Machine learning used to take place behind the scenes: Amazon mined your clicks and purchases for recommendations, Google mined your searches for ad placement, and Facebook mined your social network to choose which posts to show you. But now machine learning is on the front pages of newspapers, and the subject of heated debate. Learning algorithms drive cars, translate speech, and win at Jeopardy! What can and can't they do? Are they the beginning of the end of privacy, work, even the human race?

In this post, I'll explain my approach and show some preliminary results. This is going to be a long post, so here's an overview of the different sections. If you want to skip ahead, just click the section title to go there. Traditionally, Spotify has relied mostly on collaborative filtering approaches to power their recommendations. The idea of collaborative filtering is to determine the users' preferences from historical usage data. For example, if two users listen to largely the same set of songs, their tastes are probably similar. Conversely, if two songs are listened to by the same group of users, they probably sound similar. This kind of information can be exploited to make recommendations.

Back in March of this year, Google DeepMind had its AI system, AlphaGo, "sit down" with international Go champion Lee Sedol in a 5-game Go match with a purse of a cool $1 million. Despite Go being far more difficult than say, chess, to program for (due in part to number of possible moves), it destroyed Sedol 4-1. Now, the same company that took down the best in what many consider to be the most difficult board game in the world, is turning its sights on Starcraft II. The company made the announcement at this year's BlizzCon 2016 in Anaheim, California, and in an associated press release, says that it has established a "collaboration with Blizzard Entertainment to open up StarCraft II to AI and Machine Learning researchers around the world." For anybody paying attention to Google DeepMind, or one of its central driving forces, Demis Hassabis, the leap to 3-D video games has been expected for some time.

Artificial Intelligence (AI) has had a major resurgence in the past few years. If we look at the history of AI, we see a repeated cycle – it was at the forefront in the 80's too – it has been going through cycles of over-promise, investment, under delivery and investment reduction. However this time around, the technology advancements, scale and attention being paid to AI are much larger than before. The advances in big data technologies combined with cheap massively scalable infrastructure and storage is now helping us tremendously to tackle bigger and bolder problems in Artificial Intelligence. However, we are really just at the tip of the iceberg with AI.

There are a lot of solar chargers out there, but the Water-Resistant Dual-USB Solar Charger is one of the best deals I've seen. It's much more compact than your typical solar chargers, which tend to be very brick-like. And yet it still packs an impressive 5000mAh of power.The charger itself can be powered via solar energy or USB depending […]

Robotic process automation (RPA) can be the true antidote to manual, rote work, or it can be our worst nightmare if you listen to all the drama or the hype. RPA centers on the use of artificial intelligence (AI) to apply human-like thinking to streamline a typically manually intensive process or activity; and whether we like it or not, it's here to stay. Take, for instance, the process of data extraction from documents such as invoices. Application of advanced optical character recognition (OCR) and intelligent document recognition can automate a significant amount of the job of data entry typically performed by clerks or specialized data entry staff. Interestingly, human effort is still involved with attaining the ability to hand off a process or task to a machine.

Improvements and convergences in machine learning and neurosciences combined with the availability of massive datasets and the ubiquity of high-performance scalable computing are propelling us into a new age of Artificial Intelligence (AI). The promise these developments hold is immense; so too are the risks. These challenges require both immediate and future action. Computing systems are already outperforming humans in many tasks that profoundly shape our everyday lives in the fields of transportation, communication, energy, finance, healthcare as well as defense and security. There are clear upsides and opportunities, but the unintended socio-political consequences are serious, disrupting the fabric of our social contracts, sense of human identity, dignity, and considerations of agency and personal empowerment.

This article was written by Stephanie Kim. Stephanie has a professional experience with data mining and processing including natural language processing along with a small amount of machine learning and script automation. Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let's find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets. This walkthrough will provide a basic introduction to help developers of all background and abilities get started with the NLP microservices available on Algorithmia.

There may be bad news for horror film writers - computers could soon be taking your job. A new independent horror film called'Impossible Things' has been produced in part by an artificial intelligence tool. The creators have billed the film as the'the scariest and creepiest horror film out there.' The AI software was used to develop'perfect plot twists' for the film, which is about a grieving mother who, after the death of her daughter, is driven to insanity by a supernatural being Impossible Things is a horror film which was reportedly produced by an artificial intelligence (AI) software tool. The tool analysed audience response data to help the writers craft plot points that connect with viewer demand. The AI software was used to develop'perfect plot twists' for the film, which is about a grieving mother who, after the death of her daughter, is driven to insanity by a supernatural being.

AI good for internal back office and some limited front office activities; however, still need to see more adoption of QC in the Net and infrastructure in companies to expose their services and information to the public net & infrastructure. Deep learning, as explained by tech journalist Michael Copeland on Blogs.nvidia.com, is the newest and most powerful computational development thus far. It combines all prior research in artificial intelligence (AI) and machine learning. At its most fundamental level, Copeland explains, deep learning uses algorithms to peruse massive amounts of data, and then learn from that data to make decisions or predictions. The Defense Agency Advanced Project Research (DARPA), as Wired reports, calls this method "probabilistic programming."