As bot technology improves, businesses finding their way into more use cases where human judgment and effort have traditionally been required. Some relevant business use cases are assistant bots, finance compliance, supplementing HR practices etc. The use cases can be classified and explained in terms of automation and augmentation. Automation of routine tasks can improve overall productivity and performance. Augmentation bots powered by artificial intelligence and natural language processing are better than humans at switching task and sifting through gigabytes of data.

The Conference and Workshop on Neural Information Processing Systems (NIPS) is a machine learning and computational neuroscience conference held every December. The conference is a single track meeting that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track workshops that up to 2013 were held at ski resorts. According to Microsoft Academic Search, NIPS is the top conference on machine learning. US tops the chart and has been the top research contributor with the highest number of accepts. UK stands second with an marginal increase in paper accepts from 2014, whereas India and China both dropped, with lesser number of accepts in 2015.

Google is known for seemingly wild investments like stratospheric Internet balloons and face computers. This week it began a project that is less flashy but bolder: rethinking the conventional search engine that has become embedded into daily life and provided the revenue that made Google into a 546 billion behemoth. On Wednesday the company launched a virtual helper similar to Apple's Siri. Called Google Assistant, the awkwardly named aide exists only as a "preview" inside Google's new messaging app Allo, and early impressions show that it still needs work. But Google is committed to rolling out Assistant much more widely, in a bid to make us as dependent on it as we are on the search box.

Robots assisted by artificial intelligence may eliminate 6 percent of all U.S. jobs by 2021, according to a new analysis by Forrester. "Solutions powered by AI/cognitive technology will displace jobs, with the biggest impact felt in transportation, logistics, customer service and consumer services," read part of the analyst firm's report, as quoted by The Guardian. Many of these industries already seem on the verge of technological disruption. Otto, a young startup run by engineers from Google's autonomous-vehicle project (and recently acquired by Uber for 670 million), is testing out self-driving trucks. If all goes according to plan, humans won't entirely cede control of their big rigs to software; rather, the technology will take over at periodic intervals so that drivers can catch some shut-eye. It's also not impossible to imagine a future in which trucks lack drivers altogether.

Customer support is one the most resource-intensive departments in a company. Staff spend their day answering queries, on the telephone with customers, communicating with other departments, and much more. It is also a part of the operation that is hard to link to an ROI. Although the concept of "customer success" is gaining traction in the startup world (particularly in SaaS), it is still tough to report the real value a customer support team can deliver. Therefore, anything reducing the need for manpower in customer support is considered a good thing.

London-based Almax Analytics has launched a news analysis tool for financial markets, it has announced. The solution "works like a human" by combining natural language processing (NLP), machine learning and network and data visualisation to help clients act on news and social media.

Much as the steam engine helped power the Industrial Revolution, semiconductors are in many ways the backbone that power today's information revolution. A semiconductor is a microchip that allows any computing device to carry out the instructions a program sets forth. The size of the semiconductor industry should continue to climb with the levels of technology in our daily lives, though the growth won't be evenly distributed. This is a diverse industry, but for now let's focus on some of the more compelling opportunities for investors in the semiconductor space today. QCT, the chip business, dominates revenue production, having generated between 68% and 71% of Qualcomm's total sales in each of the past three fiscal years.

The most common threat to your digital property comes in the form of cracks and keygens, tools created by hackers to outright penetrate your software's registration system and enable unauthorized users to freely access your software without actually paying for it. The gaming industry is one of the largest victims of this form of piracy, with cracked versions of virtually every single game available in some format on a torrent website. A single search on any popular torrent search engine will reveal cracked versions of any and every popular software or video game you may come across, and even the biggest giants in the genre, such as EA or Bethesda, are not safe from this threat. If your software uses some form of technology that requires it to download regular updates from a secure database, as antiviruses tend to do, you are in luck. Otherwise, it can be really painstaking to fend off this kind of attack.

Random forests or random decision forests[1][2] are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set.[3]:587–588 The first algorithm for random decision forests was created by Tin Kam Ho [1] using the random subspace method,[2] which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.[4][5][6] An extension of the algorithm was developed by Leo Breiman[7] and Adele Cutler,[8] and "Random Forests" is their trademark.[9] The extension combines Breiman's "bagging" idea and random selection of features, introduced first by Ho[1] and later independently by Amit and Geman[10] in order to construct a collection of decision trees with controlled variance.

In supervised learning, the learning algorithm is provided outcome data in advance, in the form of a pre-labeled set of instances. It is from this set that the algorithm is expected to learn what to do when it encounters future, previously unseen instances. Classification is a form of supervised learning. As an example, take the biological taxonomic hierarchy. Organisms are grouped into successfully more specific ranks of domain, kingdom, phylum, etc.