In the first place, to understand the context of adversarial machine learning, you should know about Machine Learning and Deep Learning in general. Adversarial machine learning studies various techniques where two or more sub-components (machine learning classifiers) have an opposite reward (or loss function). Most typical applications of adversarial machine learning are: GANs and adversarial examples. In GAN (generative adversarial network) you have two networks: generator and discriminator.
In this article we'll look at what it would take for an artificial intelligence to generate truly creative content marketing, and how close we are to living in a world with AI content marketers. For the purposes of this article, let's keep this restricted to a typical content marketing campaign that involves idea generation and content production in the forms of a video, some text-based articles with accompanying images, and social media promotion. News outlets can depend on AI to write accurate stories on events, but AI has also developed the ability to produce creative work through the usage of neural networks. Similar experiments have been undertaken to see how well an artificial intelligence system can understand the structure of literary works.
AI, Neural Networks, Machine Learning and other buzzwords are not new; they are with us from late 50s, but why did they become so much of a trend only now? The business focus changed from investing into so-called "artificial intelligence" to development of systems that could work with already gathered data, process and re-structurize it. Bayes was widely used in anti-spam, Markov's chains predicted criminal structure behavior, search engines developed decision trees to predict user input, speech and image recognition was no miracle anymore, and it was good. Basically, we returned to 50s -- we are trying to create universal structures, mimic human brain, and create entities that can process mixed data as our brains do.
Microsoft's HoloLens may have largely faded from public view, but that doesn't mean that Microsoft's halted development on it. On Sunday, Microsoft researchers disclosed that HoloLens development is moving ahead, with a new chip that emphasizes machine learning. Specifically, Microsoft said the next generation of its Holographic Processing Unit, or HPU, will support Deep Neural Network processing, with an emphasis on artificial intelligence, or AI. Harry Shum, executive vice president of the Artificial Intelligence and Research Group, recently showed off the second version of the HPU.
Since the dawn of computing technology, developers created programs and algorithms by writing code that machines translate into precise instructions. Instead of code-writing the way a program solves a problem, the program "learns" to solve it on its own. In a not too dissimilar way than the human brain, unsupervised AI would recognize new patterns, label them on its own and classify them without human prior input. Per MIT Technology Review, Quoc Le (one of Google's Brain research scientists) has identified "unsupervised learning" as the biggest challenge to developing true AI that can learn without the need for labeled data.
In a blog post Monday, Microsoft detailed plans for its HoloLens 2 mixed reality headset and confirmed that it would utilize a dedicated coprocessor for AI processing. Competitors like Google, Facebook and Nvidia have also explored including similar processors for AI. The increased investment in AI-tailored hardware speaks to the interest and importance AI now has for tech companies. For a mixed reality device like the HoloLens, the potential applications for AI are similar: AI could help power overlays to give you additional information about things you see or hear in the real world.
Count Microsoft among the companies preparing to build specialized chips for artificial intelligence (AI): The next version of the company's HoloLens augmented reality headset will come with a chip capable of complex AI computation, revealed Microsoft Research VP Harryn Shum at a computer vision conference Sunday. This will make it possible to improve hand tracking on the device, as well as run object recognition and other computer vision tasks. The next version of this chip will incorporate an artificial intelligence co-processor, Shum said. Microsoft isn't the only company building custom chips for AI and similar tasks.
All robovacs use short-range infrared or laser sensors to detect and avoid obstacles, but iRobot added a camera, new sensors and software to Roombas in 2015 to give them the ability to map while they clean. So far investors have cheered Angle's plans, sending iRobot stock soaring to $102 in mid-June from $35 a year ago, giving it a market value of nearly $2.5 billion on 2016 revenue of $660 million. So far investors have cheered Angle's plans, sending iRobot stock soaring to $102 in mid-June from $35 a year ago, giving it a market value of nearly $2.5 billion on 2016 revenue of $660 million But there are headwinds for iRobot's approach, ranging from privacy concerns to a rising group of mostly cheaper competitors - such as the $300 Bissell SmartClean and the $270 Hoover Quest 600 - which are threatening to turn a once-futuristic product into a commoditized home appliance. All robovacs use short-range infrared or laser sensors to detect and avoid obstacles, but iRobot in 2015 added a camera, new sensors and software to its flagship 900-series Roomba that gave it the ability to build a map while keeping track of their own location within it.
"Machine learning and artificial intelligence not only makes devices more autonomous and valuable but also allows them to be more personal depending on what a customer likes or needs," says Vadim Budaev, software development team leader at Scorch AI. Much of the work in the area is being led by tech's biggest companies, which are adding basic AI and machine learning applications to products as they develop them. If phones can't process data quickly enough, AI systems will run down their batteries. Google's Tensor Processing Units powers its translate and search systems, while UK startup Graphcore has developed its own machine learning chips.