As artificial intelligence gains popularity, designers will need to adapt. Here's how to get started. It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a 15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over 70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us.

Artificial intelligence has crept into our everyday lives, even though we're not always aware of it. Here are some examples of AI's many applications. Journalists might not care for this, but AI programs are becoming smart enough to compile bits of information and turn them into articles. What's critical to understand about the potential impact machine learning can have on a manufacturing business lies in the way data is studied. The use of artificial intelligence that relies on natural language understanding is vital for achieving the potential outcomes mentioned above - otherwise, market intelligence would be based purely on statistical patterns and not contextual meaning.

In what is being perceived as another attempt to tighten its control over the internet, China's internet regulator on Saturday announced new rules that ban search engines from showing subversive information and obligate them to clearly identify paid results. The new regulations, which will take effect from Aug. 1, come close on the heels of the death of a 21-year-old college student, who is believed to have undergone an unapproved, experimental cancer treatment he found using the search engine Baidu. "Some search results lack objectivity and fairness, go against corporate morals and standards, misleading and influencing people's judgment," the Cyberspace Administration of China -- the country's internet regulator -- reportedly said. "Internet search providers should earnestly accept corporate responsibility toward society, and strengthen their own management in accordance with the law and rules, to provide objective, fair and authoritative search results to users." In addition, search engines would also be required to censor "rumors, obscenities, pornography, violence, murder, terrorism and other illegal information" -- regulations that the Chinese government claims are needed to safeguard the security of its citizens.

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Gensim is deep learning toolkit implemented in python programming language intended for handling large text collections, using efficient algorithms.

Today at ICML 2016, NVIDIA announced its latest Deep Learning SDK updates, including DIGITS 4, cuDNN 5.1 (CUDA Deep Neural Network Library) and the new GPU Inference Engine. NVIDIA GPU Inference Engine (GIE) is a high-performance deep learning inference solution for production environments. Power efficiency and speed of response are two key metrics for deployed deep learning applications, because they directly affect the user experience and the cost of the service provided. GIE automatically optimizes trained neural networks for run-time performance, delivering up to 16x higher performance per watt on a Tesla M4 GPU compared to the CPU-only systems commonly used for inference today. Figure 1 shows GIE inference performance per watt of the relatively complex GoogLeNet running on a Tesla M4. GIE can deliver 20 Images/s/Watt on the simpler AlexNet benchmark.

As the automotive ecosystem pushes forward with evermore-connected vehicles, new solutions – from over-the-air software updates to connected car services – are emerging from vendors to manage the vast amounts of data that accompany that connectivity. Airbiquity recently announced a software and data management solution specifically for the automotive industry to manage large-scale data collection and software updates to vehicles. SAP introduced its cloud-based Vehicle Insights tool that leverages its HANA big data platform to analyze telematics information as well as other existing business data and external data, with the aim of better integrating connected vehicles into business processes. Last week, IBM said it will be partnering with Local Motors of Maryland on a small, autonomous bus named Olli that incorporates IBM's artificial intelligence and analytics software, Watson, in its first vehicle-based "internet of things" solution – with the big data capabilities focused on passenger interactions rather than the vehicle's self-driving capabilities. Gartner has predicted that by 2020, connected car services will generate almost 40 billion in revenue annually, driven by a combination of infotainmet, navigation, fleet management, traffic management, remote diagnostics and automotive crash notification, among others.

Today, I'm going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you'll have this blog post as a springboard to learn even more about data mining. In order to do this, C4.5 is given a set of data representing things that are already classified. A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. Sure, suppose a dataset contains a bunch of patients.

Predicting what will happen in the future is challenging. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory developed an algorithm that can predict whether two individuals will hug, kiss, shake hands or slap five in the next scene. Using a Tesla K40 GPU with the cuDNN-accelerated Caffe deep learning framework, the researchers trained their network on 600 hours of prime-time television shows including The Office and Desperate Housewives. When predicting which of the four actions the person would perform one second later, the algorithm correctly predicted the action more than 43 percent of the time – and humans who have been watching TV for years were only able to predict the next action with 71 percent accuracy. In their second study, the algorithm was shown frames from a video and asked it to predict what object will appear five seconds later.

HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver. Researchers mostly ended up avoiding the term, preferring to talk instead about "expert systems" or "neural networks". The rehabilitation of "AI", and the current excitement about the field, can be traced back to 2012 and an online contest called the ImageNet Challenge.