The above graph summarizes the number of Artificial Intelligence companies founded in a certain year. We are currently tracking 1117 Artificial Intelligence companies in 13 categories across 69 countries, with a total of 6.3 Billion in funding. Click here to see the full Artificial Intelligence landscape report and data.

If your opinion of artificial insemination (AI) for the beef cattle herd is "been there, done that," you may want to give it another look. New protocols and synchronization methods have eased the pressure. "There's no question that fixed-time AI has got easier," says Cliff Lamb, University of Florida animal scientist. That's important for those who tried other AI programs in the past but did not find success, and also noteworthy for those who have never tried AI. Fixed-time protocols allow the average producer who doesn't know how to AI to synchronize them and schedule a technician to come out and breed the cows.

With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense. One problem with drawing them as node maps: it doesn't really show how they're used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they "remember". I should add that this overview is in no way clarifying how each of the different node types work internally (but that's a topic for another day).

This article was originally published on TechRepublic. Remember the famous scene in Stanley Kubrick's 1968 2001: A Space Odyssey, when Hal 9000--the intelligent-turned-malevolent computer--regresses to his "childhood" and sings "Daisy Bell" as he's decommissioned by astronaut Dave Bowman? Its inspiration was a real-life Bell Labs demonstration of speech synthesis on an IBM 704 mainframe in 1961, witnessed by Arthur C Clark, who later incorporated it into his 2001 novel and screenplay. Although Bell Labs' involvement in the field stretches back to the 1930s with Homer Dudley's keyboard-and-footpedal-driven Voder speech synthesis device, it's undoubtedly the classic Kubrick/Clarke movie that cemented the ideas of artificial intelligence (AI) and conversing with computers into the public mind. Depending on how old you are, we're now familiar with computerised voices, thanks to devices like Texas Instruments' popular 1978 Speak & Spell educational toy, Stephen Hawking's speech synthesiser (memorably sampled in the Pink Floyd song Keep Talking), GPS navigational systems in your car, and any number of public information and call handling systems. More recently, the combination of automatic speech recognition (ASR), natural-language understanding (NLU) and text-to-speech (TTS) has come to mainstream attention in virtual assistants such as Apple's Siri, Google Now, Microsoft's Cortana, and Amazon's Alexa. To get a handle on how speech technologies work, we clearly need to know something about the mechanics of human speech and the structure of language. When we speak, air from the lungs passes through the vocal tract to produce "voiced" or "unvoiced" sounds (depending on whether the vocal cords are vibrating or not) that may then be modulated by the tongue, teeth and lips.

In just five years, intelligent systems and robots may have taken up to 6 percent of U.S. jobs, according to Forrester Research in a report released this week. As artificial intelligence (AI) advances to better understand human behavior and make decisions on its own in complicated situations, it will enable smart software and robots to take on increasingly challenging jobs. That means robotics should be able to take over some jobs traditionally held by humans by 2021. For instance, Forrester predicts that smart systems like autonomous robots, digital assistants, AI software and chatbots will take over customer service rep jobs and eventually even serve as truck and taxi drivers. "Intelligent agents have emerged, but wide adoption is not yet mainstream," Forrester analysts wrote in their report.

The 2015 version of the Barbie Dreamhouse was pretty rad. It had a slot that let you use a phone as a TV screen, a bay window that flips down and becomes a pool, plug-and-play appliances, and a roomy three-level floorplan. It's hard to see how the next-gen BDH could improve, and savvy shoppers will likely scoff at the proposition of buying new real estate for their dolls after only a year. But slow your roll and bust out your wallet, because Mattel's new Barbie Hello Dreamhouse makes last year's version seem like a rat-infested hovel. This mini-mansion is dope AF.

Just this weekend i submitted my week 3 assignment for the Stanford University's Machine Learning course that i enrolled at Coursera. It wasn't easy for me as i don't have any strong background in data science or programming -- but Andrew Ng made it very simple. Recently i wrote about How Machine Learning is used in IT Services? There are many influencing factors that made me learn Machine Learning concepts and if you continue reading this article -- i am sure you would agree. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.

When contracts need to be reviewed, renewed or revised, the process tends to be labor intensive and time consuming. Contract delivery and analytics specialist Seal Software is launching version 5.0 of its contract analysis software, which uses machine learning to speed up the process. It also includes a new add-in which allows contract data extraction and review capabilities to be used within Microsoft Word. Analyze This Now (ATN) pulls existing contracts directly into Word, converting all documents -- including PDF and TIFF images -- into MS Word format. From there, business users can review text, update terms, change metrics, and create new versions all within their familiar Word interface. With ATN, the Seal platform is running in the background, analyzing the text and showing users the status of clauses and provisions using color-coded shading for clear visibility.

Imagine if you could easily add voice recognition to your app or service even if you have no experience with voice-based sevices. Amazon hopes you'll do just that with the new Alexa Skills Kit, a collection of self-service APIs, tools, and code samples that make it a snap to add skills to Alexa. Alexa is the voice service that powers Amazon's Echo device. The Echo is a small, internet-connected speaker that responds to voice requests and can do things like manage calendar appointments and shopping lists. The Echo had been available in limited fashion to Amazon Prime subscribers until this week, when the online retailer made Echo available to anyone for 179.

Yusuke Sugomori is a creative technologist with a background in information engineering. When he was a graduate school student, he cofounded Gunosy with his colleagues, which uses machine learning and web-based data mining to determine individual users' respective interests and provides an optimized selection of daily news items based on those interests. This algorithm-based app has gained a lot of attention since its release and now has more than 10 million users. The company has been listed on the Tokyo Stock Exchange since April 28, 2015. In 2013, Sugomori joined Dentsu, the largest advertising company in Japan based on nonconsolidated gross profit in 2014, where he carried out a wide variety of digital advertising, smartphone app development, and big data analysis.