Who says artificial intelligence doesn't involve humans? Try telling that to Silicon Valley startup Unanimous AI. After recently achieving the rare "superfecta" -- picking the top four finishers in the Kentucky Derby -- using UNU, a new form of human-based AI using algorithms, the company is ready to share its formula with the public. After more than a year of testing, the online platform is now available in open beta. UNU relies on an artificial "swarm" of human group intelligence that comes together in real time to make predictions, said Louis Rosenberg, its creator.

The analysis of interconnection structures of entities connected through relationships has proven to be of immense value in understanding the inner-workings of networks in a variety of different data domains including finance, health care, business, computer science, etc. These analyses have emerged in the form of Graph Analytics -- the analysis of the characteristics in these graph structures through various graph algorithms. Some examples of insights offered by graph analytics include finding clusters of entities closely connected to each-other, calculating optimal paths between entities (the definition of optimal depending on the dataset and use case), understanding the hierarchy of entities within an organization as well as figuring out the impact each entity has inside the network. Graph structured data is a specialized type of dataset in terms of the way we need to access it; therefore it needs to be stored in ways that complements these access patterns. This has sparked the emergence of a wide variety of specialized graph databases such as Neo4j, OrientDB, Titan etc.

We are all'algos' now: traders at work in the New York Stock Exchange in May When Bruce Bittles first started trading in the 1960s, the US stock market was a largely human affair. Exchange floors were the chaotic maelstrom of shouts, frantic phone calls and finger waving, later made famous by 1980s films such as Wall Street and Trading Places. But now the machines have taken over. Nasdaq became the world's first electronic stock market when it opened its doors in 1971, but since then, the trading world has been revolutionised several times over. The old bourses and trading pits now are largely shuttered.

This DataFrame-based API for MLlib provides functionality for saving and loading models that mimics the familiar Spark Data Source API. We will demonstrate saving and loading models in several languages using the popular MNIST dataset for handwritten digit recognition (LeCun et al., 1998; available from the LibSVM dataset page). This dataset contains handwritten digits 0–9, plus the ground truth labels.

Po, the wisdom-seeking hero of the "Kung Fu Panda" films, might recognize this temple in China where the world's first robot monk dwells. For Po's Jade Palace, there is Longquan (Dragon Spring) Temple, a place of Buddhist worship in the mountains northwest of Beijing, where gnarled gingko and cypress trees tower over red-walled buildings underneath rocky Phoenix Ridge. For his Hall of Warriors, there is the Comic Center deep inside the temple, at the end of winding stone paths and steps, past a flower-shaped audio device that crackles sutras. As for Po himself, there is Xian'er, the two-foot-tall, advice-dispensing robot whose full title is Worthy Stupid Robot Monk. A childlike creature in an orange Buddhist robe, Xian'er is an object of fascination in China amid an increasingly urgent pursuit of spirituality and, more recently, artificial intelligence.

RANCHO PALOS VERDES, Calif. - The emergence of artificial intelligence and machine learning to household gadgets is "gigantic," according to Amazon founder Jeff Bezos. Speaking to the Code conference here, Bezos, whose company has a huge hit on its hand with the Echo connected speaker, said "it's hard to overstate how big of an impact this will have on society over the next 20 years. It doesn't mean phones are going to go away or that voice actions will replace screens. As long as people have eyes, they have screens." The Echo speaker can turn on lights, access online music and answer queries by awaking the speaker and waking it by stating the word Alexa.

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. One of the themes coming out of the recent Google I/O conference was that Google plans to make a more aggressive push into the consumer hardware business. The company announced the Home product, an AI-oriented service to compete with Amazon Echo; a VR headset; and a new smartphone division that will build and ship its modular Ara phones. Former Motorola CEO Rick Osterloh will lead the new hardware division. In a Recode post last week, Mark Bergen argued that one of the key unanswered questions coming out of I/O is how these exciting new products are going to be distributed. Getting products such as Nexus and Chromecast into consumers' hands is something Google has "never done well," Bergen wrote, further suggesting that if Google wants to more directly compete with Apple, the company will also need to think about its retail strategy.

According to MarketandMarkets, the artificial intelligence (AI) market is estimated to grow from 419.7 million in 2014 to 5.05 billion by 2020, growing at a CAGR of 53.65% from 2015 to 2020. The Media and Advertising sector is expected to drive the growth of AI during this period. IBM, Microsoft, and Google are key players in the market, and now Salesforce is trying to make inroads into it. For the first quarter of fiscal 2017, Salesforce's revenue grew 27% over the year to 1.92 billion, above analyst estimate of 1.89 billion. Net income was 38.8 billion or 0.06 per share. Non GAAP EPS was 0.24, beating analyst forecast of 0.25.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

Users who don't want to pay the 180 for the Amazon Echo smart-speaker can now test the system's features in their web browser with Echoism, according to Mashable. Amazon's 9.25-inch cylindrical Echo speaker uses voice interaction, where users ask Alexa questions or request music. Browser testing of these features is just one way Amazon is trying to get Alexa into cars, homes and phones. Popular Science reported Amazon's release of two seperate toolkit packages: Alexa Voice Service and Alexa Skills Kit. These will allow companies to add new functionality and integrate Alexa into various devices including smart yard products and robotic vacuums.