Do we need an Asimov's Law for chatbots? And how do they compare with a talking parrot? What can dairy farmers learn from outfitting cows with pedometers? How can algorithms better explain to humans not just what they're predicting, but why? Some of the biggest names from the Seattle area's growing machine learning and artificial intelligence community tackled these questions and more Wednesday at a Madrona Venture Group summit. The sprawling array of challenges and opportunities in this fast-growing, fascinating, and sometimes frightening field defy easy summary.

A field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming. In this article, we're going to go over three aspects of machine learning pipeline design that tend to be tedious but nonetheless important. After that, we're going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science.

In his famous book – Think and Grow Rich, Napolean Hill narrates story of Darby, who after digging for a gold vein for a few years walks away from it when he was three feet away from it! Now, I don't know whether the story is true or false. But, I surely know of a few Data Darby around me. These people understand the purpose of machine learning, its execution and use just a set 2 – 3 algorithms on whatever problem they are working on. They don't update themselves with better algorithms or techniques, because they are too tough or they are time consuming.

The race to "cure death" has gripped Silicon Valley. In 2012, Google hired Ray Kurzweil, the'futurist' inventor best known for popularizing the idea of the "technological singularity," a hypothetical'super-intelligence' that will one day vastly outstrip the capacities of human beings. As Google's Director of Engineering, Kurzweil's job is to turn the fantasies of science fiction into consumer products -- and Google has invested billions in hopes that Kurzweil's dreams could one day become reality. One notable project, called "Calico," was announced the year after Kurzweil joined Google: a secretive biotech firm researching age-related diseases and developing anti-aging technology. Soon, Kurzweil promises, age and disease will disappear altogether, giving way to "software-based humans" with holographically projected bodies.

Algorithms help us to choose which films to watch, which music to stream and which literature to read. But what if algorithms went beyond their jobs as mediators of human culture and started to create culture themselves? In 1950 English mathematician and computer scientist Alan Turing published a paper, "Computing Machinery and Intelligence," which starts off by proposing a thought experiment that he called the "Imitation Game." In one room is a human "interrogator" and in another room a man and a woman. The goal of the game is for the interrogator to figure out which of the unknown hidden interlocutors is the man and which is the woman.

Japan's three leading automakers expect a stronger yen will cost them around 14 billion in lost operating profit this year alone -- just as they need to invest more in everything from cleaner fuel to driverless cars. After three years of supernormal profits on the back of a weaker currency, Toyota Motor, Nissan Motor and Honda Motor now face a reality check after the yen has turned around. While the recent years' currency boon filled automakers' coffers -- Toyota alone has around 10 billion in cash -- a squeeze on margins will put them under pressure to focus their investments, analysts say. "How to respond to yen rises while securing profits and continuing future investments -- this balance is important," Toyota Executive Vice President Takahiko Ijichi said this past week. The dollar climbed roughly 60 percent against the yen between late 2011 and mid-2015, a huge windfall for Japan's carmakers, but so far this year it is down roughly 9 percent against the yen.

Software development begins as a quest for capability, doing what could not be done before. Once that what is achieved, the engineer is left with the how. In enterprise software, the most frequently asked questions are, "How fast?" and more importantly, "How reliable?" Questions about software performance cannot be answered, or even appropriately articulated, without statistics. Yet most developers can't tell you much about statistics. Much like math, statistics simply don't come up for typical projects. Between coding the new and maintaining the old, who has the time? Engineers must make the time. I understand fifteen minutes can seem like a big commitment these days, so maybe bookmark it. Insistent TLDR seekers can head for our instrumentation section or straight to the summary. For the dedicated few, class is in session.

The Turing test has always been an approximate benchmark for good AI. In the test, a human is supposed to converse with a machine over text for five minutes; if the human doesn't realize that they are talking to a machine, then the computer passes as AI "indistinguishable" from human intelligence. DON'T MISS: To make the iPhone exciting again, Apple has to launch... an Android phone? Earlier this year, Georgia Tech professor Ashok Goel noticed he was spread thin for teaching assistants for his computer science course. So Goel programmed IBM's Watson system to work as an online chatbot, answering some of the 10,000 online questions submitted by students during the course.

It has come to the attention of digital marketers that Google have ventured even further into the world of tomorrow with their latest updates to how search engine optimisation works. Utilising artificial intelligence and machine learning, the new system is called RankBrain and might be about to change SEO completely. I interviewed Owen Radford, Online Marketing Manager here at Elementary Digital to get the lowdown. JH: So what exactly is RankBrain? OR: RankBrain is the name for the artificial intelligence system that Google are using to process search engine results.

Baker & Hostetler is the first law firm to announce that it will use a ground-breaking artificial intelligence product for legal research. The law firm will license Ross Intelligence in its bankruptcy practice, report the Am Law Daily (sub. The research product uses IBM's Watson technology, which is designed to get smarter as it is used. Ross responds to lawyers' questions in natural language by reading through the law, gathering evidence and drawing inferences. The program learns from the lawyers who use it to refine its search results.