Lately, it seems that every time you open your browser or casually scroll through a news feed, someone is writing about machine learning and its impact on both humans and the advancement of artificial intelligence. Machine learning has been highlighted in articles covering everything from Virtual Assistant solutions to self-driving cars and robots that can perform the same tasks as humans. A number of large companies are defining machine learning as'the future' – but what does that really mean? Machine learning is nothing new. The history, in fact, dates back over sixty years to when Alan Turing created the'Turing test' to determine whether a computer had real intelligence.

DEDHAM, MA--(Marketwired - Sep 7, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced that a leading multinational financial services firm has selected its Artificial Intelligence platform (RAGE AI) to drive improved results for its investment customers by using artificial intelligence to discover signals captured in a wide variety of data sources with Rage's innovative deep learning capabilities. RAGE AI significantly extends the frontier of deep learning and machine intelligence technology as it incorporates proprietary linguistics-based machine learning innovations to understand market developments in the context of individual companies and interpret those signals as a human would. After demonstrating via historical back-testing that the Rage AI platform repeatedly delivered returns in excess of what the firm's quantitative team was able to produce, Rage's solution was integrated in order to drive significant lift in the returns generated for the firm's clients. In fact, Rage has repeatedly shown that its deep background in computational linguistics and Natural Language Understanding can systematically discover Alpha by forming assessments of a company's financial projections that effectively predict future performance for businesses such as Wal-Mart (attached), where Rage AI predicted an upward trend in stock price months in advance. The RAGE AI platform does this by continuously interpreting unstructured content from over 100,000 sources and translating it into valuable intelligence.

After crossing 1.5M downloads, the free music-syncing app AmpMe now allows users to party without an internet connection using a new machine learning-powered predictive syncing technology. AmpMe streams music to multiple devices in perfect sync so friends can enjoy music together anywhere, without setting up a sound system. In the latest version, the new Predictive Sync" feature allows AmpMe to synchronize directly with other devices using a local Wi-Fi hotspot created by the phone that is hosting the party. "We think of it as a portable Sonos," AmpMe's founder and CEO Martin-Luc Archambault told The New York Post. The "Predictive Sync" capability was created by training their neural network models using the NVIDIA CUDA Deep Neural Network library (cuDNN) to analyze thousands of devices and hardware configurations.

Editor's note: This is Part II of a guest series written by legendary Silicon Valley investor Vinod Khosla, the founder of Khosla Ventures. In Part I, he laid the groundwork by describing how artificial intelligence is a combination of human and computer capabilities. In Part III, he will talk about how technology will sweep through education. I was asked about a year ago at a talk about energy what I was doing about the other large social problems, namely health care and education. Surprised, I flippantly responded that the best solution was to get rid of doctors and teachers and let your computers do the work, 24/7 and with consistent quality.

We identified over 90 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things. A few investment highlights: Increasingly crowded imaging & diagnostics: 17 out of the 22 companies under imagining & diagnostics raised their first equity funding round since January 2015 (this includes 1st Seed or Series A rounds, as well as a first round raised by stealth startup Imagen Technologies). In 2014, Butterfly Networks raised a 100M Series C, backed by Aeris Capital and Stanford University. This was the third-largest equity round to AI in healthcare companies, after China-based iCarbonX's 154M mega-round and two 100M raises by oncology-focused Flatiron Health . VCs invest in drug discovery: Startups are using machine learning algorithms to reduce drug discovery times, and VCs have backed 6 out of the 8 startups on the map.

Intel is not just content to build the silicon brains that will power the Rise of the Machines – it is bidding to own their eyes too, with the acquisition of computer vision developer Movidius. Movidius is 11 years old, having been founded in Dublin and transplanted to San Mateo. No price was disclosed, but the 140-strong firm has raised around 90m in funding in the past, and according to the Irish Times was valued at around 250m last year. As for its technology, it was previously used in Google's Project Tango computer vision platform, and according to its website, works with the likes of Lenovo, and DJI to "give sight to smart devices including drones, security cameras, AR/VR headsets and more". Intel's new technology group boss Josh Walden describes the deal in a blog post, saying it gives Intel "low-power, high-performance SoC platforms for accelerating computer vision applications" as well as "algorithms tuned for deep learning, depth processing, navigation and mapping, and natural interactions, as well as broad expertise in embedded computer vision and machine intelligence."

Long hailed as a leader in technology, we've seen many interesting developments come out of the Cupertino office, especially today with the news about the new iPhone 7. Though there are plenty of new updates and products, there's also the question of what goes on behind the scenes to make these products possible. The answer can largely be summed up in two words: "machine learning." Machine learning has been around for quite some time, whether we realize it or not. In fact, the very idea that we don't notice it means the technology is effective, processing and learning from incredibly large amounts of data in real time, right underneath our noses. However, the term has just recently become commercialized and recognized throughout the media, resulting in a more indepth conversation around artificial intelligence among both consumers and technology professionals.

VIENTIANE – U.S. President Barack Obama and Philippine President Rodrigo Duterte shook hands and had a brief chat on Wednesday, officials said, easing a standoff after Duterte called Obama a "son of a bitch" ahead of a summit of Asian leaders in Laos. The presidents of the two longtime allies were due to hold talks on Tuesday but the White House canceled the meeting after Duterte's insult. "I'm very happy that it happened," Philippines' foreign minister, Perfecto Yasay, said of their short meeting. "It all springs from the fact that the relationship between the Philippines and the United States is firm, very strong." Duterte had his outburst on Monday when he was defending his war on drugs that has killed at least 2,400 Filipinos.

Two Cornell experts in artificial intelligence (AI) have joined a nationwide team setting out to ensure that when computers are running the world, they will make decisions compatible with human values. "We are in a period in history when we start using these machines to make judgments," said Bart Selman, professor of computer science. "If decisions are properly structured, the horrors we've seen in the movies won't happen." Selman and Joseph Halpern, professor of computer science, have become co-principal investigators for the Center for Human-Compatible Artificial Intelligence, a nationwide research effort based at the University of California, Berkeley. Initially they will collaborate with scientists at Berkeley and the University of Michigan.

This paper introduces a patent-free¹ positional (ordered) dithering algorithm that is applicable for arbitrary palettes. Such dithering algorithm can be used to change truecolor animations into paletted ones, while maximally avoiding unintended jitter arising from dithering. For most of the article, we will use this example truecolor picture and palette. The scene is from a PSX game called Chrono Cross, and the palette has been manually selected for this particular task. You may immediately notice that the palette is not regular; although there are clearly some gradients, the gradients are not regularly spaced.