What if a robot could automatically adjust its grip based on the size and shape of the object? In other words, a robot that could fine-tune how it is holding an object so as not to drop it, much in the same way humans do. According to Siemens, it's quite possible, and it all comes down to artificial intelligence (AI) based on neural networks. Neural networking is a technology that mimics the human brain in that it is able to recognize complex patterns. With that in mind, Siemens says that by adding AI via neural networks to traditional control programs--which were designed to execute a set task--the capabilities of the system can be extended to change based on the parameters of the product or process. Bottom line: machines become naturally flexible.

For many years, the capacity of predicting the future was reserved to few people and their tools were limited to crystal balls, hand palms and tarot cards. But for the last 50 years, new tools have emerged and forecasting is now accessible to many more people and this is great! In this article, I will show you how to perform basic timeseries forecasting on a simple example. We will analyze, visualize and forecast road accidents in Switzerland using the open-source library Facebook Prophet and a LSTM neural network using Keras / Tensorflow. The jupyter notebooks I used for this article are available on my github.

The most recent update from the Office of Management and Budget on the President's Management Agenda shows just how automation is taking hold in the government. OMB says out of the more than100 initiatives under the leaner government portion of the PMA, the 24 civilian CFO Act agencies reported 30 projects using robotics process automation, artificial intelligence or other innovative software. And agency financial management offices are right in the middle of this transformation. For example, the Department of Housing and Urban Development used RPA to improve financial controls and reduce the time required to prepare financial statements. OMB says automation allowed HUD to cut nearly 6 months and more than 2,000 hours of employee time out of the process.

Truly, it triggered the worst of times. What was thought to be the most stable of industries – finance – was brought to its knees. It triggered a new era of corporate financial scrutiny. The oft-quoted term'more for less' was permanently inscribed on the heads of all legal professionals. During the great recession in the West, companies started to look at all areas to cut costs to survive.

But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition. In pursuit of a more performant system, researchers at Alibaba, Zhejiang University, and the Stevens Institute of Technology devised a method dubbed Lip by Speech (LIBS), which uses features extracted from speech recognizers to serve as complementary clues. They say it manages industry-leading accuracy on two benchmarks, besting the baseline by a margin of 7.66% and 2.75% in character error rate. LIBS and other solutions like it could help those hard of hearing to follow videos that lack subtitles. It's estimated that 466 million people in the world suffer from disabling hearing loss, or about 5% of the world's population.

LONDON--(BUSINESS WIRE)--The artificial intelligence market in the US education sector is expected to post a CAGR of nearly 48% during the period 2018-2022, according to the latest market research report by Technavio. The increasing emphasis on customized learning paths using AI will be one of the major drivers in the global artificial intelligence market in the US education sector. The education system of the US is well developed and teachers and students in the country are aware about AI technology. This increases the adoption of artificial intelligence in the education sectors of the US. Moreover, the growing reliance on machine learning technologies for the collection of data about student performance will contribute to expanding the artificial intelligence market in the US education sector.

Last week, the Australian state of New South Wales announced a plan to crack down on drivers using their phones on the road. The state's transport agency said it had integrated machine vision into roadside cameras to spot offenders. The AI automatically flags suspects, humans confirm what's going on, and a warning letter is sent out to the driver. "It's a system to change the culture," the assistant police commissioner of New South Wales, Michael Corboy, told Australian media, noting that police hoped the technology would cut fatalities on the road by a third over two years. It seems an admirable scheme, top to bottom.

They are an interesting piece of technology. The latest trend in technology says that these chatbots will be made sure that they match the level of human behavior so that they can offer almost human-like services but better versions of them. According to the chatbot trends 2019, the conventional services which humans offer come with their own set of limitations. Whereas, the chatbots with the help of machine learning and artificial intelligence can gain an edge over the general services of the intelligence. There are completely no issues with chatbots because they can offer the services without any time constraint.

Deep learning has met increasing hype in the last few years, and with lots of practical success. But does that necessarily indicate an exponential growth in AI over the next few years? "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run" In order to understand what's to come, we need to figure out where we are exactly in the development of not only deep learning, but AI in general. The Gartner hype curve gives some perspective about the adoption of a technology in the enterprise over a five- to 10-year perspective. One way to look at AI is to stop considering it as an invention where we endlessly invent new techniques, but instead view it as more are a discovery where mankind is slowly (and quite empirically) discovering, step by step, the behaviour of self-learning systems.

California [USA], December 4: The holiday season is here, which means prying relatives asking awkward questions. To keep such situations under control for its employees, Facebook has built a special chatbot. The new'Liam Bot' chatbot teaches Facebook workers official company answers for dealing with thorny questions like, "Is Facebook destroying democracy?", Using artificial intelligence, the chatbot instructs the employee to answer with points including Facebook consults with experts on the matter, It has hired more moderators, It is working on AI to spot hate speech, and so on. It also provides a step-by-step guide to help those asking easier questions related to account recovery.