If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
However, there are a growing number of large but innovative companies that are driving measurable value through "operational machine learning"--embedding machine learning on big data into their business processes. It includes machine learning models to customize offers, an open-source solution for run-time decisioning, and a scoring service to match customers and offers. In order to help create these capabilities, the company created both a Chief Data Officer and a Chief Loyalty and Analytics Officer within the marketing function. Building these capabilities on top of a big data stack (including data lake storage and data transformation capabilities) is transformational in terms of the availability of information to support the decision, the performance of the decision request, and the performance of the learning service.
Fig.1: Distributed Memory Model of Paragraph Vectors (PV-DM) (from: Distributed Representations of Sentences and Documents) With distributed bag-of-words (PV-DBOW), there even aren't any word vectors, there's just a paragraph vector trained to predict the context: Fig.2: Distributed Bag of Words (PV-DBOW) (from: Distributed Representations of Sentences and Documents) Like word2vec, doc2vec in Python is provided by the gensim library. I've trained 3 models, with parameter settings as in the above-mentioned doc2vec tutorial: 2 distributed memory models (with word & paragraph vectors averaged or concatenated, respectively), and one distributed bag-of-words model. These are the words found most similar to awesome (note: the model we're asking this question isn't the one that performed best with Logistic Regression (PV-DBOW), as distributed bag-of-words doesn't train word vectors, – this is instead obtained from the best-performing PV-DMM model): So, what we see is very similar to the output of word2vec – including the inclusion of awful. Same for what's judged similar to awful: To sum up – for now – we've explored how three models: bag-of-words, word2vec, and doc2vec – perform on sentiment analysis of IMDB movie reviews, in combination with different classifiers the most successful of which was logistic regression.
After 2 years of R&D and €13m investments, PIQ's 50 engineers developed a revolutionary technology, protected by 10 international patents allowing to identify athletes' Winning Factors, highlighting the key strength they should leverage on to succeed. From a world where connected sports were limited to the capture of basic data, PIQ's two cutting edge innovations are opening new horizons to the Sport Wearables industry: The combination of GAIA and PIQ ROBOTTM enables athletes to identify their Winning Factors, highlighting the key strength they should leverage on to succeed. GAIA – GAIA is the first Artificial Intelligence system that autonomously understands sports movement. Thanks to GAIA's statistical intelligence and PIQ ROBOTTM's measurement capacity, millions of actions generated in every hour of game can now be thoroughly analyzed.
The advancements in data analytics: As technology becomes fast and cost-effective enough to collect and analyze vast quantities of data, talent acquisition leaders are increasingly asking their recruiting teams to demonstrate data-based quality of hire metrics such as new hires' performance and turnover. The advancements in data analytics: As technology becomes fast and cost-effective enough to collect and analyze vast quantities of data, talent acquisition leaders are increasingly asking their recruiting teams to demonstrate data-based quality of hire metrics such as new hires' performance and turnover. Candidate sourcing is still a major recruiting challenge: a recent survey found 46% of talent acquisition leaders say their recruiting teams struggle with attracting qualified candidates. AI for candidate sourcing is technology that searches for data people leave online (e.g., resumes, professional portfolios, or social media profiles) to find passive candidates that match your job requirements.
Advertising agencies that use AI, machine learning, and image recognition are hyper-targeting consumers by learning their interests and tastes. An everyday example is Facebook's targeted ads, which use artificial intelligence to narrow target segments down in a matter of hours. For example, in May 2016 a millennial taskforce at McCann Japan developed the world's first artificial intelligence creative director, AI-CD ß. For instance, Mondelez asked a real life creative director to develop the creative direction for AI-CD ß's ad and to explain the product's benefits.
Machine learning is changing the balance of labor between the decision-making role of humans, and the number-crunching roles of computers. The High Performance Computing (HPC) Center Lunch and Learn seminars are opportunities for students and professional developers to meet with HPC industry experts. Take the difficulty out of managing IoT development by using IoT cloud services from Microsoft Azure* with Intel IoT Technology. Intel Developer Zone experts, Intel Software Innovators, and Intel Black Belt Software Developers contribute hundreds of helpful articles and blog posts every month.
Machine learning is, in essence, the very advanced application of statistics to learning to identify patterns in data and then make predictions from those patterns. The virtual assistant in your pocket -- be it Siri, Cortana, or Google Now -- represents a major leap forward in the ability of computers to understand natural human speech, and they're continuously improving. People are already doing very exciting things with machine learning algorithms. This ability to learn, coupled with advances in robotics and mobile technology, means that computers can now help humans perform complex tasks faster and better than ever before.
Machine learning (ML) has achieved remarkable breakthroughs, which have, in turn, driven performance improvements across AI components. However, it is still early days, and there are still several challenges: Most breakthroughs are in "narrow" applications and use supervised methods that require big labeled data sets (which are often expensive to create), most algorithms (still) achieve (just) sub-human performance, training requires considerable computing resources and most approaches are based on heuristics with lack of theoretical frameworks. New image recognition techniques powered by deep learning have enabled startups like Netra to improve visual intelligence and search, enhancing overall user experience. Talla is aiming to revolutionize enterprise knowledge management, starting with a seemingly simple conversational agent that will eventually become a full-fledged proactive knowledge agent.Wade & Wendy has created a two-sided conversational agent for recruiting that aims to reduce the overall recruiting time while improving the level of satisfaction on both sides of the table.
With the evolution of computer vision, improved training data, and deep learning algorithms, computers are now able to automatically classify NSFW image content with greater precision. In the spirit of collaboration and with the hope of advancing this endeavor, we are releasing our deep learning model that will allow developers to experiment with a classifier for NSFW detection, and provide feedback to us on ways to improve the classifier. Our general purpose Caffe deep neural network model (Github code) takes an image as input and outputs a probability (i.e a score between 0-1) which can be used to detect and filter NSFW images. We observe that the performance of the models on NSFW classification tasks is related to the performance of the pre-trained model on ImageNet classification tasks, so if we have a better pretrained model, it helps in fine-tuned classification tasks.
For recruiters, the Talla AI software offers a streamlined solution to internal productivity. Beamery's features include Sourcing, Recruitment CRM, Candidate Engagement and Employer Branding, which all sync and connect to your existing ATS. You can connect careers websites, content, recruiters and candidates together in a tightly integrated platform. Even cooler, it features integrated learning techniques that help you learn as you work.