Train Neural Networks With Backpropagation I said that you could also calculate the gradient of a neural network by using dual numbers or finite differences. The post I already linked to explains backpropagation. Since the fundamentals are explained in the links above, we'll go straight to the code. We'll be getting the gradient (learning values) for the network in example 4 in the backpropagation post: Note that I am using "central differences" for the gradient, but it would be more efficient to do a forward or backward difference, at the cost of some accuracy. I didn't compare the running times of each method as my code is meant to be readable, not fast, and the code isn't doing enough work to make a meaningful performance test IMO.

IBM recently announced that it reached a new industry record in conversational speech recognition, which could have big implications for the future of artificial intelligence (AI). The IBM team's system achieved a 5.5% word error rate--down from 6.9% last year. The benchmark was measured on a difficult speech recognition task, with the machine deciphering recorded conversations between humans discussing day-to-day topics such as buying a car. This recording is known as SWITCHBOARD, and has been used for more than two decades to test speech recognition systems, according to a blog post by George Saon, a principal research scientist at IBM. IBM used deep learning technologies to reach the 5.5% record.

A report from research firm IDC has thrown more light on the emerging importance of Artificial Intelligence (AI). According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, spending on AI technologies is all set to surge from $8 billion in 2016 to $47 billion in 2020. With healthcare being one of the industries that will invest the most on cognitive/ AI systems, it is apparent that this industry is betting big on AI technologies. Virtual assistants, intelligent automation and cognitive computing all are going to impact various facets of healthcare – from operations to patient-centric care to precision medicine. Data is a huge driving force in the world of healthcare today.

Interventional radiologists at the UCLA Medical Center are leveraging artificial intelligence to create a "chatbot" that automatically communicates with referring clinicians, providing them with evidence-based answers to frequently asked questions. Currently, the AI-powered prototype is being tested by a small UCLA team of hospitalists, radiation oncologists and interventional radiologists. The machine learning application, which acts like a virtual radiology assistant, enables clinicians to rapidly access valuable information while enabling them to perform other duties and to focus on patient care. The information is delivered in multiple formats, including relevant websites, infographics, and subprograms within the application. And if the tool determines that an answer requires a human response, contact information for an actual interventional radiologist is provided.

MOBILE WORLD CONGRESS, Barcelona and REDWOOD CITY, California, February 27, 2017 -- Aricent, a global design and engineering company, today announced the launch of Cognitive Services to enhance customer engagement and brand loyalty in a digital era. Artificial Intelligence (AI) is top of mind today for most business leaders who seek to provide unique value around their products and services for a truly compelling customer experience. AI is a wide ranging category containing many capabilities including Natural Language Processing, Natural Language Understanding, Machine Learning, Deep Learning and Computer Vision. However, launching successful products and services powered by AI is challenging. As per Gartner Research report titled Top 10 Strategic Technology Trends for 2017*, "Significant investment in skills, process and tools is needed to successfully exploit these techniques in terms of setup, integration, algorithm/approach selection, data preparation and model creation."

Artificial intelligence has a long history of boom and bust cycles. During A.I. booms, money flows through universities and industry labs, fueling promised advances that often sound like magic, if not panacea. Extreme optimism was particularly common in the field's early years. In 1960, for example, A.I. pioneer Herbert Simon suggested that "machines will be capable, within twenty years, of doing any work that a man can do," a claim echoed by the founder of the field, Marvin Minsky, in 1961. Despite the advances that have occured since that time -- most recently, breakthroughs in neural networks, a form of machine learning inspired by the biological structure of the brain -- today's leading researchers tend to be more circumspect about the potential of artificial intelligence in the near-term.