If you're like many people, you have thousands of photos on your phone, long forgotten after you've posted a few on Instagram or Facebook. They don't have to stay forgotten. Apple and Google are both applying a form of artificial intelligence called "machine learning" to organize your pictures and video -- and along the way, help you rediscover last year's vacation, dinner with close friends and a casual summer outing to the park. Apple's tools are part of last month's iOS 10 system update for iPhones and iPads. The Google Photos app for Apple and Android devices has a digital assistant to automatically organize these memories -- and Google signaled last week that it will only get smarter.

We're going to be blunt: Chatbots in their current form aren't great. We were promised bots that would change the way we interact with businesses and services, but instead we have interactive bots that perform worse than apps. They are primarily focused on taps or interactive graphical interfaces, and conversing with them using natural language is nearly impossible. Take an example of Poncho Weather on Facebook Messenger. Let's say I'm going to a conference next Monday in San Diego and want to know what the forecast is.

There are many clichés about lawyers, but one that even lawyers buy into is that we are slow to adopt new technology. It may be heresy for me to say this, but I do not believe it is true. Yes, the behemoth that is the profession as a whole is slow to adopt change of any kind – tech, business, and otherwise. But the behemoth is a creature of the lowest common denominator. It does not define us all.

You know you reversed over me in the Yaris a year ago today. I assume it was an accident. That's what the coroner decided. So, er Roger, how are we managing to have this conversation if I've been dead for a year? I was feeling a little guilty about the way things had worked out, Deirdre, and discovered this new technology: griefbots.

A startup from California is using GPUs and big data to predict what homes are likely to buy solar panels. PowerScout is using GPUs on the Amazon cloud and cuDNN to train their deep learning models on a mix of data from commercial databases and LIDAR to detect solar panels from satellite images, and to also detect the presence of trees near homes that could cast shade onto roofs. The tools the startup developed can also help estimate how much energy could be harvested from a home's rooftop without needing to take measurements in person with a decent degree of accuracy. From the information, they can target direct mail and online marketing to the most promising customers and quickly give them online estimates. Then, those who are interested in rooftop solar can choose a financing plan and get connected to a local installation partner to have it installed.

Artificial-intelligence systems can do increasingly complex tasks but they can't yet figure much out on their own without help from humans. In a paper published Wednesday in the journal Nature, researchers at Alphabet Inc.'s Google DeepMind describe experimental software that they say gets closer to that goal and could be more accurate and less costly than current systems. "There's a lot of things it could be used for," he said. One obvious future application is "chatbots," software that answers questions autonomously, he said. The new DeepMind prototype couples so-called artificial neural networks -- which are widely used for image and speech recognition -- with an external memory.

Baidu, a China-based search engine business, took the wraps off a digital health tool to field medical queries and conversations between physicians and their patients called Melody medical assistant. The company claimed in a news release that the app uses deep learning to help doctors gather information from patients about their medical conditions and help physicians arrive at a diagnosis. To give an idea how the bot is designed to work, a spokeswoman provided an overview, in response to emailed questions. When a patient opens the app to pose a question, Melody asks the patient relevant follow-up questions to clarify information such as the duration, severity, and frequency of symptoms. The questions can also touch on additional symptoms related to the condition, even though the patient may not have mentioned them. The point is to give the doctor a more detailed sense of the patient's condition to decide whether to recommend the patient for an appointment sooner rather than later.

The White House National Science and Technology Council released a report on the future of artificial intelligence Wednesday that predicted regulatory challenges, future job losses, more capable U.S. cyber-defenses and little chance of a Terminator-esque super-intelligent computer apocalypse. "If computers could exert control over many critical systems, the result could be havoc, with humans no longer in control of their destiny at its best and extinct at its worst," the report, titled "Preparing for the Future of Artificial Intelligence" said. "This scenario has long been the subject of science fiction series, and recent pronouncements from some influential industry leaders have highlighted these fears." The report was likely referencing comments from Microsoft founder Bill Gates, theoretical physicist Stephen Hawking and Tesla CEO Elon Musk--and, for what it's worth, several Arnold Schwarzenegger-led dystopian films--warning of the dangers of AI. But the NSTC, a Cabinet-level group that coordinates science and tech policy, held "a more positive view" of AI's future, with the technology's systems serving as "helpers, assistants, trainers and teammates of humans."

IBM (NYSE:IBM) has a lot of moving parts, and a vociferous crowd of critics. In the process of analysis, it's easy to be overwhelmed by complexity, or sidetracked into refuting mindless attacks by the ill-informed. In the interest of simplicity, this article focuses on hard evidence of the company's progress in exploiting the developing market for Artificial Intelligence, Machine Learning, or Cognitive Computing. The quarterly and annual financial results provide segment information, to include year over year revenue growth and pre-tax margins. Looking at 2Q 2016, Cognitive Solutions at 4.4% is the only segment showing growth, and at 27.5% the second highest (after Global Financing) in pre-tax margins.

You must know how to load data before you can use it to train a machine learning model. When starting out, it is a good idea to stick with small in-memory datasets using standard file formats like comma separated value (.csv). How to Load Machine Learning Data From Scratch In Python Photo by Amanda B, some rights reserved. The standard file format for small datasets is Comma Separated Values or CSV. In it's simplest form, CSV files are comprised of rows of data.